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10 Commits
lab3
...
18a911f9d3
| Author | SHA1 | Date | |
|---|---|---|---|
 ION606
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18a911f9d3 | ||
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ION606
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414a4ac5a3 | ||
 ION606
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4eff5a6378 | ||
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ION606
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5adb4119f5 | ||
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ION606
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cd3ababd59 | ||
|
ION606
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88f2975b86 | ||
|
ION606
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dc2ceac7de | ||
|
ION606
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9abd1a6df6 | ||
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ION606
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555650ac3c | ||
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ION606
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8b274f6bd6 |
Vendored
+2
-1
@@ -1,3 +1,4 @@
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{
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"r.rpath.linux": "/usr/bin/R"
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"r.rpath.linux": "/usr/bin/R",
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"r.editor.tabSize": 4
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}
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Binary file not shown.
@@ -0,0 +1,166 @@
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# bleh
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suppressPackageStartupMessages({
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library(tidyverse)
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library(broom)
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library(caret)
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library(class)
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});
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# helpers
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find_col <- function(df, patterns) {
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cols <- names(df)
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for (pat in patterns) {
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hit <- cols[str_detect(tolower(cols), regex(pat, ignore_case = TRUE))]
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if (length(hit) > 0) return(hit[[1]])
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}
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return(NULL)
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}
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sanitize_filename <- function(x) {
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gsub("[^A-Za-z0-9_.-]+", "_", x)
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}
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pick_regions <- function(df, region_col, a = NULL, b = NULL) {
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if (!is.null(a) && !is.null(b)) return(c(a, b))
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cnt <- df %>% filter(!is.na(.data[[region_col]])) %>%
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count(.data[[region_col]], sort = TRUE)
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stopifnot(nrow(cnt) >= 2)
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c(cnt[[1,1]], cnt[[2,1]])
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}
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save_txt <- function(path, txt) {
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dir.create(dirname(path), showWarnings = FALSE, recursive = TRUE)
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writeLines(txt, path)
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}
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# args
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args <- commandArgs(trailingOnly = TRUE)
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opt <- list(
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data = NULL, region_col = NULL, region_a = NULL, region_b = NULL,
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response = NULL, predictors = NULL, knn1 = NULL, knn2 = NULL,
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k = 5L, render = "none" # "none" | "html" | "pdf"
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)
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parse_flag <- function(flag) {
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key <- sub("^--", "", flag[[1]])
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val <- if (length(flag) > 1) flag[[2]] else TRUE
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opt[[key]] <<- val
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}
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if (length(args)) {
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kv <- split(args, cumsum(grepl("^--", args)))
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lapply(kv, parse_flag)
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}
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stopifnot(!is.null(opt$data))
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# load data
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message("reading: ", opt$data)
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df <- if (grepl("\\.csv(\\.gz)?$", opt$data, ignore.case = TRUE)) {
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suppressMessages(readr::read_csv(opt$data, show_col_types = FALSE))
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} else {
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suppressMessages(readxl::read_excel(opt$data))
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}
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# auto-detect columns
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region_col <- opt$region_col %||%
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find_col(df, c("^region$", "regions?$", "world\\s*bank\\s*region"))
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if (is.null(region_col)) stop("could not detect a 'region' column; use --region-col")
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response_col <- opt$response %||%
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find_col(df, c("^epi$", "overall\\s*score$", "index$", "score$"))
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if (is.null(response_col)) {
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nums <- df %>% select(where(is.numeric)) %>% names()
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if (length(nums) == 0) stop("no numeric columns found; set --response explicitly")
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response_col <- nums[[1]]
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}
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gdp_col <- find_col(df, c("^gdp", "gdp.*per.*cap", "gdppc"))
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pop_col <- find_col(df, c("^pop", "^population$"))
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# predictors set(s)
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pred_sets <- list()
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if (!is.null(opt$predictors)) {
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pred_sets <- list(strsplit(opt$predictors, ",", fixed = TRUE)[[1]] %>% trimws())
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} else {
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ps <- c(na.omit(gdp_col), na.omit(pop_col))
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if (length(ps) >= 1) pred_sets <- append(pred_sets, list(ps[1]))
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if (length(ps) >= 2) pred_sets <- append(pred_sets, list(ps[1:2]))
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}
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# choose regions
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regions <- pick_regions(df, region_col, opt$region_a, opt$region_b)
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region_a <- regions[[1]]; region_b <- regions[[2]]
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# dirs
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fig_dir <- "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures"; dir.create(fig_dir, showWarnings = FALSE)
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stats_dir <- "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/stats"; dir.create(stats_dir, showWarnings = FALSE)
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# distributions and qq
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fa <- df %>% filter(.data[[region_col]] == region_a) %>% pull(all_of(response_col)) %>% as.numeric()
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fb <- df %>% filter(.data[[region_col]] == region_b) %>% pull(all_of(response_col)) %>% as.numeric()
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# le plot de box
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p_box_a <- tibble(val = fa) %>%
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ggplot(aes(x = "", y = val)) +
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geom_boxplot() +
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labs(title = paste0("boxplot: ", response_col, " (", region_a, ")"),
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x = NULL, y = response_col)
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ggsave(file.path(fig_dir, sprintf("box_%s_%s.png",
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sanitize_filename(region_a), sanitize_filename(response_col))),
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p_box_a, width = 5, height = 4, dpi = 160)
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p_box_b <- tibble(val = fb) %>%
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ggplot(aes(x = "", y = val)) +
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geom_boxplot() +
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labs(title = paste0("boxplot: ", response_col, " (", region_b, ")"),
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x = NULL, y = response_col)
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ggsave(file.path(fig_dir, sprintf("box_%s_%s.png",
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sanitize_filename(region_b), sanitize_filename(response_col))),
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p_box_b, width = 5, height = 4, dpi = 160)
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# hist and density
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p_hist_a <- tibble(val = fa) %>%
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ggplot(aes(x = val)) +
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geom_histogram(aes(y = after_stat(density)), bins = 30, alpha = 0.6) +
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geom_density() +
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labs(title = paste0("histogram + density: ", response_col, " (", region_a, ")"),
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x = response_col, y = "density")
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ggsave(file.path(fig_dir, sprintf("hist_%s_%s.png",
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sanitize_filename(region_a), sanitize_filename(response_col))),
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p_hist_a, width = 6, height = 4, dpi = 160)
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p_hist_b <- tibble(val = fb) %>%
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ggplot(aes(x = val)) +
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geom_histogram(aes(y = after_stat(density)), bins = 30, alpha = 0.6) +
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geom_density() +
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labs(title = paste0("histogram + density: ", response_col, " (", region_b, ")"),
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x = response_col, y = "density")
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ggsave(file.path(fig_dir, sprintf("hist_%s_%s.png",
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sanitize_filename(region_b), sanitize_filename(response_col))),
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p_hist_b, width = 6, height = 4, dpi = 160)
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# qq plot between the two regions (2 sample qq)
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png(file.path(fig_dir, sprintf("qq_%s_%s_vs_%s.png",
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sanitize_filename(response_col), sanitize_filename(region_a), sanitize_filename(region_b))),
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width = 700, height = 500, res = 160)
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n <- min(sum(is.finite(fa)), sum(is.finite(fb)))
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q <- seq(0.01, 0.99, length.out = max(10, n))
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xq <- quantile(fa, q, na.rm = TRUE); yq <- quantile(fb, q, na.rm = TRUE)
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plot(sort(xq), sort(yq), pch = 19, cex = 0.6,
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xlab = paste0("quantiles: ", region_a),
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ylab = paste0("quantiles: ", region_b),
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main = paste("qq plot:", region_a, "vs", region_b))
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abline(0, 1)
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dev.off()
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@@ -0,0 +1,122 @@
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# TODO: organize this file better bc I just kinda dumped everything in here
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suppressPackageStartupMessages({
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pkgs <- c("tidyverse", "readr", "readxl", "broom", "jsonlite", "ggplot2", "class", "optparse", "markdown")
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to_install <- pkgs[!pkgs %in% rownames(installed.packages())]
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if (length(to_install)) install.packages(to_install, repos = "https://cloud.r-project.org")
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lapply(pkgs, library, character.only = TRUE)
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})
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sanitize <- function(x) {
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gsub("[^A-Za-z0-9_.-]+", "_", x)
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}
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save_plot <- function(p, path, w = 7, h = 5, dpi = 160) {
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dir.create(dirname(path), recursive = TRUE, showWarnings = FALSE)
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ggplot2::ggsave(path, p, width = w, height = h, dpi = dpi)
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}
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hist_density_plot <- function(v, lbl) {
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tibble(x = v) |>
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ggplot(aes(x = x)) +
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geom_histogram(aes(y = after_stat(density)), bins = 30, alpha = 0.6) +
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geom_density() +
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labs(title = paste("histogram + density:", lbl), x = lbl, y = "density") +
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theme_minimal()
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}
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box_plot <- function(v, lbl) {
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tibble(x = v) |>
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ggplot(aes(y = x)) +
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geom_boxplot(width = 0.3) +
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labs(title = paste("boxplot:", lbl), y = lbl, x = NULL) +
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theme_minimal()
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}
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qq_two_sample <- function(a, b, title = "qq plot", n_q = NULL) {
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a <- a[is.finite(a)]
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b <- b[is.finite(b)]
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n <- min(length(a), length(b))
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if (is.null(n_q)) n_q <- max(10, floor(n))
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if (n_q < 10) return(NULL)
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probs <- seq(0.01, 0.99, length.out = n_q)
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qa <- quantile(a, probs, na.rm = TRUE, names = FALSE)
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qb <- quantile(b, probs, na.rm = TRUE, names = FALSE)
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d <- tibble(x = sort(qa), y = sort(qb))
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ggplot(d, aes(x, y)) +
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geom_point(size = 1.6) +
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geom_abline(slope = 1, intercept = 0) +
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labs(title = title, x = "region a quantiles", y = "region b quantiles") +
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theme_minimal()
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}
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tf_pos <- function(s) {
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s <- as.numeric(s)
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if (all(s[is.finite(s)] > 0)) log1p(s) else s
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}
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strat_split <- function(d, label_col, test_prop = 0.25) {
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d <- d |> tidyr::drop_na({{label_col}})
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idx_tr <- integer(0)
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idx_te <- integer(0)
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for (lev in unique(d[[label_col]])) {
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rows <- which(d[[label_col]] == lev)
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nte <- max(1, floor(length(rows) * test_prop))
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te <- sample(rows, nte)
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tr <- setdiff(rows, te)
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idx_tr <- c(idx_tr, tr)
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idx_te <- c(idx_te, te)
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}
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list(train = d[idx_tr, , drop = FALSE], test = d[idx_te, , drop = FALSE])
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}
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run_knn <- function(d, label_col, vars, k, tag, fig_dir) {
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use <- d |> dplyr::select(all_of(c(label_col, vars))) |> tidyr::drop_na()
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split <- strat_split(use, label_col, 0.25)
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tr <- split$train
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te <- split$test
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mu <- sapply(tr[, vars, drop = FALSE], mean, na.rm = TRUE)
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sdv <- sapply(tr[, vars, drop = FALSE], sd, na.rm = TRUE)
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sdv[sdv == 0] <- 1
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trX <- scale(as.matrix(tr[, vars, drop = FALSE]), center = mu, scale = sdv)
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teX <- scale(as.matrix(te[, vars, drop = FALSE]), center = mu, scale = sdv)
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trY <- factor(tr[[label_col]])
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teY <- factor(te[[label_col]], levels = levels(trY))
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pred <- class::knn(train = trX, test = teX, cl = trY, k = k)
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acc <- mean(pred == teY, na.rm = TRUE)
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cm <- table(truth = teY, pred = pred)
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cm_df <- as.data.frame(cm)
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cm_fig <- file.path(fig_dir, paste0("knn_confusion_", sanitize(tag), ".png"))
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p_cm <- ggplot(cm_df, aes(x = pred, y = truth, fill = Freq)) +
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geom_tile() + geom_text(aes(label = Freq)) +
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labs(
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title = paste0("confusion matrix (k=", k, ") – ", tag),
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x = "predicted", y = "true"
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) + theme_minimal() +
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theme(
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axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)
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)
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save_plot(p_cm, cm_fig, w = 6, h = 5)
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list(tag = tag,
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k = k,
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vars = vars,
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accuracy = unname(acc),
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confusion_fig = cm_fig,
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n_test = nrow(te))
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}
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@@ -0,0 +1,121 @@
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source("/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/R/00_utils.R")
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# TODO: hard-code me
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# NOTE: The options were generated by chatGPT from my horrendous hard-coded options
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option_list <- list(
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optparse::make_option("--data", type = "character", default = "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/epi_results_2024_pop_gdp_v2.csv"),
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optparse::make_option("--region-col", type = "character", default = NA),
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optparse::make_option("--region-a", type = "character", default = NA),
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optparse::make_option("--region-b", type = "character", default = NA),
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optparse::make_option("--response", type = "character", default = NA),
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optparse::make_option("--predictors", type = "character", default = NA),
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optparse::make_option("--knn1", type = "character", default = NA),
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optparse::make_option("--knn2", type = "character", default = NA),
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optparse::make_option("--k", type = "integer", default = 5)
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)
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opt <- optparse::parse_args(optparse::OptionParser(option_list = option_list))
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if (is.na(opt$data)) stop("--data is required")
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read_any <- function(p) {
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ext <- tolower(tools::file_ext(p))
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if (ext %in% c("csv", "txt")) {
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suppressMessages(readr::read_csv(p, show_col_types = FALSE))
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} else if (ext %in% c("xls", "xlsx")) {
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readxl::read_excel(p)
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} else {
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stop("unsupported extension: ", ext)
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}
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}
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df <- read_any(opt$data)
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nms <- names(df)
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find_col <- function(nms, pats) {
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for (pat in pats) {
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idx <- which(stringr::str_detect(tolower(nms), pat))
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if (length(idx)) return(nms[idx[1]])
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}
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# I hate it here
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NA_character_
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}
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region_col <- if (!is.na(opt$`region-col`)) opt$`region-col` else
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find_col(nms, c("^region$", "regions?$", "world\\s*bank\\s*region"))
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if (is.na(region_col)) stop("could not detect a region column; pass --region-col")
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response <- if (!is.na(opt$response)) opt$response else if ("EPI.new" %in% nms) {
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"EPI.new"
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} else {
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find_col(nms, c("^epi", "epi.*score", "index$", "score$"))
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}
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if (is.na(response)) {
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num <- df |> dplyr::select(where(is.numeric)) |> names()
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if (!length(num)) stop("no numeric columns; pass --response")
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response <- num[1]
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}
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gdp_col <- find_col(nms, c("^gdp", "gdp.*per.*cap", "gdppc"))
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pop_col <- find_col(nms, c("^pop", "^population$"))
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counts <- sort(table(df[[region_col]]), decreasing = TRUE)
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region_a <- if (!is.na(opt$`region-a`)) opt$`region-a` else
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if ("Sub-Saharan Africa" %in% names(counts)) "Sub-Saharan Africa" else names(counts)[1]
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region_b <- if (!is.na(opt$`region-b`)) opt$`region-b` else
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if ("Latin America & Caribbean" %in% names(counts)) "Latin America & Caribbean" else names(counts)[2]
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pred_sets <- list()
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if (!is.na(opt$predictors)) {
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pred_sets <- list(strsplit(opt$predictors, ",", fixed = TRUE)[[1]] |> trimws())
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} else {
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plist <- c()
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if (!is.na(gdp_col)) plist <- c(plist, gdp_col)
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if (!is.na(pop_col)) plist <- c(plist, pop_col)
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if (length(plist) >= 1) pred_sets <- append(pred_sets, list(plist[1]))
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if (length(plist) >= 2) pred_sets <- append(pred_sets, list(plist[1:2]))
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}
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pred_sets <- pred_sets[lengths(pred_sets) > 0]
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choose_knn_vars <- function(df, exclude, k = 3) {
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cands <- names(df)[endsWith(names(df), ".new") & names(df) != exclude]
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cands <- cands[sapply(cands, function(c) is.numeric(df[[c]]))]
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miss <- sapply(cands, function(c) mean(is.na(df[[c]])))
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ord <- order(miss, cands)
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head(cands[ord], k)
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}
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knn1 <- if (!is.na(opt$knn1)) {
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strsplit(opt$knn1, ",", fixed = TRUE)[[1]] |> trimws()
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} else {
|
||||
choose_knn_vars(df, response, 3)
|
||||
}
|
||||
|
||||
knn2 <- if (!is.na(opt$knn2)) {
|
||||
strsplit(opt$knn2, ",", fixed = TRUE)[[1]] |> trimws()
|
||||
} else {
|
||||
setdiff(choose_knn_vars(df, response, 6), knn1)[1:3]
|
||||
}
|
||||
|
||||
ctx <- list(
|
||||
data = normalizePath(opt$data),
|
||||
region_col = region_col,
|
||||
response = response,
|
||||
region_a = region_a,
|
||||
region_b = region_b,
|
||||
predictors = pred_sets,
|
||||
knn1 = knn1,
|
||||
knn2 = knn2,
|
||||
k = opt$k,
|
||||
fig_dir = "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures",
|
||||
stats_dir = "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/stats"
|
||||
)
|
||||
|
||||
|
||||
writeLines(jsonlite::toJSON(ctx, pretty = TRUE, auto_unbox = TRUE), "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/ctx.json")
|
||||
message("wrote ctx.json")
|
||||
@@ -0,0 +1,27 @@
|
||||
source("/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/R/00_utils.R")
|
||||
ctx <- jsonlite::fromJSON("/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/ctx.json")
|
||||
df <- suppressMessages(readr::read_csv(ctx$data, show_col_types = FALSE))
|
||||
|
||||
va <- df |> dplyr::filter(.data[[ctx$region_col]] == ctx$region_a) |> dplyr::pull(all_of(ctx$response)) |> as.numeric()
|
||||
vb <- df |> dplyr::filter(.data[[ctx$region_col]] == ctx$region_b) |> dplyr::pull(all_of(ctx$response)) |> as.numeric()
|
||||
|
||||
box_a_path <- file.path(ctx$fig_dir, paste0("box_", sanitize(ctx$region_a), "_", sanitize(ctx$response), ".png"))
|
||||
box_b_path <- file.path(ctx$fig_dir, paste0("box_", sanitize(ctx$region_b), "_", sanitize(ctx$response), ".png"))
|
||||
hist_a_path <- file.path(ctx$fig_dir, paste0("hist_", sanitize(ctx$region_a), "_", sanitize(ctx$response), ".png"))
|
||||
hist_b_path <- file.path(ctx$fig_dir, paste0("hist_", sanitize(ctx$region_b), "_", sanitize(ctx$response), ".png"))
|
||||
qq_path <- file.path(ctx$fig_dir, paste0("qq_", sanitize(ctx$response), "_", sanitize(ctx$region_a), "_vs_", sanitize(ctx$region_b), ".png"))
|
||||
|
||||
save_plot(box_plot(va, paste0(ctx$response, " (", ctx$region_a, ")")), box_a_path)
|
||||
save_plot(box_plot(vb, paste0(ctx$response, " (", ctx$region_b, ")")), box_b_path)
|
||||
save_plot(hist_density_plot(va, paste0(ctx$response, " (", ctx$region_a, ")")), hist_a_path)
|
||||
save_plot(hist_density_plot(vb, paste0(ctx$response, " (", ctx$region_b, ")")), hist_b_path)
|
||||
qp <- qq_two_sample(va, vb, paste0("qq plot: ", ctx$region_a, " vs ", ctx$region_b))
|
||||
if (!is.null(qp)) save_plot(qp, qq_path)
|
||||
|
||||
ctx$box_a <- box_a_path
|
||||
ctx$box_b <- box_b_path
|
||||
ctx$hist_a <- hist_a_path
|
||||
ctx$hist_b <- hist_b_path
|
||||
ctx$qq_fig <- qq_path
|
||||
writeLines(jsonlite::toJSON(ctx, pretty = TRUE, auto_unbox = TRUE), "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/ctx.json")
|
||||
message("plots done")
|
||||
@@ -0,0 +1,59 @@
|
||||
source("/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/R/00_utils.R")
|
||||
ctx <- jsonlite::fromJSON("/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/ctx.json")
|
||||
df <- suppressMessages(readr::read_csv(ctx$data, show_col_types = FALSE))
|
||||
|
||||
fit_ols <- function(data, y_col, x_cols, name_tag, fig_dir, stats_dir) {
|
||||
d <- data |> dplyr::select(all_of(c(y_col, x_cols))) |> tidyr::drop_na()
|
||||
for (xc in x_cols) d[[xc]] <- tf_pos(d[[xc]])
|
||||
f <- as.formula(paste(y_col, "~", paste(x_cols, collapse = " + ")))
|
||||
m <- lm(f, data = d)
|
||||
|
||||
# residuals vs fitted
|
||||
res_path <- file.path(fig_dir, paste0("residuals_", sanitize(name_tag), ".png"))
|
||||
p_res <- tibble(fitted = fitted(m), resid = resid(m)) |>
|
||||
ggplot(aes(fitted, resid)) +
|
||||
geom_point(size = 1.6) +
|
||||
geom_hline(yintercept = 0) +
|
||||
labs(title = paste("residuals vs fitted:", name_tag),
|
||||
x = "fitted", y = "residuals") +
|
||||
theme_minimal()
|
||||
save_plot(p_res, res_path)
|
||||
|
||||
# scatter first predictor vs response
|
||||
first <- x_cols[1]
|
||||
sc_path <- file.path(fig_dir, paste0("scatter_", sanitize(name_tag), "_", sanitize(first), ".png"))
|
||||
p_sc <- d |>
|
||||
ggplot(aes(.data[[first]], .data[[y_col]])) +
|
||||
geom_point(size = 1.6) +
|
||||
labs(title = paste(first, "vs", y_col), x = first, y = y_col) +
|
||||
theme_minimal()
|
||||
save_plot(p_sc, sc_path)
|
||||
|
||||
# summary to text
|
||||
summ_path <- file.path(stats_dir, paste0("ols_", sanitize(name_tag), ".txt"))
|
||||
capture.output(summary(m), file = summ_path)
|
||||
|
||||
gl <- broom::glance(m)
|
||||
list(
|
||||
name = name_tag,
|
||||
rsq = unname(gl$r.squared),
|
||||
aic = unname(gl$AIC),
|
||||
bic = unname(gl$BIC),
|
||||
nobs = stats::nobs(m),
|
||||
summary_file = summ_path,
|
||||
residuals_fig = res_path,
|
||||
scatter_fig = sc_path
|
||||
)
|
||||
}
|
||||
|
||||
ols <- list()
|
||||
if (length(ctx$predictors)) {
|
||||
for (p in ctx$predictors) {
|
||||
tag <- paste0("full: ", ctx$response, " ~ ", paste(p, collapse = " + "))
|
||||
ols <- append(ols, list(fit_ols(df, ctx$response, p, tag, ctx$fig_dir, ctx$stats_dir)))
|
||||
}
|
||||
}
|
||||
|
||||
ctx$ols <- ols
|
||||
writeLines(jsonlite::toJSON(ctx, pretty = TRUE, auto_unbox = TRUE), "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/ctx.json")
|
||||
message("ols (full) done")
|
||||
@@ -0,0 +1,72 @@
|
||||
source("/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/R/00_utils.R")
|
||||
ctx <- jsonlite::fromJSON("/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/ctx.json")
|
||||
df <- suppressMessages(readr::read_csv(ctx$data, show_col_types = FALSE))
|
||||
reg_df <- df |> dplyr::filter(.data[[ctx$region_col]] == ctx$region_a)
|
||||
|
||||
fit_ols <- function(data, y_col, x_cols, name_tag, fig_dir, stats_dir) {
|
||||
d <- data |> dplyr::select(all_of(c(y_col, x_cols))) |> tidyr::drop_na()
|
||||
for (xc in x_cols) d[[xc]] <- tf_pos(d[[xc]])
|
||||
f <- as.formula(paste(y_col, "~", paste(x_cols, collapse = " + ")))
|
||||
m <- lm(f, data = d)
|
||||
|
||||
res_path <- file.path(fig_dir, paste0("residuals_", sanitize(name_tag), ".png"))
|
||||
p_res <- tibble(fitted = fitted(m), resid = resid(m)) |>
|
||||
ggplot(aes(fitted, resid)) +
|
||||
geom_point(size = 1.6) +
|
||||
geom_hline(yintercept = 0) +
|
||||
labs(title = paste("residuals vs fitted:", name_tag),
|
||||
x = "fitted", y = "residuals") +
|
||||
theme_minimal()
|
||||
save_plot(p_res, res_path)
|
||||
|
||||
first <- x_cols[1]
|
||||
sc_path <- file.path(fig_dir, paste0("scatter_", sanitize(name_tag), "_", sanitize(first), ".png"))
|
||||
p_sc <- d |>
|
||||
ggplot(aes(.data[[first]], .data[[y_col]])) +
|
||||
geom_point(size = 1.6) +
|
||||
labs(title = paste(first, "vs", y_col), x = first, y = y_col) +
|
||||
theme_minimal()
|
||||
save_plot(p_sc, sc_path)
|
||||
|
||||
summ_path <- file.path(stats_dir, paste0("ols_", sanitize(name_tag), ".txt"))
|
||||
capture.output(summary(m), file = summ_path)
|
||||
|
||||
gl <- broom::glance(m)
|
||||
list(
|
||||
name = name_tag,
|
||||
rsq = unname(gl$r.squared),
|
||||
aic = unname(gl$AIC),
|
||||
bic = unname(gl$BIC),
|
||||
nobs = stats::nobs(m),
|
||||
summary_file = summ_path,
|
||||
residuals_fig = res_path,
|
||||
scatter_fig = sc_path
|
||||
)
|
||||
}
|
||||
|
||||
region_models <- list()
|
||||
if (length(ctx$predictors)) {
|
||||
for (p in ctx$predictors) {
|
||||
tag <- paste0("region ", ctx$region_a, ": ", ctx$response, " ~ ", paste(p, collapse = " + "))
|
||||
region_models <- append(region_models, list(fit_ols(reg_df, ctx$response, p, tag, ctx$fig_dir, ctx$stats_dir)))
|
||||
}
|
||||
}
|
||||
|
||||
best_note <- "no region-level comparison available."
|
||||
if (length(region_models) >= 1) {
|
||||
ord <- order(
|
||||
sapply(region_models, `[[`, "rsq"),
|
||||
-sapply(region_models, `[[`, "aic"),
|
||||
-sapply(region_models, `[[`, "bic"),
|
||||
decreasing = TRUE
|
||||
)
|
||||
best <- region_models[[ord[1]]]
|
||||
best_note <- sprintf(
|
||||
"on region `%s`, the better model is **%s** (r²=%.3f, aic=%.1f, bic=%.1f).",
|
||||
ctx$region_a, best$name, best$rsq, best$aic, best$bic
|
||||
)
|
||||
}
|
||||
|
||||
ctx$best_region_note <- best_note
|
||||
writeLines(jsonlite::toJSON(ctx, pretty = TRUE, auto_unbox = TRUE), "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/ctx.json")
|
||||
message("ols (region) done")
|
||||
@@ -0,0 +1,11 @@
|
||||
source("/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/R/00_utils.R")
|
||||
ctx <- jsonlite::fromJSON("/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/ctx.json")
|
||||
df <- suppressMessages(readr::read_csv(ctx$data, show_col_types = FALSE))
|
||||
|
||||
set.seed(42)
|
||||
knn1 <- run_knn(df, ctx$region_col, ctx$knn1, ctx$k, "model A", ctx$fig_dir)
|
||||
knn2 <- run_knn(df, ctx$region_col, ctx$knn2, ctx$k, "model B", ctx$fig_dir)
|
||||
|
||||
ctx$knn <- list(knn1, knn2)
|
||||
writeLines(jsonlite::toJSON(ctx, pretty = TRUE, auto_unbox = TRUE), "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/ctx.json")
|
||||
message("knn done")
|
||||
@@ -0,0 +1,89 @@
|
||||
library(markdown)
|
||||
|
||||
source("/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/R/00_utils.R")
|
||||
ctx <- jsonlite::fromJSON("/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/ctx.json")
|
||||
|
||||
md <- c(
|
||||
"# exploratory data analysis and models on the epi dataset",
|
||||
paste0("date: ", as.character(Sys.Date()), " "),
|
||||
"",
|
||||
"## dataset and choices",
|
||||
# I regret my life choices
|
||||
paste0("- **file**: `", basename(ctx$data), "`"),
|
||||
paste0("- **region column**: `", ctx$region_col, "`"),
|
||||
paste0("- **response var**: `", ctx$response, "`"),
|
||||
paste0("- **regions**: `", ctx$region_a, "` vs `", ctx$region_b, "`"),
|
||||
"",
|
||||
"## 1) variable distributions",
|
||||
"### 1.1 boxplots and histograms (with density!)",
|
||||
paste0("![](", ctx$box_a, ")"),
|
||||
paste0("![](", ctx$box_b, ")"),
|
||||
paste0("![](", ctx$hist_a, ")"),
|
||||
paste0("![](", ctx$hist_b, ")"),
|
||||
"",
|
||||
"### 1.2 qq plot (two-sample)",
|
||||
paste0("![](", ctx$qq_fig, ")"),
|
||||
"",
|
||||
"## 2) linear models"
|
||||
)
|
||||
|
||||
# Normalize data.frame because NOTHING WORKS
|
||||
row_list <- function(x) {
|
||||
if (is.null(x)) return(list())
|
||||
if (is.data.frame(x)) {
|
||||
lapply(seq_len(nrow(x)), function(i) as.list(x[i, , drop = FALSE]))
|
||||
} else if (is.list(x)) {
|
||||
x
|
||||
} else {
|
||||
list()
|
||||
}
|
||||
}
|
||||
|
||||
if (!is.null(ctx$ols) && length(ctx$ols)) {
|
||||
for (m in row_list(ctx$ols)) {
|
||||
md <- c(md,
|
||||
paste0("### ", m$name),
|
||||
sprintf("- **r²**: %.4f | **aic**: %.2f | **bic**: %.2f", m$r2, m$aic, m$bic),
|
||||
""
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
md <- c(md,
|
||||
"### 2.2 same models on one region (comparison)",
|
||||
if (!is.null(ctx$best_region_note)) ctx$best_region_note else "no note available.",
|
||||
"",
|
||||
"## 3) classification (knn, label = region)"
|
||||
)
|
||||
|
||||
if (!is.null(ctx$knn) && length(ctx$knn)) {
|
||||
for (k in row_list(ctx$knn)) {
|
||||
md <- c(md,
|
||||
paste0("### ", k$tag),
|
||||
sprintf("- **k**: %d | **accuracy**: %.4f | **test n**: %d",
|
||||
k$k, k$accuracy, k$n_test),
|
||||
paste0("variables: `", paste(k$vars, collapse = ", "), "`"),
|
||||
paste0("![](", k$confusion_fig, ")"),
|
||||
""
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
# I hate markdown sometimes man
|
||||
md <- gsub("/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/", "", md)
|
||||
|
||||
# writeLines(md, "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/report.md")
|
||||
# writeLines(jsonlite::toJSON(ctx, pretty = TRUE, auto_unbox = TRUE),
|
||||
# file.path(ctx$stats_dir, "summary.json"))
|
||||
|
||||
md_file <- "output/report.md"
|
||||
html_file <- "output/report.html"
|
||||
pdf_file <- "output/report.pdf"
|
||||
|
||||
setwd("/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/")
|
||||
markdownToHTML(
|
||||
md_file,
|
||||
html_file
|
||||
)
|
||||
|
||||
message("done")
|
||||
@@ -0,0 +1,181 @@
|
||||
code,iso,country,region,population,gdp,EPI.old,EPI.new,ECO.old,ECO.new,BDH.old,BDH.new,MKP.old,MKP.new,MHP.old,MHP.new,MPE.old,MPE.new,PAR.old,PAR.new,SPI.old,SPI.new,TBN.old,TBN.new,TKP.old,TKP.new,PAE.old,PAE.new,PHL.old,PHL.new,RLI.old,RLI.new,SHI.old,SHI.new,BER.old,BER.new,ECS.old,ECS.new,PFL.old,PFL.new,IFL.old,IFL.new,FCL.old,FCL.new,TCG.old,TCG.new,FLI.old,FLI.new,FSH.old,FSH.new,FSS.old,FSS.new,FCD.old,FCD.new,BTZ.old,BTZ.new,BTO.old,BTO.new,RMS.old,RMS.new,APO.old,APO.new,OEB.old,OEB.new,OEC.old,OEC.new,NXA.old,NXA.new,SDA.old,SDA.new,AGR.old,AGR.new,SNM.old,SNM.new,PSU.old,PSU.new,PRS.old,PRS.new,RCY.old,RCY.new,WRS.old,WRS.new,WWG.old,WWG.new,WWC.old,WWC.new,WWT.old,WWT.new,WWR.old,WWR.new,HLT.old,HLT.new,AIR.old,AIR.new,HPE.old,HPE.new,HFD.old,HFD.new,OZD.old,OZD.new,NOD.old,NOD.new,SOE.old,SOE.new,COE.old,COE.new,VOE.old,VOE.new,H2O.old,H2O.new,USD.old,USD.new,UWD.old,UWD.new,HMT.old,HMT.new,LED.old,LED.new,WMG.old,WMG.new,WPC.old,WPC.new,SMW.old,SMW.new,WRR.old,WRR.new,PCC.old,PCC.new,CCH.old,CCH.new,CDA.old,CDA.new,CDF.old,CDF.new,CHA.old,CHA.new,FGA.old,FGA.new,NDA.old,NDA.new,BCA.old,BCA.new,LUF.old,LUF.new,GTI.old,GTI.new,GTP.old,GTP.new,GHN.old,GHN.new,CBP.old,CBP.new
|
||||
4,AFG,Afghanistan,Southern Asia,41454761,2116,18,30.7,21.1,31.2,25.6,32.1,NA,NA,NA,NA,NA,NA,0.3,0.3,0.6,9.1,0.3,11.7,24.6,24.6,79.5,79.5,80.9,80.9,75.8,75.6,67.8,66.4,50.1,50.5,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,5.2,42,19.4,18,39.9,37.6,0,34.2,0,55.5,45,41.1,35.3,31.2,100,97.2,60.6,52,40.2,41.9,9,9,89.7,89.7,0,0,0,0,0,0,17.7,18.2,16.8,15.8,22.8,18.8,1.1,3.6,7.5,10.3,33.4,34.9,63.5,58.7,51,50.2,36.5,37.1,26.2,32.3,20.7,31,22.3,33.1,0,0,0,0,25.2,25.2,60.6,60.6,0,0,2.4,2.4,13.7,40.2,13.7,40.2,0,38.4,0,100,4.2,48,36.8,4.3,9.3,50.8,1.2,39.3,42.2,44.4,0.7,35,8.1,46.7,17.9,22.6,97.7,97.7
|
||||
8,ALB,Albania,Eastern Europe,2811655,22730,45.9,52.1,50.3,51.8,50.9,50.6,21.2,25.5,24.4,24.4,100,96.7,46.4,46.4,54.4,56.8,54.2,60.7,58.4,58.4,65.2,65.2,63.4,63.4,70.5,70,78.4,53.5,44.3,45.1,58.4,62.4,NA,NA,NA,NA,65,71.7,36.5,36.5,67.5,67.5,13.7,15.8,NA,NA,24.2,24,9,3.5,5.6,7.8,100,100,80.3,74.5,25.8,25.9,23.3,21.8,100,100,100,69.2,48.4,50.4,20.3,22,35.7,36.2,67,65.7,68.6,73.6,21.8,39.2,64.8,56.6,19,26.5,10.9,49,33.4,33.4,40,43.8,32,36.5,32.8,36.6,19.3,27.6,43.6,60.2,37.5,34.5,39,44.6,59.6,64.4,48.9,47.2,68.1,71.3,67.1,73.2,65.4,70,50.5,51.5,50.7,51.5,16.4,16.4,35.6,35.6,0,0,5.3,5.3,44.1,59.4,44.1,59.4,37.8,54.7,47.5,77.2,82.4,87.6,36.8,3.8,72.3,100,65.5,100,49.7,50,43.3,56.9,44,56.3,32.7,39.5,87.6,87.6
|
||||
12,DZA,Algeria,Greater Middle East,46164219,18340,38.6,41.9,39.7,42.2,33.1,33,0,0,0,0,100,100,6.1,6.1,73.2,73.8,4.2,4.3,6,6,20.3,20.3,58.8,58.8,74.3,73.8,78.1,74,54.7,54.5,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,53.1,51.6,85.6,75.7,40.8,37.7,44.8,49.2,45.3,50.1,20.8,58.2,49.7,63.7,25.8,18.7,12,7.8,7.1,60.7,100,86.8,42.9,46.7,35,38.6,100,100,76.7,74.6,39.8,38.7,53.1,55.9,62.7,62.7,65,72,41.7,41.7,41.7,41.7,48,48.1,47.3,46.1,46.8,37.1,52.8,64.5,30.5,29.6,33.6,31.3,36.5,24,54,42.7,33.2,27.9,61.6,64.7,61,68.8,57.4,62,26.6,29.2,24.4,29.2,34.3,37.6,44.6,47.8,61.6,71.6,10.3,10.5,29.3,36.2,29.3,36.2,34.8,40.7,21.3,30.4,48,40.5,34.3,12.6,9.4,46.1,3.8,59.8,49.3,49.7,29.8,32.1,32.1,34.7,7.7,6.9,51.5,51.5
|
||||
24,AGO,Angola,Sub-Saharan Africa,36749906,9910,31.6,39.7,35.9,43.2,42.3,41.5,0,0,0,0,NA,NA,35.6,35.6,37.1,37.1,34.1,34.1,74.7,74.7,82.1,82.1,89.8,89.8,77.8,77.7,85.1,71.6,57.7,55.6,50.8,49.2,68.5,50.5,41.1,49.3,58.1,49.1,27.9,27.9,83.5,83.5,38.8,37.6,21,29,48.1,45.4,27.2,26.9,38.5,43.4,59.3,45.3,13.5,73.2,69.5,69.1,87.5,84.4,0,69.6,0,75.4,39.3,41,33,35.2,81.6,97.5,97,97.8,24,18.4,13.8,13.8,86.5,86.5,7.6,7.6,4.3,4.3,4.3,4.3,19.4,21.6,18.9,19.9,14.4,15.4,8.2,13.8,24.4,32.7,43.7,43.6,63.7,60.3,37.5,43.2,4.3,9.8,16.2,22.9,10,23.4,10,22.6,28.7,30.4,26.9,30.4,26.1,26.1,64.1,64.1,0,0,1.1,1.1,35.2,49.4,35.2,49.4,26.4,54.7,78.8,100,55.5,54.7,36.8,3.7,49.2,49,5.8,52.5,47.9,48,38.3,48.8,42.5,52.9,15.3,20.4,88.5,88.5
|
||||
28,ATG,Antigua and Barbuda,Latin America & Caribbean,93316,31474,54.4,55.5,52.4,53.2,52.5,52.8,74.7,74.7,33.1,33.1,60.3,88.5,NA,NA,19.6,19.9,51.5,51.5,72.9,72.9,28.3,28.3,90,90,67.3,64,NA,NA,93.7,91.4,35.4,28.7,0,0,NA,NA,70.6,59,30.2,30.2,46.1,46.1,97.6,97.3,81.5,100,NA,NA,100,100,100,100,51.3,56.4,64.6,72,40,36.2,44.4,39.6,39.5,84.8,76.3,72.9,30.2,31.4,5.2,3.8,40.2,39.8,29.4,41.2,54.1,54.1,48.2,48.2,56.5,56.7,50.8,50.8,44.4,44.4,44.4,44.4,65.7,69.6,72.8,77.8,93.9,93.5,57.3,62.7,100,100,42.2,35.1,63,68.8,82.6,85.8,87.3,91,55.6,56.9,53.3,56,56,57.5,45.8,48.5,46.1,48.5,35.6,35.6,39,39,100,100,0,0,47.1,46.4,47.1,46.4,42.1,46.3,22.8,28.8,67.9,57.4,NA,NA,52.1,57.5,57.2,59.9,50.3,50.2,39.1,42.6,36.8,39,51.5,51.6,54,54
|
||||
32,ARG,Argentina,Latin America & Caribbean,45538401,30380,45.9,46.8,41.7,47.1,32,35,13.4,17.1,9.5,33,68.2,89.1,14.9,14.9,30.9,32.9,26,27.9,40.5,40.5,37.6,37.6,71.6,71.6,54.4,54.2,69.6,48.2,39.9,39.3,35.6,48.9,47.2,62.7,44.9,51.3,21.4,44.2,0,0,72.2,72.2,52,38.5,77.5,70.5,53.4,50.8,12.8,26.8,15.8,24.6,49.9,48.3,57,80.5,80.1,77,94.4,92.5,36.7,69,62,90.4,84.1,81.4,82.6,87.7,100,100,37.3,29.5,80.8,95.3,48.5,48.5,32.8,32.8,55,55,55,55,11.8,11.8,52.1,52.9,48.1,47.6,47.9,44.6,47.8,54.5,49,48.6,11.6,18.2,65.6,66.9,66.4,64.5,14.2,17.4,64.4,68.3,61.6,70.2,60.2,67.1,68,72.8,65.8,72.8,26.6,26.6,32.5,32.5,56.1,56.1,6,6,47.1,41.4,47.1,41.4,41.8,50.8,30.7,44.8,87.6,46.6,42.6,6.8,58.4,27.3,41.7,54.3,45.1,48.5,44.9,43.4,39.9,39.1,7.4,6.6,53,53
|
||||
51,ARM,Armenia,Former Soviet States,2943393,24970,42.5,44.7,46.8,47.8,48.4,47.4,NA,NA,NA,NA,NA,NA,14.4,14.4,38.7,38.7,76.4,77.2,39.5,39.5,49.8,49.8,71.5,71.5,49.9,47.9,92.4,80.4,41.5,43.4,80.3,83.4,NA,NA,NA,NA,88.8,99.3,58.2,58.2,54.2,54.2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,38.7,54.2,40.9,37.3,45.3,40.6,41.2,18.6,50.8,95.8,56.6,35.5,29.7,13.1,100,100,66.3,49.3,59.9,46.7,31.9,35.8,0,0,38,42.9,38,42.9,14.6,14.6,38.5,41.2,29.1,30.9,22.9,19.3,26.5,38.5,27.2,35.2,29.6,28.3,35.6,35.2,58.4,59.7,47.1,43.2,69.1,74.2,59.8,68.7,67.1,77.8,49.9,54.8,45.7,54.8,23.5,24.8,54.3,57.4,0,0,4.5,4.5,39.3,42.8,39.3,42.8,37.3,39,38.9,41.9,60.3,79.3,36.7,3.8,6.7,39.2,36,74.4,43.3,46.9,35.1,38.9,36,38.1,31.8,31.3,69.2,69.2
|
||||
36,AUS,Australia,Global West,26451124,71310,59,63,60.7,63.3,50.6,55.4,36.5,57.2,30.1,42.5,45,57.2,50.9,50.9,53.5,65.8,44.2,67.1,58.1,58.1,89.1,89.1,92.2,92.2,48,39.5,82.7,50.3,36.1,35.8,60.2,42.3,97.5,97.4,21.1,0,64.7,20.1,44.9,44.9,72.2,72.2,48.5,48.1,29.4,28.6,45.4,47.7,46.3,51.1,49,53.3,77.6,57.2,87.4,95.9,79,77.3,86.2,82.1,91.2,98.2,79.7,100,53.8,65.3,52.9,49.7,53.4,51.8,57.5,59,58.9,84.7,88.9,89.1,26.2,26.2,100,100,92.4,92.9,92.9,92.9,79.6,82,78.5,81,90,94.9,80.6,85.1,85.5,71,16.5,26.4,33.3,42.2,77.5,86.5,16.8,18.3,89.8,90.9,89,91.6,89.3,90.5,80.4,86.3,75.4,86.3,45.4,45.7,25.2,27.6,90.2,91.3,43.1,41.1,39.1,46.6,39.1,46.6,47.8,50.8,20.1,24,44.6,82.4,39.8,31.9,39.7,77.7,41.7,49.6,47.7,49.5,38,47.5,20.4,30.2,2.7,5.9,43.5,43.5
|
||||
40,AUT,Austria,Global West,9130429,74981,68.9,69,78.4,78.2,74.6,74.4,NA,NA,NA,NA,NA,NA,86.4,86.4,78.9,83.6,85.7,86.9,70.6,70.6,76.7,76.7,57.9,57.9,62.6,59.1,74,59.9,48.1,48,58.9,47.5,NA,NA,NA,NA,66.7,53.4,38.7,38.7,35.6,35.6,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,93.1,92.9,56.3,54.2,65.5,61,100,100,100,100,68.9,72.5,64.6,64,41,40.9,75.3,71.6,71.5,84.1,88.1,89.5,0,10.8,100,100,95.2,96,100,100,65.4,70,56.4,61.5,30.8,46.1,83.7,88.9,41.6,39.3,20.1,25.5,54.2,61.1,51.7,57.4,50.8,50.2,89,92.6,79.9,87.7,88.5,95.8,82.5,88.3,78.4,88.3,67.2,63.8,23.8,12.2,97.9,99.5,95.2,97.6,57.3,54.1,57.3,54.1,62.1,52.5,50.8,36.9,72.7,77.3,47.1,40.4,55.2,60.5,100,100,51.5,51.4,57.4,53,47.6,44.5,23.2,20.6,61.5,61.5
|
||||
31,AZE,Azerbaijan,Former Soviet States,10318207,25480,40.4,40.4,44.7,44.4,36.3,36.9,0.8,0.8,20,20,50,50,7.2,7.2,42.1,43.1,31.3,31.4,41.3,41.3,55.2,55.2,31,31,80.8,81.2,86,80.1,24.1,25.8,79.5,82.3,NA,NA,NA,NA,92.1,98.3,50.7,50.7,65.4,65.4,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,77.1,67,45,39.3,56.4,49.8,70.5,60.3,100,82.7,62.7,63,45.8,48.1,100,100,80.7,64.2,55.3,74.4,23.2,28.9,43,44,18.9,25.9,18.9,25.9,38.1,38.1,38.1,40.2,36.1,38.2,36.3,37.9,24,36.8,41.2,37.1,37.6,36.4,35.5,39.3,60.9,63.1,40.3,41.6,45,48.2,39.8,45.2,44.6,50.2,40.9,46.8,37,46.8,31.2,21.6,47.7,34.5,22.7,13.8,18.9,12.6,36.1,34.7,36.1,34.7,52.8,44.5,50.5,37.3,0,21.2,23,25.8,24,11.3,28.3,50.7,48.4,47.2,36.9,33.5,36.6,33,18.9,16.9,48.4,48.4
|
||||
44,BHS,Bahamas,Latin America & Caribbean,399440,37517,54.6,56,54.7,53.9,47,46.8,25.1,25.1,17.4,17.4,53,77.8,80.1,80.1,21.6,21.6,79.3,82.6,46.2,46.2,100,100,100,100,8.1,3.1,99.4,98.2,100,100,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,91.5,91.6,64.7,59,100,100,100,100,100,100,46.9,56,70,66.1,20.2,16.5,13.6,8.4,63.9,79.8,80,73.9,50.8,50.2,37.8,39.9,29.7,30.8,23.6,23.3,73.3,73.3,61.9,61.9,28.6,28.6,68.9,68.9,63,63,63,63,67.7,69.4,74.7,76.4,99,97.1,63,65.4,55.9,65.9,22.9,23.6,62.3,70.9,78,83,79.5,86.5,62.7,63.8,65.7,68.7,59.3,60.5,50.4,53.9,47.9,53.9,8.7,8.7,18.8,18.8,0,0,2.9,2.9,42.7,47.6,42.7,47.6,45.9,49,32.6,37.3,33.9,40.9,NA,NA,38.3,69.4,70.5,58.5,50,50.1,43.6,47.9,38.4,42.7,41.9,42.9,61.3,61.3
|
||||
48,BHR,Bahrain,Greater Middle East,1569666,66975,37.1,35.9,45.9,38.6,26.8,26,67.3,67.3,44.3,59.4,100,30.8,NA,NA,3.7,3.7,8.7,8.7,0,0,NA,NA,NA,NA,21.7,13.3,NA,NA,46.2,45.6,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,66.4,68.9,NA,NA,27,30.6,100,100,65.5,67.6,18.7,75.8,73.3,32.5,0,0,0,0,76,60.7,66.4,17.2,38,42.5,8.2,21.7,45.3,35.5,61.1,58.5,57.2,57.2,83.2,80.9,20.4,20,88,81,98.6,100,64.9,64.9,39.6,40.9,32.8,32.7,9.5,3.8,63.6,71.7,13.9,15.2,5.6,7.9,11,9.2,45.5,37.5,27,22.8,65.3,66.9,65.6,69.9,62.7,64.9,42.8,48,36.3,48,20,34.6,0,3.2,100,98.5,0,34,23.4,27.9,23.4,27.9,27.7,39.5,0,5.8,12.9,42.9,48.3,10.2,28.5,35.7,100,100,NA,NA,10.2,24.7,0,1.5,16,17,3.8,3.8
|
||||
50,BGD,Bangladesh,Southern Asia,171466990,10370,25.5,27.8,27.3,31.4,20.7,29.1,21,32.6,4.8,61.1,50,91.2,0,0,19.8,19.8,13.1,14,23.9,23.9,1.4,1.4,51,51,25.4,13.2,92.7,80.1,0.3,0.4,61.9,51.4,62.9,59.9,NA,NA,65.4,31.2,75,75,53.9,53.9,62.4,63.2,98.2,88.6,60.3,58.4,69.8,58.6,71.2,60.4,43.2,49,11.9,13.3,33.1,27.3,41.5,35.4,18.8,19.5,0,0,71.1,72.2,52.9,54.4,50.6,29.7,74.2,56.6,85.3,100,14.1,14,48,48.9,2.9,2.9,19.9,19.4,2,2,13.4,15,5.6,6.3,0,0,3.2,7,1.1,2.4,40.8,40,21.6,17.8,5.1,3.2,16.8,15.8,28.8,31.9,25,30.9,27,32.6,22.3,27,17.8,27,51.5,52.9,96.5,99.5,58.6,59.1,3,3.1,32.8,33,32.8,33,24.7,27.4,63.7,69.1,31.3,42.1,36.7,3.7,23.1,32.7,36.4,46.9,48.7,46.1,30.3,34.2,33.3,35.5,8.3,6.8,87.1,87.1
|
||||
52,BRB,Barbados,Latin America & Caribbean,282336,22035,50.5,53.1,34.1,35,11.8,12.5,0,0,0,0,50,100,NA,NA,1,1.2,1.6,1.6,1.7,1.7,NA,NA,NA,NA,58.9,54.7,NA,NA,19,19.5,42.5,45.4,NA,NA,NA,NA,36.6,47.6,60.1,60.1,5,5,77.9,80.4,11.8,6,86.4,77.4,100,100,100,100,NA,NA,67.8,70,48.5,43.6,54.7,48.8,41,74.8,73.1,74.6,48.7,47.9,61.6,41.4,23.8,20.3,0,13.5,71,71,49.5,49.5,33.8,33.8,55.1,55.1,48.2,48.2,48.2,48.2,76.5,77.4,85.2,85.8,100,100,75.6,76.5,100,100,20.8,25,87.1,92.5,83,85.4,94.1,96.3,59.1,59.8,58.7,61.1,58.1,59,62.7,65.7,62.2,65.7,42.9,46,32.6,8.9,100,100,24.7,56.2,50.5,56.7,50.5,56.7,47.3,59.2,38.1,56.4,61,49.5,NA,NA,42.4,53.8,55.1,54.8,54.6,66.4,40.5,56.1,39.8,55.4,44,55.2,82.3,82.3
|
||||
112,BLR,Belarus,Former Soviet States,9115680,33600,49.3,58.1,60.4,68.1,53.9,70.3,NA,NA,NA,NA,NA,NA,36.2,36.2,13.4,90.6,44.8,45.5,93.7,93.7,23.9,23.9,72.3,72.3,88.9,92.4,87.7,77.8,0,0,63.5,52.5,NA,NA,NA,NA,61.6,44.5,80.6,80.6,36.2,36.2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,79.8,81.9,56.1,58.1,65,68.2,56,100,100,71.4,49,45.2,30.2,29.7,33.2,44.2,70.7,73.1,56.7,49.2,66.4,63.2,23.6,16.3,78.5,78.5,65.8,59.6,62.8,62.8,49.6,55.8,43.7,51.3,26.1,44.4,46.4,58.1,48.7,66.3,21.8,15.9,47.7,50.3,61.6,65.4,63.6,65.2,74.3,74.5,71.4,71.9,76.5,76.2,48.9,53,44.6,53,29.1,44.3,25.5,32,70.8,95.1,11.9,31.1,32.1,44.5,32.1,44.5,47,50.8,29.9,35.5,32.8,50.5,2,30.1,0,49.9,10.3,69.5,49.7,49.4,26.8,41.4,24,38.4,12.8,17.1,50.5,50.5
|
||||
56,BEL,Belgium,Global West,11712893,75199,62,66.7,61.6,69.1,53.2,66.4,45.5,45.5,80,80,48.5,47.1,70.4,70.4,38.7,96,24,51.6,46.1,46.1,3,3,70.8,70.8,93.8,92.7,45,48,16.5,16.7,48.8,43.6,NA,NA,NA,NA,47.8,47.5,48.5,48.5,13.9,13.9,7.8,8,NA,NA,15.2,15.7,0,0,0,3.2,44.5,51.4,95.4,94.3,68.1,61.6,80.8,70.2,100,100,100,100,68.9,68.5,44.7,43.6,27.6,29.3,62.1,61.6,99.6,99.6,81.6,83.6,29.3,29.3,95.2,98,81.9,84,78.2,78.2,66.5,70.8,59.5,64.8,30.4,48.7,87.1,92.7,44.6,44.8,10.7,24.9,43.1,54.7,49.9,60.2,63.4,67,86.4,88.2,82.9,87.4,86.3,88.7,74.4,81.3,68.9,81.3,70,65.1,31.9,14.3,96.6,99.3,94.9,98.9,59,59.7,59,59.7,70,56.8,59.9,41,63.3,100,28.6,60.5,39.6,61.8,100,100,48.8,47.2,59,55.8,48.1,46.2,23,19.8,64.7,64.7
|
||||
84,BLZ,Belize,Latin America & Caribbean,411106,14958,46.5,47.4,55.8,57.3,57.6,58.4,25.9,25.9,36.1,36.7,50,97.4,100,100,90.3,90.3,94.6,94.6,39.8,39.8,89.5,89.5,99.4,99.4,31.7,29.9,26,0,87.6,83.2,42,42.3,46.7,48.2,63.1,56.3,28.9,28.6,8.1,8.1,60.5,60.5,86.3,86.7,NA,NA,40.4,54.4,75.1,100,99.7,100,61.4,55.6,66,71,63.3,62.4,71,69.6,31.2,71.2,74.9,72.9,55.8,60.9,66.8,57.4,41,23.4,39.2,42,68.1,75.5,37.9,37.9,50.9,50.9,43.2,43.2,31,31,31,31,41.5,43.3,39.8,41.3,43.6,47.7,23.5,28,49.6,46.7,45.4,50.3,81.2,84.4,68.8,75,21,18.9,48.2,50.4,46.8,51.4,46.3,49.7,50.5,54.3,48.9,54.3,19.6,19.6,44.9,44.9,5.2,5.2,1.4,1.4,36.5,35.8,36.5,35.8,42.6,32.2,55.9,37.7,2.7,40.4,44.1,28.6,0,42.7,35.4,48.1,47.6,46.3,27.1,32.1,29.4,34.3,44.7,43.8,63.8,63.8
|
||||
204,BEN,Benin,Sub-Saharan Africa,14111034,4501,37.7,37.4,51,54.6,63.8,63.7,NA,NA,0,0,NA,NA,62.6,62.6,75.5,75.5,86.1,86.1,91.7,91.7,36.3,36.3,79.3,79.3,70.7,70.7,83.9,80.4,12,10.7,54.4,53.2,92.9,85.5,NA,NA,19,28.9,14.4,14.4,58.4,58.4,87.8,88.3,NA,NA,100,100,72.7,77.2,97.4,97.9,42.4,30.3,27.4,50.4,66.5,63.5,71.7,68.9,0,44.6,17.6,49.8,50.1,54,41.3,52.6,100,100,79,71.7,42.3,44.2,10,10,97.9,97.9,0.4,0.4,0,0,0,0,24.1,25.8,23.6,25,46.8,36.1,4.5,6.1,35.8,24.5,51.8,55.6,62.4,59.4,42.2,38.4,25.8,20.1,16.4,20.1,12.1,18.9,13.6,20.9,36,38.1,34.5,38.1,48.1,41.9,100,84.3,40.6,40.6,0,0.1,30.5,22.9,30.5,22.9,23.2,22,58.5,56.2,17.3,25.4,36.8,3.8,37.7,12.1,18.5,35.9,0,0,23.4,20.4,30,26.2,26.7,23.3,82.1,82.1
|
||||
64,BTN,Bhutan,Southern Asia,786385,16754,36.4,43.3,56.4,59.5,68,67.2,NA,NA,NA,NA,NA,NA,40.9,40.9,68.2,68.2,80.2,80.2,77.8,77.8,100,100,99,99,42.3,42.3,98,87.7,100,100,87.8,86.7,93.9,94.1,70.3,87.6,87,90.1,52.5,52.5,88.4,88.4,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,25.4,50.9,32,15.6,46.4,25.4,54.8,51.5,15.4,62.4,50.5,44.7,39.8,33.8,100,67.5,85.7,74.9,34.2,41.1,23.9,23.9,64.9,64.9,25.5,25.5,14.4,14.4,14.4,14.4,18.5,22.3,13,16.8,1.1,5.2,12.4,22.2,3.3,1.7,65.7,71.1,42.9,39.4,18.3,15.9,10.5,12.2,30.3,35,20.9,27.6,28.8,40,28.3,30.5,26.6,30.5,33.7,35.8,62.3,65.5,32.4,35.5,5.7,6.2,19.6,35.3,19.6,35.3,3.8,38.2,0,58.2,35.7,17.7,NA,NA,37.1,37.5,20.4,51.3,48.3,49.6,23,31.6,25.1,32.6,39.6,39.3,64.8,64.8
|
||||
68,BOL,Bolivia,Latin America & Caribbean,12244159,11323,41.6,44.9,58.4,55.7,67.6,63.6,NA,NA,NA,NA,NA,NA,73.3,73.3,69.1,69.4,81.5,85,66.5,66.5,46.1,46.1,95.5,95.5,53.2,51.9,69,24.3,51.8,49.9,53.2,33.7,59.9,47.1,44.3,23.6,44.4,24.4,21.9,21.9,84.7,84.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,61,73.9,100,100,100,100,27.6,55.9,57.4,81.5,62.3,59.6,49.5,61.3,82,98.6,52.8,42.8,63.9,61.7,24.2,24.2,66.7,66.7,26.4,26.4,14,14,14,14,29.5,29.4,24.8,23,23,14.9,14.8,21.9,59.1,39.2,20.9,20.4,95.3,91,63.2,53.1,0,0,42.1,46.5,36.1,44.7,39.5,47.7,41,43.3,39.3,43.3,24.8,24.5,49.7,49.8,12.6,11.8,5.9,5.5,25.8,41.4,25.8,41.4,29.6,45,33.7,60.8,24.9,45.6,10,24.3,32.7,26.1,22.7,72.6,48.7,49.2,22.2,35,29.5,40.7,17.2,18.8,62.1,62.1
|
||||
70,BIH,Bosnia and Herzegovina,Eastern Europe,3185073,22610,42.4,45.6,48.7,51.3,43.8,45.7,NA,NA,31,31,NA,NA,55.1,55.1,19.2,22,11.2,13.2,67.5,67.5,76.1,76.1,75,75,64.6,63.5,82.7,75.4,36.7,36.7,76.3,76.2,NA,NA,NA,NA,89.9,88.4,53.7,53.7,59.9,59.9,92.5,89.9,NA,NA,82.2,81,100,100,100,100,80,4.5,77.3,75.2,28.6,29.6,57.2,54.7,55,100,50.7,63.7,31.8,52.3,21,29.1,46.8,51.2,63,52.1,22.9,75.8,20.4,23,42.8,42.8,29.6,36,2.9,2.9,31.3,31.3,32.5,36,19.3,22.9,8.6,10.5,19,24.4,39.9,40.1,39.9,38.8,25.7,31.9,56.7,60.8,36.8,35.7,75.5,78.7,72.1,77.9,74.7,79.3,45.1,48.8,43.5,48.8,17.7,17.7,37.2,37.2,14.1,14.1,0,0,41.8,45.9,41.8,45.9,32.1,50.5,8.8,36,39.2,40.8,100,43.3,6,100,17.3,34.4,50.2,49.7,22.6,43.5,25.7,42.1,21.9,26.9,57.7,57.7
|
||||
72,BWA,Botswana,Sub-Saharan Africa,2480244,20800,50.9,49,68.2,74,85.9,85.8,NA,NA,NA,NA,NA,NA,84.1,84.1,93.7,93.7,87.4,87.4,78.8,78.8,46.2,46.2,54.5,54.5,92.3,92.2,95.3,94.2,59,59.6,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,52.5,83.5,65.6,61.9,71.3,65.6,43.6,74.9,100,100,33.7,35.3,1,6,56.1,56.8,97.6,93.8,38.5,38.5,42,42,53.3,53.3,46.7,46.7,36,36,36,36,27.2,31.7,29.2,34.5,28.8,38.9,17.4,25.5,30.1,31.3,57.5,61.2,45.3,45.1,66.2,60.8,13.5,12.3,18.1,20.1,14.8,18.4,16.9,21.3,31.4,37.3,25.6,37.3,31.4,31.4,53.8,53.8,49,49,0.3,0.3,44.1,25.1,44.1,25.1,38.6,45.1,35.1,45.7,0,0,36.6,3.7,0,0,31.6,80.7,0,0,6.4,19.3,6.6,18.7,22.4,22.9,27.5,27.5
|
||||
76,BRA,Brazil,Latin America & Caribbean,211140729,22930,46.2,53,58.2,63.8,58.1,62.2,47.4,66.4,26.7,39.9,46.9,73.4,100,100,66.7,69.6,75.6,76.7,59.7,59.7,68.5,68.5,94,94,63.8,62.2,34.2,0,23.2,22.6,55.4,44,67.4,56.3,61.7,40.2,47.8,37,20.3,20.3,75.1,75.1,48.1,47.9,58,58.6,18.3,16.5,59,54.2,61,56.8,46.7,48.4,60.3,90.6,100,100,100,99.5,43.7,82.2,76.3,95.3,80.1,81,81.3,78.8,34.4,30.2,42.8,53.5,88.5,99,49.6,55.3,15.1,15.4,63,72.2,46.9,52,40.8,40.8,40.2,42.2,36.1,36.2,44.5,39.3,27.4,35.8,50.4,35.9,9.8,15,42.8,43.8,62.8,59.8,16.5,14.2,52.6,59.4,44.9,57.6,47.9,60.6,51.5,57.4,46.6,57.4,26.2,26.2,35.3,35.3,57.8,57.8,1.4,1.4,32.9,45.5,32.9,45.5,33.1,53.4,32.4,66.8,45.3,41.8,12,26.6,35.1,30.2,34.1,69.7,47.1,47.9,34.5,45,33.5,44.4,0,0,64.3,64.3
|
||||
96,BRN,Brunei Darussalam,Asia-Pacific,458949,95046,51.8,48.5,51.5,48.9,49.1,47.4,0,0,0.6,1.1,50,57.9,20.8,20.8,89.6,89.6,100,100,44.5,44.5,100,100,99.9,99.9,52.8,51.7,74.9,33.3,100,100,54,64.2,68.7,80.3,47,51.1,52.3,65.1,47.1,47.1,75.8,75.8,44.3,41.4,NA,NA,37,35.6,24.8,34.2,31.7,48.7,28.8,50.1,64.6,41.9,83.2,84.4,79.7,83.1,68.2,67.1,0,0,20,24.1,0,0.7,12.7,10.7,16.1,22.4,20.1,49.4,67.2,67.2,27.3,27.3,76.1,76.1,68.1,68.1,68.1,68.1,62.9,70.9,58.2,68.7,71.4,76.8,70.9,72.3,70,67.7,40.6,42,56,57.8,45,52.5,0,0,85.8,87.9,84.5,88,85.6,87.9,59.5,65,57,65,35,35,34.9,34.9,100,100,2.5,2.5,43,29.2,43,29.2,31.1,30.4,0,0,46.4,73.3,36.8,3.9,31.1,38.8,20.1,72.5,47.5,47.9,28.6,27.9,4.9,5,27.4,26.8,1.6,1.6
|
||||
100,BGR,Bulgaria,Eastern Europe,6795803,41510,57.6,56.3,70.5,70.8,69.4,69.1,86.7,86.7,0,0,32.3,33,48.7,48.7,91.3,91.5,99.9,99.9,99.2,99.2,74.4,74.4,0,0,80.5,80.1,86,77.5,18.2,18.9,71.9,72.1,NA,NA,NA,NA,82,80.3,57,57,61,61,19.1,20.9,NA,NA,57.2,55.5,12.1,4.3,16.5,8.8,50,50,92.7,92.3,47.3,49,58,59,100,100,100,100,71.5,74.2,56.1,60.8,71.2,57.1,85.7,70.1,62.2,90.7,66,67.4,39.4,39.4,86.8,90.4,54.6,54.6,54.6,54.6,41.3,42.5,30.3,32,18.6,21,28.1,36.4,40.3,49.3,38.5,37.8,11.4,16,54.5,59.6,41,41.4,80.6,79.3,90.8,87.8,78.7,73.7,33.8,37.3,31.1,37.3,47.7,47.3,33.6,33.4,99.9,90.1,35.6,39.8,51.7,45.7,51.7,45.7,56.3,54.1,41.7,38.4,76,60.2,0,7.9,53.9,37.6,80.5,48.5,51.6,51,50.4,48,47.7,41.9,24.3,21.4,56.3,56.3
|
||||
854,BFA,Burkina Faso,Sub-Saharan Africa,23025776,2850,41.5,41.5,58,56.9,72.3,73.3,NA,NA,NA,NA,NA,NA,50.7,50.7,83.3,89.8,54.1,54.4,88.8,88.8,17.2,17.2,85,85,95.5,95.4,80.1,74.9,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,47.8,39.8,73.2,72.3,90.1,90.8,5.2,35.7,55.6,27.2,66.5,64,45.8,46.3,100,100,93.9,90.2,73.6,67.7,12.4,12.4,100,100,3.4,3.4,2,2,2,2,31.1,33.1,38,39.8,79.1,72.8,4.5,6.2,48.5,34.2,50.7,42.2,77.8,75.4,52.6,51.5,19.7,13.6,12.3,15.7,9,14.7,10.1,16.3,21,22.3,20.7,22.3,27.4,27.4,66.4,66.4,1.8,1.8,1.2,1.2,25,24.9,25,24.9,6.8,15.1,61.2,78.6,23.3,25.7,36.7,3.7,31.6,31.6,44.1,64.2,0,0,18.5,19.8,34.9,33.7,21.7,19.5,81.5,81.5
|
||||
108,BDI,Burundi,Sub-Saharan Africa,13689450,986,34.3,33,52.2,48.1,51.2,51.7,NA,NA,NA,NA,NA,NA,28.3,28.3,34.6,37,20.4,26.4,89.4,89.4,38.5,38.5,95.7,95.7,67.6,67.6,90.4,80.7,3.3,3.7,68.3,63.7,91.4,85.7,NA,NA,65.2,50.7,39.9,39.9,44.4,44.4,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,82.7,59.6,72.6,70.5,84.5,82.7,57.4,53.2,69.4,59.3,46.2,45.4,40.9,45,100,72.1,84.8,77.1,12.3,30.4,11.5,11.5,97.2,97.2,4.5,4.5,0,0,0,0,12.5,12.9,9.5,9.1,0,0,3.6,5.2,40.1,21.5,55,53.9,32.9,32.9,20.1,20.4,17.5,17.7,14.1,16.8,10.8,15.9,11.9,17.4,28.2,29.6,26.9,29.6,24,24,59,59,0,0,0.9,0.9,25.1,26.6,25.1,26.6,12.9,4.9,100,100,3.3,29.4,36.7,3.8,0,20.4,42,61.9,44.3,45.4,13.3,19.7,34.2,43,32.3,31.6,99.7,99.7
|
||||
132,CPV,Cabo Verde,Sub-Saharan Africa,522331,11397,39.6,37.9,26.4,23.1,19.1,19.8,0.1,0.1,0,0,100,100,NA,NA,0.4,3.5,9.4,9.6,1.3,1.3,0,0,91.3,91.3,69.3,69.7,NA,NA,100,100,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,80.6,70.2,93.5,61.5,48.2,38.6,NA,NA,100,100,63.6,14.8,38.5,14.3,15.5,14.8,NA,NA,4.1,12.6,60.5,15.9,26.7,28,11.4,8.1,46.9,50.5,33.1,51.9,36.1,36.1,25.6,28.7,90.4,90.4,20.9,28.6,16.5,16.5,16.5,16.5,53.8,54.7,62.8,63.1,100,100,16.6,21.4,62.8,47.8,82.7,79.9,85,88.9,77.5,81.3,94.1,95.5,33.2,36.5,28.3,34.2,31.6,38.1,46.1,45.2,48.9,45.2,20.2,20.2,49.4,49.4,0,0,1.2,1.2,45,43.4,45,43.4,41.4,43.2,77,80.5,34.6,41.9,0,21.3,39.8,48.9,48.9,41.2,42.9,52.7,38.9,42.3,40,43,49,49.1,89.7,89.7
|
||||
116,KHM,Cambodia,Asia-Pacific,17423880,8680,31,31,36.3,44,38,57.3,27.8,63.1,13.8,17.6,50,50,66.7,66.7,58.4,91.1,46.5,100,72.6,72.6,4.4,4.4,86.1,86.1,39.7,32.3,0,0,57.5,55.2,25.6,37.6,15.7,32.2,57.8,77.6,0,5.9,0,0,63.3,63.3,33.7,31.8,33.6,23,88.8,86.7,15.5,12,15.4,15.4,78.3,52,46.7,14.5,64.1,65.1,73.2,72.9,31,4.4,56.1,2.9,62,64.6,48.8,51.4,71.5,100,65.7,48.6,66.7,81.5,11.7,11.7,77.8,77.8,9.9,9.9,0,0,0,0,23.9,24.1,19.5,18.2,17,20,3.9,6.5,34.3,31.8,41.7,41.1,45.1,44.8,39.7,46.1,9,4.5,35,40.2,28.6,38.9,31.2,41,27.6,28.9,26.5,28.9,36.3,36.3,85.7,85.7,0,0,5.1,5.1,28.8,16.7,28.8,16.7,9.4,0,9.6,0,30.6,38.8,36.8,3.8,28.5,31.2,30.9,33.8,47.3,45.8,23.4,12.2,34.1,21.4,21,16.3,60.7,60.7
|
||||
120,CMR,Cameroon,Sub-Saharan Africa,28372687,5566,36,38.1,45.2,48.1,46,45,NA,NA,53.8,75.6,100,100,30.9,30.9,30,31,30.3,33.1,67.7,67.7,62.3,62.3,89.1,89.1,49.8,49,75.1,0,46.8,44.9,67.5,58.4,78.2,58.9,79.4,69.9,72.1,46.6,41.4,41.4,80,80,72.2,81.6,NA,NA,85.2,90.1,56.8,76.5,75,88,32.8,28.8,44.2,71.7,86.7,84.3,88.5,85.6,33.5,60.9,25.9,77.1,48.4,50.4,39.2,44.7,100,100,56.3,59.1,47,48.4,10.2,10.2,100,100,0.6,0.6,0,0,0,0,17.3,19.5,15.7,17,17.3,14.6,4.5,7.5,37.7,25,63.5,63,49,47.4,41.5,38.7,2.5,4.7,17.5,23,12.1,21.8,13.4,23.8,25.1,28.3,22.7,28.3,26.8,26.8,65.5,65.5,2.6,2.6,0.2,0.2,38.6,39.4,38.6,39.4,31.6,43.6,100,100,51.8,40.5,58.7,7.7,62.3,40.7,54.7,28.6,48.9,49.1,40.1,36.7,46.9,41.9,23.3,20.6,88.2,88.2
|
||||
124,CAN,Canada,Global West,39299105,64570,57.7,61.1,57.8,60.6,48.2,52.1,11.2,21.7,15.7,26.9,50.7,60.8,40.3,40.3,66.8,82.1,29.2,36.2,40.7,40.7,89.8,89.8,99,99,89.8,89.3,79.5,47.1,57.8,56.9,42.5,47.6,NA,NA,24.2,29.2,67.3,65.9,36.2,36.2,89.9,89.9,31.7,33.1,4.7,0,64.6,57.3,31.1,29,35.9,34,53.2,49.4,89.5,90.8,22.2,41.1,43,48.6,100,100,100,100,72.5,72.3,54.1,61.7,77,60,41.5,33.6,96.1,100,80.4,80.4,6.9,6.9,98.2,98.3,84,84,68,68,73.5,77.3,68.4,72.3,62.5,67,90.7,96.7,50.1,51.9,2.3,7.8,37.1,41.7,55.9,61,57.3,57.3,90.6,94.7,80.1,88,91.3,99.1,93.3,97.3,91,97.3,36,36,17.6,17.6,96,96,24.4,24.4,44.3,48.2,44.3,48.2,51.7,52.5,26,27.1,60.1,65.7,43.7,41.4,38,44.3,100,100,49.7,49.8,45.2,48.3,29.3,33.1,3.2,4.1,45.2,45.2
|
||||
140,CAF,Central African Republic,Sub-Saharan Africa,5152421,1296,43.3,38.3,58.7,56.1,68.7,71,NA,NA,NA,NA,NA,NA,37,37,59.4,76.7,59.5,59.5,94.5,94.5,87.5,87.5,99.4,99.4,79.5,79.5,93.9,77.5,48.4,47.2,77.7,68.6,79,73.4,81.2,59.6,79.5,78.7,43.4,43.4,92.7,92.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,63.3,47.7,95.1,88.3,100,100,38.5,40.6,70.8,36.2,38.3,38.3,15.8,16.2,68.7,67.9,94.6,93.7,34.8,34.8,10,10,100,100,0,0,0,0,0,0,12.1,14.4,13.2,15.6,13.4,19.9,0,1.2,13.3,7.5,56.4,61.1,79.5,77.3,32.2,32.7,0,0,6.3,8.9,4.4,8.4,4.7,9.2,13.7,15.3,12.7,15.3,20.7,20.7,50.9,50.9,0,0,0.9,0.9,45.6,31,45.6,31,71.5,19.4,100,100,50.7,49.1,36.8,3.8,60.3,55.2,56.5,83.4,49.8,49.9,2.4,0,49.5,47.1,29.8,28.2,75.1,75.1
|
||||
148,TCD,Chad,Sub-Saharan Africa,19319064,2832,33.2,35.2,50.9,49.4,61.8,60.1,NA,NA,NA,NA,NA,NA,29.8,29.8,68.8,68.8,61.5,61.5,72,72,4.4,4.4,87,87,72.3,71.6,88.6,70.2,25.8,24.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,56.6,52.4,44.6,35.5,99,93.4,27.6,32.7,62.6,67.3,41.1,42.7,30.7,30.2,100,100,60.4,73,37.8,37.8,10,10,99.9,99.9,0,0,0,0,0,0,23.5,26.4,30.3,33.6,56.6,57.1,2.6,4,34.6,24.7,67.6,66.6,84.9,86.1,49.8,54.4,0,0,1.8,4.3,0,4,0,4.5,16.1,17.5,14.8,17.5,31.2,31.2,77.1,77.1,0,0,1,1,14.2,21,14.2,21,15.8,45.8,100,100,5.6,0,36.7,3.8,8.6,0,44.8,56.7,0,0,0,0,24,18.9,15.6,11.8,39.7,39.7
|
||||
152,CHL,Chile,Latin America & Caribbean,19658835,34790,47,50,54.8,58.4,39.7,42.5,13.4,28.4,17.4,37.8,100,87.7,34.6,34.6,30.4,33.6,48.2,54.9,63.9,63.9,64.8,64.8,95.6,95.6,30,20.6,78.1,46.8,100,100,50.6,70.3,NA,NA,46.1,83.6,51.9,62.1,49.6,49.6,73.5,73.5,82.7,81.9,31,22.7,85.5,88.8,100,100,96.5,94.3,57.7,55.2,75.7,80.9,74.2,71.1,93.3,86.6,32.2,63.4,100,99.1,69.1,66.3,39.7,45.4,30.7,29.5,60.6,43.2,91,100,88.3,88.4,0,0,99.9,99.9,99.9,100,84,84,44.8,44.7,31.2,29.2,21,8.3,37.9,48,55.6,62.3,11.8,12.4,0,0,25.2,22.4,25.8,27.5,75.8,80.1,68.8,77.6,74.9,81.8,89,94,85.5,94,31.9,31,34,31.3,90.7,91.3,0.4,0.5,37,41.5,37,41.5,36.5,46.7,21.8,37.5,55.5,49.5,0,0,45.3,56,14,66.6,49.3,49.5,34.3,42.3,30.2,38.1,12.3,13.5,54.8,54.8
|
||||
156,CHN,China,Asia-Pacific,1422584933,28010,29.9,35.5,34.5,35.9,11.4,9.5,24,24,2.4,2.5,47.9,54.8,0,0,2.5,3.1,1.5,2.6,3.8,3.8,4.4,4.4,0,0,20,10.4,54.1,24.9,39.8,40.3,69.7,73.1,61.1,85.3,76.9,73.2,64.1,66,54.8,54.8,71.4,71.4,40.5,39.6,59,57.5,76.6,68.3,29.6,26.3,28.7,24.1,46.3,46,73.6,87.3,0,0,45.2,47.2,40.9,100,100,100,64.8,69,50.1,58.8,25,30.1,71.2,60.5,77.7,85.9,48.4,48.4,42.6,42.6,49,49,49,49,49,49,26,29.5,11,14.3,0,1.1,13.8,25.1,0.9,16.5,24.3,31.9,0,0,2.5,4.5,28,29.2,69.8,74.5,61.1,71.6,66.5,76.5,35.4,39.4,31.7,39.4,44.7,43.3,61.8,58.1,89.5,91.4,5.1,4.5,26,39.8,26,39.8,21,43.1,0,20.9,22.9,38.7,10.8,36.3,42.6,65.3,54.6,100,50.1,49.4,3.4,33.5,7.8,31.1,0,0,39.3,39.3
|
||||
170,COL,Colombia,Latin America & Caribbean,52321152,22190,44.9,49.4,52.9,56.4,56.1,55.5,97.1,97.2,40.9,51,67.8,75.6,67.6,67.6,45.1,54.1,48.7,53.5,81.2,81.2,64,64,97.1,97.1,17.6,14.3,56.4,0,54.9,53.5,73,57,79.5,67.1,84.5,52.7,62.4,52.1,39,39,82.6,82.6,51.6,46.2,8.1,0,63.8,42.9,52.6,51.3,66.1,62.9,51.9,55.5,42.5,84.9,100,100,100,100,38.6,64.3,39.6,99.4,57.1,59.3,52.5,47.4,25.8,28.8,40.1,48.2,64.9,78.4,28,28.1,60.3,61.8,31.8,31.8,20.2,20.2,11.3,11.3,42.8,45.2,37.6,39.6,38.5,37.6,28.9,40,66.7,58.8,20.8,23.1,57,57.9,51.7,49.2,11.9,9.4,56.5,59.7,55.8,62.1,54.3,58.1,61,65.3,55.7,65.3,27,27,30.3,30.3,56.8,56.8,8.8,8.8,34.2,42.2,34.2,42.2,30.7,49.2,32.7,65.1,42.1,34.9,36.4,4.6,55,21.5,0.8,97.2,48.9,49.2,35,41.5,35.1,40.6,9,9.3,65.8,65.8
|
||||
174,COM,Comoros,Sub-Saharan Africa,850387,3861,44,37.9,48.8,44.4,53.9,49.9,70.1,70.1,23.3,23.3,86.6,100,NA,NA,43.3,43.3,100,100,41.1,41.1,100,100,99.6,99.6,23.4,10.2,86.5,30.4,100,100,46.1,65.1,NA,NA,NA,NA,57,72.1,41.3,41.3,77.9,77.9,74.9,73.4,100,64.1,100,5.9,100,100,100,100,52.6,51.8,55.7,33.4,65,53.8,NA,NA,48.2,37.8,61.7,25,49.7,50.6,42.3,45.1,100,100,77.8,77.8,40.7,40.7,9.5,9.5,83.9,83.9,2.8,2.8,0,0,0,0,44,41.2,51.8,46.8,71.8,70.9,5.3,6.7,88.2,54.9,90.4,79.6,85.2,89.6,81.8,82.1,82.4,85.9,23.7,25.8,20,24.1,22.4,26.9,36.9,39,35,39,28,28,69.1,69.1,0,0,1,1,36.5,25.2,36.5,25.2,42.3,3.4,100,29,18.2,35.1,NA,NA,33.5,35.3,47.7,61.7,49.9,50,32.6,22.1,36.7,26.9,48.6,44.6,86.1,86.1
|
||||
188,CRI,Costa Rica,Latin America & Caribbean,5105525,31090,55.3,55.5,63.2,62.5,65.7,63.9,86,86.1,34.1,37.5,63.3,100,99.9,99.9,58.6,58.8,84.4,84.8,54.7,54.7,60.9,60.9,97.2,97.2,51.7,49.1,59.9,14,79.1,77.1,77,72.3,85.6,83.6,88.6,83.8,57.5,64,38.1,38.1,46.4,46.4,41.9,35.7,19,18,9.4,3.8,57.9,47.4,51.4,50.2,50,56,72.8,79.8,66.8,64.9,75.5,78.7,51.1,62.7,40.6,100,52.8,57,49.7,42.7,32.2,33,35,49.1,76.7,76.7,40.3,38.7,20.5,17.7,84.2,81.4,7.6,7.1,15.2,15.2,51.5,53.7,47.6,49.6,55,56.3,37.5,45.3,65.9,59.8,18.8,23.8,50.1,51.4,67.5,68.1,20.6,20.4,65.9,68,67,73.3,62.2,64.4,59.7,64.4,55.6,64.4,31.2,31.2,42.9,42.9,63.6,63.6,3.2,3.2,46.1,46.3,46.1,46.3,46.4,50.5,68.3,75.6,30.9,39.3,36.7,4.2,24.6,46.3,45.8,82.5,49.7,49.7,42.3,47.1,41.4,45.9,28.9,29.7,78.4,78.4
|
||||
384,CIV,Cote d'Ivoire,Sub-Saharan Africa,31165654,8060,35,42.5,38.4,48.9,49.7,56.3,NA,NA,2.4,2.4,100,100,37.1,37.1,48.1,84.4,76.2,76.3,94,94,68.3,68.3,96.2,96.2,70.8,70.7,0.6,0,27.6,25.6,20.7,26.2,28.6,39.5,25.6,27.2,32.2,11.4,15.3,15.3,36.5,36.5,49.3,46.4,51.2,28.4,68.8,61.8,17.7,15.9,73.9,69.2,49.4,31,27.2,75.6,76.5,76.3,87.5,87.8,24.4,49.3,63.6,99.4,44.9,42.2,34.4,32.4,100,100,51.9,45.5,49.8,45.8,11.5,11.5,90.2,90.2,6.1,6.1,0,0,0,0,30.8,33.5,34.8,37,73.8,66.9,4.1,7,53.3,31.1,45.9,38.2,69.1,67.3,49.8,47.7,17.4,13.1,19.9,24.4,15.4,23.1,16.9,25.2,28.3,30.9,26.4,30.9,23.7,23.7,57,57,1.5,1.5,1.5,1.5,33.4,40.9,33.4,40.9,43.8,42.7,100,100,27.2,39.8,36.8,3.7,38.3,38.9,11.8,60.2,45.5,46,37.1,39.8,40.2,41,23.4,22.3,92.5,92.5
|
||||
191,HRV,Croatia,Eastern Europe,3896023,51220,58.1,62.6,70.2,72.8,69.5,69.8,66.7,66.7,32.8,33.4,55.3,55.3,55.8,55.8,81.3,84.6,40.4,100,95,95,63.2,63.2,52.5,52.5,65.1,63.6,85.4,78.4,34.4,34.5,68.5,61.7,NA,NA,NA,NA,80.9,69.4,48.7,48.7,49,49,59.3,62.1,60,61.5,46.3,68.5,45.3,61.9,47.8,63,37.1,33.8,73.7,91.1,39.7,37.3,59.4,56.4,100,100,100,100,64,67.9,53.5,61.5,37.7,56.2,78.8,78.2,53.2,71.1,77.5,77,37.3,39.8,98.3,96.5,81,80.9,20.6,20.6,48.5,51.7,36,40.6,23.2,29.3,45.7,52.8,37.7,36.2,34.3,34.9,35.3,41.9,50.7,55.2,38.2,37.2,87.7,85.4,96.3,91.1,89.5,81.6,62.3,68.1,56.5,68.1,39.1,39.1,31.6,31.6,96.8,96.8,17.8,17.8,47.5,56,47.5,56,54.6,52.9,48.5,45.8,51,90.5,30.5,46,58.3,40.2,89.5,94.1,52.1,51.1,53.1,52.7,49.4,47.5,29.6,28.9,68.1,68.1
|
||||
192,CUB,Cuba,Latin America & Caribbean,11019931,,49.8,52.3,52.1,49.8,47.3,43.4,80.8,80.8,33.9,33.9,50,26.4,40.8,40.8,39.2,39.2,41.7,41.7,71.9,71.9,98.1,98.1,97.1,97.1,0,0,71.6,21.6,47.7,46.5,80.9,66,85.6,73.8,96.9,65.5,64.6,66.6,49.2,49.2,53.6,53.6,83.6,81.4,33.1,31.4,98.9,99.1,86.4,88.9,87.9,89.8,69.1,63.7,62.7,78.8,29.5,24.6,38,32.5,84.5,84.5,68.5,93.1,50.5,47,41.8,45.7,63.4,71.2,43.8,54.5,55.2,43.2,23.3,20,6.6,3.7,16.8,18.7,33.7,24.3,24.6,24.6,50.1,52,50.6,52.5,62.9,62.8,37.7,44.9,46.8,55.1,20.9,22.1,54,56.9,72.4,76.3,43.6,43.1,57,58.8,55.9,59.8,56.1,58.1,42.9,45,42.1,45,22.1,23.5,21.1,25.8,48.6,49.9,9.9,7.9,46.1,56.4,46.1,56.4,42.6,62.3,54.4,88.7,42.3,62,36.8,3.8,53.2,60.4,100,55.9,51,51.5,38.8,58.9,40,59.6,20.1,33.6,90.1,90.1
|
||||
196,CYP,Cyprus,Eastern Europe,1344976,62290,54.3,54,55.7,57.2,48.7,51.6,7.8,7.8,16,16,44.2,36.4,37.6,37.6,49.4,69.3,57.2,100,59.9,59.9,25.2,25.2,37.1,37.1,73.3,73.4,73.1,60.2,74.9,76.2,75.8,60.6,NA,NA,NA,NA,83.7,60,55.3,55.3,74.5,74.5,41.5,43.6,8.9,10.9,31.8,26.4,40.7,74.9,98.6,51.3,94.2,0,79.6,83.3,0,0,0,0,100,100,100,100,39.1,35.7,13.7,9.2,28.9,27.7,55.3,48.6,57.6,57.6,70.4,70.8,61,61,82.7,83.5,62.5,62.5,62.5,62.5,61.9,62,57,55.9,42.9,37.1,82.7,87.2,27.9,30.4,20.1,21.8,29.9,40.5,62,67.7,36.8,42.4,85.1,86.7,84,87.5,84,86.2,60.4,67.1,56,67.1,31.7,31.7,19.9,19.9,85.4,85.4,16.6,16.6,45.9,42.6,45.9,42.6,54.6,48.6,43.9,35.1,0,30.3,15.7,33.8,59.6,35.9,100,52,57.8,54.5,49.2,45.2,44.9,39.6,36,33.4,55.6,55.6
|
||||
203,CZE,Czech Republic,Eastern Europe,10809716,59210,65.4,65.6,79,78,78.5,78.7,NA,NA,NA,NA,NA,NA,63.2,63.2,94.9,96.8,63.8,69.9,98.4,98.4,65.7,65.7,31.2,31.2,91.1,89.6,75.7,68.1,8.4,8.6,49.4,22.5,NA,NA,NA,NA,57.8,17.1,38.6,38.6,17.1,17.1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,93.4,93.8,62.8,59.3,70.3,66.8,100,100,100,100,74.7,74,60.3,58.5,96.1,73.5,74.5,70.6,67.6,90.9,77.1,80.2,27.3,27.3,80.8,84.7,80.8,84.8,96.8,96.8,55.3,58.8,44.7,50.4,21.1,37.7,62.5,69.7,43.7,38.4,22,27.1,33.6,40.4,48.5,54.1,54.1,53.2,83.3,80,98.3,89.6,83.7,73.6,75.7,80.9,71.2,80.9,55.5,51.2,38.9,23.4,100,100,49.9,54.5,53,52.2,53,52.2,61.8,57.8,44.9,39.4,57.2,63.4,0,36.3,52.3,69.1,69.3,65.9,54.9,54.6,53.8,53.3,44.7,43.3,20.1,18.6,59.5,59.5
|
||||
180,COD,Dem. Rep. Congo,Sub-Saharan Africa,105789731,1842,33,39,47.9,53.1,53,51.8,NA,NA,59.6,59.6,NA,NA,33.9,33.9,51.1,62.7,41.6,45.8,66.8,66.8,74.9,74.9,97.6,97.6,62.1,62.1,71.9,0,60,54.8,57,47.1,69.5,55.4,64.7,45.8,55.7,39,32.2,32.2,75.6,75.6,73.2,73,NA,NA,100,100,19.9,18,100,100,41.5,51.3,50.6,100,100,100,100,100,39.5,100,38.3,100,39.4,39,37.7,37.5,100,100,64.8,57.8,26.9,27.6,10,10,100,100,0,0,0,0,0,0,12.3,13.8,8.1,8.2,0,0,3.5,5.1,26.5,16.3,47.6,53.9,27.3,28,24.8,26.1,0,0,18.3,25.7,10.5,24.1,11.6,26.7,27.5,27.7,27.4,27.7,23.9,23.9,58.2,58.2,1,1,1,1,29.5,40.1,29.5,40.1,29.3,51.8,100,100,20.3,29.3,36.8,3.8,57.7,42.1,42.4,40.9,47.9,47.1,23.3,30,41.8,45.5,17.3,16.4,98.3,98.3
|
||||
208,DNK,Denmark,Global West,5948136,85791,68.3,67.9,64.2,63.5,53.4,53.3,28.9,29,17.3,17.3,21.2,21.4,41.5,41.5,74.9,75.4,48,48,52.4,52.4,24.9,24.9,67.4,67.4,93.6,93.8,90.6,87.1,5.3,6.3,56,51,NA,NA,NA,NA,49.9,45.9,87,87,4.3,4.3,49.6,44.7,9.5,27.3,68.8,72.7,34.7,39.2,24.5,41.2,57.9,36.6,90.9,90.3,39.2,38.9,42.7,44.1,100,100,100,100,80.6,77.8,65.9,61.3,44.6,41,75,71.2,96.8,100,85.1,85.8,20.4,21.4,99.6,99.8,90.8,92,69.3,69.3,73.9,76.9,67.7,70.9,54.3,59,90.6,95.6,37.5,40.7,26.6,32,59.1,66.8,62.9,68.2,78,80.3,89.7,91,87.7,91.1,89.1,90.9,92.3,98.8,86.4,98.8,64.4,65.5,12.3,13.9,100,100,98.8,99.9,69.9,67.1,69.9,67.1,63.3,77.9,59.6,81.7,60.3,56.5,54.3,80.4,57.5,49.6,72.2,100,47.6,48.2,63.1,64.3,53.1,56.9,37.6,40.5,83.7,83.7
|
||||
262,DJI,Djibouti,Sub-Saharan Africa,1152944,8601,32,32.2,25.2,24.4,19.7,18.1,0,0,0,0,NA,NA,0,0,6.8,6.8,0.3,4.4,2.1,2.1,100,100,100,100,46.7,39,90.1,81.3,43.8,42.9,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,95.9,96.4,NA,NA,100,100,45.2,100,100,100,13.5,38.8,18.7,16.8,34.4,10.4,NA,NA,17.1,17.1,17.7,17.7,58.4,65,49.9,59.3,23.9,42.4,68.9,86.3,63.6,63.6,14.8,14.8,74.1,74.1,14.5,14.5,3.2,3.2,3.2,3.2,27.1,28.9,28.2,29.2,29.1,33.2,14.3,16.8,49.2,34.3,33.3,30,60.5,60.5,67.1,61.6,56.3,56.9,21.1,25.8,16,24.4,17.7,26.8,31.6,34.5,29.2,34.5,29.1,29.1,71.6,71.6,0,0,1.1,1.1,47.9,47.9,47.9,47.9,51.4,51.4,100,100,37.1,37.1,NA,NA,46.2,46.2,48.6,50.3,NA,NA,43,43,46.5,46.5,44.4,44.4,88.9,88.9
|
||||
212,DMA,Dominica,Latin America & Caribbean,66510,18391,49.4,49.2,42.9,40.6,27.8,27.8,0,0,3.2,3.2,50,78.3,NA,NA,24.7,24.7,70.4,70.4,43,43,52.2,52.2,95.7,95.7,4.8,0,NA,NA,100,100,79.4,56.4,89,62,NA,NA,83.5,64.3,43.2,43.2,10,10,39.6,49.6,NA,NA,18.2,33,100,100,0,24.9,45.9,37.6,75.6,68.1,44,39.8,NA,NA,49,68.6,91.5,73.3,40.9,45.7,35.5,37.5,57.6,55.6,19.8,38,56.4,56.4,44.5,44.5,50.2,50.2,49.9,49.9,39.1,39.1,39.1,39.1,55.8,57.6,59.6,62,84.1,80.8,28.5,32.2,86.3,100,50.2,43,80,85,82.3,85.5,78,83.2,51.2,51.3,51.5,53,50.8,50.2,42.9,44.8,41.8,44.8,39.6,38.4,43.9,43.5,100,95.2,5,4.8,52.8,53.6,52.8,53.6,45.3,50.7,55.1,64.4,40.7,45.9,100,100,46.7,47.3,66.4,52.1,50.1,50.6,42.1,48.1,42.6,48.8,58.8,60.9,79.1,79.1
|
||||
214,DOM,Dominican Republic,Latin America & Caribbean,11331265,28950,48.7,47.6,58.4,56.8,59.4,57.6,82.6,82.6,43.5,43.5,65.8,50,56.8,56.8,51.6,51.8,67,69.9,95.3,95.3,34.6,34.6,91,91,15.5,12,81.3,56.7,70.5,67.9,54.6,61.1,68.3,68.8,64,82.1,42.2,39.9,37.3,37.3,41.8,41.8,95.2,95.7,80.8,86.6,100,100,100,100,100,100,54.6,54.6,68,51.7,50.3,46.2,56.3,53.1,72.5,43.5,79.5,60.8,74.5,78.7,65.1,85.4,46.3,68.8,49.9,55.4,82.5,82.5,21.1,25.2,24.4,27.2,20.9,20.9,23.4,32.8,9.7,9.7,40.8,43.2,43.9,46.3,56.1,53.9,27.8,37.1,58.4,66.9,17.1,19.5,54,58.2,63.9,64.7,31.6,29.9,39.4,42.4,36,42.8,37.1,42.1,28.6,30.5,27.4,30.5,18.3,18.3,35,35,7.1,7.1,7.1,7.1,40.3,37.3,40.3,37.3,41.4,45.2,48.5,54.9,20.2,34.3,36.8,3.8,34,24,57.2,24.9,50.7,50.7,35.9,40.4,35.8,38.9,21,20.7,67,67
|
||||
218,ECU,Ecuador,Latin America & Caribbean,17980083,16890,44.1,51.2,51.2,56.7,50.7,50.3,84.2,84.2,64.4,66.3,93.4,47.8,50.1,50.1,33.8,39.2,53.7,63.9,52.4,52.4,73.3,73.3,96,96,0,0,68.4,30.1,80.3,77.6,74.8,71.3,81.6,76.7,87.3,78.5,61.6,66.5,42.5,42.5,76.4,76.4,65.4,69.9,0,0,64.8,74,72.6,83.4,80.8,91.6,48.6,43.2,41.5,85.7,100,100,92.5,87.9,31.8,68.1,33.9,100,51.1,50.1,37.5,37.9,46.5,40.3,39,34.2,50.2,69.7,37,38.7,37.5,35.5,48.9,48.9,25,29.8,36.8,36.8,42.4,44.3,37.2,37.3,33,29.3,31,39.1,79.8,65.3,15.4,23.9,68.3,69.1,55.2,50.8,16.1,14.3,56.1,63.5,47.6,62.7,50.3,64,55.3,62.3,50.1,62.3,36.4,32.4,47.5,44.4,58.9,49.5,14,11.8,34.6,48.5,34.6,48.5,36.5,52.3,37.6,64.3,35.6,62.1,36.8,3.8,32.8,45.2,29,59.1,48.7,49,32.6,48.8,33.7,50.3,16.1,20.9,77.4,77.4
|
||||
818,EGY,Egypt,Greater Middle East,114535772,21610,40.5,43.8,46.9,51.7,48.4,47.4,30.2,30.2,27.5,27.5,100,100,22.5,22.5,41.7,41.7,42.7,42.7,44.6,44.6,82.8,82.8,98.7,98.7,75.4,68.7,93.9,90.1,64.1,64,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,30.1,32.4,58.6,41.9,36.6,34.5,29.1,31.2,26.8,23.3,36.9,64.9,35.8,73.3,0,0,14.8,0,35,76,45.5,100,51.1,48.9,57.5,54.1,100,73,49.2,44.4,39.9,43.5,57.1,57.1,18.8,18.8,91.8,91.8,32.1,32.1,56.7,56.7,29.5,33.3,28,31.7,6.4,10.3,44.8,58,22.4,26.3,17.3,20.8,6.2,3.5,30.1,27.2,39.3,38.3,47.3,53,39.7,50.3,45.2,54.8,0,1.9,0,1.9,25.2,25.2,53,53,6.8,6.8,6.5,6.5,39.7,40.4,39.7,40.4,36.2,43.8,36.4,49.3,44.3,46.1,36.7,10.6,34.7,60.2,0.8,51.3,19.5,32,33.2,40.7,34.7,40.9,5.2,5.5,71.7,71.7
|
||||
222,SLV,El Salvador,Latin America & Caribbean,6309624,13173,46,41.5,45.8,44.7,36.9,36.3,13.4,13.4,38.9,38.9,50,84.2,35,35,18.1,18.8,22.8,24.1,45.5,45.5,77.9,77.9,85.2,85.2,41,37,84.5,66.7,58.5,57.9,68.1,67,82.8,79.4,NA,NA,59.3,67.6,41.8,41.8,40.2,40.2,24.8,21.6,0,0,28.8,22.6,21.3,30.9,30,18.8,53.7,55.3,91.5,85.1,54.9,54.4,57.7,55.6,94.8,82.3,94.1,100,56.5,62.3,49.8,55.8,49.9,53.3,56.1,50.1,76.3,74.7,21.2,21.2,50,50,37.4,37.4,2.5,2.5,2.5,2.5,28.5,29.8,22,22.9,8.5,10.3,19.7,27.3,53.2,45.9,21.9,32.9,33.9,35.6,47.8,48.3,3.8,2,46.7,50,41.6,48.7,45,50.9,38.2,39.6,37.8,39.6,29.2,27.4,45,40.5,53.1,53.1,1.4,1.4,61.1,46.4,61.1,46.4,51,40.8,85.6,66.7,94.1,79.8,36.9,3.7,53.3,78.2,69,45.9,49.4,51.1,53.6,44.2,54.7,44.9,35.5,30.5,85.3,85.3
|
||||
226,GNQ,Equatorial Guinea,Sub-Saharan Africa,1847549,21751,44.7,41.6,53,43.7,49,45.6,3.5,3.5,25.3,25.3,0,10.5,33.3,33.3,70.1,70.1,59.2,59.2,100,100,85.7,85.7,97.3,97.3,40,40,72.2,8.8,84.1,82.7,61.2,66.6,76.9,75.6,74.4,68.9,64.3,59.8,42.5,42.5,80,80,45,44.7,39.7,7.2,85.4,95.7,32,17.1,52.2,43.9,63.3,97.4,90.1,22.3,74.1,69.3,NA,NA,81.1,18.5,100,16.8,41.5,43.7,32.3,34.5,100,100,54.8,64.9,39.4,39.4,34.8,34.8,52.8,52.8,34.8,34.8,31.1,31.1,31.1,31.1,32.9,33.3,33.3,32.4,19.2,14.9,34.4,37.1,56.8,32.1,92.7,95,65.8,66.4,57.7,53.5,18.7,18.3,30.7,35.3,25.7,36.3,26,34.7,38,39.5,37.1,39.5,26.4,26.4,64.7,64.7,0,0,1.4,1.4,42.1,45.5,42.1,45.5,42,54.9,38.8,59.9,41.5,55.9,36.8,3.7,29.5,54.9,100,0,49,49.2,35.5,48,26.7,47.7,26.9,35.6,69.9,69.9
|
||||
232,ERI,Eritrea,Sub-Saharan Africa,3470390,1832,28.9,28.6,27.3,25.1,18.5,16.8,0,0,0,0,NA,NA,3.1,3.1,0,0,0,0,0,0,0,0,65.5,65.5,68.7,61.4,95.9,86.3,27.2,26.8,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,63.1,66,0,28.4,100,100,9.4,15.7,100,100,49.3,57.1,64.6,56.3,35.4,12,57.7,30.6,58.1,52.2,96.3,74.5,33.9,31.9,19.3,20.5,75.9,100,95.9,90.4,15.1,13.2,7.6,7.6,76.2,76.2,0,0,0,0,0,0,23.2,24.6,26.2,26.9,37.9,40.6,4.2,5.9,29.4,26.3,46.5,46.7,67.6,63.7,60,52.2,20.2,22.9,13,15.9,9.7,14.9,10.9,16.6,24.7,27.7,21.9,27.7,21.3,21.3,52.2,52.2,0,0,1,1,36.6,38.1,36.6,38.1,55.6,44.4,100,100,35.3,38,NA,NA,40.1,40.5,30.5,29.2,0,0,22.9,24,45,43.9,35,34.3,84.8,84.8
|
||||
233,EST,Estonia,Eastern Europe,1367196,49700,60.6,75.3,75.8,76.6,77.1,78.8,94.9,94.9,82.6,82.6,66.2,69.3,48.4,48.4,79.6,95.9,60.7,66.2,95.6,95.6,88.2,88.2,92,92,95.2,95.2,69.3,43.3,11.5,12,39.4,28.9,NA,NA,NA,NA,44.9,25.9,35.8,35.8,30.3,30.3,69.9,70.4,80.1,73.2,55.6,56.6,90.8,90.2,74.1,69,68.4,27.7,87.6,91.5,42.3,46,44.5,52.5,83.5,100,100,100,71.1,71,38.5,51.6,51.3,54.7,70.7,68.7,69.8,92.8,74.5,72.2,25.2,25.7,88,83,83,82,36,36,60.8,63.9,57,60.9,58.8,69.3,45,55.4,68.4,77.8,25.4,22.8,34.7,37.3,57.8,62.6,64.4,70.6,71.8,71.9,69.5,70.4,73.1,72.9,63.9,68.8,59.3,68.8,62.6,65.1,35.8,34.4,87.4,98.7,77.1,79,37.3,82.8,37.3,82.8,45.9,100,25.4,100,27.3,100,27.9,49.1,40.3,45.3,30.4,100,50.3,49.6,33.5,78.9,23.7,69.6,25.1,100,99.1,99.1
|
||||
748,SWZ,Eswatini,Sub-Saharan Africa,1230506,12963,40.8,38.5,39.9,38.7,30.5,30.7,NA,NA,NA,NA,NA,NA,35.2,35.2,15.2,17,13.3,14,26.6,26.6,79.5,79.5,98.1,98.1,39.6,39.6,34.5,26,64.7,65,41.1,44.6,NA,NA,NA,NA,32.1,49.1,34.9,34.9,41.3,41.3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,83.2,75.3,70.4,69.5,82.3,82,100,62.8,89.4,87.7,52.3,51.7,33.3,26.4,100,100,71.5,71.8,64.6,64.6,14.5,14.5,65.7,65.7,11.9,11.9,6.4,6.4,6.4,6.4,21,21.9,23.2,22.5,26.7,24.5,6.6,12.1,35.9,23.6,55.3,62.9,35.8,36.5,43.4,40.9,19.9,16,14.2,18.1,10,17.3,10.9,18.7,18.2,25.6,13.2,25.6,22.8,22.8,55.8,55.8,0,0,1.2,1.2,60.1,52.9,60.1,52.9,40.8,42.6,72.1,75.3,100,81.1,NA,NA,41,50.2,41.9,70.9,49.8,50.1,49.9,48.3,52.6,49.6,43.6,42.9,85.2,85.2
|
||||
231,ETH,Ethiopia,Sub-Saharan Africa,128691692,4045,32,35.8,42.3,45.9,44.1,46,NA,NA,NA,NA,NA,NA,31.2,31.2,45.2,51.4,41.2,56.5,29,29,30.4,30.4,8.8,8.8,50.4,50,95,78.1,23.2,23.1,58,63.8,69.3,67.5,56.9,63.3,65.2,71.3,31.9,31.9,71.7,71.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,44.8,53.9,79.4,70.1,84.5,74.7,30.4,41.4,34.4,59.1,52.8,58.8,41,48.2,90.2,87.2,65.6,62.8,40.6,65.3,10.8,10.8,100,100,2,2,0,0,0,0,23.5,25.1,24.6,25.4,30.6,33,4.8,7.4,45.4,30.6,61.9,63.3,70.3,69.4,36,36,14.1,14,14.9,18.5,11.1,17.2,12.5,19.4,29.7,33.1,26,33.1,36.1,36.1,89.2,89.2,0,0,1,1,22.8,28.9,22.8,28.9,23.2,22.6,100,100,9.6,23.1,NA,NA,6.9,18.8,35.3,43.2,43.4,41.8,11.3,23.6,28.6,34,9.4,8.3,84.5,84.5
|
||||
242,FJI,Fiji,Asia-Pacific,924145,16003,48.1,45.8,39.6,36.9,17.9,16.2,4.3,4.3,16.8,16.9,80.6,99.5,0,0,7.5,7.5,8.1,8.1,5.5,5.5,59,59,99.3,99.3,5.7,0.1,90.9,64.9,40.2,38.4,79.2,75.3,92.8,87.6,NA,NA,80.3,72.8,40.8,40.8,83.7,83.7,76.4,75.8,53.9,33.9,41.7,40.6,100,100,100,100,48.9,51.6,81.8,70.7,78.7,75,92.8,87.8,100,66.2,100,71,62.6,63,71.9,68.2,58.6,44.2,27.3,30.3,73,73,42.4,42.4,47.9,47.9,49.1,49.1,36,36,36,36,54.8,55.7,61.2,61.7,100,100,10.2,15.8,77.2,66.2,91.8,93.8,63,64.1,95.5,94.2,46.2,47.8,34.3,36.1,33,37.5,32.7,35.1,56.7,59.6,54.5,59.6,45.2,44.7,58.5,57.5,100,100,4.4,4.2,55.6,51.1,55.6,51.1,60.5,51,100,87.6,47,61.1,36.8,3.8,68.6,75,56.8,59.9,49.7,49.8,52.6,50.9,53.4,51,45.5,45.3,86.2,86.2
|
||||
246,FIN,Finland,Global West,5601185,67077,70.5,73.7,70.1,68.4,61.8,58.7,64.7,64.9,44.4,44.7,69.4,64.2,27.4,27.4,64.1,67.9,33.9,42.4,84,84,100,100,100,100,97,97,72.5,13.5,40,38.9,61.1,60.8,NA,NA,81,93.2,45.9,31.6,41.3,41.3,50.9,50.9,89.6,90.4,88.6,80.8,94.2,95.8,94.4,91.4,100,100,59.4,25.6,92.1,92.8,36.4,51.6,49.6,62.3,100,100,100,100,68.5,66.6,43.8,37.7,51.6,50.4,60.6,59,94.5,100,84.1,84.5,32.4,32.4,99.9,100,84,85,72.6,72.6,82.5,85.6,78.5,82.2,73.4,81.9,90.6,95.3,62.5,79.6,21.9,20,64,69.3,62.7,66.9,71.3,76.7,93.4,95.2,89.2,93.5,93.3,96.3,95.2,99.3,91.9,99.3,69.5,68.4,27.1,21.5,100,100,96.7,99.4,61.3,71.8,61.3,71.8,62.7,73.7,52.5,68.5,80.4,100,41.2,64.5,57.3,54.4,63.5,100,49.4,49.3,58.2,66.6,45.6,56.9,28.1,100,87.5,87.5
|
||||
250,FRA,France,Global West,66438822,67669,65.6,67.1,68.3,68.4,60.2,61.6,47.4,48,57.4,58,43.3,44.1,71.5,71.5,67.6,71.6,64.4,83,68.4,68.4,45.3,45.3,0,0,59.6,46.2,80.7,72.8,27.1,27.6,64,58.6,NA,NA,NA,NA,66.3,65.3,49.3,49.3,43.5,43.5,45.7,43.2,23,20,31.5,39.9,42.6,51.5,37.8,48.9,53.6,44.7,93.3,92.8,61.4,60.8,55.9,53.1,100,100,100,100,77.1,72.8,65,63.7,65.2,57.4,56.6,59.8,95.1,88.6,84.2,84.2,33.8,33.8,100,100,82,82,80.3,80.3,67.9,71.5,61.3,65.2,36.3,49,86.8,91.5,55,57.5,13.8,19,50.4,62,53.6,60.6,56.8,57.4,84.4,85.9,82.8,86.7,83.2,85.3,89,94.8,84.4,94.8,56.6,59.6,24.4,23.9,92.6,100,70.8,75.2,59.6,61.3,59.6,61.3,63.4,60.9,63,59.1,67.9,69.1,36.2,73.3,71.4,70.1,100,100,51.2,51.1,57.4,59.1,50.5,53.6,10.3,12.8,77.7,77.7
|
||||
266,GAB,Gabon,Sub-Saharan Africa,2484789,24129,46,53.1,57.2,64.8,63,63.9,0,2.2,40.9,50,28.9,45,50.5,50.5,87.1,87.1,72.9,73.2,90.5,90.5,47.2,47.2,84.2,84.2,84.9,84.9,94.4,77.8,68.5,65.8,75.6,80.5,88.8,89.1,76.7,81.2,82.1,80.2,47.9,47.9,90.7,90.7,46.2,47.5,54.1,37.2,88.2,88.1,51.4,33.9,35.8,35,41.5,70.5,48.8,98.9,84.1,86.4,100,100,20.1,100,11,100,28.5,28.2,29.2,24.9,80.3,55.6,18.6,32.3,27.5,27.6,42.5,42.5,62.1,62.1,45.8,45.8,35.9,35.9,35.9,35.9,31.5,32.1,31.4,30.6,15.6,15.1,29,35.1,67.9,42.2,71.4,67.4,70.1,69.4,51.8,48.6,11.1,8.8,29.6,34.1,23.9,31.9,27.1,35.6,38.1,41.4,35.6,41.4,29,29,71.1,71.1,0,0,1.4,1.4,40.8,52.8,40.8,52.8,51.4,57.5,61.4,71.8,100,81.4,36.7,3.8,17.9,55.1,14,81.8,49.7,49.8,51.5,47.3,52,47.8,42.5,32.3,67.6,67.6
|
||||
270,GMB,Gambia,Sub-Saharan Africa,2697845,3491,36.5,37.1,40.1,35.5,40.6,38.3,0.7,0.7,56.4,56.4,NA,NA,40,40,30.9,30.9,17.9,25.3,15.5,15.5,52.2,52.2,99,99,87.4,87.3,93,41.8,0,0,41,49.9,NA,NA,NA,NA,43.7,55.4,37.3,37.3,48,48,90.8,91.1,NA,NA,98.6,100,75.7,73.9,95.8,100,1.7,78.9,48.9,26.4,50.9,51.2,66.2,65.7,20.2,18.9,67,21.1,40.3,33.7,30.5,15.4,100,96.6,67.4,55,51.1,37.8,9.5,9.5,92.6,92.6,0.7,0.7,0,0,0,0,37.5,40,44.3,46.9,96.7,93.2,4.4,5.2,41.5,28.9,50.2,58.3,32.4,29.8,54.8,51.9,28.8,29.7,21.2,24.9,17.7,23.6,19.4,25.8,22.7,23.4,23,23.4,32.6,32.6,80.4,80.4,0,0,1,1,29.6,37.2,29.6,37.2,34,26.6,100,99.3,0,43.7,NA,NA,44.4,44.5,41.5,52,0,0,21.6,33.8,30.2,41.5,39.5,40.1,94.5,94.5
|
||||
268,GEO,Georgia,Former Soviet States,3807492,29530,41,46.9,45.8,50.2,42.6,44.3,46.4,46.4,14.3,14.3,33.5,67.3,28.5,28.5,24.5,28.7,26.7,36.5,31.9,31.9,89,89,98,98,79.1,76.6,92.4,85.7,53.1,53.8,87.9,85.1,NA,NA,88.4,84,95.2,97.4,61.6,61.6,77.9,77.9,70.9,61.2,NA,NA,48.4,38.1,79.5,67.6,81,76,39.6,17.9,48.3,73.9,49.6,47.3,60,56.9,17.4,68,75.8,88.5,24.8,26,16.7,19.2,77,51.7,55.3,42.1,14.8,24,34.3,38.2,51.3,51.3,32.1,37,32.1,37,34.5,34.5,39.7,42,33,34.5,41.6,39.9,13.1,25.5,48.3,41.8,33.2,26.5,44.3,44.8,62.7,65.3,40,35.2,66.7,70.6,59,66.6,64.9,73.3,32.1,37,27.1,37,35,36.6,43.1,42.8,79.4,88,4.6,4.6,34.8,46,34.8,46,24.8,38.1,11,32.7,64.3,81.7,56.7,19.7,46.8,77.8,17.5,88.2,52,52.1,31.2,40,31.9,38.7,27.4,27.7,63.4,63.4
|
||||
276,DEU,Germany,Global West,84548231,72661,70.5,74.6,80.9,80.5,81.9,82.5,85,85.3,95,95.1,38.2,43.2,76.3,76.3,94.5,98.3,100,100,76.7,76.7,15.9,15.9,0,0,92.8,91.6,74.3,71.9,17.9,18.5,64,38.5,NA,NA,NA,NA,68.7,37.1,49.9,49.9,22.8,22.8,37.5,36.4,8.2,0,32.9,48.6,6.9,26.6,35.7,49.6,60.6,52.8,89.2,92.6,56.9,51.7,65.1,58.9,92.1,100,100,100,78.7,78.8,65.5,67.1,50,51.3,66.8,65.4,95.2,98.4,90.7,90.9,25.7,25.7,99.3,99.3,96.8,97.3,97,97,70.9,75.4,61.9,66.9,36,50,92.8,96.7,45.4,43.5,20.9,26.9,51,62.1,53.8,61.3,59.4,60.4,94.9,97.9,90.9,97.7,92.3,98.1,90.1,94.6,86.5,94.6,65.9,67.4,20.3,19.7,100,100,94.4,98.9,54.4,64.9,54.4,64.9,55.3,68.3,38.3,56.9,86.8,87,54.1,55.7,65.6,85.7,86.6,97.8,52.3,52.1,53.5,62.6,42.1,53.4,3.3,14.9,78.4,78.4
|
||||
288,GHA,Ghana,Sub-Saharan Africa,33787914,8260,34.8,36.6,45.5,46.9,48.1,45,0,0,0.1,0.1,100,56.7,40.8,40.8,79.2,79.2,49,49,90.4,90.4,69.4,69.4,92,92,46.4,45.5,78.1,42.1,17.5,16.9,41.6,25.1,65.4,35.9,NA,NA,39.9,8.5,24.5,24.5,45.2,45.2,57.2,58.6,37.1,45,21,22.2,68.7,72.7,73.9,78.4,49.2,35.8,50.6,78.3,69.3,68.5,83.6,84.6,0,57.4,19.3,100,61,70.7,46.1,51.9,100,100,68.8,64.9,61.4,89.4,14.5,14.5,65.5,65.5,12.1,12.1,6.3,6.3,6.3,6.3,29.7,31.9,30.6,32.4,58.4,49.5,5.7,9.4,51.3,35.9,56.8,55.6,62.6,60,43.8,39.6,22.1,17.4,23.6,27.4,19.7,26.6,21.1,27.9,36.1,39.5,33.2,39.5,31.5,31.3,60,59.9,28.1,27.5,4.8,4.7,22.7,24.7,22.7,24.7,21.3,26.5,55.6,65.8,0,9.1,37.6,4.1,26.6,30.4,28.3,26.9,46.2,49.2,21.2,23.6,23.4,25.9,19.3,16.6,82,82
|
||||
300,GRC,Greece,Eastern Europe,10242908,43800,58.7,67.4,65.6,67.9,62.9,62.7,72.2,72.2,39.7,39.7,43.4,47.2,36.5,36.5,56.4,56.5,100,100,85.2,85.2,75.6,75.6,18.5,18.5,57.7,56.6,90.6,85.1,44.9,44.8,68.2,58.2,NA,NA,NA,NA,73.9,60.7,48.1,48.1,66.1,66.1,42.8,47.8,45.8,30.2,36.2,46.2,50.8,49.7,37.4,51.1,59.3,73.6,68.9,88,23.5,24,37.4,38.4,79.7,98.7,100,100,63.8,61.4,55.9,52.2,100,100,55.5,50.1,62.7,72,84.3,85.4,18.1,18.1,93.4,94.7,93.4,94.7,78.1,78.1,61,61.9,53.3,53.8,44.9,41.2,71.3,73.7,30.8,44.8,19.4,26.6,32.5,37.4,58.1,63.3,46.6,45.9,91,92,82.9,85.3,95,96.4,62.9,67.3,58.4,67.3,38,39.4,27.4,26,100,100,17.7,22.4,46.1,71.3,46.1,71.3,51.4,69,38.6,64.8,50.5,96.8,14.3,58,58.6,69.1,65.3,100,51.3,52.7,47.7,66.7,41.3,60.8,17.5,100,89.9,89.9
|
||||
308,GRD,Grenada,Latin America & Caribbean,117081,20306,45.6,46,39.2,40.4,20.6,21.4,0,0,1.8,3.4,100,100,NA,NA,11.2,14.2,27.3,27.3,60.6,60.6,28.3,28.3,74.6,74.6,0,0,NA,NA,84.8,81.5,62.6,63.1,NA,NA,NA,NA,63.1,76.2,40,40,44,44,83.5,84,NA,NA,53.4,50.8,100,100,100,100,48.6,24.6,73,76.5,47.8,42.5,NA,NA,36.5,87.3,82.8,72.6,48.3,51,43.8,50.6,59.7,55.8,4.1,21.6,63.3,63.3,46.8,46.8,23.4,23.4,55.6,55.6,44.5,44.5,44.5,44.5,64.3,66.1,72.5,74.4,100,100,39.4,47.1,74.5,82.7,56.3,51.7,83.8,88.4,83,84.7,90.4,92.3,53.8,55.2,51.7,55.7,52.7,54.9,33.1,36.9,30.3,36.9,38.8,38.8,48.3,48.3,96.8,96.8,0.2,0.2,38.5,36.7,38.5,36.7,36.1,39,32.2,36.9,46.9,43.6,0,20.3,12.5,50.2,55.6,50.7,52,51.5,23.7,30.3,24.1,29.9,51.1,50.5,56.1,56.1
|
||||
320,GTM,Guatemala,Latin America & Caribbean,18124838,15390,35.3,32.6,42.8,38.6,38.2,36.8,53.8,53.8,17.8,19.2,89.6,20.6,57.6,57.6,31.9,33,65.5,66.6,35.6,35.6,46,46,88,88,11.3,7.7,0,0,82.5,78.7,46.6,33.3,39.2,41.1,61,32.2,31.7,29.5,19.8,19.8,38.5,38.5,40,39,70.9,32.3,42,35.2,21.3,44.1,31.2,38.2,66.4,54.8,63.5,49.8,79.9,80.9,63.9,60.9,65.5,37.8,70.4,53.4,59.2,56.8,62,64.7,44.9,44.3,50.2,49.5,47.4,53.1,28.7,28.7,25.3,25.3,35.5,35.5,23.9,23.9,23.9,23.9,22.2,24,19.2,20.1,7,10.9,11.9,15.8,58,59.5,34.3,38.6,37.7,37.9,41,43.1,3,0.7,27.4,31.4,25.5,31.8,25.3,31.1,33.9,38.2,32.9,38.2,24.3,24.3,58.6,58.6,1.7,1.7,1.3,1.3,34.7,30.6,34.7,30.6,49.3,33.3,85.9,56.1,18.5,26.7,36.9,3.8,22.1,32.2,21.7,17.2,46.7,48.2,38.3,32,40.1,33.3,22.5,19,73.2,73.2
|
||||
324,GIN,Guinea,Sub-Saharan Africa,14405465,4321,39.1,36.2,51,47.4,64,61.4,81.4,81.4,7.7,7.7,100,100,67.1,67.1,58.5,65,95.6,95.6,69.6,69.6,33.4,33.4,91.1,91.1,65.9,65.6,87.1,25.4,66.8,65.7,39.7,30.6,69.1,56.4,NA,NA,44.2,5,8.3,8.3,48.8,48.8,43,38.2,53.9,55.4,39.7,28.6,12.4,25.6,52.8,43.7,52.5,48.7,44.9,33.8,62.5,66.9,80.3,85.6,34.7,28.7,20.5,22,42.4,45.9,38,41,100,100,95.6,81.4,21.1,31.6,9.6,9.6,95.7,95.7,0,0,0,0,0,0,29.7,32.4,34.5,36.8,71.6,67.4,2.5,4.6,40.6,27.6,61.8,62.4,62.2,62.7,35.2,38.9,5.5,4.5,15.1,20.1,10.5,19,11.6,20.8,20.9,23.2,19.3,23.2,39.3,39.3,95.5,95.5,1.8,1.8,1.8,1.8,28.7,22.2,28.7,22.2,35.9,7.1,100,49.3,0,26.6,36.8,3.8,14.3,22.2,40.3,56,47.8,48.3,6.2,16.8,25.2,31.4,21.8,20.6,73,73
|
||||
624,GNB,Guinea-Bissau,Sub-Saharan Africa,2153339,3110,42.1,41.6,48.7,44.8,54.9,54.2,47.7,47.7,21.4,21.4,100,100,63.3,63.3,60.5,79.4,71.9,87.9,23.4,23.4,46.2,46.2,96.7,96.7,70.5,70.4,60.5,0,59.3,56.6,39.2,33.7,65.5,50.9,NA,NA,33.9,17.3,11.8,11.8,56.3,56.3,64.8,67,46.7,49.2,100,100,43.3,14.4,100,100,35.9,19.5,57.5,35,50.4,45.3,65,64.3,36.2,35.9,79.7,26.1,44,42.2,28.6,28.2,81.2,67.5,90,76.3,40,40,10,10,100,100,0,0,0,0,0,0,33,35.4,41.2,42.9,87.8,83.5,1.3,3.2,36.8,24.4,74.1,71.3,54,51.6,51.5,51.4,14.7,14,14.8,19.8,9.7,19,10.5,20.4,13.4,16,11.7,16,25,25,61.5,61.5,0,0,1,1,39.4,41.8,39.4,41.8,41.8,39.2,100,100,24.4,37.1,NA,NA,36.3,43.6,53.1,67.9,48.2,48.8,21.3,29.3,38.8,43.6,39.3,39.6,88.8,88.8
|
||||
328,GUY,Guyana,Latin America & Caribbean,826353,91380,46.4,48.6,53.3,56.2,57.2,56.1,NA,NA,0,0,NA,NA,96.9,96.9,38.3,38.3,27.8,27.8,100,100,71.3,71.3,98.5,98.5,62.5,59.4,89.2,79.8,44.1,41,81.4,79.8,92.2,89,78.3,74.6,88.7,84.4,48.2,48.2,95.8,95.8,26,31.1,55.4,50.9,29.1,29.6,19.4,22.5,22.9,25.9,55.4,57.8,35.2,61.2,100,100,84,87.1,31.4,47,40,62.4,77.6,74,57.2,55.3,66.6,100,69.4,51.4,77.9,100,27.3,27.3,52.7,52.7,29.8,29.8,20.3,20.3,20.3,20.3,53.7,55.8,63.9,65.4,100,100,18.6,27.5,100,100,39.3,37.1,97.3,99.3,78.5,77.2,23,16.1,38.4,42.2,35.5,43.5,35.4,41.4,16.2,20.3,13.8,20.3,31.1,31.1,49.6,49.6,55.1,55.1,0.5,0.5,29.4,30.6,29.4,30.6,43.4,19.6,32,0,22.3,59.9,NA,NA,36.5,44.9,51.1,39.6,49.7,49.6,31.8,30.3,34.1,24,36.6,32.8,35.5,35.5
|
||||
332,HTI,Haiti,Latin America & Caribbean,11637398,3039,28.5,36.2,29.2,36.8,26.7,30.6,42.1,52.6,22.3,26.8,50,50,45.4,45.4,11.5,21.2,6.9,25,30.9,30.9,0,0,62.9,62.9,13.7,10.5,63.7,22.2,70.1,67.7,56.6,51.4,64.8,53.3,NA,NA,55.9,52.6,48.5,48.5,40.1,40.1,87.6,86.8,64.1,47.1,NA,NA,100,100,100,100,53.9,47.2,13,55.7,27,22.9,38.4,31.6,41,57.9,0,64.8,50.9,50.6,30.9,27.5,41.2,52.9,93.3,98.3,53.7,53.7,10.7,10.7,89.9,89.9,4.2,4.2,0,0,0,0,20,22.1,23.2,25,37.1,38.4,2.3,3.4,34.4,35.9,15.5,14.4,73,75.9,57.2,59.9,33,33.8,14.6,18.1,10.5,17.4,11.3,18.6,6.4,8.4,5,8.4,21.6,21.6,52.3,52.3,1.2,1.2,1.1,1.1,34.8,47.7,34.8,47.7,30.2,51.2,100,100,29.2,42.2,NA,NA,38,47,26,42.4,43.9,48,31.8,42,37.7,48,27.8,30.2,94.3,94.3
|
||||
340,HND,Honduras,Latin America & Caribbean,10644851,7605,37.4,40.2,47,49,51.8,51,76.9,76.9,24,25.6,51.5,11.1,64.7,64.7,60.5,61.9,59.3,60.2,85.3,85.3,39.9,39.9,95.3,95.3,20.4,16.7,11.1,0,80.4,77.5,36.2,20.2,42.4,27.1,28.8,0,41.7,25.5,35,35,44.7,44.7,55,45.3,43.6,29.2,53.2,37.4,63.6,53.1,65.2,55.1,61.6,18.3,53,87.8,74.1,74.4,86.5,89.8,45.2,77.9,55.2,100,39.3,38.6,36.7,34.9,48,40.5,41.2,41.3,45.6,40.9,28.3,28.3,47.1,47.1,34.7,34.7,19.5,19.5,19.5,19.5,21.8,22.8,18.3,18.7,11.2,14.5,9.7,11.5,40.4,33.1,34.4,41.1,52.1,54.5,56.5,61.9,0,0,34.3,37.8,29.3,36.1,32.5,38.9,17.5,18.7,17.5,18.7,25.7,25.7,50.5,50.5,25.3,25.3,1.2,1.2,35.6,41.2,35.6,41.2,38.1,44.1,66.5,77.7,27.9,38.4,36.8,3.8,25.8,34.1,17.1,84,49.4,48.6,30.5,37,36.2,41.4,24.8,24.8,79.8,79.8
|
||||
348,HUN,Hungary,Eastern Europe,9686463,49150,61.6,60.1,73,73.8,66.9,67,NA,NA,NA,NA,NA,NA,38.1,38.1,83.3,83.3,75,75,83.6,83.6,8,8,38.5,38.5,71.7,71.6,74.9,77.2,6,6.3,53.7,50.1,NA,NA,NA,NA,55.5,47.4,70.6,70.6,22.5,22.5,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,94.1,93.3,60.8,56.8,67.7,62.9,100,100,100,100,75,69.2,64.5,67,94.6,89.7,73.7,75.6,59.8,67.1,75.8,87.1,49.3,49.3,76.6,83.5,76.6,97.9,96,96,43.7,48.1,31.9,38.7,22.5,35.5,36.5,43.6,27.6,30.8,25.9,27,24.4,31.8,52.9,58.3,48.5,49.8,76.3,74,83.1,76.9,80.5,72.1,57.3,61.9,54.4,61.9,54,51.7,35.5,32.7,100,98.3,49.4,47.3,59,49.2,59,49.2,63.8,47.5,62.3,37.4,65.8,73.3,14.6,33.7,80.5,37.2,30,100,53.6,53.6,64.2,46.4,60.1,41,100,19.4,58.4,58.4
|
||||
352,ISL,Iceland,Global West,387558,80000,62,64.3,56.8,60.9,54,54.8,46,46.1,29.2,29.2,34.8,40.9,63,63,46.7,49.7,44.6,47.6,32.2,32.2,100,100,99.7,99.7,62.6,62.5,100,100,100,100,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,57.6,47.5,17.9,39,67.6,78.5,41.9,30.9,48.6,45.1,54.2,48.1,63.5,89.8,33.5,46.6,40.5,54.6,100,95.2,10.1,100,33.6,36.5,0,0,29,28.9,29.8,46.8,69.4,69.4,76.7,76.7,20.4,20.4,86.9,86.9,79.7,79.7,79.7,79.7,87.4,89.3,88.3,89.7,100,100,92.6,96.6,51.7,58.8,32.7,29.6,74.6,87.8,70.1,75.2,94.3,95.8,93.5,95.2,89.3,93.7,93.5,96.2,88.2,95.7,82.2,95.7,40.1,39.3,19.3,21.2,94.5,84.9,33.6,34.7,48.5,48.2,48.5,48.2,43.5,49.9,19.3,28.4,63,50.9,18.3,45.8,54.2,49.2,100,100,NA,NA,40.1,48.2,26.9,35.8,36.7,38.8,46.2,46.2
|
||||
356,IND,India,Southern Asia,1438069596,11940,23.5,27.6,28.1,30.5,13,11.4,1.6,1.9,0.2,0.2,100,100,0,0,0.9,0.9,0.7,0.7,0.8,0.8,88,88,94.5,94.5,3,0,85.7,61.1,15.6,15.7,75.4,73.8,76.2,71.9,90.2,90.4,74.2,63.6,56.6,56.6,70.9,70.9,37,37,84.1,81.3,54.1,58.9,22.1,16.8,25.1,19.5,45.2,40.8,31.9,55.3,32.5,25.1,29.9,22,22.8,66.4,30.6,57,59.7,65.1,45,51.6,38.1,39.4,62.9,45.8,76.7,88.8,29.9,29.9,71.8,71.8,32.7,32.7,19.2,19.2,19.2,19.2,11.4,13.3,5.9,6.8,0,0,5.8,10,0,0,33.8,37.9,11.1,8,3.7,0,20.4,17.8,20.2,25.6,14.8,25.3,16,25.8,26.3,28.2,25.7,28.2,31.3,31.8,64.3,65.6,9.3,9.3,9.3,9.3,26.4,35,26.4,35,24.1,37.1,20.3,42.6,39.6,39.2,0,18.3,21.3,31.2,15.6,56.8,48.5,49.2,19.6,31.1,26.1,34.9,0,0,70.6,70.6
|
||||
360,IDN,Indonesia,Asia-Pacific,281190067,17520,28.1,33.8,31.7,39.3,25.5,31.5,24.9,28.6,11.3,13,84.6,96.1,14.9,14.9,3.3,43.2,38.2,38.2,43.7,43.7,77.8,77.8,97.4,97.4,31.2,19.6,34.5,0,93.7,91.8,41.2,52.7,47.5,65,63.1,60.9,16.7,31.8,36.9,36.9,66.1,66.1,40.2,39.9,64.1,60.3,60.2,58.3,27.2,26.3,33.1,31.7,50.2,31.1,21.5,46.2,90.6,92.6,67.1,62.3,0,38.9,27,41,74,72.2,50.5,52.5,74.6,49.3,65.2,59.3,97.3,99.3,36.9,36.9,39,39,45.2,45.2,29.8,29.8,29.8,29.8,20.6,25.7,16.9,22.8,24.2,23.2,9.6,17.5,37.4,28.6,40.8,47.1,18.2,17.6,26.2,40.1,8.7,8.8,29.3,33.4,26.7,36.6,24.5,31.3,26.9,30,24.5,30,26.7,26.7,48,48,31.1,31.1,3.1,3.1,28.8,32.1,28.8,32.1,30.2,34.7,23.2,30.7,9,25.9,37.4,8.5,25,21.3,48,100,45,44.2,22.9,28.9,25.2,29.6,0,0,55.3,55.3
|
||||
364,IRN,Iran,Greater Middle East,90608707,20370,41.6,41.6,45.5,45.9,43.8,42.9,66.4,66.4,22.2,22.2,100,100,10.1,10.1,24,25.4,23.2,23.2,68,68,65.5,65.5,80.2,80.2,61.9,54.2,81.6,69.6,45.7,44.9,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,62.4,63.3,55.5,76.5,61.5,62.6,50.2,58.4,70.2,67.8,42.6,20.2,63.9,70.7,18.7,2.4,25.2,5.2,44.1,80.1,78.3,88.1,41.2,37.8,26.9,31.6,49.4,83.7,54.4,50.2,28.7,35.1,28.1,28.7,51.8,51.8,30,31.5,21.8,21.8,21.8,21.8,40,41.6,36.6,36.9,19.8,15.8,56.8,66.6,32.6,29.3,21.8,21.8,11.7,6.7,33.3,22.9,24.4,22.6,60,65.6,52.2,61.5,59.6,68.3,22.8,26.8,20.8,26.8,31.7,31.7,52.4,52.4,19.8,19.8,17,17,37.1,35.1,37.1,35.1,37,44,12.7,22.6,40.6,35.4,35,14,100,35.9,37.8,68.7,52.9,53.4,25.4,30.5,26.5,30.4,0,0,37.5,37.5
|
||||
368,IRQ,Iraq,Greater Middle East,45074049,15260,24,30.4,27.4,33.1,21.4,20.2,0,0,0.4,0.4,NA,NA,8.8,8.8,12.8,12.8,1.9,4.9,17,17,68.1,68.1,78.2,78.2,38.4,31.6,94.7,87.1,41.5,38.3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,74.2,72.7,NA,NA,72.3,68.2,81.9,75.8,82.7,76.8,100,47.1,8.7,57.3,10.2,0,26.6,9.6,0,51.1,34.9,84.6,52,49.6,28.5,31.5,53.6,52,90.5,84.2,41.4,53.2,48.9,44.1,64.9,55,27.1,28.9,71.8,60.5,28.2,28.2,30.9,32.6,26.7,27.7,10.4,4.2,31.7,51,36.6,35.8,26,24.1,14.8,10.7,36.8,33.4,22.6,23.2,52.1,57.7,46.5,58.2,48.7,57.3,24.1,24.3,22,24.3,10.9,8.6,22.5,16.7,0,0,4.8,4.8,13,24.6,13,24.6,35.3,38.7,21.4,26.8,0,12.1,36.7,5.5,0,24.7,36.4,48.1,0,0,14.6,19,20.1,25.5,5.4,4.1,36.7,36.7
|
||||
372,IRL,Ireland,Global West,5196630,133550,63.4,65.7,61,67.5,50.7,62.9,30.9,33.1,42.6,46.9,70.9,60.7,46.8,46.8,25.1,93.8,43.9,47,70.3,70.3,66.9,66.9,95.5,95.5,60.2,60.5,96.8,97.7,70,72.9,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,62.2,40.6,41.2,14.6,40.3,43.6,59.5,28.9,60.6,56.8,42.1,52,86.6,87.3,15.5,22.4,19.3,25.3,100,100,100,100,75.7,72.9,50.6,46.6,29.6,23.5,82.8,80.9,89.9,100,72.7,73.8,10.4,10.4,93.5,94.8,62.1,63.6,94.3,94.3,77.2,79.8,74.7,76.8,62.3,67.1,87.8,93.5,57.6,85.4,20.2,25.6,55.2,61.3,67.2,72.5,80.3,83.4,87.1,88.4,78.2,82,90.9,92.7,84.1,93.2,78.6,93.2,56.4,60.7,22.9,19.8,95.8,98.7,70.1,82.6,55.6,51.1,55.6,51.1,61.1,50.5,47.1,32,64.3,51.5,48.5,80.9,60.9,47.9,100,100,55.8,52.5,59.6,51,46.4,36.9,29.2,20.5,48.3,48.3
|
||||
376,ISR,Israel,Greater Middle East,9256314,54446,47.6,48.1,45.1,43.6,31.4,30,0,0,50,50,57.7,100,18.7,18.7,32.8,34,68.3,69.5,23.3,23.3,46.2,46.2,92.1,92.1,10.4,10.7,54.4,0,48.1,47.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,23.7,27.5,NA,NA,19.3,25.4,11.8,22.1,15.7,25.6,51.2,75.5,61.7,65.2,31.2,18.3,51.4,32.4,52.2,46.4,99.3,100,63,45.3,35.9,24.5,40.4,51.6,37.2,45.2,92.5,65.6,90.3,90.5,32.9,32.9,97.7,97.9,96.8,97,92.3,92.3,60.3,62,52.3,53.7,36.7,33.3,83,88.5,35.9,41.6,17.7,24,0,0,27.9,26.9,38.5,41.4,78.7,80.2,74.5,77.9,79.7,81.7,94.3,100,89.9,100,36.6,37.1,21.2,19.2,100,100,20.4,23.5,40.7,43.3,40.7,43.3,42.2,60.6,24.3,51.5,21,2.1,0,14.6,62.3,42.7,100,95.2,58.1,11.4,36.1,48.9,27.9,41,15.3,18.6,55.8,55.8
|
||||
380,ITA,Italy,Global West,59499453,62600,55.8,60.5,58.4,63.4,53.5,58.9,66.3,66.8,37.3,41.8,44.1,45.3,55.4,55.4,26.7,57.6,57.4,72.1,71.9,71.9,70,70,44.7,44.7,63.4,57.8,77.5,59.4,34.3,34.4,65,55,NA,NA,NA,NA,77.8,61.3,48.5,48.5,36.5,36.5,35.5,34,20.8,11.3,45.3,48.8,28.2,35.5,24.4,30.1,67.3,63.1,72,89.5,38.3,32,51.3,41.9,86.5,100,100,100,62.1,56.4,45.5,41.7,46.4,43,65.7,61.2,72.8,70.3,70.9,73.9,28.5,28.5,86.9,86.9,62.5,70,82.6,82.6,61.1,63.9,49.1,52.3,29,31.7,75.6,79.4,43.7,38.6,24.8,34.3,41.5,50.8,45.3,54.7,45.3,44.8,97.9,98.2,94.7,95.6,100,100,74.9,79.6,71.1,79.6,52.5,57.5,27.5,28.1,90,90.9,58.7,70.2,47.5,53.2,47.5,53.2,54.1,55.9,42.7,45.4,55.4,51.9,23.9,40.5,76.8,56.7,64.1,100,53.4,52.9,54.5,54.6,47.7,48,9.3,9.2,67.6,67.6
|
||||
388,JAM,Jamaica,Latin America & Caribbean,2839786,12283,47.7,48.5,49.9,49.7,39.7,38.9,76,76,19.2,20.3,50,50,44.5,44.5,31.2,32.4,62.1,63.4,58.1,58.1,52.2,52.2,52.2,52.2,0,0,45.3,0,80.6,77.8,65.2,68.7,74.8,83.2,NA,NA,58.5,64.3,45.6,45.6,50.1,50.1,84,83.2,82.3,20.6,91.4,88.5,100,100,100,100,46.1,47.5,74.9,77.4,21.9,19.1,25.2,22.6,89.1,77.5,88.7,100,60.3,55.1,67.5,54.4,46.8,42.5,53.6,55.6,53.3,56.6,40.5,40.5,49.1,49.1,46.8,46.8,33.7,33.7,33.7,33.7,38.1,39.5,34,35.8,35.2,35.9,25.9,29,49.3,50.9,27.3,33.8,38.1,41.2,63.6,67.3,41.7,43.4,53,53.8,51.9,53.4,53.3,54.1,42.2,42.7,43,42.7,25.5,25.5,37,37,50.5,50.5,1.4,1.4,52.4,54.3,52.4,54.3,52.1,58.8,63.9,75.3,61.2,54.4,30.6,24,71.6,51.8,56.6,41.1,47.1,50.2,50.6,56.8,52.7,58.1,36.3,41.5,90.2,90.2
|
||||
392,JPN,Japan,Asia-Pacific,124370947,54910,57.6,61.7,56.9,59.9,46.8,47.5,42.6,42.9,55.9,58.7,33.7,37,25,25,45.3,58.9,88.5,95.1,26.9,26.9,68.8,68.8,78.5,78.5,27.3,17.7,70.8,43.3,72.8,72.5,79.6,80.1,NA,NA,92.8,98,81.4,76.4,47.3,47.3,57.4,57.4,41,48.5,32.6,49.8,40.5,44.2,39.8,40.7,50.8,54.5,47.7,58.4,72.5,86.4,17.8,17.9,19.8,18.6,100,100,59.3,100,61.9,63.3,48.9,50.3,24.6,25.1,29.4,38.5,89.9,89.8,76.6,78.4,24.6,24.6,89.9,91.7,77.8,80.6,70.7,70.7,65.4,67.4,57.4,59.9,40.5,42.2,83.2,86.7,59.2,62.9,12.7,17.7,29.2,33.9,50.2,58.9,40.2,43.9,77.5,78.7,77.9,80.5,75.8,77.5,100,100,98.8,100,72.5,73.6,38.8,39.6,99.3,100,92.7,94.4,52.2,59.7,52.2,59.7,49.9,63.1,29.8,48.4,70.8,72.3,62.5,40.6,71.1,63.3,100,100,51,50.5,47.9,61.1,38.4,52.8,0,10,76.9,76.9
|
||||
400,JOR,Jordan,Greater Middle East,11439213,11380,37.7,47.5,36.7,50.1,30.5,32.9,NA,NA,0,0,NA,NA,31.6,31.6,5.2,13.5,3,12.2,14.9,14.9,57,57,87.1,87.1,87.6,87.4,82.5,62.3,64.9,64.5,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,96.2,95.7,NA,NA,100,100,100,100,100,100,49.2,26.1,13.4,85.5,11.3,4.6,27.4,21.1,21.8,100,52.1,100,34.1,38.1,31.5,31.1,51,88.4,52.6,49.9,29.1,35.9,72.5,73.3,53.4,54.3,63,63,89.3,91.2,62.1,62.1,44.6,46.6,41.1,42.9,22.6,20.4,65.2,74.2,33.6,41.1,12,18.6,0,0,37.2,32.3,37.1,36,62.3,64.3,61.1,65.6,60.1,63.5,38.8,42.8,33.8,42.8,27.3,27.3,47.4,47.4,29.4,29.4,6.2,6.2,33.2,44.6,33.2,44.6,42.1,58,50.7,78.1,0,15.7,36.7,3.8,20.2,42.4,100,100,0,0,33,45.3,34.1,47.1,21.9,24.3,78.7,78.7
|
||||
398,KAZ,Kazakhstan,Former Soviet States,20330104,43610,43.3,47.5,51.5,49.2,50.2,50,87.6,87.6,41.7,41.7,50,50,12.6,12.6,57.4,58.3,32.3,32.6,34,34,31.9,31.9,16.1,16.1,63.7,62.3,93.3,88.9,22.4,22,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,76.9,61.7,35.7,42.1,44.8,54.2,48.9,55.5,100,73.4,43.2,44.7,28.7,37.4,100,95.1,87.6,66.4,18.2,38.7,32.2,32.6,0,0,57,56,11.3,13.2,48.9,48.9,46.6,50.8,41.1,44.5,43.5,48.2,31.1,45,26.4,22.4,43.3,41.6,32.4,33,62,64.5,53.4,53.1,66.7,73.1,56,68.4,62.5,76.3,51.6,58.6,45.1,58.6,28.5,30.8,48,47,31.1,34.2,7.8,13,28.6,42.3,28.6,42.3,38.3,51,8,25.1,14.9,44.1,0,41.9,52.3,20.4,0.3,96.4,43.2,45,21.1,38.9,14.6,31.1,4.6,8.1,40.1,40.1
|
||||
404,KEN,Kenya,Sub-Saharan Africa,55339003,7520,37.3,36.9,48,49.1,44.7,43.9,58.6,58.6,20.3,20.3,43.4,59.9,38.4,38.4,51.3,54.4,40.1,40.2,54.6,54.6,31.2,31.2,5.5,5.5,34,24.9,90.1,80.7,14.9,15.2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,73.9,77.1,64.1,78.7,65.5,70.9,100,90.6,74.2,78.2,30.5,21.4,60.5,72.6,78.6,70.2,84.8,74.9,13.8,45.1,40.9,100,49.3,44.2,42.7,44,54.1,44.9,85.2,83.8,38.8,27.8,38.2,38.7,48.5,48.5,7.5,8.5,76,76,0,0,27.5,27,27.1,25,31.3,32.4,6.6,8.8,58.6,31.1,60.6,64.5,49,48,28.4,26.5,18.4,17.4,16.8,21.2,12.2,20.1,13.4,22,42.2,45.7,39.6,45.7,59,51.8,100,87.7,74.9,73.4,10,5.2,29,26.5,29,26.5,25.1,24,75.4,73.3,5.4,17.2,0,24.2,0,13.2,19.4,30.1,40.9,46.7,19.5,24.6,25.1,29.3,13.6,11.8,76.9,76.9
|
||||
296,KIR,Kiribati,Asia-Pacific,132530,3612,45.2,44.1,46.8,45,33.2,31.4,23.6,23.6,8,8,99.8,100,NA,NA,NA,NA,57.7,58.3,29.7,29.7,NA,NA,NA,NA,27.9,18.9,NA,NA,100,100,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,92,91.4,79.3,94.1,82.7,82.2,100,100,100,100,62.1,16.4,86,81.1,89.1,94.3,NA,NA,100,89.9,87.5,69.7,58.3,58.6,23.1,23.8,100,100,77.8,77.8,81.9,81.9,19.5,19.5,81.9,81.9,20.4,20.4,6.3,6.3,6.3,6.3,45.9,46.2,54.9,54.5,100,100,2.5,5.1,27.9,14.6,82.5,72.9,100,100,95.4,97.6,100,100,19.6,21.6,17.1,21,18.4,22,48.6,50.3,47.3,50.3,18.8,18.8,42.3,42.3,0,0,4.7,4.7,42.6,40.9,42.6,40.9,40.5,37.9,100,95.7,28.9,30.2,NA,NA,32.6,37.8,41.7,66.7,NA,NA,34,31.7,43.6,40.3,64.8,62.8,96.6,96.6
|
||||
414,KWT,Kuwait,Greater Middle East,4838782,49736,46.8,44.9,56.7,54.8,50.7,50.6,0,0,6.5,7.4,50,100,44.2,44.2,71.2,71.5,54.8,55.3,86.8,86.8,87,87,99.8,99.8,61.8,55.3,81,67.5,48.3,49,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,19.6,23.3,NA,NA,0,0,21.1,25.8,24.5,29.1,47,63.6,63.4,51.8,0,0,0,0,42.2,44.9,72.9,79.3,62.9,59,44.5,37.1,16.2,7.7,84.3,75,78.4,78.4,87.4,87.4,30.9,30.9,100,100,88.7,88.7,88.7,88.7,50.1,50,41.6,41.5,12.4,7,76,82.5,54.7,62.4,5.2,9,0,0,34.7,30.2,13.7,11.1,75.3,76.5,77.5,81.5,72.1,73.1,54.6,60.3,48.8,60.3,59,42.6,28.9,17.2,79,59.6,79,59.6,28.5,24.9,28.5,24.9,36.6,38.6,2.1,4.7,47.8,42.6,36.2,5.8,20.5,29.9,0,75.5,NA,NA,27.6,22.1,5.2,1,11.7,9.7,0,0
|
||||
417,KGZ,Kyrgyzstan,Former Soviet States,7073516,7773,29.5,42.2,35.5,43.3,37.5,36.5,NA,NA,NA,NA,NA,NA,36.3,36.3,13.7,13.7,11.7,11.7,13.7,13.7,64.1,64.1,74.8,74.8,64.2,63.6,91.2,83.9,39.8,40.8,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,30.3,77.3,3,2.6,31.3,27.6,0,100,33.1,79.4,54.7,51.9,43.4,39.5,72.9,59.9,80.7,51.8,42,63.6,22.7,22.7,75.9,75.9,26.9,26.9,8.7,8.7,8.7,8.7,32.2,36.7,26.2,30.6,37.4,45.7,7.3,14.2,12.4,21.5,32.3,25.5,37.5,38.8,61.9,61.9,45.5,45.8,53.3,59.4,43.4,54.8,49.5,62.4,38.4,43.1,34.2,43.1,16.2,14.8,37.2,33.9,0,0,3.2,3.2,17.5,45.4,17.5,45.4,25.1,56.2,33.1,89.6,19.4,22,NA,NA,14.9,35.2,0,52.4,46.5,47.6,14.9,39,23.9,45.9,24.5,28,79.1,79.1
|
||||
418,LAO,Laos,Asia-Pacific,7664993,9727,24.2,26.1,35,40,39.1,50.8,NA,NA,NA,NA,NA,NA,32.9,32.9,44.8,66.2,37.3,63,63.2,63.2,74.4,74.4,97.3,97.3,44.2,43.4,0,0,93,90.5,31.8,24.5,52.3,40.4,35.9,25.7,24.6,2,14.2,14.2,55.8,55.8,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,16.2,16.2,54.2,56.1,53.3,55.5,30.8,16.6,15.4,0,75.4,78.5,59.8,78.1,65.9,45.5,72.2,72.2,84.4,84.4,20.9,20.9,51.2,51.2,24.7,24.7,11.8,11.8,11.8,11.8,16.5,19.2,11.8,13.7,5.3,9.9,2.7,6.8,22.9,22.2,49.8,49,47.3,49.4,18.9,34,0,0,26.2,32.3,19.6,30.9,21.7,33.3,19.1,21.9,16.7,21.9,42.6,42.6,94.9,94.9,12.1,12.1,5.5,5.5,13.4,9.6,13.4,9.6,0,0,0,0,21.8,28.8,NA,NA,14.3,24.4,22.6,52.3,47.9,48,19.9,0,24.6,0,24.9,16.5,27.6,27.6
|
||||
428,LVA,Latvia,Eastern Europe,1882396,45450,59,59.9,70.3,68.6,69.3,68.3,86.8,86.8,42.9,42.9,39.1,37.2,42.6,42.6,83,84.2,60.3,60.4,95.2,95.2,71,71,55.7,55.7,96.3,96.3,63.2,44.2,10.2,11.3,39.8,31.8,NA,NA,NA,NA,35.9,26.5,50.7,50.7,20.6,20.6,72.6,69,43.2,34.4,37.7,54.9,88.4,87.4,96.1,81.2,41.6,51,86.3,82.4,43,46.8,46.6,52.1,89.8,100,100,77.9,67,64.4,45.5,60.3,55.7,52.3,78.1,73.4,63.5,65.8,68.1,69.8,22.2,22.2,75.1,77.3,74.8,76.9,59,59,49.3,52.8,40.9,45.1,29.4,37.8,33.7,45.1,69.7,70.5,31.9,30.6,53.5,56.6,53.2,59.3,59.6,67.5,74.9,75.1,73.3,73.8,76.2,75.9,63.4,68.1,58.4,68.1,36.1,42.8,28.2,24.7,77.5,84.4,23.2,40.1,49.8,52.4,49.8,52.4,52,52.4,51,51.7,46.2,73.1,6,48.1,34.3,43.5,66.3,60.7,49.6,49.1,48.2,51.3,44.4,46.5,32.3,33.7,67.2,67.2
|
||||
422,LBN,Lebanon,Greater Middle East,5733493,11784,32.2,40.1,34.1,38.1,25.3,24.1,5,5,0,0,100,50,14.9,14.9,4.2,4.3,5.8,6.2,4.3,4.3,42.6,42.6,79.2,79.2,87.1,87.6,88.9,63.2,48.9,49,64.5,48.5,NA,NA,NA,NA,68.5,45.7,60.8,60.8,37.5,37.5,96.9,96.2,NA,NA,92.6,94,100,100,100,100,55.4,60.2,22.8,62.5,29.4,23.2,33.9,27,0,68,59.3,72,53,50.6,39.2,44.6,29.6,52.7,41.3,37.5,62.6,61.8,47.3,47.3,38.6,38.6,55,55,42.8,42.8,42.8,42.8,44.1,46.3,37.9,40,29.6,27.4,52.8,62.1,26.6,35.5,5.2,0,10,10.1,31.7,33.5,41.4,45.7,60.7,63.2,66.4,73.2,53.4,56.6,58.3,62.4,54.1,62.4,36.6,36.6,40,40,57,57,23,23,19.3,38,19.3,38,32.8,47.8,16.2,39.3,16.5,22.3,36.6,4.5,11.5,38.2,0,67,48.5,45.5,26.6,37.3,24.8,37.7,21.4,23.1,56.2,56.2
|
||||
426,LSO,Lesotho,Sub-Saharan Africa,2311472,3260,36.6,36.6,45.7,46.3,51.5,60.4,NA,NA,NA,NA,NA,NA,25.6,25.6,0.5,60.1,69.9,69.9,66.6,66.6,64.1,64.1,77.2,77.2,81,80.9,93.6,53.2,79.6,79.7,69.7,59.6,NA,NA,NA,NA,81.1,64.8,53.3,53.3,45.9,45.9,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,55.4,35,59.9,54.7,64.2,61.4,0,29.6,0,31.3,35.4,34.3,11.2,11,59,73.9,84.9,84,33.5,33.5,9.6,9.6,84.8,84.8,2.9,2.9,0,0,0,0,13.7,12.8,14,11.8,11.5,6.1,2.4,4.3,24.5,15,67.7,68.4,16.6,15,53.5,47.7,26,20.2,7.6,9.4,5.4,8.9,6.1,9.8,13,16.1,11.8,16.1,40.4,40.4,100,100,0,0,1,1,41.9,41.7,41.9,41.7,33.7,43.7,49.4,67.5,47.6,55.8,35.2,0,56.1,60.9,41.9,60.2,0,0,24.2,32.5,38.9,49.3,35.7,38.2,85.4,85.4
|
||||
430,LBR,Liberia,Sub-Saharan Africa,5493031,1902,32.9,34.1,32.2,30,26.5,26.5,NA,NA,4.5,4.5,100,100,24.6,24.6,14.7,15,12.7,12.7,10.3,10.3,55.9,55.9,96.9,96.9,72.4,72.4,18.7,0,70.7,69.5,50.7,42.4,69.3,49.2,77.9,68.4,33.9,6,32.1,32.1,47.8,47.8,65.9,68.8,65.3,49,99.3,96.2,13.8,21.1,99.6,99,41.2,45.7,42.3,34.1,69.3,69.8,90.2,91.6,23.5,26.8,61.7,22.7,38.4,34,24.3,22.2,91.2,70.6,72.7,49.8,36.2,36.2,9.9,9.9,99.1,99.1,0,0,0,0,0,0,32.7,36.3,39.5,43.2,78.4,76.9,4.3,5.6,60.2,33.7,77,73.6,79.2,79.8,54.7,51.7,19.7,16.2,14.2,18.7,9.9,18,10.6,19.1,24.2,25.5,22.6,25.5,28.2,28.2,69.6,69.6,0,0,1,1,34.2,38.4,34.2,38.4,29.5,39.9,100,100,6,21,NA,NA,0,35.2,38.1,55.7,48.3,48.4,18.5,28.4,29.1,40.3,35.6,36,96.1,96.1
|
||||
440,LTU,Lithuania,Eastern Europe,2854099,56000,59.6,63.9,70.3,74.3,68.6,74.8,61.1,61.1,100,100,55.5,68.8,41.7,41.7,53.4,94.5,56,56.5,86.9,86.9,50.2,50.2,41.8,41.8,96.7,96.8,89.5,77.5,0,0,51.1,45.9,NA,NA,NA,NA,45.4,38.6,79,79,16.4,16.4,81.1,80.1,NA,NA,40.5,74.4,77,81.2,92,89.8,64.3,28.9,81.3,83.2,45.7,50.8,50.2,57.1,80.4,78.2,98,100,66.7,67,42.6,61,64,78.5,74.2,76.1,65.7,68.3,70.8,73.8,35.2,35.2,76.7,79.8,72.4,77,75.9,75.9,53.4,58.8,46.3,53.2,31.3,45.2,46.4,58.4,58.3,73.8,32.2,29.5,49.1,50.9,56.2,60.4,67.5,69.7,72.1,72.5,69.2,70.4,74.1,73.9,65.4,71.5,60.2,71.5,56.8,61.3,30.5,28.1,94,93.5,64.6,78.3,48.4,52.4,48.4,52.4,50.6,48.9,42.7,40,70.1,100,0,45.8,38.1,55.5,45,41.9,48.9,48.6,47.7,50.9,41.9,43.9,27.5,28.8,61,61
|
||||
442,LUX,Luxembourg,Global West,665098,154915,71.7,75,83.1,83.6,82.7,84.9,NA,NA,NA,NA,NA,NA,89.6,89.6,93.1,100,100,100,89.2,89.2,0,0,0,0,94.3,93.2,60.8,66.4,31.3,32,52.6,46.1,NA,NA,NA,NA,57.5,51,51.4,51.4,11,11,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,95.1,94.3,66.5,61,77.1,70.2,100,100,100,100,66,62.8,51.4,44.8,36.2,35.1,84.6,80.3,72.7,75.9,92,92.4,40.8,40.8,99.4,99.4,98.5,99.4,87.8,87.8,69.7,74.6,61.2,67.1,34.4,52.9,89.5,94.5,43.2,45,13.9,23.1,59.5,68.2,51.5,58.6,57.7,58.5,91.4,93.2,88.9,93.2,90.8,93.2,90.1,97,85.3,97,63.3,63.8,12.9,13.6,100,99.8,95.4,95.9,55.8,62.4,55.8,62.4,59.6,67.4,38.7,49.3,50.7,68.7,29.7,53.2,66.5,78.6,100,100,49.8,49.6,55.9,62.5,34.9,48.4,36.5,47.5,74.1,74.1
|
||||
450,MDG,Madagascar,Sub-Saharan Africa,31195932,1990,29.2,29.9,28.4,27.7,27.4,27,15.9,19.8,10.5,14.8,81.9,55.2,34.5,34.5,38,38.9,23.5,24.3,24.5,24.5,68.3,68.3,91.1,91.1,25.2,14.9,0,0,75.1,75.2,26.7,23.5,41.9,33.8,36.9,20.6,26.5,11.3,20.5,20.5,46.3,46.3,53.8,49.3,94.9,33.1,67,54.9,65.4,51.1,63.5,52.3,44.3,45,29,31.2,81.2,71.2,97.9,85,30.7,24.9,0,18.7,52.5,48.2,47,42.6,100,100,90,80.6,42.7,36,10,10,100,100,0,0,0,0,0,0,26.2,26.5,31.6,30.8,47.7,49.6,2.2,3.5,63.9,31.4,51.3,47.1,82.1,85.7,57,64.2,16.6,18.2,9.7,12.9,6.5,12.1,7.5,13.5,21.8,23.9,20.1,23.9,25.7,25.7,63.4,63.4,0.6,0.6,0.6,0.6,33,36.1,33,36.1,25.2,34.1,100,100,36.9,38.8,36.8,3.8,38.1,75,33.5,8.2,47.6,48.9,24.6,29.9,41.7,44.7,22.2,22.2,92.4,92.4
|
||||
454,MWI,Malawi,Sub-Saharan Africa,21104482,1714,41.3,34.9,57.8,53.8,68.3,68.3,NA,NA,NA,NA,NA,NA,63.9,63.9,79.2,81.4,67.3,77.6,79.9,79.9,24.5,24.5,90.9,90.9,37.5,37.6,96.2,86.5,16.6,17.1,45.2,28.2,70.7,30.7,NA,NA,45.6,26.4,10.6,10.6,57.3,57.3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,68.4,47.5,88.2,84.8,100,98.2,45.9,35.3,85.1,42.2,47.1,46.6,43.9,43.6,100,56.3,73.2,65.8,41.2,40.8,21.7,29.4,100,100,16,16,13.2,32.5,0,0,21.1,20.8,21.9,20,24,21.4,3.4,5.3,45.1,36.9,58.1,48.8,64.3,64.6,43,45.5,14.5,15.8,13.4,17.1,9.7,16,11,17.8,27.2,29.9,25.1,29.9,33.9,33.9,83.7,83.7,0,0,1,1,32.8,17.7,32.8,17.7,38.1,15.3,100,100,6.6,2.7,36.8,3.8,0,0,46.8,66.3,37,42.9,20,5.9,33.5,17.7,28.1,22.9,90.3,90.3
|
||||
458,MYS,Malaysia,Asia-Pacific,35126298,43100,34.1,41.2,33.8,39.7,28.3,28.8,6.2,6.2,6.6,12.4,39.8,86.1,14.6,14.6,56.8,57.7,31.3,42.8,40,40,80,80,96.7,96.7,11,0.1,0,0,100,100,25.8,41,36,49.9,33.8,55.9,0.7,14.5,31.1,31.1,50.1,50.1,52.4,52,51.3,51.5,87.9,87.8,39.5,39.4,41.8,41.3,54.5,48.7,31.8,60.7,88.1,88.9,60.9,53.8,28.3,68.6,12.3,48.6,65.2,63.6,56.7,63.9,46,45,14.6,19.7,86.7,83.2,45.1,48,35.3,33.2,75.4,83.1,22.9,22.9,22.9,22.9,39.2,45.7,33.8,43.2,24.2,33.7,52.8,60.2,47.6,35.1,24.6,29.5,35,37.5,31.8,46,0,0,53.5,54,54.9,57.7,52,51.6,48.6,51.3,46.2,51.3,40.1,35.4,31.9,32.7,100,47.2,18.3,32.2,30.2,39.9,30.2,39.9,35.7,41.3,9.6,17.5,52.4,44,19.8,43.5,29.1,64.3,42.4,90.2,46.1,44.8,29.3,36.2,24.6,29.2,5.3,5.1,36,36
|
||||
462,MDV,Maldives,Southern Asia,525994,34322,35.3,38.1,40.5,39.4,15.5,12,0,0,0.9,1.3,81,26.8,NA,NA,NA,NA,1.3,5.9,0,0,NA,NA,NA,NA,59.6,47.3,NA,NA,NA,NA,71.3,72.9,75.3,70.1,NA,NA,77.6,84.5,52,52,NA,NA,90.3,90.5,75.7,52.2,100,99.5,100,100,100,100,54.3,54.9,68.2,65.9,NA,NA,NA,NA,60.5,58,68.5,73.8,48.7,55.8,47,46.4,72.7,73.4,77.8,77.8,54.5,54.5,40.6,40.6,38.4,38.4,45.4,45.4,37.1,37.1,37.1,37.1,45.9,48,46.5,47.8,41.8,44.2,28,40.4,34.7,21.6,93.3,96.9,94.2,97.2,72.2,74.5,91.8,93.5,47.8,51.9,46.8,61.3,41.2,45.7,52.2,57.3,47.6,57.3,13.4,13.4,29.4,29.4,2.7,2.7,2.7,2.7,19.4,27.9,19.4,27.9,24.2,30.4,8.5,18.5,1,15.9,NA,NA,0,28.3,0.9,38.5,49.9,50,16.2,25.2,15.1,23.5,42.2,40,54.6,54.6
|
||||
466,MLI,Mali,Sub-Saharan Africa,23769127,2843,37.1,33.9,45.9,43.2,51.7,50.8,NA,NA,NA,NA,NA,NA,15.9,15.9,70.5,71,18.6,19,16.6,16.6,22,22,92.2,92.2,93.3,92.9,94.7,85.4,27.7,26.3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,47.6,36.7,58.4,56.4,89.5,91.2,3,26.3,62.3,32.2,67.9,66.4,41.7,47,100,100,99.1,99.2,73.2,69.4,8.9,8.9,89.4,89.4,0,0,0,0,0,0,35.2,37.5,42.8,44.7,92.3,88.3,3.9,5.5,39.5,19.8,54.1,48.2,84.5,82.6,54.7,54.6,9.4,8.4,14.1,18.3,10.1,17.3,11.1,19,25.9,27.3,25.2,27.3,30,30,74,74,0,0,1,1,25.4,16.5,25.4,16.5,20.9,4.6,83.6,50,19,11.9,36.8,3.8,25.6,7.8,44.1,65.4,0,45.5,15.1,6.4,33.7,25.3,20.5,16.3,70.3,70.3
|
||||
470,MLT,Malta,Global West,532956,75822,62.1,66.6,55.1,65.5,47,67.7,41.7,99.3,56.6,57.9,45.5,36.8,NA,NA,10.3,34.6,94.1,95.2,99.8,99.8,0,0,0,0,75.1,75.1,NA,NA,27.1,26.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,57.3,56.9,11.2,33.6,16.9,33.2,42.2,74.4,0,73,40.2,21.8,83.3,83.3,0,0,0,0,82.3,100,100,100,57.2,43.5,48.5,16.4,23.8,21.6,45.5,39.5,74,74,51.8,51.9,17.8,18.5,100,100,0,0,100,100,68.6,72,66.2,69.9,68.4,69.9,74.5,83.4,44.3,42.6,23.7,27.4,36.3,48.2,67.6,70.5,80.2,80.9,89.7,91.7,83.9,88.9,90.5,93.6,57,63.7,51.2,63.7,27.1,27.1,14.7,14.7,93.1,93.1,6.5,6.5,66.1,63.6,66.1,63.6,56.4,67.2,61.3,78.7,59,34.7,0,44.7,89.2,79.7,33.4,100,100,100,51,63.6,45.4,59.8,43.6,57.7,89.7,89.7
|
||||
584,MHL,Marshall Islands,Asia-Pacific,38827,6688,41.9,42.6,38.2,34.9,14.9,13.4,0.2,0.2,5.1,5.1,100,100,NA,NA,3.8,3.8,2.9,2.9,0,0,NA,NA,NA,NA,49.3,39.6,NA,NA,92.5,93.1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,91.7,86.9,51.6,56.2,71.4,70.6,100,100,100,100,NA,NA,95.1,83,80.4,88,NA,NA,79.6,92.5,100,72.5,77.8,77.8,NA,NA,NA,NA,77.8,77.8,NA,NA,39.8,39.8,54.9,54.9,47.2,47.2,30.8,30.8,30.8,30.8,54.8,55.4,63.1,63.4,100,100,5.8,8,100,100,88.1,78.4,100,100,91.7,93.9,100,100,35.3,36.5,34,36.2,34.9,36.7,41.5,44.2,39.3,44.2,36.4,36.4,57.9,57.9,22.1,22.1,22.1,22.1,35,41,35,41,41.1,46.7,32.8,41.7,0,17.5,NA,NA,9.5,28.3,44,69.9,NA,NA,24.7,33.9,35.7,41.8,60.2,61.2,67.9,67.9
|
||||
478,MRT,Mauritania,Sub-Saharan Africa,5022441,8233,37.7,34.2,37.9,33.7,37.1,36.2,45.4,45.4,20.7,20.7,100,100,0.9,0.9,5.1,5.2,2,2,52.3,52.3,NA,NA,NA,NA,91,90.4,90.1,80.4,58.8,58.3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,78.7,77.1,66.3,60.5,14,54.4,74.9,81.7,31.1,95.9,18,63.2,47.8,23,33.2,34.5,57.5,56.1,24.1,14.4,58.9,22.7,41.3,42.1,16.1,21.5,65,62.4,97.2,95.7,38.7,38.7,11.9,11.9,90.8,90.8,7.1,7.1,0,0,0,0,45.7,46.3,54,53.5,100,100,9.3,11.8,61.9,42.4,36.1,35.2,70.9,69.4,69,68.8,28.1,30.7,22.8,27,17.8,25.7,19.5,27.9,40.1,40.8,39,40.8,30.1,30.1,71.6,71.6,4.5,4.5,1.4,1.4,30.5,24.8,30.5,24.8,25.5,17.9,46.9,32.5,36.4,34.7,36.7,3.8,35.1,36.2,37.3,42.5,0,0,27.5,22.9,37,31.5,28.6,25.5,61.5,61.5
|
||||
480,MUS,Mauritius,Sub-Saharan Africa,1273588,32063,44.5,47.3,33.2,34.3,16.4,14.6,0.8,0.8,17.1,17.1,73.9,1.7,NA,NA,11,11,15.7,15.7,6.7,6.7,100,100,99.7,99.7,0,0,NA,NA,79.9,75.8,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,78.3,75.7,46.4,16.1,87.9,70.9,91.8,87.2,82.3,94.6,34.7,83.7,58.7,69.6,62.8,47,NA,NA,59.2,77.7,49.6,66,64.2,68.6,86.7,92.4,42.4,45.6,0,17.6,67.9,67.9,35.6,36.9,51.6,51.6,25,28.1,40.9,40.9,40.9,40.9,68.1,69.8,74,75.8,88.1,88.5,46.3,56.4,100,100,58.6,60.9,71,79.9,83.9,92.1,95.8,99.8,59.3,61.4,54.6,61.2,59.7,61.6,55.5,57.6,54.1,57.6,38.5,34.8,35.3,34.3,100,100,10.9,2.7,39.7,45.8,39.7,45.8,39.8,50.1,32.8,49.2,44.8,47.1,0,24.4,45.5,51.6,61.4,73.5,48.4,48.1,29.4,43.8,27.5,40.9,33.2,35.2,62.4,62.4
|
||||
484,MEX,Mexico,Latin America & Caribbean,129739759,25560,42.4,44.7,46.5,47.7,33.2,32.5,51.6,55.5,34.3,48.6,29.8,53.7,18,18,27.4,28.3,44.5,46.5,30.7,30.7,34.7,34.7,77.9,77.9,2.9,0,66.7,8.2,82.7,82,60.2,48.8,60.5,52,66.2,45.1,53.3,47.5,43.8,43.8,68.2,68.2,35,38.4,58,52.8,34.2,31.7,44,34.9,47.7,39,51.2,35.6,74.6,89.1,58.2,54.7,60.9,55.1,71,100,67.9,91.9,51.7,56.8,42.3,48.5,64.3,50.1,55,50.1,60.8,68.5,67.3,71.5,35,35,91.7,91.7,57,67.5,43.4,43.4,35,36.9,28.2,29.7,25.5,27.5,30.3,34.4,28.6,36.9,15.9,22.2,0,0,41.7,40.7,11.2,10.6,54.9,58.6,51.5,59.6,51.9,58,46.7,49.1,44.7,49.1,26.3,26.3,31.3,31.3,59.6,59.6,4.7,4.7,42.5,46.4,42.5,46.4,46.3,51.2,38.5,46.1,42,46.3,0,24.9,22.2,38.2,45.2,100,49.5,47.9,39.6,45.4,37.2,42.8,0.2,1.5,61,61
|
||||
583,FSM,Micronesia,Asia-Pacific,112630,4689,40.4,40.6,29.5,27.6,5.3,5,0,0,0,0,100,88.5,NA,NA,0,0,0,0,0,0,NA,NA,NA,NA,0,0,NA,NA,100,100,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,84.4,85.3,65.3,66,63.9,64.8,100,100,100,100,29.3,49.2,88.2,76.6,79.3,86.6,NA,NA,100,81.6,100,69.7,46.1,47,43.5,24,38.7,100,0,1.7,84.4,84.4,24.8,24.8,57.1,57.1,27.6,27.6,16.1,16.1,16.1,16.1,55.7,56.5,64.1,64.8,100,100,6.1,8.5,91,100,100,100,100,100,90.5,94,99,100,38.7,39.3,38.5,39.3,38.9,39.3,43.7,45.8,42,45.8,21.8,21.8,49.5,49.5,0,0,4.9,4.9,41.7,44.4,41.7,44.4,51,45.2,82.2,71.6,33.2,37.8,NA,NA,31,37.9,76.7,58.1,59.4,62,39.6,34.3,47.7,43.5,61,58.9,84.2,84.2
|
||||
498,MDA,Moldova,Former Soviet States,3067070,19910,42.8,45.6,44.1,48.4,41.1,53.3,NA,NA,NA,NA,NA,NA,16.2,16.2,16.1,68.4,35.3,35.3,52.9,52.9,NA,NA,NA,NA,90.2,91,57.6,58.8,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,68.7,54.2,43.1,44,46.6,48.7,51.3,60.3,80.9,51.3,47.6,49.9,46.6,55.9,100,66.1,66.4,66,17.4,35.8,23,23.3,2.4,0,36.1,36.1,16.6,18.1,16.6,16.6,37.2,40.6,30.1,34.5,27.5,35.3,14.9,23.4,35.9,57.9,39.8,37.3,30.9,32.1,60.6,65.2,64.5,64.7,61.1,62.2,57.3,59,62.8,64.3,46.9,51.2,42.3,51.2,17.2,16.2,24.8,21.7,31.9,31.8,2.3,2.9,45.6,45.4,45.6,45.4,50,43.2,63.6,51.9,71.4,71,0,23.3,43.4,29.6,33.7,61.5,51.3,55,48,42.2,49.4,42.4,35.5,32.8,72.7,72.7
|
||||
496,MNG,Mongolia,Asia-Pacific,3431932,20510,31.5,37,52.6,54.4,63.1,63.4,NA,NA,NA,NA,NA,NA,26.5,26.5,80.4,82.5,55.3,59.2,73.4,73.4,26.8,26.8,63.3,63.3,85.7,84.9,81.6,58.5,29.5,29.3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,41.9,48.2,11.2,21.1,29,41.7,38.7,54.4,23.2,48.6,23.7,29,1.3,0.2,35.6,28.2,96.9,88.1,58.3,33.2,44.1,44.1,42.1,42.1,53.5,53.5,36.9,36.9,36.9,36.9,24.8,28.3,15.2,17.8,0,0,9.2,20.5,29,41,39,33.4,27.8,23.6,64,67.5,58.5,56.7,56.4,61.8,48.3,58.3,53.4,64.1,34.5,41.8,28.1,41.8,11.6,12.3,22.6,24.3,0,0,6.4,6.4,4.6,17.6,4.6,17.6,0,38.2,0,10.1,0,8.1,0,5,28.3,23.4,0,39.6,50,50.6,0,0,0,8.8,11.2,14,1.8,1.8
|
||||
499,MNE,Montenegro,Eastern Europe,633552,33620,48.3,47.6,48.2,50.2,42.2,42.1,14.5,14.5,0,0,100,75.1,65,65,43.9,47.1,27.2,42.5,50.4,50.4,84.1,84.1,86.9,86.9,49.4,48.2,72.2,53.2,40.6,40.5,65.7,67.9,NA,NA,NA,NA,79.4,79.2,40.9,40.9,65,65,30.4,47.9,NA,NA,57.2,46.4,34.4,47.4,37.3,49.7,41.2,44.6,78,87.3,24.4,24.7,23.2,22.7,0,100,0,100,44.6,43,16,17.3,11.1,12.7,75,64.1,62.4,62.4,44.1,44.1,43.8,43.8,61,61,30.6,30.6,30.6,30.6,44.9,48.5,31.1,35.3,23.3,32.4,23.2,28.4,61.7,57.7,48,43.2,38.9,43.6,60.4,64.3,43.5,42.3,94.3,97.5,93.2,100,87,95.8,55,56.1,55.5,56.1,12.7,12.7,25.6,25.6,4.1,4.1,4.1,4.1,51.3,43.1,51.3,43.1,43.1,44.6,35.2,37.5,50.9,39.4,100,20,69.8,51.5,8.6,83.7,51.9,52.7,43.7,41.8,42.1,38.8,40.7,39.3,58.3,58.3
|
||||
504,MAR,Morocco,Greater Middle East,37712505,11100,37.1,39.7,34.8,40.7,22.6,30.4,4.8,5.2,6.6,6.6,100,100,12.6,12.6,4.1,57.9,7,7.1,7.7,7.7,43.5,43.5,66.1,66.1,65.7,62.2,56.7,49.7,56.2,56.4,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,48.7,49.6,51.6,51,46.4,48.4,28.6,34.5,59.5,63.6,45.4,28.7,59.4,69.1,22.7,27.4,26.6,33.4,38.8,63.8,77.2,89.8,52.6,35.6,39.1,40.4,54.9,71.3,66,62.3,18.5,16.6,50,57.6,61.2,61.2,60,79,40.1,40.1,38,38,42.1,43.8,44.4,44.7,70.9,62.1,23.6,34.3,34.8,27.1,38.3,40.8,27.7,21.9,55.7,48.6,34.4,34.2,43.8,50.6,39,52.6,37.1,49.2,24.9,27.1,23.6,27.1,28.4,28.4,55.2,55.2,18.8,18.8,6.3,6.3,36.5,34.9,36.5,34.9,37,39.7,45.3,50.1,28.7,31.6,36.8,3.8,41.8,39.3,30,39.9,50.2,50.6,31.9,33.9,35,36.6,13.9,12.9,70.8,70.8
|
||||
508,MOZ,Mozambique,Sub-Saharan Africa,33635160,1730,33.6,38.6,48.4,47.8,57.3,57.3,34.2,34.2,16.6,16.6,63,54.7,68.4,68.4,56.6,69.6,86.7,89,81.8,81.8,81.7,81.7,96.8,96.8,38,29.2,80.1,67,63.8,61.5,44.1,41.5,57.2,58.3,NA,NA,40.9,32.5,0,0,69.2,69.2,59.1,66.9,59.3,96.5,31.2,50.3,45,68.3,47.1,69.8,68.3,17.1,49.1,41.3,83.5,79,97.1,91,37.8,4.1,85.3,61,36,40.1,34.2,32.1,100,100,95.2,94.2,23,20.6,9.6,9.6,96.1,96.1,0,0,0,0,0,0,24.6,25,27.9,26.9,35.9,35.2,2.5,4.3,56.9,41.7,56.7,58,79.1,81.2,59.4,62.5,17.3,17.1,16.4,20.5,12.2,19.3,13.5,21.3,13.5,16.7,11.6,16.7,31.5,31.5,78.1,78.1,0.5,0.5,0.5,0.5,18.4,35.7,18.4,35.7,19,36.8,90.7,100,7.1,34.3,70.4,22.5,43.4,46.9,100,28.9,48.2,48.7,10.8,22.9,32.8,42.6,20,20.4,90.4,90.4
|
||||
104,MMR,Myanmar,Asia-Pacific,54133798,5206,28.4,26.9,31.5,26.6,26.5,23.4,4.8,5.1,1.6,2.1,93,98.1,7.1,7.1,32.8,34.3,17.6,19.6,27.7,27.7,54.9,54.9,95.3,95.3,39.8,31.5,71.1,17,69.3,68.4,54.5,51.5,68.2,65.3,65.4,56.7,43.3,32,32.7,32.7,71.7,71.7,38.6,36,72.7,50.5,67.2,55.3,22.1,22.8,24.5,25.3,39.1,56.4,28.8,12.5,70.7,69.9,76.6,80,51,0,28.9,0,68.9,59.6,71.6,54.7,100,62.6,69.2,58,67.1,64.8,12,12,72.4,72.4,12,12,0,0,0,0,15.3,17,8.4,9.1,0,0,2.7,7.1,13.2,11.9,49.7,47.3,51.8,48.2,30.9,37.7,0.2,0,30.6,35.5,25.6,34.2,27.6,36.3,25.7,29.2,22.1,29.2,34.7,34.7,78.1,78.1,7.2,7.2,5,5,34.5,35.6,34.5,35.6,47.2,3.6,100,19.2,36.4,75.1,26.1,27.8,19.8,92.3,49.8,34.7,46.6,47.3,32.4,38.1,41.9,45.7,14.1,14.4,83.3,83.3
|
||||
516,NAM,Namibia,Sub-Saharan Africa,2963095,11730,43.8,43.8,58.6,62,70.4,69.8,7.5,7.5,7.7,7.7,96,73.4,100,100,84.4,84.6,100,100,99,99,46.2,46.2,80.1,80.1,91.2,91.1,80.1,77.2,70.9,72.4,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,36.7,34.2,11.1,18,59.1,58.6,28.2,20.5,34.3,35.8,52.2,42.1,59.4,84,68.6,65.8,69.2,67.5,48.3,74.9,46.5,100,22.6,25.5,12.9,10.1,72.3,69.9,99.1,97,1,7.5,29.3,29.3,59.6,59.6,32.4,32.4,20.7,20.7,20.7,20.7,25,26.6,26.4,27.4,32.1,33,10,16.2,20.8,20.7,46.5,45.6,68.2,67.8,60.2,59.9,9.2,11.1,16.8,19.8,13.2,19.2,14.2,20.2,31.1,34.3,28.9,34.3,30.5,30.5,72.1,72.1,2.7,2.7,2.7,2.7,36.8,30.3,36.8,30.3,34.4,41.1,46.5,58.5,11.7,22.2,36.9,3.7,0,23.7,4.5,50.1,0,0,20.8,26.6,23.9,31.1,26,27,48.7,48.7
|
||||
524,NPL,Nepal,Southern Asia,29964614,5348,32.2,32.9,47.4,47.4,55.6,55.6,NA,NA,NA,NA,NA,NA,13.8,13.8,48.4,48.8,55.9,56.5,80.3,80.3,53.6,53.6,76.9,76.9,50.6,50.3,97.7,95.9,85.3,84.7,81.1,80.6,93.9,93.4,55.7,67.8,88.3,91.7,57.3,57.3,72.3,72.3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,15.7,15.8,21.7,9.5,31,17.1,22.3,32.6,28.3,0,65.3,65.6,76.3,70.7,100,71.7,85.6,67,46.2,59.3,11.4,11.4,81.5,81.5,8.1,8.1,0,0,0,0,13.1,14.4,6,6.2,0,0,4.4,7.1,0,0,55.7,42.7,27.3,23.8,0,0,8.8,7.6,28.2,33.8,22.2,33,23.8,34.4,20.9,21.4,21,21.4,42.7,42.7,98.4,98.4,14.6,14.6,1,1,24.4,25.8,24.4,25.8,5.6,5.2,32.3,31.4,35.1,42.6,NA,NA,18.3,27,37.3,28.6,48.7,49.1,29,27.6,35.8,32.9,20,17.5,82.1,82.1
|
||||
528,NLD,Netherlands,Global West,18092524,83823,63,67.2,64.7,67.8,54.4,61,40.2,77.7,52.9,53.2,32.7,32.6,52.8,52.8,40.2,40.5,53.5,90.1,76.8,76.8,28.3,28.3,61.4,61.4,73.8,69.6,55.6,52.9,38.6,39.5,71.2,62,NA,NA,NA,NA,69.5,64.1,84.8,84.8,5.7,5.7,25,22.5,8.3,0,26.2,29.8,7.4,11.8,34,31.9,66.3,47.9,94.3,92.6,57.7,50.5,70.1,60.9,100,100,100,100,68.4,68,46.4,45.2,16.9,17.8,57.6,55.9,99.5,100,91.4,91.3,19.4,18.1,100,100,99.4,99.5,97,97,70.3,74.2,62.7,67.4,37.4,51.1,92,96.5,49.7,44.1,12.9,20.4,52.8,64.3,56.2,65.3,66.8,70,87.1,88.2,84.8,87.7,87.1,88.5,93.9,99,89.2,99,69.5,69.6,26.2,26.4,100,100,97.5,97.7,54.4,60.7,54.4,60.7,54.8,68.3,39.8,59.1,66.1,100,83.5,17.9,100,61.9,100,100,36.6,40.5,55.5,58.3,43.2,47.9,15.6,18.6,68.7,68.7
|
||||
554,NZL,New Zealand,Global West,5172836,52983,56.6,57.7,51.3,51.3,37.7,39.6,15,27.2,12.6,24.5,83.6,61.2,28.5,28.5,49.4,50.2,86.2,86.8,16.8,16.8,97.1,97.1,99.5,99.5,0,0,80.1,59.4,79.1,81.7,60.3,67.4,NA,NA,81.1,87,53.6,52,45.1,45.1,70.9,70.9,34.3,31.4,54.6,34.4,40.2,38.8,19.4,25.6,33.4,26.7,72.4,54.7,78.7,69.1,72.3,60.2,92.5,85.2,77.5,65.6,100,71.2,75.6,72.9,63.7,52,53.1,50.9,58.8,62.3,100,100,72.7,72.7,20.2,20.2,77.1,77.1,84.1,84.1,61.8,61.8,80.6,81.5,83,83.1,97.8,96.2,80.8,85,74.5,65.2,31.2,40.6,42.8,45.1,90.6,94.6,43.8,53.5,82.4,84.8,80.4,85.6,80.5,84.2,76.5,80.8,73.2,80.8,40,39.5,16.4,15.1,100,100,33.6,33.6,44.6,47.6,44.6,47.6,53.5,53,39.9,39.2,51.1,64.6,29.1,31.8,58,54.1,57.8,68.4,52.6,51.7,44.5,48.5,30.9,35.2,18.8,19.6,47.3,47.3
|
||||
558,NIC,Nicaragua,Latin America & Caribbean,6823613,8950,46.2,47.4,58.6,58.5,65.9,66.1,97.1,97.1,40.6,40.6,50,100,85.3,85.3,72.9,72.9,69.3,69.5,92.9,92.9,66.1,66.1,96.3,96.3,48.2,40.9,0,0,64,61.6,33.8,16.7,43.2,22.8,31.8,5.7,24.7,25.2,0,0,36.1,36.1,51.7,48.9,73.4,67,35.1,43.3,28.3,41.2,43,50.1,60.5,47.6,72.3,82.4,74.3,74.5,71.6,73.7,58,68.1,93.4,100,44.8,51.6,30.3,36.8,56.8,48.3,50.9,58.8,52.1,63.6,41.3,41.3,31.5,31.5,53.7,53.7,33.3,33.3,33.3,33.3,37,39,35.7,36.7,47.1,47.7,13.6,17.6,62.2,58.3,42.1,48.2,37.1,41,69.8,73.5,13.9,12.1,42.4,47,36.4,44.7,40,48.5,41.7,47.6,38.1,47.6,21.6,21.6,49.2,49.2,7.4,7.4,1.2,1.2,34.8,37.6,34.8,37.6,47,45.9,95,92.9,24.2,30.6,36.8,3.7,23.6,30,36.7,41.1,45.4,47.5,31.3,33.3,38.3,39.5,26.6,25.5,72.4,72.4
|
||||
562,NER,Niger,Sub-Saharan Africa,26159867,1978,32.2,39.2,46.1,57,61,69.7,NA,NA,NA,NA,NA,NA,53.9,53.9,54.9,78.1,29.2,59.7,85.2,85.2,36.3,36.3,89.6,89.6,79.4,76.9,85.7,72.2,30.4,28.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,24.2,57.9,52.3,41.7,70,59.7,8.1,48.2,10.5,70.4,57.5,55.9,31.6,33.7,100,100,91,77.7,62.4,65.4,10,10,100,100,0,0,0,0,0,0,28.9,30.4,35.5,36.5,76.1,65.4,4.8,5.7,38.7,26.1,50.4,45.9,70.4,69.7,50.5,51.6,21.4,24.1,8.3,12.2,4.2,11.4,4.7,12.8,23.2,23.3,23.8,23.3,31.9,31.9,78.4,78.4,0.9,0.9,0.9,0.9,13.6,19.4,13.6,19.4,0,33.2,79.4,100,15.3,5.2,36.8,3.8,13.1,3.7,19.8,14.1,0,0,3.6,2.8,30.1,26.9,20.9,17.5,78.8,78.8
|
||||
566,NGA,Nigeria,Sub-Saharan Africa,227882945,6710,32.9,37.5,38.7,43.3,39.5,47.1,NA,NA,0.4,0.4,NA,NA,28.2,28.2,23.2,68.8,44.5,44.5,88.7,88.7,32.9,32.9,41.4,41.4,53.2,52.7,78.4,62,0,0,53.8,32.8,63.1,46.6,64,25.7,52.3,24.4,19.7,19.7,61.8,61.8,58.2,63.4,83.9,47.6,88.5,95.6,38.1,56.9,40.8,57.9,43.4,52.8,32.7,54.4,59.1,53.8,61.1,55.6,39,53.6,72.4,55.1,49.1,47.9,45.5,50,100,100,56.1,47.3,42.7,41.8,13.4,13.4,74.8,74.8,10.6,10.6,3.4,3.4,3.4,3.4,18.2,20,17,18,30.6,18.3,5.8,8.8,36.5,30,41.8,40,55.4,51.2,26.2,20.4,19.2,16.7,9.2,14.4,5.2,14,5.2,14.6,44.7,47,43,47,29.7,29.7,63.7,63.7,19,19,1.1,1.1,36.9,43.9,36.9,43.9,38.6,44.9,91.9,100,59.1,59.6,36,5.8,26.4,30,29.3,41.1,44.8,47.6,41.5,43.7,46.5,47.8,6.2,6.3,88.1,88.1
|
||||
807,MKD,North Macedonia,Eastern Europe,1831802,28720,49,50,54.1,55.1,47.3,52.8,NA,NA,NA,NA,NA,NA,42.3,42.3,34.9,53.2,28.6,44.4,26.3,26.3,76.1,76.1,94.5,94.5,77.3,75.2,80.4,67,30,30.6,66,64.6,NA,NA,NA,NA,65.9,71.1,43.3,43.3,74.3,74.3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,87.5,74.7,41.9,43.7,47.2,49.7,77,100,77.4,60.6,42.1,46.7,36.8,37.7,44.9,50.6,53.5,56.7,38.2,51.3,41.9,41.9,62.5,62.5,46.9,46.9,33.7,33.7,33.7,33.7,33.6,39.1,24,29.9,12.4,21.5,19,28.7,34,56.1,35.7,36.8,15.1,21.1,54.7,61.2,40.1,38.6,65.4,70.4,63.1,71,63.3,70,37.4,43,34.3,43,31.6,31.6,42.2,42.2,73,73,0.2,0.2,54.2,51.3,54.2,51.3,53.3,47.5,51.9,42.7,48.4,78.8,22.9,24.4,68.3,79.2,50.5,100,51.2,51.1,48.1,45,47.9,43.5,34.4,32.5,65.5,65.5
|
||||
578,NOR,Norway,Global West,5519167,106540,66.7,70,67.2,72.6,63.9,71.6,93.6,93.9,9.9,11.3,62,62,98,98,19.4,67.4,55.6,58.5,73.4,73.4,99.5,99.5,99.9,99.9,84.2,83.8,83,74,100,100,71,61.4,NA,NA,78.6,72.9,69.3,53.3,43.2,43.2,69.7,69.7,50,54.2,31.6,40.2,73.3,91.9,65.9,50.5,64.4,39.6,48.3,65.7,79.2,90.9,35.1,48.4,48.3,49.2,65.4,98.6,100,100,52.7,52.3,19,18.9,44.7,43.7,32.2,26.8,73.9,97,81.9,83.3,10.1,10.1,99.2,100,84.1,86.7,76.1,76.1,84.5,86.3,81.1,82.9,78.7,85.1,97.1,100,47.5,58.1,22.6,28.9,41.8,54.5,62.9,67.3,59,59.4,96.6,97.6,94.7,97.4,97,97.8,93.9,100,86.8,100,61.3,58.3,14.9,13.2,95.1,90.4,90.9,87.3,51.1,52.6,51.1,52.6,49.3,58.8,30.9,44.4,72.7,51,64.4,52.1,86.2,47.8,94.5,100,49.8,49.7,51.5,53.2,37.6,40.3,20.8,21.7,54.6,54.6
|
||||
512,OMN,Oman,Greater Middle East,5049269,41652,39.3,51.9,48.6,65.6,38.7,56.7,37.8,73.7,26.2,65.8,97,100,42.1,42.1,12.7,19.1,9.3,72.9,53.5,53.5,71.3,71.3,98.6,98.6,68.2,61.6,95.6,61.1,75.8,74.6,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,93.9,88.8,100,68.8,79.8,83.6,69.1,100,70.4,100,26.5,35.3,32.2,73.6,26.1,13.7,0,0,1.1,73.9,100,100,67.1,70.7,31.2,46.4,49.8,49.3,63.1,62.8,100,100,88.6,88.4,60.7,58.6,99,99,99,99,33.1,33.1,49.7,50.1,47.7,47.5,38.4,34.4,59.3,68.3,34.6,37.2,27,29.5,29.6,24.7,60.3,51.7,36.3,33.6,67.4,69.5,73.8,78.4,61.5,63.5,30,36.7,25.8,36.7,33.8,22.7,36,19.8,32.3,24.7,32.3,24.7,16.3,32.6,16.3,32.6,15.5,47.9,0,20.1,46.7,27.6,31.9,9.4,3.1,25.2,52.3,100,100,0,11.4,32.4,0,18.9,11.7,14.2,21.9,21.9
|
||||
586,PAK,Pakistan,Southern Asia,247504495,6920,29.5,25.5,33.1,29.4,27.6,25.7,0,0,0.7,0.7,50,50,6.6,6.6,28.1,28.3,35.5,35.5,62.8,62.8,34.1,34.1,18.7,18.7,54,41.6,43.6,32.7,38.5,39,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,56.7,62.2,62,67,88.4,91.1,42.9,52.1,44.7,53.4,58.1,45.3,51.6,32.2,20.3,13.2,26.1,21.1,53.5,47.2,51.6,23.1,46.4,46.8,34.1,33.9,46,35.3,34.8,28.9,66.4,68.2,21.1,21.1,55.9,55.9,29,29,7.9,7.9,7.9,7.9,11.2,13,5.7,6.4,0,0,5,8,1.5,0,38.2,43.9,11.8,8.7,2.6,0,17.7,17.2,22.3,28.2,18.1,28.2,18.4,28.2,20.5,22.4,19.2,22.4,29.3,29.3,64.2,64.2,10.8,10.8,3.6,3.6,39.2,30,39.2,30,43.6,35.7,86.2,71.1,23.7,25.1,35,9.9,24,9.2,33.1,29.9,44.9,37.5,32.7,28.6,38.5,33,4,1,76.2,76.2
|
||||
591,PAN,Panama,Latin America & Caribbean,4458759,41292,47.6,52.9,56.4,59.1,59.1,57,76.8,76.8,23.4,23.5,52.4,88.1,68.9,68.9,70.2,71.8,93.1,93.4,84,84,72.2,72.2,97.9,97.9,11.8,8.4,51.2,0,64.9,63.5,67.4,60,77.7,72.3,71.8,66,53.8,48.3,32.5,32.5,63.3,63.3,67.1,71.6,15.9,57,42.8,50,52.7,77.1,67,82.2,81.4,100,52.1,80.9,65.5,65.6,68.2,67.5,33.1,67.5,51.7,100,40.5,50,26.4,31,49.8,41.2,49.7,53.9,53.8,68,42.5,42.9,28.6,28.5,38.3,38.3,53.4,54.4,29.6,29.6,51.2,54.9,53.3,57.5,70.2,70.6,33.5,46.2,70,60.3,33.7,43.6,61.6,65.5,66.5,69.3,31.3,32.5,46,49.1,42.1,48.4,45.2,49.5,58.4,61.6,56.3,61.6,25.7,27.5,38.4,42.3,46,47.5,2.8,2.8,31.3,41.9,31.3,41.9,25.9,45,15.5,47.2,30.3,48.1,36.7,3.7,30,40.9,2,61,48.2,48.8,27.2,43.4,24.3,40.4,24.9,27,65.6,65.6
|
||||
598,PNG,Papua New Guinea,Asia-Pacific,10389635,3542,40.1,36.5,38,35.5,23.1,19.8,0,0,3.5,3.5,100,100,0,0,7.2,8.4,10.3,11.8,3.9,3.9,79.5,79.5,98.6,98.6,51.6,41.2,91.2,50,100,100,67.8,65.3,83,73.3,71.3,59.1,75.6,64.7,49.6,49.6,88.6,88.6,85.7,88.6,84.7,66.6,67.9,75.6,99.1,98.9,97.2,98.2,5.1,87.2,74.3,68.2,100,100,100,100,100,54,90.3,69.6,58.2,61.8,48.2,57.3,100,100,67.9,75.2,63.7,57.5,9,9,86.1,86.1,0.9,0.9,0,0,0,0,37,37,40.8,40,60.4,58.1,1.6,2.9,86.2,95.4,83.1,78.4,45.5,44.7,80.6,77.6,0,0,19.3,21.2,16.8,20.2,18.3,21.9,49.6,51.7,48,51.7,36.8,36.8,71.6,71.6,38.4,38.4,1.2,1.2,46.2,37.7,46.2,37.7,48.6,36.4,99.4,76,100,29,NA,NA,63.3,47.9,34.7,44.9,49.1,49.3,53,33,57.1,37,37.7,27.9,88.7,88.7
|
||||
600,PRY,Paraguay,Latin America & Caribbean,6844146,17360,38.2,39,44.4,43.6,47.9,47.8,NA,NA,NA,NA,NA,NA,43.3,43.3,36.9,36.9,46.8,47.3,61.7,61.7,38.2,38.2,82.6,82.6,84.3,84.2,0,0,46.3,43.1,12.9,20.6,23.7,33.9,0.2,9.2,1.4,18,0,0,63.9,63.9,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,69,62.3,100,100,100,100,36.7,23.2,91.4,86.3,80.6,71.6,80.9,86.7,52.9,100,85.9,79.7,46.1,50.7,11.5,11.5,46.2,46.2,11.2,11.2,4.8,4.8,4.8,4.8,37.4,39.3,32.3,33.6,46.4,39.6,16.8,24.2,58.8,43.2,20.8,20.8,83.7,83.8,60.2,57.2,0,0,53.2,56.7,47.7,55.1,51.2,57.7,48.9,52.6,47.6,52.6,23.4,23.4,43.5,43.5,26.8,26.8,1.5,1.5,29.4,31.9,29.4,31.9,34.5,25.7,51.6,35.5,13.9,46.1,36.8,4,14.2,38.6,51.8,31.4,46.7,47.3,23.7,34.8,25.5,35.4,19.3,20.2,50.6,50.6
|
||||
604,PER,Peru,Latin America & Caribbean,33845617,18390,42.4,46.6,53.2,56.4,50.2,48.9,92.1,92.1,13.5,16.6,23.2,53.4,48.4,48.4,42.3,46.2,62.5,71.8,65.6,65.6,83.4,83.4,98.4,98.4,15.3,15.2,72.2,10.5,77,75.6,65.2,60.1,73.4,69.1,66,53.9,62.6,57,45.1,45.1,88.6,88.6,75.8,85,52.4,80.8,46.4,62.3,95.2,94.9,95.3,94.8,81.1,69.5,53.7,74.7,100,100,100,99.1,32.9,65.8,77.9,73.7,42.6,45.3,34.8,34.2,52.9,46.8,36,37.4,51.2,59.5,53.6,64,51.9,48.9,53.5,66.9,53.5,66.9,56.3,56.3,35.5,35.6,28,26.6,16,10.5,22.3,33.2,89.1,66.2,6.2,6.2,61.4,56.4,66.6,57.2,11.3,10.9,50.9,55.1,45.2,52.9,49.7,56.6,66.2,68.3,66.3,68.3,25.2,25.9,51,48.6,22.4,28.5,0.7,1.8,31.5,40.7,31.5,40.7,27.5,40.2,29.4,51.7,29.1,48.3,36.7,3.8,33.2,36.6,29.3,81.5,49.2,49.5,29.3,40.2,29.8,40.3,13.4,14,72,72
|
||||
608,PHL,Philippines,Asia-Pacific,114891199,12910,31.7,32,33.9,33.7,22.8,25.6,16.4,19.4,7.2,12.5,67,53.1,28.5,28.5,10.6,26.3,39.4,51.4,37.1,37.1,56,56,78.7,78.7,3,0,58.8,0,77.2,76.3,62.8,50.6,74.7,67.4,66.3,36.3,50.1,49.2,42.8,42.8,59.1,59.1,74.5,76.4,86.6,72.7,85.4,86.2,74.6,76.7,76,77.2,56.2,41.8,43.1,39.7,60.2,61.5,71.6,72.2,53.8,32.8,73.2,35.7,72.1,72.3,79.4,73.7,100,86.1,49.5,49.8,71.4,79.2,10.6,10.6,80.9,80.9,2.8,2.8,2.8,2.8,2.8,2.8,26.9,28.5,21.7,22.8,20.4,17.4,9.4,13.1,54.7,79,22.8,24.2,11.7,11.3,56,61.4,18.9,19,38.5,42.7,35.8,45.5,34.1,40.8,40.3,41.6,39.7,41.6,30.8,30,56.5,55.9,13.7,12.8,13.7,12.8,32.4,32.2,32.4,32.2,37.1,31.4,57.3,46.8,32.9,39,35.7,8.3,50,30.5,27.6,45.2,46.1,48.5,35.4,31.3,37.8,32.8,9,5.9,75.2,75.2
|
||||
616,POL,Poland,Eastern Europe,38762844,54500,62.7,64.4,78.8,79.3,81.3,81.3,82.1,82.2,79.5,79.5,69.9,58.5,66.6,66.6,98.4,99,100,100,90.9,90.9,0,0,0,0,89.5,92.3,87.1,83.6,0,0,56.2,48.4,NA,NA,NA,NA,56,45.3,69.2,69.2,22.4,22.4,58.4,57.8,85.6,50.2,68,78.5,73.8,84.2,32.7,33.6,64.4,34.8,92.8,93.5,55.8,55.8,67.1,65.7,86.9,100,100,100,59.7,68.3,52.5,60.8,39.4,43.1,71.1,72,59.8,76.3,77.2,79.2,37.9,36.3,94.2,96.9,72.6,75.2,67.4,67.4,45.6,49.9,31.6,38.5,13.8,28.8,36.5,46.7,46.4,49.6,16.2,10.8,25.8,34.9,52.8,60,61.6,63.2,84.5,80.7,91.5,83.9,89.5,78.6,60.6,65.3,56.6,65.3,57.9,58.8,41.8,36.9,100,100,53,60,52.3,53.5,52.3,53.5,50.2,49.3,30.1,28.8,93.6,85.3,14.8,100,48,49.6,47.4,64.8,53.8,53.4,45.8,48.7,39.6,39.9,7.5,7.7,53.2,53.2
|
||||
620,PRT,Portugal,Global West,10430738,51260,58.2,62.2,61.4,63.4,59.7,60.4,70,70.1,28.6,30.5,25,26.4,46.3,46.3,61.9,65.2,75.3,76.6,80,80,38.1,38.1,47.9,47.9,68.1,66.7,71.6,64,63.1,63.6,18.7,16.5,NA,NA,NA,NA,17.7,11.4,33.1,33.1,8.6,8.6,36.5,31.1,24.3,5.5,25,21.7,62.8,42.5,49.1,36.7,54.8,49,75.9,88.7,22.8,30.3,24.6,34.4,100,100,100,100,46.1,49.7,13.4,15.1,35.8,35.6,72.4,72.1,53.8,76.1,87.3,87.3,40.5,40.5,97.1,97.1,91.8,91.8,76.8,76.8,64.7,68.2,57.4,61.1,55.7,55,69.3,77,42.2,52.8,13.3,22.8,37.1,44.2,57.9,65.3,47,48,92.3,94.4,87.7,92.5,92.9,95.6,65.2,71.6,59.7,71.6,50.7,50.8,28.9,26.5,95,100,50.4,50.4,48,55.3,48,55.3,61.5,62.3,62.2,63.5,62.2,41.6,0,31,57.9,59.3,93,100,51.8,51.7,61,56.6,56.3,51.9,31.7,24.7,75.2,75.2
|
||||
634,QAT,Qatar,Greater Middle East,2979082,118760,41.5,47.2,54.1,57.4,50.5,50.2,42.9,42.9,36.2,36.2,92.3,84.9,55.3,55.3,13,25.2,44.9,44.9,94.5,94.5,64.1,64.1,98.6,98.6,50.4,39.4,86.3,66.1,54.7,54.6,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,88.3,88.6,NA,NA,61.7,62,100,100,100,100,0,57.4,41.7,65.3,0,0,0,0,0,56.7,90.6,100,35.5,35.7,6.1,6.7,43.9,30.8,43,39.9,63.3,63.3,86.7,86.7,20.3,20.3,100,100,89.3,89.3,89.3,89.3,49.1,50.7,41.3,42.4,0,0,88.7,96.8,26.4,31.7,5.3,6.6,21.7,19.7,46.1,37,22.1,18.8,73.7,75.1,76,80.6,70.4,71.5,57.4,64.8,46.6,64.8,42,42,30.8,31.3,100,100,24.3,23.6,15.1,28,15.1,28,21,40.7,0,5.8,0,47.2,31.2,7.2,0,14.9,100,91.2,NA,NA,0,30.5,0,0,6.3,10.2,4.1,4.1
|
||||
178,COG,Republic of Congo,Sub-Saharan Africa,6182885,6404,40.2,41.2,55.4,63.8,71.5,71.4,NA,NA,22.7,22.7,100,100,69.8,69.8,80.6,81.4,77,85,75,75,61.8,61.8,87.7,87.7,89.3,89.2,93.3,72.8,62.8,58.8,71.9,67.9,85.3,79.5,74.5,64.4,75.4,64,43.1,43.1,88.9,88.9,51.3,61,NA,NA,71.3,59.7,NA,NA,75.2,63.4,54.2,49,16,76.6,100,100,88.8,97.2,0,68.8,0,75.7,47.5,48.1,36.7,37.6,100,100,56.7,66.1,46,46.8,21.3,21.3,88.4,88.4,21.3,21.3,7.9,7.9,7.9,7.9,16.2,17.5,12.3,12.2,1.3,2.5,6.6,10.3,36.4,22.9,36.8,48.1,49.9,50.5,33.7,32.7,0,0,18.6,24.4,14.6,22.9,16.4,25.4,32.7,35.4,30.6,35.4,37.8,37.8,79.1,79.1,10.3,10.3,10.3,10.3,38.8,29.3,38.8,29.3,34,35,53,55,40.1,25.3,36.7,3.8,27.2,55.4,0,48.9,49.5,49.6,25.3,12.5,37.4,33.2,25.2,23,57.6,57.6
|
||||
642,ROU,Romania,Eastern Europe,19118479,49940,60.2,57.2,67.7,68.4,71.3,71.9,98.8,98.8,90,90,31.2,53.7,43.8,43.8,77.9,78.8,63.5,68.9,81.1,81.1,56.4,56.4,71.1,71.1,63.4,63,69.6,62.3,8.4,8.5,60.2,57.1,NA,NA,52.2,44.1,71.6,72.1,57.3,57.3,59.6,59.6,25.6,24.5,NA,NA,32.3,88.1,40,0,100,1.9,60.2,50,93.3,86.8,53.3,51.3,62.5,60,100,86,100,100,58.2,67.8,43.8,68.5,55.2,51.3,82.4,84.4,32,61.6,44.7,52.5,24.1,25.1,47.8,58.3,46.4,55.2,45.7,45.7,43.1,46.4,34.7,39.4,26.7,29.6,31.9,42.8,44.2,66.6,23.6,24.4,33,38.5,54.1,60,49.1,50.1,69.1,68.5,67.5,65.9,73,70.2,48.6,53.4,44.1,53.4,43.1,42.3,46.6,42.3,87.1,92.7,17.7,17.1,63.1,49.3,63.1,49.3,62.9,54,65.4,51.4,62.6,63,73.7,23.6,54.3,36.1,44.9,58.3,51.1,51.5,59.7,51.1,56.5,46.4,26.2,17.1,67.4,67.4
|
||||
643,RUS,Russia,Former Soviet States,145440500,48960,46.5,46.5,48.2,48.2,41.8,41,10.6,11.4,4,4.4,90.6,85.6,27.2,27.2,45.7,47.2,31.9,34,31,31,79,79,91.8,91.8,82.9,82.4,88,67.3,68,67.1,46.4,43.9,NA,NA,27,18.2,71.7,63.1,49.9,49.9,90.2,90.2,59.9,63,31.5,51.3,28.7,24.5,77,74.4,85.7,84.6,64.6,43.7,67.3,65.2,32.4,46.9,57.1,65.2,70.4,65.1,63.1,69,50.9,62.9,33.6,43.9,73.9,82.5,68.1,53,42.1,84.4,53,53,33.9,33.9,55.1,55.1,55.1,55.1,55.1,55.1,50.7,54.7,46.3,50.5,41.2,48.4,44.4,55,42.2,59.5,22.6,23.7,29.5,31.5,58.2,59.1,57.2,56.3,71.1,73.8,63,68.3,72,77.5,55.3,61.8,48.4,61.8,15.5,15.5,32.8,32.8,4,4,4,4,40.5,36.9,40.5,36.9,47,48.8,21.7,24.2,37.1,32.9,66.6,30.6,38.3,36.2,39.5,40,50,49.9,37.2,38.1,29.7,29.6,0,0,35.7,35.7
|
||||
646,RWA,Rwanda,Sub-Saharan Africa,13954471,3747,33.7,33.4,45.7,44.5,51.8,49.9,NA,NA,NA,NA,NA,NA,34.4,34.4,44.5,44.7,29.4,29.8,57,57,100,100,99.3,99.3,63.1,63.1,97.1,67.3,0,0,67,58.3,94.4,84.4,NA,NA,56.1,39.6,35.7,35.7,39.8,39.8,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,47.3,51.2,69.8,66.7,80.1,77.2,34,46.2,47.4,47.9,42.1,41.6,50.4,48.8,100,61.7,68.9,65.9,39.7,22.6,8.6,8.6,79.6,79.6,1.6,1.6,0,0,0,0,13.4,14.2,8.8,8.5,0,0,5.3,7,42.4,21.5,46.6,53.6,0,0,11.8,13.1,11.7,14.5,20.5,24.6,15.8,23.2,17.3,25.5,34,36.2,31.7,36.2,15,15,36.5,36.5,0,0,1,1,32.1,32.3,32.1,32.3,32.2,18,100,100,21.8,26.3,31.5,29.2,7,31,32.9,32.2,43.6,47.4,28.8,32.1,40.8,41.1,31.9,30.2,98.3,98.3
|
||||
662,LCA,Saint Lucia,Latin America & Caribbean,179285,27052,48.8,51,45.4,45.1,30.4,30.3,12,12,13.2,13.2,50,70.2,NA,NA,21.1,21.1,52,52,44.5,44.5,0,0,82.2,82.2,31.8,27.4,NA,NA,92.7,91.2,78.5,80.7,87,92.4,NA,NA,76.3,85.5,43.2,43.2,62,62,92.7,94,NA,NA,100,80.5,100,100,100,100,56.1,76.4,71,68.6,46.5,41.5,51.9,45.9,47.9,75,95,72.1,41,39.3,41.5,27.8,44.7,39.3,22.7,21.7,58.2,58.2,38.4,38.4,43.5,43.5,41.9,41.9,34.6,34.6,34.6,34.6,63.5,64.9,72.1,73.7,100,100,35.4,42.3,88.1,100,54.3,53.1,71.3,77.4,82.5,85.4,82.1,87.5,52.9,53.8,51.7,54.8,52.1,53.2,43.1,44.3,41.4,44.3,11.7,12.5,29.1,31,0.1,0.1,0.1,0.1,41.3,47.5,41.3,47.5,39.1,50.4,38,56.8,55.5,51.4,43.3,24.3,34.6,49.1,43.7,49.9,51.3,51.2,36.7,46.2,36.3,45.6,50.2,52.5,73.8,73.8
|
||||
670,VCT,Saint Vincent and the Grenadines,Latin America & Caribbean,101323,19425,53.4,54.1,49.8,50.6,31.7,35.6,9.6,9.6,10.7,10.7,65.9,100,NA,NA,9,26.4,58.1,58.1,61.6,61.6,100,100,99.6,99.6,19.3,16.2,NA,NA,96.4,95.2,81.1,75.5,90.3,81.9,NA,NA,77.3,83.9,38.3,38.3,69.9,69.9,90.6,86.1,NA,NA,42.8,43.4,100,100,100,100,45.3,90.2,76.7,72.8,46.6,41.6,NA,NA,56.7,78.5,84.7,73.3,75.8,76.9,76.4,79.7,61.8,56.5,77.8,77.8,75.5,75.5,41.5,41.5,47.2,47.2,46.7,46.7,36.1,36.1,36.1,36.1,63,64.5,72.6,74.1,100,100,34.7,40.9,100,100,66.3,67.6,69.4,74.6,82.7,85.2,88.1,91.3,50.1,51.7,48.2,51.9,49.3,51.6,26.4,28.3,25.8,28.3,37.1,37.1,42.7,42.7,100,100,0.1,0.1,49.9,49.9,49.9,49.9,49.1,49,58.9,58.7,41.6,47.4,NA,NA,36.5,56.5,60,53.3,50.2,50.2,46.8,47.9,47,47.5,58.4,58.3,79.5,79.5
|
||||
882,WSM,Samoa,Asia-Pacific,216663,6998,40.9,46.8,35.9,37.2,26.2,25.9,7,7,5.2,5.2,89.3,100,NA,NA,8.2,8.2,25.8,25.9,14.5,14.5,28.3,28.3,89.8,89.8,26.3,22.1,100,100,44.8,46,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,85.3,75.9,33.3,29.2,82.4,42.5,100,100,100,100,54.4,60.1,65.4,75.2,87.4,82.4,NA,NA,94.3,79.3,64.4,69.7,51.2,57.1,23.5,40.9,80.5,54.2,77.8,77.8,65,65,17.6,17.6,50.1,50.1,0,0,25.2,25.2,25.2,25.2,57.2,57.7,57.5,57.9,100,100,6.4,7.8,34.1,39.2,88.3,76.8,92.5,97.1,100,100,100,100,56.6,57.3,61.9,64.4,52.2,52.5,57,58.2,56.5,58.2,56,55.7,86.1,83.4,98.8,98.9,4.6,6.5,33.9,51.3,33.9,51.3,43.4,39.4,74.2,66.7,34.9,92.6,12.9,28.5,19.9,79.2,41.9,62.2,NA,NA,35.3,43.5,39.9,48.6,52.9,55.1,85.7,85.7
|
||||
678,STP,Sao Tome and Principe,Sub-Saharan Africa,NA,6205,34.2,35.9,36.2,31.6,33.6,33.6,10.1,10.1,0,0,100,100,NA,NA,0,0,100,100,59.3,59.3,4.4,4.4,82.1,82.1,33.4,33.4,NA,NA,100,100,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,83.5,72.6,15.9,0,99.7,100,86,65.2,90.7,100,50.8,26.9,41.6,20.4,54.4,51.4,NA,NA,4.4,17.7,68.3,17,39.9,31.7,41.4,20.1,98,57.4,28.7,44.8,35.6,35.6,19.8,19.8,81.7,81.7,20.6,20.6,6.7,6.7,6.7,6.7,35.4,39.5,37,40.9,52.7,56.3,6.5,10.7,34.6,20.6,100,100,100,100,74.4,70.2,84.9,88.1,31.4,37.5,24.2,36.1,26.2,38.5,36,38.1,34.8,38.1,27.8,27.8,68.4,68.4,0,0,1,1,30.4,38.6,30.4,38.6,31.8,44,79.2,100,30.3,27.6,23.6,0,12.5,26,45.1,46.4,48.7,49.9,29.2,35.9,32.8,38.6,57.6,57,92.7,92.7
|
||||
682,SAU,Saudi Arabia,Greater Middle East,32264292,65880,33.2,42.6,39.2,50.3,37.7,45.4,50.4,50.4,5.8,5.8,87.9,100,34.1,34.1,13.3,54.9,7.2,29.7,15.6,15.6,72.9,72.9,95.4,95.4,72.6,65.2,98,89.2,47.7,47.3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,53.4,56,56.4,54.6,52.2,53.6,33,61.1,49.9,55.2,49.7,50.4,17.8,60.5,0,0,2.4,0,4.6,65.4,31.1,79.9,54.2,53.8,10.9,8.9,40.6,41.3,61.2,53.1,100,100,57.4,57.7,30.8,26.8,60.1,61,60.1,61,62.1,62.1,38,40,33.1,34.7,5.4,2.7,59.6,70.2,23.2,23,24.4,31.1,5.3,4.6,58.6,54.4,41.7,40.8,61.7,64,62.4,69.2,57.2,60.6,21.4,26.3,17.2,26.3,37.4,37.4,28.4,28.4,100,100,15,15,20,33.2,20,33.2,33.9,46.5,0.6,17,22.1,35.6,35.9,6.7,23,32.3,0,95.9,100,0,24,36.3,7.9,18.8,0,0,21,21
|
||||
686,SEN,Senegal,Sub-Saharan Africa,18077573,5056,38.6,43.3,46.6,50.9,51.5,56.5,8.6,8.9,14.3,18.4,45.4,44.9,54.3,54.3,48.5,78.2,83.6,86.8,63.8,63.8,16.8,16.8,61.5,61.5,80,78.2,93.9,89.2,7.8,7.1,67.4,63.5,97.4,97,NA,NA,48.8,28.6,46.2,46.2,71.2,71.2,56,58.4,25.2,11.3,56.8,57.5,65.7,55.8,69.3,79.8,71.1,66.2,35.8,39.7,60.9,60.1,59.6,60.7,24.3,44.9,55.5,26.2,53.5,71.1,29.5,43.9,100,86.6,87.6,86.5,65.8,90.6,12.8,12.8,67.9,67.9,4.4,4.4,8.6,8.6,8.6,8.6,40.5,42.9,47.7,49.5,100,100,4.8,6.8,48.9,33.6,50.6,46.5,15.1,14.5,57.4,55.5,29.4,30.2,21.6,27.2,15.6,25.5,17.7,28.3,33.7,35.5,32.6,35.5,24.5,24.5,59.9,59.9,0.6,0.6,1,1,24.9,32,24.9,32,26.9,33.9,63.1,76.8,27.1,33.6,36.7,3.9,34.3,24.3,0,51,0,19.6,24.1,27.8,33.6,34.7,23.3,21.6,82.1,82.1
|
||||
688,SRB,Serbia,Eastern Europe,6773201,30910,54.8,49.3,54.7,56.1,50.5,53.5,NA,NA,NA,NA,NA,NA,34.7,34.7,46.6,55.9,21.5,29.7,48.5,48.5,53.2,53.2,85.4,85.4,75.2,73.8,90.4,84.5,13.4,13.7,72.3,69.3,NA,NA,NA,NA,85.1,78,55.9,55.9,52.8,52.8,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,90,86.3,48.2,46.8,61.3,56.1,89,100,71.9,86.6,66.7,71.4,59.7,78.1,50.6,47.7,58.3,46.3,77.1,77.1,13.9,15.4,34.9,34.9,12.1,15.9,10.1,10.1,14.8,14.8,39.1,43.4,26.6,31.6,16.9,24.2,23.2,30.6,35.5,55.4,32.8,36.4,14.5,24.3,54,60.1,40.7,39.9,79.6,82.6,78.3,83.9,77.7,81.8,50.6,54.1,47,54.1,26.4,26.4,39.4,39.4,52.3,52.3,0.5,0.5,68.1,43.6,68.1,43.6,71.8,47.6,66.1,30.8,50.6,44.9,100,25.7,45.2,95.8,53.5,40.7,53.1,52.7,56.9,42.9,56.4,39.4,41.8,20.5,53.5,53.5
|
||||
690,SYC,Seychelles,Sub-Saharan Africa,127951,41078,52.3,48.2,46.3,48.3,33.1,51.8,47.9,94.7,4.7,50.2,100,50.5,NA,NA,12.1,18.7,73.4,73.4,89.2,89.2,28.3,28.3,95.8,95.8,6.6,0,NA,NA,66.1,64.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,75.2,75.8,10.8,16.1,58.7,56.6,100,98,100,100,47.9,51.5,74.6,17.5,71.8,62.1,NA,NA,68.8,5.7,100,20.3,54.9,58,31.4,52.3,39.3,38,77.8,77.8,57.1,57.1,51.5,51.5,34.6,34.6,58,58,49.7,49.7,49.7,49.7,71.9,71.4,82.5,80.2,91.8,92.6,58.4,66.6,91.1,59,95,97.7,94,97.3,87.3,89.1,99.8,100,51.4,53.9,51.8,58,48.2,51.1,53.9,59.6,52.4,59.6,29.6,32,24.2,30.1,95,95,2.3,2.3,43.5,27.3,43.5,27.3,44.4,30.9,25.9,6.3,36.3,33.3,NA,NA,100,29.4,82.4,11,50,50,42.4,25.6,38.9,19.8,53.2,47.2,30.8,30.8
|
||||
694,SLE,Sierra Leone,Sub-Saharan Africa,8460512,3505,34.4,39.7,41.6,38.9,47,46,5.5,5.5,39.1,39.1,75,100,24.6,24.6,59.5,67.8,42.8,42.8,63.3,63.3,85.1,85.1,96.9,96.9,79.3,79.1,76.9,0,58.4,56.7,24.1,17.4,61,34.5,NA,NA,28.2,0,4.9,4.9,27.4,27.4,77.1,75.3,64.1,79.4,52.7,96.4,66.3,38.4,52.6,98.7,17.6,0,41.2,33,56.3,59.6,52.8,48.4,23.5,30.6,64.2,27,45.9,40.1,34.2,29.1,100,100,71.8,52.8,40.7,40.7,10,10,100,100,0,0,0,0,0,0,29.5,33.1,34.4,38.2,73.5,70.3,2.3,4.5,45.2,34.3,51.8,51.8,63.6,67.8,35.1,40.4,11.9,10,13.5,17.7,9,16.9,9.8,18.3,25.9,28.2,23.9,28.2,32.8,32.8,81.1,81.1,0,0,1,1,26.9,46.8,26.9,46.8,20.1,50.3,100,100,22.8,36.4,NA,NA,12.6,49.1,49.5,64,44.9,48.1,24.6,36.2,33.5,46.8,31.8,33.4,96.8,96.8
|
||||
702,SGP,Singapore,Asia-Pacific,5789090,153737,47.5,53.8,59.2,55.9,38.3,36.6,NA,NA,0,0,NA,NA,NA,NA,63.2,63.2,16.5,16.5,14.9,14.9,100,100,91.7,91.7,57.7,48.1,NA,NA,64,65.3,61,28.8,92,37.3,NA,NA,41.9,18.7,40.7,40.7,4.1,4.1,97.9,97.1,NA,NA,NA,NA,100,100,100,100,73.5,62.6,78.6,81.8,30,27.1,28.9,26.9,64.8,85.5,100,100,60.2,61.3,33.6,34.1,0,0,38.6,46,100,100,92.4,92.4,23.7,23.7,100,100,100,100,100,100,56.8,65.8,42.3,53.6,11.3,32.7,77.9,84.7,70.6,59.9,5.3,10,0,6.5,35.7,61.2,25,30.5,97.2,99.9,94.6,100,94.5,99.8,70.3,78.6,63.3,78.6,74.6,75.5,39.3,42,100,100,97.2,96.7,25.5,41.2,25.5,41.2,43.7,51.9,19.5,31,16.1,29.1,37.7,10.6,100,26.2,26.4,100,45.6,42.8,41.2,45.6,28.2,32,17.6,18.1,39.2,39.2
|
||||
703,SVK,Slovakia,Eastern Europe,5518055,47440,66.5,65,77.5,77.8,81.9,81.8,NA,NA,NA,NA,NA,NA,62.4,62.4,94,95.8,90.1,90.1,91.4,91.4,56.5,56.5,70.8,70.8,90.1,89.4,79.2,73.7,18.4,18.2,54.2,53.5,NA,NA,NA,NA,58.7,60.1,42.1,42.1,43.2,43.2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,94.5,94.1,64.3,60.4,73.6,68.3,100,100,100,100,65.6,67.4,55.3,62.9,76.6,100,82.1,79.6,47.7,64.2,57.1,59.4,26.1,26.1,57.4,57.4,64.5,70.2,57.3,57.3,55,60.5,43.3,50.6,20.2,35.8,59.3,68.7,43.7,51,31.8,32.7,29.5,36.7,51.3,57.9,47.6,48,92.9,93,100,100,89.3,88.4,63,67.1,59,67.1,48.4,53.4,38.1,27.6,98.8,97.6,33.4,57,59.3,48.9,59.3,48.9,66,51.7,58.2,37.3,70.5,48.3,13.8,44.5,46.6,44.3,48.8,82.9,50.8,51,56.3,49.5,50.8,43,29,23.6,59.1,59.1
|
||||
705,SVN,Slovenia,Eastern Europe,2118396,58150,62.6,62.5,68.3,67.7,65.3,64.8,12,12,25.9,27.8,100,100,69.9,69.9,93.9,94,100,100,87.3,87.3,58.7,58.7,54.3,54.3,72.8,71.2,75.7,52.6,42.9,42.2,67.2,58.9,NA,NA,NA,NA,85.8,67.6,47.6,47.6,37.9,37.9,46.7,36.4,NA,NA,32.5,51.9,54.2,29,97.5,32.1,56.3,41.5,93,92.7,53.6,51.7,62.2,60.6,94.6,100,100,100,58.6,56.7,39.6,43.8,32,34.6,74,70.9,67.4,65.5,67.3,72.7,37.9,38,91.7,94,56.5,67.6,42.5,42.5,55.2,59,40.4,45.8,28,35.8,52.3,58.6,44.3,42.4,28.6,33,37.1,45.7,46.6,51.9,39.8,39.2,92.5,91.5,98.1,95.7,93.1,88.7,86.6,92.5,82.3,92.5,58.2,53.6,30,26.7,83.8,77,73.5,68.9,59.9,57.5,59.9,57.5,53.3,57.9,40.4,47.1,75.3,69.6,91.8,59.1,54,50.2,81.1,100,48.8,48.9,53.3,55.7,45.9,48.4,32.1,33.6,68.5,68.5
|
||||
90,SLB,Solomon Islands,Asia-Pacific,800005,2627,40.3,41.8,34,30.2,18.8,13.2,9.5,9.5,7.3,7.3,100,99.6,0,0,0.5,0.8,0.9,1.7,5,5,100,100,99.7,99.7,30,20.4,75.7,0,100,100,51.2,42,71.5,52,50.1,23.3,61.8,44.9,45.8,45.8,72.3,72.3,80.1,84.8,83,84.7,44.1,48.8,100,100,100,100,55,47.5,84.8,83,92.7,98.1,98.7,100,100,90,100,69.7,40.1,44.6,16.5,19.4,100,100,33.6,42.1,66.2,66.2,9.7,9.7,83,83,3.5,3.5,0,0,0,0,49.9,50.6,59.9,60.1,100,100,1.2,1.6,99.2,100,97.2,98.3,94.5,96.6,97.3,95.6,31.8,33.4,31.1,33.6,27.8,31.7,30.7,34.8,27.2,28.6,26.7,28.6,19.4,19.4,43.8,43.8,0,0,4.7,4.7,42,52.8,42,52.8,37.2,59.7,100,100,16.9,25.4,NA,NA,35.9,41.5,46.8,73.2,48.9,48.8,29.2,46.1,34.9,54.1,50,54.4,100,100
|
||||
710,ZAF,South Africa,Sub-Saharan Africa,63212384,16010,38.7,42.9,44.8,49.8,38.8,40.1,35,35.2,45.4,54.6,76.8,70.3,16.3,16.3,28.9,34.8,23,29,37.8,37.8,87.3,87.3,81.8,81.8,31.3,24.4,87.7,77.2,59.3,59.9,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,41.1,47.8,0,22.9,54,60.1,52.6,48.1,54.6,50.2,53,54.7,61.1,85.3,65.8,60.7,67.3,62.7,56.7,97.5,60.9,82.6,49,57.2,40.5,45.8,62.7,55,72.9,73.5,57.6,62.1,54,52.4,34,34,58,56,58,56,42.3,42.3,22.5,24.2,19.4,20.4,10.4,10.3,19,25,39.4,33.6,46.6,50.4,4.8,2.8,22.2,13.3,22.5,18.4,21.5,25.3,18.2,24.9,18.8,25.5,44.6,48.6,40.4,48.6,34.5,34.5,39.9,39.9,59.5,59.5,16.5,16.5,42.8,48,42.8,48,47.6,56,30,42.3,43.4,60.2,37.9,10.6,55,42.4,34.8,88.7,47.9,49.8,34.1,43.7,35.4,45,3.4,7.2,62.3,62.3
|
||||
410,KOR,South Korea,Asia-Pacific,51748739,65580,46.2,51,47.8,49.9,28.8,32.8,41.4,41.4,14.4,15.1,20.5,27,19.1,19.1,27.2,54.4,47.6,55.8,29.1,29.1,54,54,65.4,65.4,9,0,48.8,35.4,42,40.5,63.4,57.5,NA,NA,NA,NA,71.1,62,44.9,44.9,60.3,60.3,30.5,34.9,45,31.2,34.4,40,18.2,21.2,29.8,39.5,48.8,61.5,89,87.3,31.9,22.3,36.5,25.2,100,100,97.2,100,62.9,61.8,47.6,43.6,25.9,21.1,41.4,48.3,80.4,89,85.1,86.3,14.9,14.9,92.9,94.4,92.9,94.4,93,93,55.6,57.9,42.3,44.5,9.9,12.6,81.8,89.7,45.7,39.2,6.4,13.9,0,0,10,14.3,21.7,22.2,89.2,91.1,88.9,92.7,87.7,90.1,78.6,85.4,71.6,85.4,67.5,64.7,35.2,31.4,99.9,96.7,83.6,82,36,47,36,47,37.2,52.6,8.3,29.2,48.5,42.2,23.5,41.9,58.5,38.7,100,100,51.6,51.5,30,49.3,19.3,37.6,0,4.1,49.7,49.7
|
||||
724,ESP,Spain,Global West,47911579,56660,62.1,64.2,67.5,68.5,67.2,67.3,91.3,91.4,46.3,47.4,29,33.3,59.5,59.5,70.8,75.1,79.8,93.5,64.3,64.3,49,49,63.5,63.5,57.5,55.3,83.6,67.3,37.8,38.4,50.8,44.5,NA,NA,NA,NA,53.4,42.2,51.3,51.3,42.3,42.3,33,33.7,22.5,21.9,40.4,41.3,39.5,40.3,33.8,27.4,53.3,49.7,82.2,89.3,29.8,33.9,32.3,38.1,100,100,100,100,51.8,54.1,31.3,34.9,42.8,38.5,68.4,67.8,54.3,69,80.7,80.7,25.6,25.6,89.4,89.4,88.3,88.3,71,71,63.3,64.8,55,56.2,55.1,48.8,69.7,74.2,36.3,36.1,18.1,25.5,35.8,45.4,60.7,64.5,45.7,44.3,90.1,91.6,84.7,88.6,91.8,93.6,73,77.8,69.5,77.8,50.2,50.8,29.6,29,100,100,45.9,48.1,52.8,57.2,52.8,57.2,63.7,55.1,59.4,46.3,54.1,45.7,43.3,100,69,47.2,92.6,100,51.4,51.5,61.8,56.6,55.7,50.7,22.8,12.5,72.2,72.2
|
||||
144,LKA,Sri Lanka,Southern Asia,22971617,14255,36.9,38.7,40.9,39.3,37.2,33.7,2.5,2.5,1.5,1.5,75.1,44.8,59.8,59.8,36.1,36.2,82.8,82.8,48.3,48.3,32.2,32.2,81,81,0,0,75.4,30.7,78.1,78.5,66.1,70,81.4,88.7,NA,NA,62,65.4,31,31,58.4,58.4,63.1,63.4,66,68.1,49.8,62.6,51.5,58.7,65.8,67.2,53.5,50.3,47.7,49.1,42,36.2,42.2,30.9,41.7,54.2,63.4,50.2,64.2,62.5,55.1,65.6,61.8,100,55,61.3,68.5,56.9,10.9,10.9,87.3,87.3,4,4,1.1,1.1,1.1,1.1,30.1,30.9,20.2,20.1,8.8,12.3,11.6,20.3,48.9,26.9,44.9,48.1,22.1,20.8,41.8,38.6,25.5,23.6,50.7,53.7,47.9,55.6,47.8,52.5,60.9,65.2,57.7,65.2,32.6,32.6,68.2,68.2,8.9,8.9,8.9,8.9,36.6,44.1,36.6,44.1,38.1,44.2,79.2,91,32.2,46.1,36.7,3.8,43.7,51.1,53.3,61.9,46,48.6,38.3,44.8,38.4,44.8,22.5,23.4,90.4,90.4
|
||||
729,SDN,Sudan,Greater Middle East,50042791,2513,34.7,38.6,35.5,42.3,28.7,39.1,1.9,95.5,26.5,28,50,65.6,11,11,22.5,22.5,6.6,6.6,16.9,16.9,0,0,58.1,58.1,78.4,71.7,78.7,70.2,15.6,14.6,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,96.6,95.8,53.9,83.9,100,100,100,100,100,100,31.7,64,62.9,62.4,56.6,49.3,63.5,52.5,37.6,41.2,19.1,88.3,40.2,48.5,17,26,100,100,83.2,74.7,35.7,55.8,9.4,9.4,75.7,75.7,4.6,4.6,0,0,0,0,32.4,34.6,34.4,35.3,53.6,55.5,7.2,11.7,37.1,28.3,48.5,48.4,69.9,63.1,59.1,58.7,17.4,22.3,32.5,39.2,24.5,36.4,27.7,41,9.7,12,7.6,12,44.5,44.5,84.8,84.8,44.8,44.8,4,4,35.4,36.2,35.4,36.2,30.4,40.3,85.3,100,40.7,41.9,38.4,3.1,42.5,43.7,34.8,33.2,0,0,32.6,32.6,41.4,43.4,14.3,13.9,79.2,79.2
|
||||
740,SUR,Suriname,Latin America & Caribbean,628886,21404,47.9,56.6,55.6,63.9,63.8,64.3,57.9,57.9,43.4,43.4,50,100,100,100,57,58.3,34.1,34.1,32.6,32.6,76.1,76.1,98.7,98.7,97.7,97.7,88.9,74.9,47.6,46.2,78.7,72.9,89.2,82.5,80.7,63.4,84.9,78.4,47.9,47.9,93.9,93.9,44.7,38.9,38.6,19.5,38.9,44.9,38.8,38.3,41.1,44.7,56.4,36.3,14.5,80.3,97,100,85.4,89.9,20.8,72.1,7.7,82.7,58.1,62.8,41.7,47.5,38,100,20.5,28.3,79.9,89.4,44.3,44.3,46.6,46.6,49.5,49.5,39.6,39.6,39.6,39.6,57.2,58.8,67.2,68.3,100,100,30.3,37,100,97.8,33,36.4,85.6,83.1,77.9,76.2,25.5,15.9,40.9,43.4,41.2,46.5,37.7,41.4,34.4,39.7,33.2,39.7,13.7,13.1,31.2,30.3,2.4,2.4,1.9,1.2,28.5,43.6,28.5,43.6,19.2,45.2,0,29.2,22.9,50.7,100,100,28.2,29,44.8,10.5,49.9,49.8,14.5,37.2,11.4,37,33.8,37.2,50.3,50.3
|
||||
752,SWE,Sweden,Global West,10551494,74147,70.3,70.5,67.3,67.3,59.5,59.9,45.1,54.8,45.8,47.5,42.8,48.9,40.2,40.2,68.3,84.9,39.7,42.8,69.3,69.3,97,97,99.4,99.4,97.5,97.5,70.5,3.5,55.8,54.9,56.7,56.2,NA,NA,73.8,83.6,44.3,33.3,32.5,32.5,53.5,53.5,56.3,52.4,82.3,26.6,73.9,48.9,74.5,76.7,41.3,50.5,66.6,35,90.2,90.6,42.2,51.9,29.2,35.4,100,100,100,100,74.4,73.2,44.4,46.6,58.6,54.6,78.3,76.5,100,100,86.3,86.3,35.5,35.5,100,99,87,88,79.8,79.8,83,85.5,78.1,81.2,73,77.1,94,99.9,55.6,66.5,22.4,17.7,68.9,76.6,64.2,69.3,74.5,77.4,96.1,97,93.8,96,96.9,97.7,96.7,100,92.5,100,72,72.7,30.6,32.7,100,99.7,99.4,99.2,64.3,62.9,64.3,62.9,64.2,64.8,69.4,70.4,72.5,96.8,56.2,59.9,53.2,35.7,60,93.3,49.5,49.5,59.1,58.1,52.3,53.1,26.1,25.7,77.1,77.1
|
||||
756,CHE,Switzerland,Global West,8870561,98146,66,68,68.7,69.4,56.9,60,NA,NA,NA,NA,NA,NA,92.9,92.9,32.8,42.9,24.7,32.8,21.7,21.7,87.5,87.5,93.5,93.5,89.7,88.3,77.2,68.7,62.3,62.3,69.8,61.1,NA,NA,NA,NA,83.5,72.3,46.2,46.2,35.2,35.2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,93.7,92.5,58.8,52.1,69.9,57.5,100,100,100,100,62.4,59.6,45.1,43.1,27.2,27.5,58.6,50.9,84.9,82.5,85.5,85.5,10.1,10.1,93.9,93.9,93.9,93.9,93.9,93.9,72.3,75.6,63.8,67.5,37.2,48.2,96.7,100,43.7,45.7,20.6,25.9,62.1,71.4,50.1,59.8,48.9,49.6,97.1,98,95.6,98.2,96.5,97.9,86.3,92.2,81.1,92.2,65.8,66.8,16.1,16.9,99,100,99,100,56.5,59.4,56.5,59.4,55.8,64.1,53.4,66.3,57.5,45.1,39.1,58,57.1,63.1,100,100,50.7,50.8,53.5,60.8,47.2,53.7,23.2,27.6,78.8,78.8
|
||||
158,TWN,Taiwan,Asia-Pacific,23317145,79031,50.4,50.3,53.2,51.8,41.5,39.4,2.2,2.2,10.5,10.5,100,88.3,24,24,37.4,37.4,65.3,65.4,79.1,79.1,95.1,95.1,98.3,98.3,NA,NA,68.4,27.8,100,100,84.8,85.1,77.3,97.3,NA,NA,80.6,88.5,50.8,50.8,64.1,64.1,47.5,46.4,22,41.3,45.3,45.9,50.9,27.3,72.7,69.1,41,0,89.5,86.5,30.2,18.8,44.8,18.8,100,100,100,100,58.2,60.3,38.9,44.2,14.2,16.2,30,34.8,90.8,90.8,39.2,39.2,31.5,31.5,69.9,69.9,16.2,16.2,16.2,16.2,47,50,36.2,40.1,19,22.2,55.5,61.2,35.2,50.8,13,18.2,10.3,10.4,34.9,44.2,35.2,36.1,69.7,70.9,68.9,72.1,69.5,70.1,68,71.8,65.9,71.8,75.4,69.7,40.8,28.4,99.2,98.5,98.1,96.7,49,48.4,49,48.4,48.7,49.2,23.8,24.4,40.3,70.3,100,49.4,48.2,34.8,63.2,100,NA,NA,53.4,47.9,42,34.8,12.3,8.7,44.4,44.4
|
||||
762,TJK,Tajikistan,Former Soviet States,10389799,5533,36,31.9,45,46.2,54,53.6,NA,NA,NA,NA,NA,NA,29.5,29.5,34.2,34.7,53,53,23.1,23.1,71.3,71.3,88.8,88.8,96.4,96.4,94.4,87.7,57.3,58.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,29.5,41,4.3,0,52.2,47.5,14.5,22.7,20.8,66.3,64.8,57,71.6,57.9,74,51.9,82.6,57.2,48.2,56.5,18.6,18.6,64.3,64.3,23.9,23.9,5.2,5.2,5.2,5.2,21.1,21.7,18.1,17.4,21.4,15.1,6.1,10.9,15.8,22.9,37.2,30.9,34,34.3,53.6,54,33.2,34.4,28,31.2,22.5,28.1,26.7,33.3,28.8,33.8,23.5,33.8,22.8,22.8,53.8,53.8,0,0,3.1,3.1,34.5,18.5,34.5,18.5,47.9,0,87.3,0,0,25.4,100,89.6,0,4,18.4,35.1,26.2,31.9,45.9,9.5,53.1,16.2,34.2,22.2,70.4,70.4
|
||||
834,TZA,Tanzania,Sub-Saharan Africa,66617606,4134,37.7,43.1,52.3,59.6,61.5,65.3,67.5,67.5,37.8,37.8,100,100,100,100,45.5,72.1,96.4,98.1,82.1,82.1,56.1,56.1,79.4,79.4,5.9,0,87.7,73.4,24.7,24.8,46,54.4,73,62.6,49,73.6,42.4,40,0,0,71.3,71.3,82.2,71.7,88.6,75.2,70.8,60.7,86.1,77.7,87.2,78.9,43.6,24.1,38.3,77.7,86.9,81.8,93.4,87.6,28.5,52.7,72.4,100,48.6,48.5,39.7,44,100,100,99.7,99.6,22.6,27.4,19.4,16.1,100,100,0.9,0.9,22.7,14.4,0,0,24.5,24.9,24.9,23.9,23.8,26.1,5.1,6.9,62.9,42.2,54.7,53.7,69.8,71.6,57.9,57.9,20.4,19.6,16.7,20.6,12.6,19.4,13.9,21.4,41.6,44.1,39.4,44.1,24.1,24.1,59.2,59.2,0,0,1,1,25.6,32.5,25.6,32.5,19.1,24.2,91.8,100,19.4,31.5,NA,NA,25.7,35.6,26.4,32.2,43.5,44.7,19.6,28.5,33.2,38.1,14.2,13.6,86.3,86.3
|
||||
764,THA,Thailand,Asia-Pacific,71702435,26420,41,45.4,49,50.8,46.9,46.2,57.8,68.3,17.2,28.6,53.8,33,27,27,69.9,70.2,60.5,60.6,69.6,69.6,63.9,63.9,94.2,94.2,30.5,22,41.5,0,30.2,30.2,66.6,70.7,76,89.9,69.7,85.2,41.4,39.8,51.9,51.9,60.1,60.1,47.2,44.2,58.9,38.8,79,79.2,32.7,31.5,34.7,33.3,49,60.9,61.3,75.8,66.3,64.2,58.4,56.6,52.6,57.7,88.9,100,59.8,58.8,46.8,48.9,64.9,60.7,58.1,47.5,76.6,73.2,21.5,21.5,31.3,31.3,23.4,23.4,18.1,18.1,18.1,18.1,33.3,34.9,24.1,25.5,5.7,11.9,27.9,37,38.7,35.7,33.6,32.7,18.7,16.2,28.1,31.4,10.5,10.4,49.1,51.2,54.1,61.2,42.3,44.6,72.1,75.4,68.7,75.4,32.6,33.6,35.2,38,41.8,40.1,25.3,26,35,46,35,46,39.5,50,29.5,46.1,27.1,70.7,36,6.4,9.6,57.8,31.8,55.9,46.2,48.1,31.7,45,30.9,43.2,3,6,62.3,62.3
|
||||
626,TLS,Timor-Leste,Asia-Pacific,1384286,4697,40.2,49.7,43.9,47.6,42.9,45.3,59.7,59.9,6.5,17.7,50,100,14.8,14.8,27.3,32.2,42.8,53.5,56.2,56.2,80.2,80.2,93.1,93.1,58.1,47.6,81.6,69.3,72.1,70.7,63.3,62.6,NA,NA,NA,NA,65.6,66.1,49.5,49.5,71,71,95.5,95.5,100,100,NA,NA,NA,NA,100,100,45.3,50,50.3,67,78.5,74,94.2,89.4,36,57.9,100,70.2,50.5,48.8,26.1,18.9,54.3,45.9,95,97.1,58.9,58.9,18.9,18.9,71.5,71.5,18.5,18.5,8.7,8.7,8.7,8.7,36,36.1,38.9,38.3,65.1,62.2,8.2,8.1,50.8,38.1,44.3,44.9,86.2,90.7,76.3,75,15.4,16.9,30.3,33.1,25.8,31.5,28.7,34.1,23.6,23.8,24.1,23.8,39.7,39.7,94.3,94.3,0,0,5,5,37.9,65.2,37.9,65.2,30.4,46.2,83,100,0,100,NA,NA,31.7,39.8,44.8,45.5,46.2,47.1,0,65.7,3.5,71.8,33,100,100,100
|
||||
768,TGO,Togo,Sub-Saharan Africa,9304337,3290,36.4,35.2,45,45.9,55.3,54.7,NA,NA,0,0,NA,NA,45.7,45.7,62.4,62.6,82.6,83.1,85.4,85.4,30,30,26.3,26.3,58.9,57.7,90.1,81,9.3,8.6,50.6,42.4,83.6,61,NA,NA,49.9,25.2,20.7,20.7,59.6,59.6,56.3,58.9,NA,NA,86.3,87.7,22.4,24.3,65.6,66,45,67.4,32.9,45.3,75.3,75.5,78,76.8,0,0,38.1,78.3,41,40.3,28.7,31.9,100,99.5,82,76.1,26.4,28.8,10,10,100,100,0,0,0,0,0,0,22.8,25.3,24.9,26.8,50.8,41.6,3.4,5.5,34.6,21.1,56.9,58.8,61.5,59.1,42.4,39.8,25.2,20.4,13.2,17.8,9,17.1,9.8,18.2,28.1,30.4,26.1,30.4,26.4,26.4,64.1,64.1,1.2,1.2,1.2,1.2,35.6,28.5,35.6,28.5,35.8,28.4,100,100,19,23,36.8,3.7,14.3,30.4,8.3,21.7,43.3,47.2,22.1,23.4,33.2,32.1,30.5,28.4,88.1,88.1
|
||||
776,TON,Tonga,Asia-Pacific,104597,7811,47.5,40.2,37.3,34,15.1,15.3,0.2,0.2,3.7,3.7,100,100,NA,NA,2.9,7.9,33.1,33.1,0.5,0.5,100,100,98.1,98.1,15,9.9,NA,NA,0,0.4,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,95.2,95.7,94.9,98.2,100,100,100,100,100,100,28.3,18.8,96.5,70.4,71.4,63,NA,NA,100,74.7,100,67.5,42,49.9,23.8,53.7,100,53.9,0,0,66.6,66.6,34.8,34.8,51.9,51.9,39.6,39.6,27.6,27.6,27.6,27.6,57.6,58.2,59.8,60.3,100,100,10.7,13.4,40.1,46.1,80.5,68.8,98.2,100,100,100,100,100,49.4,50.2,49.3,50.9,49,49.7,71.4,74,69.1,74,32,32,60.5,60.5,28.4,28.4,5.3,5.3,53.4,32.5,53.4,32.5,54,23.7,76.2,23.3,46.3,44.4,NA,NA,54,48.3,60.1,60.8,NA,NA,45.4,24.5,50.4,30.4,60.7,53.8,65.6,65.6
|
||||
780,TTO,Trinidad and Tobago,Latin America & Caribbean,1502932,34987,50.9,52.1,47.6,48.9,48.1,49.5,0,0,3.4,3.4,79.8,100,100,100,15.4,32.9,97.3,97.3,70.5,70.5,84.9,84.9,93.4,93.4,60.6,57.3,54.1,20.8,51.5,49.1,70.9,72.6,82.7,86.5,NA,NA,56.3,72.5,34.1,34.1,66.3,66.3,57.3,58.1,40.9,42,28.3,26.5,70.6,65.6,76.6,78.8,48.7,49.8,74.8,72.5,48.9,44.3,48,42,43.6,66.3,100,90.4,13.5,22.5,4,5.4,23.4,20.7,22.7,29.1,27.9,36.9,13.2,13.2,9.8,9.8,25.2,25.2,4.2,4.2,4.2,4.2,75,77,85,87.2,100,100,75,82.4,89.6,94.6,32.2,46.4,75,82.5,78.6,81.2,48.5,53.1,58.5,60.2,58.7,63,55.6,58.4,62.2,65.2,59.4,65.2,11.8,11.8,27.6,27.6,2.4,2.4,0.7,0.7,36,36.1,36,36.1,38.3,52.2,4.8,22.8,25.9,55.4,36.7,3.7,49.1,40.1,85.3,54.7,51.2,50.8,8.9,27.6,0,21.3,16.3,23.2,36.8,36.8
|
||||
788,TUN,Tunisia,Greater Middle East,12200431,14720,45.2,45.7,47.5,48.1,38.5,38.3,15.7,15.7,25.7,25.7,100,100,10.7,10.7,15.4,15.4,21,21.1,59.6,59.6,61.8,61.8,95.8,95.8,83.6,83.5,78.1,71.9,29.1,28.6,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,49.5,50.3,42.5,45.5,44.5,44.8,42.5,51.1,56.7,54.8,54.9,51.6,69.3,70,17.4,16.3,28,24.6,53.7,59.7,100,100,31.5,40.2,22.6,24.3,65.4,63.7,55,59.7,47.5,45.9,75,75,58.8,58.8,80.7,80.7,73.7,73.7,73.7,73.7,46.9,46.5,45.9,44.3,43.5,33,50.6,61.6,34.6,32.8,31.5,35.4,23.8,19.2,52.2,46.3,47.6,43.3,60.5,62.4,59.5,64.3,58.6,61.1,29.8,33.2,27,33.2,30.8,30.8,50.2,50.2,41.8,41.8,5.9,5.9,40.3,41.4,40.3,41.4,38.2,43.6,34.2,43,40.2,65.3,36.8,4,42.7,38.2,47.2,45.3,41.2,48.5,34.4,39.2,35.9,41,20,20.1,67.4,67.4
|
||||
792,TUR,Turkiye,Eastern Europe,87270501,41910,36,37.6,40.8,35.6,21.1,20.1,2.1,2.1,21,21,70.9,61.5,5.4,5.4,0.8,0.8,0.6,0.6,1,1,46.2,46.2,18.7,18.7,68.7,67.7,81,64,29.4,30.4,65.4,54.7,NA,NA,NA,NA,70.7,53.4,53.3,53.3,63.8,63.8,40.1,49.6,48.6,24.4,54.9,56.2,37.2,51.3,41.5,54.2,53.1,57.4,84.2,50,32.7,32.9,42.5,44.7,86.4,69.7,67.6,34.7,61.2,59.2,50.2,49.8,63.7,52.1,57.9,42.9,63.9,76,65.9,69.1,38.7,38.7,69.9,74,69.9,74,60.7,60.7,38.5,41.8,31.8,34.8,16.9,16,41.8,57.2,19.9,21.3,14.9,13.1,32.5,38.8,57.5,62.4,41,40.8,59,63.7,55.6,66.9,53.6,61.6,49.2,52.2,47.2,52.2,28,29.7,32.7,33.1,56.4,57.8,9.2,12.3,26.5,37,26.5,37,35.2,40.9,16.5,25,0,28,0,30.2,5.2,16.9,26,100,51.5,51.9,27,36.6,23.2,30.8,1,0.7,44.2,44.2
|
||||
795,TKM,Turkmenistan,Former Soviet States,7364438,26881,35.5,40.7,36.9,40.8,39.7,39.5,72.8,72.8,12.5,12.5,50,50,2.3,2.3,17.9,18.2,10.7,10.7,24.1,24.1,64.1,64.1,65.6,65.6,92.2,91.9,93,89.2,42,42.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,16.6,43.5,30.7,32.2,37.5,38.8,33.4,15.7,25.4,74.6,49.9,48.1,31.8,27,69.7,43.5,86.6,72.3,59.6,59.6,39.2,39.2,43.2,43.2,44.7,44.7,34.1,34.1,34.1,34.1,51.6,53.8,53.3,54.1,48.5,45.5,51.4,67.8,48.1,43.1,36,34.1,57,56.3,62.8,64.2,48.3,48,51.3,58.5,40.2,54.2,45.7,61.4,39.8,41.9,38.7,41.9,48.7,48.7,79.8,79.8,75.3,75.3,4.3,4.3,20.1,29.6,20.1,29.6,34.5,49.5,5.1,25.7,42.9,37.7,36.8,7.6,19.9,28.3,0,11.5,0,0,4.8,23.4,14.9,26.4,12.4,14.7,32.3,32.3
|
||||
800,UGA,Uganda,Sub-Saharan Africa,48656601,3642,31.3,35.4,43.8,44.5,54.6,51.9,NA,NA,NA,NA,NA,NA,39.1,39.1,69.5,72,59,59,77.5,77.5,17.6,17.6,62.2,62.2,24.9,15.7,89.8,63.7,0,0,31.3,34.1,40.7,46.2,31.2,35.5,44.8,29.7,0,0,43.5,43.5,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,44.7,54.5,83.4,76.4,83.2,77.2,9.3,30.7,46.6,69.4,47.8,48.3,36.4,40.9,100,100,97.7,92.7,31.6,32.9,12.8,12.8,96,96,8.1,8.1,0,0,0,0,17.7,18.3,14.4,14,4.5,7.7,5.5,7.5,48.9,29.2,60.5,64.7,22.8,25.4,27.1,28.9,3.5,5.9,20.5,23.6,16.3,22.1,18.1,24.6,35.9,38.3,33.7,38.3,24.3,24.3,57.3,57.3,2.6,2.6,2.2,2.2,22.8,35.7,22.8,35.7,13.4,29.5,100,100,0,33,NA,NA,0,37.1,13,24.5,46.6,48.1,12.2,33.2,21.9,40.4,17.1,18.7,92.9,92.9
|
||||
804,UKR,Ukraine,Former Soviet States,37732836,20760,47,54.6,49.4,58.5,44.8,53.8,81.5,81.5,34.2,34.2,48.4,44.1,22.4,22.4,8,64.9,43.8,43.8,54.5,54.5,42.5,42.5,0,0,79.3,79.8,73.1,71.1,0,0,58.4,54.8,NA,NA,NA,NA,58.5,53.1,70.4,70.4,32.2,32.2,33.6,33.4,79.4,18.8,37.4,27.7,29.7,40,42.1,34.8,60.7,57.6,67.5,92.9,54,54.7,56.2,59.7,70.7,100,49.6,100,69.8,76.4,56.1,71.1,100,100,63,64.7,56.6,84.5,41.4,41.7,19.5,22,55.9,55.9,34.2,34.2,34.2,34.2,43.6,48.1,34.1,40,25.1,37.9,28.8,35.6,40.8,58.8,29.3,31,30.4,35.9,59.9,64.2,63.3,64.4,74.9,76,69.9,72.2,76.3,78.5,56.2,60.7,52.9,60.7,22.6,22.2,39.7,37.5,30.2,33.3,1.8,1.3,46.3,53.9,46.3,53.9,52.5,62.6,50,65.9,53.8,73.4,0,0,28.1,41.6,55.1,62.6,50,49.8,41.3,51.7,43.1,55.3,8.1,23.9,84.9,84.9
|
||||
784,ARE,United Arab Emirates,Greater Middle East,10642081,82000,43.3,52,54.3,63.5,49.2,59.3,90.2,90.2,20.6,31.1,100,100,42.5,42.5,23.1,37.3,2.6,55.1,95.3,95.3,88,88,99.3,99.3,55.8,48.4,88,78.7,41,41.2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,79.4,80,45,37.1,89.8,89.4,100,100,77.5,82.8,44.7,50.6,46.2,65.1,0,0,0,0,12.5,88.9,100,67.3,34.5,37.5,11.7,13.8,33.7,29.2,31.5,36.7,62.1,62.1,91.2,91.2,36.7,36.7,93.9,93.9,100,100,100,100,49.3,50.6,46,46.2,16.9,10.3,93.2,99.3,11.9,24.2,2.3,7.1,16.3,12.4,42.8,34.8,14.9,12.6,69.5,71.9,76,80.6,63.2,66.1,36.6,49.9,33.5,49.9,29.5,20,23,23.3,53,41.6,24.2,5.9,21.5,35.6,21.5,35.6,17.2,45.9,0,14,22.2,42.7,36.1,8,6.9,35.9,60.1,89.9,100,100,7.2,36.5,0,11.3,3.4,6.5,14.4,14.4
|
||||
826,GBR,United Kingdom,Global West,68682962,64384,71.4,72.7,72.8,73.3,70.4,71.9,78.1,80,44.5,54.2,66.2,70.4,60,60,56.9,57,87.6,87.9,83.5,83.5,72,72,24.5,24.5,91.3,91.4,89.2,88,47.1,49.3,46,46.4,NA,NA,NA,NA,42.3,45.6,63.6,63.6,16.1,16.1,43.2,38.1,20.9,14.9,49.2,58.4,29.2,31.7,37.1,40.3,57.5,44.1,92.1,92,38.9,43,62.2,60.5,100,100,100,100,78.6,76.9,57.4,56,50.1,48.6,83.6,77,81.6,100,79.8,79.8,25.7,25.7,85.8,85.8,85.8,85.8,85.8,85.8,74.5,77.4,67.2,69.9,43.1,52.1,93.9,98.8,59.7,66.5,4.1,12.7,38.8,51.6,57.7,66.8,68.8,71.7,95.8,98.2,94.5,100,91.9,97,89.9,95.7,85.2,95.7,61.9,65.4,28.4,29.4,97.6,97.7,77.5,85.2,66.6,67.8,66.6,67.8,65,74.3,63.1,77.3,100,58.6,46.8,56.8,68.6,52.5,100,86.5,46.9,47,62.4,67.1,54.1,61.6,18.2,100,91.6,91.6
|
||||
840,USA,United States of America,Global West,343477335,89685,57.3,57.3,54.1,54.1,41.6,41,58.7,58.7,46.1,46.1,69.6,76.9,14.6,14.6,27,39.9,37.2,37.8,36.9,36.9,71.5,71.5,89.4,89.4,47.7,45.5,68.1,20.8,46.8,46.2,53.6,51.9,92.2,87.6,24,18.1,50.3,49.9,43.9,43.9,66.5,66.5,42.5,46.5,38.4,38,37,45.1,36,46.4,41,50.6,51.5,49.1,89,89.1,28.6,31.7,35.5,36.9,100,100,100,100,78.5,83,68,76.6,62.8,80.7,56.9,57.8,77.6,100,65.7,65.7,5.6,5.6,78.2,78.2,67.7,67.7,67.7,67.7,69.7,72,63,65.8,56.3,61.5,88.3,91.6,27.1,33.8,0,6.6,29.1,35.7,53.5,60.3,38.5,35.5,95.1,96.4,84.5,90.9,98.6,100,75.4,78.6,73.6,78.6,45.3,41.7,15.6,13.3,100,93.9,47.7,44,51.6,50.1,51.6,50.1,60.8,57.7,37.4,33.3,60.7,50.5,38,46.7,53.8,55.2,100,100,50.5,50.2,52.6,52.5,35.3,35.5,0,0,50.1,50.1
|
||||
858,URY,Uruguay,Latin America & Caribbean,3388081,36010,43.2,43.9,39.7,39.1,29.8,29.4,15.1,15.1,22.1,22.1,73.4,54.2,6.3,6.3,17.4,18.3,9.7,11.6,16.7,16.7,42.6,42.6,89.3,89.3,70.4,71.3,80.1,69.9,23.5,25.9,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,44.1,34.4,61.2,51.6,55.1,53.7,16.8,26,20.6,21.1,47.5,40.1,68.2,75.8,82.4,78.9,96.7,92.6,16.4,55.5,41,92.2,72,58.2,59.1,58.6,36.1,32.5,73.4,73.8,82.3,53.4,34.1,34.1,59,59,37.7,37.7,26.3,26.3,26.3,26.3,54.8,56.5,51.1,52.3,48.4,48.6,46.7,57.7,57.8,52.3,18.7,27,68,68.6,79.3,78.4,33.9,38.8,69.3,72.5,69.1,75.6,64.9,70.4,62.1,64.7,60,64.7,30.9,30.9,36.3,36.3,66.2,66.2,7.8,7.8,38.9,40.6,38.9,40.6,34.8,46.1,34.7,53.8,44.7,38.9,94.8,50.4,39.9,46.5,0,53.3,46.3,48.2,36,37.6,31.2,31.4,21.8,21.4,38.4,38.4
|
||||
860,UZB,Uzbekistan,Former Soviet States,35652307,11596,44.3,42.9,48.2,49.3,37.7,44.4,NA,NA,NA,NA,NA,NA,10.8,10.8,19.8,36.6,13.4,32.3,26.8,26.8,75.3,75.3,75.3,75.3,91.1,90,76.9,66.1,36,36.1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,61.9,49.2,40.3,41.5,60.8,57.1,73.6,42.8,68,55.6,64.9,57.4,59.6,49.6,42.4,35.6,70.6,45.7,72,72,60.8,62.2,54.2,53.6,39,39,93.5,97.2,23.5,23.5,35.4,37.7,26.2,27.5,25.8,23.9,13.7,23.2,46.6,45.8,35.9,29.6,28.3,30.1,56,56,38.2,40.1,66.4,71.5,55.6,65.4,64.6,75.5,39.9,44.3,36,44.3,28.7,28.7,68.3,68.3,0,0,3.5,3.5,45.8,37.5,45.8,37.5,59.2,46.8,63.5,43.6,32.6,35.6,0,21.7,4.1,11.8,53.4,47.2,54,55,32.3,32.1,45.2,38.6,13,10,57.8,57.8
|
||||
548,VUT,Vanuatu,Asia-Pacific,320409,2878,35.4,44.6,30.5,36.5,17.7,18.1,0.6,0.6,1.8,1.8,100,98.1,1.7,1.7,0.6,3.6,13.7,13.7,2.9,2.9,76.1,76.1,99.1,99.1,0.3,0,98.5,98.8,100,100,77.4,81.9,83.8,81.2,91.7,94.6,86.9,79.6,48.4,48.4,88.7,88.7,78.9,84,73.2,85.6,31.1,36.3,100,100,100,99.2,NA,NA,36.1,73.5,76,80.5,78.4,88.9,42.1,73,1,69.6,40.6,42.7,11.8,13.7,100,100,30.4,42.3,67.2,67.2,17.6,17.6,77.2,77.2,15.7,15.7,7.1,7.1,7.1,7.1,48.3,49.2,57,57.5,100,100,1.4,2.5,64.5,58.5,100,100,76.8,83.7,96.6,97.7,58.3,64.9,30.6,32.4,28.2,31.2,30.2,33.2,32.8,34.5,31.5,34.5,21.9,21.9,48.1,48.1,4.4,4.4,4.4,4.4,31.9,53.7,31.9,53.7,9.3,25.4,31.3,62.8,25.2,100,NA,NA,23.8,100,22.3,61.5,49.3,50.2,16.3,47.4,31.1,57.9,48.4,59,95.1,95.1
|
||||
862,VEN,Venezuela,Latin America & Caribbean,28300854,8404,51.6,53.1,60.2,61,63.2,61.3,10.3,10.3,14.9,14.9,58.1,76.4,100,100,88.5,88.6,96.7,96.7,75.5,75.5,69.9,69.9,91.6,91.6,43.1,37.5,82.2,56.7,54.9,52.2,79.2,71.3,89.1,82.8,84.9,69.6,70.8,69.7,37.6,37.6,87.8,87.8,84.9,82.5,36.8,27.8,75,85.9,100,99,99.7,98.5,79.3,39.3,53.7,76.1,100,100,91,90.5,44.9,72.2,27.8,72.3,47.3,46.1,25.9,18.1,40,42.5,41.2,47.5,83.1,73.9,32.1,32.1,29.1,29.1,33,33,32,32,32,32,48.3,50,51.4,54.2,58.2,62.1,49,51.6,63.3,54.2,24.3,33.3,58.4,61.9,62.6,62.9,11.5,9.4,47.7,48.5,48.5,49.3,47.7,47.9,39.9,38.2,40.5,38.2,14.6,10.3,35.3,24.6,0,0,1.2,1.2,41.4,43.5,41.4,43.5,37.8,50,35,55.1,50.4,51.7,40.7,15.5,44.8,53.6,43.3,57.2,49.3,49.4,32.6,34.4,29.2,45.5,6.4,14.2,67.2,67.2
|
||||
704,VNM,Viet Nam,Asia-Pacific,100352192,17350,28.2,24.5,32.2,27.7,27.4,25.4,27.5,27.5,7,7,76.7,40,25.4,25.4,41.2,41.6,19.7,24.9,44.3,44.3,30,30,82.7,82.7,14.5,3.2,19,0,60.2,59.4,47.4,48.5,45.2,56.8,75.5,71.4,22.4,15.6,34.3,34.3,53.9,53.9,31.4,29.4,100,98.2,36.8,31.8,15.4,9,18,13.3,35.3,29,33.2,7.5,46,43.9,52.8,46.6,36.5,0,44.6,0,73.7,73,58.9,55.2,29.9,31.9,58.1,59.4,98.7,100,14.9,14.9,58.7,58.7,10,10,10,10,10,10,24.7,26.6,13.7,15.5,1.8,8,9.7,15.4,27.3,34.3,28.8,22.9,32.8,32.3,21,24.8,17.2,15.8,51.6,53.7,46.7,52.2,51.2,54.7,40.8,43.3,38.9,43.3,46.1,46.1,74.2,74.2,27.4,27.4,27.4,27.4,25.2,17.9,25.2,17.9,23.9,10.8,6.3,0,36.4,49.7,36.7,0,33.2,35.4,35.4,51.6,45.5,41.7,24.2,10.3,28.2,12,4.8,0,36.5,36.5
|
||||
894,ZMB,Zambia,Sub-Saharan Africa,20723965,4190,42.2,46.1,63.5,65.3,84.7,83.7,NA,NA,NA,NA,NA,NA,99.6,99.6,90.7,91.2,99.2,99.2,79.4,79.4,54,54,92.1,92.1,59.6,59.6,94.2,80.1,54.6,52.6,50.4,40,57.9,45.1,NA,NA,47.4,37.5,13.5,13.5,75.1,75.1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,54.7,75.4,72.1,69.3,86.8,82.6,28.2,50.5,40.6,100,46,44.1,32.8,29.9,100,58.2,74.5,85.9,38.1,39.6,15.3,15.3,94.9,94.9,13.8,13.8,0.5,0.5,0.5,0.5,17.3,18.9,16.4,16.7,15.3,12.7,3.7,7,31.2,30.9,62.1,55.7,42,44,48.2,51.2,11.2,13.1,15.7,21.4,10,20.1,11.3,22.3,24.6,28.2,22.4,28.2,25.2,25.2,61.9,61.9,0,0,1,1,30.3,39.4,30.3,39.4,15.6,16.8,57.5,60,37.7,48.9,76.9,100,41.9,55.4,18.9,31.3,47.7,47.8,30,34.2,41.2,43.4,21.9,21.6,84,84
|
||||
716,ZWE,Zimbabwe,Sub-Saharan Africa,16340822,5071,42.4,51.7,57.1,66.3,68.7,70.5,NA,NA,NA,NA,NA,NA,64.7,64.7,67.6,72.7,92.3,94,95.4,95.4,41.8,41.8,79.4,79.4,32.7,32.7,79.2,85.1,31.8,33.6,41.4,45.7,54.1,51.6,NA,NA,36.1,45.1,20.4,20.4,63.5,63.5,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,41,88.7,76,69.5,90.9,84,37.7,82.1,50.1,100,27.3,35.5,27.5,31.7,65.9,57,83.5,80.8,4.1,18.7,62.5,56.7,76.5,75.1,75.9,69,61.2,53.9,0,0,21.5,22.9,21.9,22.8,27.4,26.8,3,4.9,38.7,33.5,68,68.9,53.7,55.4,49.6,53.1,16.8,17.5,17.2,19.5,14.3,19.2,14.9,19.7,23.9,27.1,22,27.1,31.8,31.8,74.9,74.9,3.3,3.3,3,3,37.1,53.5,37.1,53.5,45.3,69.1,100,100,34.7,54.2,36.8,3.8,35.1,59.8,34,34.1,35.7,43.5,26,46.2,42.3,59,23.3,32.2,96.9,96.9
|
||||
|
@@ -0,0 +1,62 @@
|
||||
{
|
||||
"data": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/epi_results_2024_pop_gdp_v2.csv",
|
||||
"region_col": "region",
|
||||
"response": "EPI.new",
|
||||
"region_a": "Sub-Saharan Africa",
|
||||
"region_b": "Latin America & Caribbean",
|
||||
"predictors": [
|
||||
"gdp",
|
||||
["gdp", "population"]
|
||||
],
|
||||
"knn1": ["AGR.new", "AIR.new", "APO.new"],
|
||||
"knn2": ["BCA.new", "BDH.new", "CBP.new"],
|
||||
"k": 5,
|
||||
"fig_dir": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures",
|
||||
"stats_dir": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/stats",
|
||||
"box_a": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/box_Sub-Saharan_Africa_EPI.new.png",
|
||||
"box_b": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/box_Latin_America_Caribbean_EPI.new.png",
|
||||
"hist_a": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/hist_Sub-Saharan_Africa_EPI.new.png",
|
||||
"hist_b": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/hist_Latin_America_Caribbean_EPI.new.png",
|
||||
"qq_fig": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/qq_EPI.new_Sub-Saharan_Africa_vs_Latin_America_Caribbean.png",
|
||||
"ols": [
|
||||
{
|
||||
"name": "full: EPI.new ~ gdp",
|
||||
"rsq": 0.5224,
|
||||
"aic": 1257.4369,
|
||||
"bic": 1266.999,
|
||||
"nobs": 179,
|
||||
"summary_file": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/stats/ols_full_EPI.new_gdp.txt",
|
||||
"residuals_fig": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/residuals_full_EPI.new_gdp.png",
|
||||
"scatter_fig": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/scatter_full_EPI.new_gdp_gdp.png"
|
||||
},
|
||||
{
|
||||
"name": "full: EPI.new ~ gdp + population",
|
||||
"rsq": 0.5392,
|
||||
"aic": 1246.1592,
|
||||
"bic": 1258.8864,
|
||||
"nobs": 178,
|
||||
"summary_file": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/stats/ols_full_EPI.new_gdp_population.txt",
|
||||
"residuals_fig": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/residuals_full_EPI.new_gdp_population.png",
|
||||
"scatter_fig": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/scatter_full_EPI.new_gdp_population_gdp.png"
|
||||
}
|
||||
],
|
||||
"best_region_note": "on region `Sub-Saharan Africa`, the better model is **region Sub-Saharan Africa: EPI.new ~ gdp + population** (r²=0.361, aic=265.4, bic=272.7).",
|
||||
"knn": [
|
||||
{
|
||||
"tag": "model A",
|
||||
"k": 5,
|
||||
"vars": ["AGR.new", "AIR.new", "APO.new"],
|
||||
"accuracy": 0.5581,
|
||||
"confusion_fig": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/knn_confusion_model_A.png",
|
||||
"n_test": 43
|
||||
},
|
||||
{
|
||||
"tag": "model B",
|
||||
"k": 5,
|
||||
"vars": ["BCA.new", "BDH.new", "CBP.new"],
|
||||
"accuracy": 0.5116,
|
||||
"confusion_fig": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/knn_confusion_model_B.png",
|
||||
"n_test": 43
|
||||
}
|
||||
]
|
||||
}
|
||||
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|
||||
# exploratory data analysis and models on the epi dataset
|
||||
date: 2025-10-13
|
||||
|
||||
## dataset and choices
|
||||
- **file**: `epi_results_2024_pop_gdp_v2.csv`
|
||||
- **region column**: `region`
|
||||
- **response var**: `EPI.new`
|
||||
- **regions**: `Sub-Saharan Africa` vs `Latin America & Caribbean`
|
||||
|
||||
## 1) variable distributions
|
||||
### 1.1 boxplots and histograms (with density!)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
### 1.2 qq plot (two-sample)
|
||||
|
||||
|
||||
## 2) linear models
|
||||
### full: EPI.new ~ gdp
|
||||
|
||||
### full: EPI.new ~ gdp + population
|
||||
|
||||
### 2.2 same models on one region (comparison)
|
||||
on region `Sub-Saharan Africa`, the better model is **region Sub-Saharan Africa: EPI.new ~ gdp + population** (r²=0.361, aic=265.4, bic=272.7).
|
||||
|
||||
## 3) classification (knn, label = region)
|
||||
### model A
|
||||
- **k**: 5 | **accuracy**: 0.5581 | **test n**: 43
|
||||
variables: `c("AGR.new", "AIR.new", "APO.new")`
|
||||
|
||||
|
||||
### model B
|
||||
- **k**: 5 | **accuracy**: 0.5116 | **test n**: 43
|
||||
variables: `c("BCA.new", "BDH.new", "CBP.new")`
|
||||
|
||||
|
||||
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@@ -0,0 +1,19 @@
|
||||
|
||||
Call:
|
||||
lm(formula = f, data = d)
|
||||
|
||||
Residuals:
|
||||
Min 1Q Median 3Q Max
|
||||
-22.432 -4.915 0.043 6.222 20.899
|
||||
|
||||
Coefficients:
|
||||
Estimate Std. Error t value Pr(>|t|)
|
||||
(Intercept) -22.3482 5.0070 -4.463 1.43e-05 ***
|
||||
gdp 7.0974 0.5101 13.913 < 2e-16 ***
|
||||
---
|
||||
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
|
||||
|
||||
Residual standard error: 8.023 on 177 degrees of freedom
|
||||
Multiple R-squared: 0.5224, Adjusted R-squared: 0.5197
|
||||
F-statistic: 193.6 on 1 and 177 DF, p-value: < 2.2e-16
|
||||
|
||||
@@ -0,0 +1,20 @@
|
||||
|
||||
Call:
|
||||
lm(formula = f, data = d)
|
||||
|
||||
Residuals:
|
||||
Min 1Q Median 3Q Max
|
||||
-21.810 -5.068 -0.027 6.014 19.567
|
||||
|
||||
Coefficients:
|
||||
Estimate Std. Error t value Pr(>|t|)
|
||||
(Intercept) -8.6847 7.0829 -1.226 0.2218
|
||||
gdp 6.9983 0.5047 13.867 <2e-16 ***
|
||||
population -0.7970 0.2995 -2.662 0.0085 **
|
||||
---
|
||||
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
|
||||
|
||||
Residual standard error: 7.905 on 175 degrees of freedom
|
||||
Multiple R-squared: 0.5392, Adjusted R-squared: 0.5339
|
||||
F-statistic: 102.4 on 2 and 175 DF, p-value: < 2.2e-16
|
||||
|
||||
@@ -0,0 +1,19 @@
|
||||
|
||||
Call:
|
||||
lm(formula = f, data = d)
|
||||
|
||||
Residuals:
|
||||
Min 1Q Median 3Q Max
|
||||
-8.7004 -2.6329 -0.6432 3.1700 12.7231
|
||||
|
||||
Coefficients:
|
||||
Estimate Std. Error t value Pr(>|t|)
|
||||
(Intercept) 7.9123 6.3944 1.237 0.223
|
||||
gdp 3.6412 0.7453 4.885 1.41e-05 ***
|
||||
---
|
||||
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
|
||||
|
||||
Residual standard error: 4.322 on 44 degrees of freedom
|
||||
Multiple R-squared: 0.3517, Adjusted R-squared: 0.3369
|
||||
F-statistic: 23.87 on 1 and 44 DF, p-value: 1.407e-05
|
||||
|
||||
@@ -0,0 +1,20 @@
|
||||
|
||||
Call:
|
||||
lm(formula = f, data = d)
|
||||
|
||||
Residuals:
|
||||
Min 1Q Median 3Q Max
|
||||
-8.4641 -2.8896 -0.5014 3.3435 12.5429
|
||||
|
||||
Coefficients:
|
||||
Estimate Std. Error t value Pr(>|t|)
|
||||
(Intercept) 3.4874 12.1785 0.286 0.776
|
||||
gdp 3.8097 0.8314 4.583 4.08e-05 ***
|
||||
population 0.1907 0.4558 0.418 0.678
|
||||
---
|
||||
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
|
||||
|
||||
Residual standard error: 4.375 on 42 degrees of freedom
|
||||
Multiple R-squared: 0.3611, Adjusted R-squared: 0.3307
|
||||
F-statistic: 11.87 on 2 and 42 DF, p-value: 8.205e-05
|
||||
|
||||
@@ -0,0 +1,62 @@
|
||||
{
|
||||
"data": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/epi_results_2024_pop_gdp_v2.csv",
|
||||
"region_col": "region",
|
||||
"response": "EPI.new",
|
||||
"region_a": "Sub-Saharan Africa",
|
||||
"region_b": "Latin America & Caribbean",
|
||||
"predictors": [
|
||||
"gdp",
|
||||
["gdp", "population"]
|
||||
],
|
||||
"knn1": ["AGR.new", "AIR.new", "APO.new"],
|
||||
"knn2": ["BCA.new", "BDH.new", "CBP.new"],
|
||||
"k": 5,
|
||||
"fig_dir": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures",
|
||||
"stats_dir": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/stats",
|
||||
"box_a": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/box_Sub-Saharan_Africa_EPI.new.png",
|
||||
"box_b": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/box_Latin_America_Caribbean_EPI.new.png",
|
||||
"hist_a": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/hist_Sub-Saharan_Africa_EPI.new.png",
|
||||
"hist_b": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/hist_Latin_America_Caribbean_EPI.new.png",
|
||||
"qq_fig": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/qq_EPI.new_Sub-Saharan_Africa_vs_Latin_America_Caribbean.png",
|
||||
"ols": [
|
||||
{
|
||||
"name": "full: EPI.new ~ gdp",
|
||||
"rsq": 0.5224,
|
||||
"aic": 1257.4369,
|
||||
"bic": 1266.999,
|
||||
"nobs": 179,
|
||||
"summary_file": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/stats/ols_full_EPI.new_gdp.txt",
|
||||
"residuals_fig": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/residuals_full_EPI.new_gdp.png",
|
||||
"scatter_fig": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/scatter_full_EPI.new_gdp_gdp.png"
|
||||
},
|
||||
{
|
||||
"name": "full: EPI.new ~ gdp + population",
|
||||
"rsq": 0.5392,
|
||||
"aic": 1246.1592,
|
||||
"bic": 1258.8864,
|
||||
"nobs": 178,
|
||||
"summary_file": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/stats/ols_full_EPI.new_gdp_population.txt",
|
||||
"residuals_fig": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/residuals_full_EPI.new_gdp_population.png",
|
||||
"scatter_fig": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/scatter_full_EPI.new_gdp_population_gdp.png"
|
||||
}
|
||||
],
|
||||
"best_region_note": "on region `Sub-Saharan Africa`, the better model is **region Sub-Saharan Africa: EPI.new ~ gdp + population** (r²=0.361, aic=265.4, bic=272.7).",
|
||||
"knn": [
|
||||
{
|
||||
"tag": "model A",
|
||||
"k": 5,
|
||||
"vars": ["AGR.new", "AIR.new", "APO.new"],
|
||||
"accuracy": 0.5581,
|
||||
"confusion_fig": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/knn_confusion_model_A.png",
|
||||
"n_test": 43
|
||||
},
|
||||
{
|
||||
"tag": "model B",
|
||||
"k": 5,
|
||||
"vars": ["BCA.new", "BDH.new", "CBP.new"],
|
||||
"accuracy": 0.5116,
|
||||
"confusion_fig": "/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures/knn_confusion_model_B.png",
|
||||
"n_test": 43
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,37 @@
|
||||
setwd("/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/")
|
||||
|
||||
# yes I am lazy
|
||||
dir.create("/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/stats", recursive = TRUE, showWarnings = FALSE)
|
||||
dir.create("/home/ion606/Desktop/Homework/Data Analytics/Assignments/Assignment II/output/figures", recursive = TRUE, showWarnings = FALSE)
|
||||
|
||||
|
||||
# These functions were chatgpt generated because I was having some issues with sourcing
|
||||
options(
|
||||
warn = 1,
|
||||
keep.source = TRUE,
|
||||
show.error.locations = TRUE
|
||||
)
|
||||
|
||||
safe_source <- function(file) {
|
||||
message("Sourcing: ", file)
|
||||
tryCatch(
|
||||
{
|
||||
# echo=TRUE prints each line before running, aiding pinpointing failures
|
||||
source(file, echo = TRUE, chdir = TRUE, keep.source = TRUE, local = new.env(parent = globalenv()))
|
||||
},
|
||||
error = function(e) {
|
||||
cat("\nError while sourcing: ", file, "\n", sep = "")
|
||||
cat(conditionMessage(e), "\n", sep = "")
|
||||
cat("Calls:\n"); print(sys.calls())
|
||||
stop(e) # rethrow to halt execution with context
|
||||
}
|
||||
)
|
||||
}
|
||||
|
||||
|
||||
safe_source("R/01_args_and_load.R")
|
||||
safe_source("R/02_plots.R")
|
||||
safe_source("R/03_ols_full.R")
|
||||
safe_source("R/04_ols_region.R")
|
||||
safe_source("R/05_knn.R")
|
||||
safe_source("R/06_report.R")
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -0,0 +1,41 @@
|
||||
##########################################
|
||||
### Principal Component Analysis (PCA) ###
|
||||
##########################################
|
||||
|
||||
## load libraries
|
||||
library(ggplot2)
|
||||
library(ggfortify)
|
||||
library(GGally)
|
||||
library(e1071)
|
||||
library(class)
|
||||
library(psych)
|
||||
library(readr)
|
||||
|
||||
## set working directory so that files can be referenced without the full path
|
||||
setwd("/home/ion606/Desktop/Data Analytics/Lab 4")
|
||||
|
||||
## read dataset
|
||||
wine <- read_csv("wine.data", col_names = FALSE)
|
||||
|
||||
## set column names
|
||||
names(wine) <- c("Type","Alcohol","Malic acid","Ash","Alcalinity of ash","Magnesium","Total phenols","Flavanoids","Nonflavanoid Phenols","Proanthocyanins","Color Intensity","Hue","Od280/od315 of diluted wines","Proline")
|
||||
|
||||
## inspect data frame
|
||||
head(wine)
|
||||
|
||||
## change the data type of the "Type" column from character to factor
|
||||
####
|
||||
# Factors look like regular strings (characters) but with factors R knows
|
||||
# that the column is a categorical variable with finite possible values
|
||||
# e.g. "Type" in the Wine dataset can only be 1, 2, or 3
|
||||
####
|
||||
|
||||
wine$Type <- as.factor(wine$Type)
|
||||
|
||||
|
||||
## visualize variables
|
||||
pairs.panels(wine[,-1],gap = 0,bg = c("red", "yellow", "blue")[wine$Type],pch=21)
|
||||
|
||||
ggpairs(wine, ggplot2::aes(colour = Type))
|
||||
|
||||
###
|
||||
Binary file not shown.
@@ -0,0 +1,366 @@
|
||||
has_pkg <- function(pkg) requireNamespace(pkg, quietly = TRUE)
|
||||
|
||||
has_ggplot2 <- has_pkg("ggplot2")
|
||||
has_GGally <- has_pkg("GGally")
|
||||
has_e1071 <- has_pkg("e1071")
|
||||
has_class <- has_pkg("class")
|
||||
has_psych <- has_pkg("psych")
|
||||
has_readr <- has_pkg("readr")
|
||||
|
||||
# WHY IS THIS HERE YOU MIGHT ASK???? WELL LET ME TELL YOU I SPENT TWO HOURS ON STUPID PACKAGE IMPORTS
|
||||
# OOOOOOHHH PSYCH IS IN A DIFFERENT REPO??? OH IT ISN'T??? I have a fever of 103 I DO NOT CARE
|
||||
if (has_ggplot2) { library(ggplot2) } else { warning("ggplot2 not available; plots will be skipped") }
|
||||
if (has_GGally) { library(GGally) } else { message("GGally not available; skipping ggpairs plot") }
|
||||
if (has_e1071) { library(e1071) }
|
||||
if (has_class) { library(class) } else { stop("class package not available for kNN") }
|
||||
if (!has_psych) { message("psych not available; skipping pairs.panels plot") }
|
||||
if (has_readr) { library(readr) }
|
||||
library(grid) # unit() for arrows in plots
|
||||
suppressWarnings(RNGkind(sample.kind = "Rounding"))
|
||||
|
||||
# set a reproducible seed
|
||||
set.seed(4600)
|
||||
|
||||
# 178 rows
|
||||
# col 1 is class label (1,2,3)
|
||||
# other 13 columns continuous predictors
|
||||
|
||||
possible_paths <- c(
|
||||
"wine.data",
|
||||
"./wine.data",
|
||||
"../wine.data",
|
||||
"DAN/wine.data",
|
||||
"./DAN/wine.data"
|
||||
)
|
||||
data_path <- NA
|
||||
for (p in possible_paths) { if (file.exists(p)) { data_path <- p; break } }
|
||||
if (is.na(data_path)) stop("could not find wine.data; place this script in the DAN folder or given/ and re-run")
|
||||
|
||||
if (has_readr) {
|
||||
wine <- readr::read_csv(
|
||||
file = data_path,
|
||||
col_names = FALSE,
|
||||
show_col_types = FALSE,
|
||||
progress = FALSE
|
||||
)
|
||||
} else {
|
||||
wine <- read.csv(file = data_path, header = FALSE)
|
||||
}
|
||||
|
||||
colnames(wine) <- c(
|
||||
"Type",
|
||||
"Alcohol",
|
||||
"Malic_acid",
|
||||
"Ash",
|
||||
"Alcalinity_of_ash",
|
||||
"Magnesium",
|
||||
"Total_phenols",
|
||||
"Flavanoids",
|
||||
"Nonflavanoid_phenols",
|
||||
"Proanthocyanins",
|
||||
"Color_intensity",
|
||||
"Hue",
|
||||
"OD280_OD315",
|
||||
"Proline"
|
||||
)
|
||||
|
||||
wine$Type <- as.factor(wine$Type)
|
||||
|
||||
# put here from when I accidentally read in the wrong file repeatedly
|
||||
# left because it makes it more, "robust"
|
||||
stopifnot(nrow(wine) == 178, ncol(wine) == 14)
|
||||
print(summary(wine$Type))
|
||||
|
||||
# exploratory plots (because I went down a rabbit hole and by god I'm using it)
|
||||
|
||||
if (has_psych) {
|
||||
# pairs panel (psych) – colors by class
|
||||
psych::pairs.panels(
|
||||
wine[,-1],
|
||||
gap = 0,
|
||||
bg = c("red","gold","royalblue")[wine$Type],
|
||||
pch = 21,
|
||||
main = "wine (uci) – scatterplot matrix by class"
|
||||
)
|
||||
}
|
||||
|
||||
if (has_GGally && has_ggplot2) {
|
||||
# ggpairs for nice matrix <3
|
||||
GGally::ggpairs(wine, ggplot2::aes(colour = Type), columns = 2:ncol(wine))
|
||||
}
|
||||
|
||||
# split into train/test BEFORE!!!!!!!!!!!!!!!!!!!!!! any preprocessing to avoid leakage
|
||||
|
||||
set.seed(4600)
|
||||
n <- nrow(wine)
|
||||
train_idx <- sample.int(n, size = floor(0.7 * n))
|
||||
wine_train <- wine[train_idx, , drop = FALSE]
|
||||
wine_test <- wine[-train_idx, , drop = FALSE]
|
||||
|
||||
X_train <- wine_train[, -1]
|
||||
y_train <- wine_train$Type
|
||||
X_test <- wine_test[, -1]
|
||||
y_test <- wine_test$Type
|
||||
|
||||
# yes
|
||||
if (any(sapply(X_train, function(x) var(x, na.rm = TRUE) == 0))) {
|
||||
warning("one or more predictors have zero variance in the training set; scale() would fail")
|
||||
}
|
||||
if (anyNA(X_train) | anyNA(X_test)) {
|
||||
stop("found NA values in predictors; handle missingness before PCA")
|
||||
}
|
||||
|
||||
# project both train and test using the train-fitted pca
|
||||
pca_tr <- prcomp(X_train, center = TRUE, scale. = TRUE)
|
||||
|
||||
pve_tr <- (pca_tr$sdev^2) / sum(pca_tr$sdev^2)
|
||||
pve_df <- data.frame(
|
||||
PC = paste0("PC", seq_along(pve_tr)),
|
||||
PVE = pve_tr,
|
||||
CumPVE = cumsum(pve_tr)
|
||||
)
|
||||
|
||||
print("variance explained (training pca):")
|
||||
print(pve_df)
|
||||
|
||||
# scree plot from training pca
|
||||
p_scree <- ggplot(pve_df, aes(x = seq_along(PVE), y = PVE)) +
|
||||
geom_line() + geom_point() +
|
||||
scale_x_continuous(breaks = 1:length(pve_df$PC), labels = pve_df$PC) +
|
||||
labs(title = "scree plot – variance explained by principal components (training pca)",
|
||||
x = "principal component", y = "proportion of variance explained") +
|
||||
theme_minimal()
|
||||
|
||||
# cumulative variance plot from training pca
|
||||
p_cumvar <- ggplot(pve_df, aes(x = seq_along(CumPVE), y = CumPVE)) +
|
||||
geom_line() + geom_point() +
|
||||
scale_x_continuous(breaks = 1:length(pve_df$PC), labels = pve_df$PC) +
|
||||
labs(title = "cumulative variance explained (training pca)",
|
||||
x = "principal component", y = "cumulative proportion of variance") +
|
||||
theme_minimal()
|
||||
|
||||
# ========================================================================================================
|
||||
|
||||
# choose number of pcs: default to the smallest k with >= thresh cum variance
|
||||
# you can change thresh to 0.90 or 0.99 if you prefer
|
||||
|
||||
pc_variance_threshold <- 0.95
|
||||
k_pcs <- which(cumsum(pve_tr) >= pc_variance_threshold)[1]
|
||||
if (is.na(k_pcs)) k_pcs <- ncol(X_train) # crashes if fails so...
|
||||
cat("chosen number of pcs (threshold =", pc_variance_threshold, "):", k_pcs, "\n")
|
||||
|
||||
# project train/test into the pca space
|
||||
Z_train_full <- as.data.frame(predict(pca_tr, newdata = X_train))
|
||||
Z_test_full <- as.data.frame(predict(pca_tr, newdata = X_test))
|
||||
|
||||
# for downstream modeling
|
||||
Z_train <- Z_train_full[, seq_len(k_pcs), drop = FALSE]
|
||||
Z_test <- Z_test_full[, seq_len(k_pcs), drop = FALSE]
|
||||
|
||||
scores_all <- as.data.frame(predict(pca_tr, newdata = wine[,-1]))
|
||||
scores_all$Type <- wine$Type
|
||||
|
||||
# loadings from training pca
|
||||
loadings <- as.data.frame(pca_tr$rotation)
|
||||
loadings$Variable <- rownames(loadings)
|
||||
top_pc1 <- loadings[order(abs(loadings$PC1), decreasing = TRUE), c("Variable","PC1")][1:5, ]
|
||||
top_pc2 <- loadings[order(abs(loadings$PC2), decreasing = TRUE), c("Variable","PC2")][1:5, ]
|
||||
print("top contributors to pc1 (training pca):"); print(top_pc1)
|
||||
print("top contributors to pc2 (training pca):"); print(top_pc2)
|
||||
|
||||
|
||||
# function to make convex hull data for each group
|
||||
scores <- scores_all
|
||||
hull_df <- do.call(rbind, lapply(split(scores, scores$Type), function(df) {
|
||||
pts <- df[chull(df$PC1, df$PC2), c("PC1","PC2")]
|
||||
pts$Type <- unique(df$Type)
|
||||
pts
|
||||
}))
|
||||
p_pc12 <- ggplot(scores, aes(PC1, PC2, color = Type)) +
|
||||
geom_point(size = 2, alpha = 0.85) +
|
||||
geom_polygon(data = hull_df, aes(fill = Type, group = Type), color = NA, alpha = 0.15) +
|
||||
guides(fill = "none") +
|
||||
theme_minimal() +
|
||||
labs(title = "pc1 vs pc2 by class (projected with training pca)")
|
||||
|
||||
# arrow arrow arrow arrow arrow arrow arrow arrow arrow
|
||||
loading_scalefactor <- 3 * max(abs(scores$PC1), abs(scores$PC2)) # heuristic
|
||||
load_plot_df <- loadings
|
||||
load_plot_df$PC1s <- load_plot_df$PC1 * loading_scalefactor
|
||||
load_plot_df$PC2s <- load_plot_df$PC2 * loading_scalefactor
|
||||
|
||||
p_biplot <- ggplot(scores, aes(PC1, PC2, color = Type)) +
|
||||
geom_point(size = 2, alpha = 0.85) +
|
||||
geom_segment(
|
||||
data = load_plot_df,
|
||||
mapping = aes(x = 0, y = 0, xend = PC1s, yend = PC2s),
|
||||
inherit.aes = FALSE,
|
||||
arrow = arrow(length = unit(0.02, "npc")),
|
||||
color = "black",
|
||||
alpha = 0.8
|
||||
) +
|
||||
geom_text(
|
||||
data = load_plot_df,
|
||||
mapping = aes(x = PC1s, y = PC2s, label = Variable),
|
||||
inherit.aes = FALSE,
|
||||
hjust = 0,
|
||||
vjust = 0
|
||||
) +
|
||||
theme_minimal() +
|
||||
labs(title = "pc1 vs pc2 with variable loadings (training pca projection)")
|
||||
|
||||
# 1) kNN on original variables with standardization
|
||||
# 2) kNN on first 2 principal components only
|
||||
|
||||
# helper to create metrics from a confusion matrix (rows=true, cols=pred)
|
||||
compute_metrics <- function(cm) {
|
||||
lv <- rownames(cm)
|
||||
if (is.null(lv)) lv <- as.character(1:nrow(cm))
|
||||
TP <- diag(cm)
|
||||
FP <- colSums(cm) - TP
|
||||
FN <- rowSums(cm) - TP
|
||||
precision <- TP / (TP + FP)
|
||||
recall <- TP / (TP + FN)
|
||||
f1 <- 2 * precision * recall / (precision + recall)
|
||||
acc <- sum(TP) / sum(cm)
|
||||
macro_precision <- mean(precision, na.rm = TRUE)
|
||||
macro_recall <- mean(recall, na.rm = TRUE)
|
||||
macro_f1 <- mean(f1, na.rm = TRUE)
|
||||
per_class <- data.frame(
|
||||
class = lv,
|
||||
precision = precision,
|
||||
recall = recall,
|
||||
f1 = f1,
|
||||
row.names = NULL
|
||||
)
|
||||
summary <- data.frame(
|
||||
accuracy = acc,
|
||||
macro_precision = macro_precision,
|
||||
macro_recall = macro_recall,
|
||||
macro_f1 = macro_f1
|
||||
)
|
||||
list(per_class = per_class, summary = summary)
|
||||
}
|
||||
|
||||
set.seed(4600)
|
||||
ks <- seq(1, 15, by = 2)
|
||||
Kfolds <- 5
|
||||
|
||||
# kNN on original vars
|
||||
X_train_scaled <- scale(X_train, center = TRUE, scale = TRUE)
|
||||
scale_center <- attr(X_train_scaled, "scaled:center")
|
||||
scale_scale <- attr(X_train_scaled, "scaled:scale")
|
||||
X_test_scaled <- scale(X_test, center = scale_center, scale = scale_scale)
|
||||
|
||||
n_train_orig <- nrow(X_train_scaled)
|
||||
folds_orig <- sample(rep(1:Kfolds, length.out = n_train_orig))
|
||||
cv_acc_orig <- sapply(ks, function(k) {
|
||||
mean(sapply(1:Kfolds, function(f) {
|
||||
tr <- which(folds_orig != f)
|
||||
va <- which(folds_orig == f)
|
||||
pred_cv <- knn(train = X_train_scaled[tr, , drop = FALSE],
|
||||
test = X_train_scaled[va, , drop = FALSE],
|
||||
cl = y_train[tr], k = k)
|
||||
mean(pred_cv == y_train[va])
|
||||
}))
|
||||
})
|
||||
|
||||
best_k_orig <- ks[which.max(cv_acc_orig)]
|
||||
cat("[Original vars] best k:", best_k_orig, "cv acc:", max(cv_acc_orig), "\n")
|
||||
|
||||
pred_orig <- knn(train = X_train_scaled, test = X_test_scaled, cl = y_train, k = best_k_orig)
|
||||
acc_orig <- mean(pred_orig == y_test)
|
||||
cm_orig <- table(truth = y_test, pred = pred_orig)
|
||||
|
||||
cat("[Original vars] held-out accuracy:", round(acc_orig, 4), "\n")
|
||||
print(cm_orig)
|
||||
|
||||
metrics_orig <- compute_metrics(cm_orig)
|
||||
print(metrics_orig$summary)
|
||||
print(metrics_orig$per_class)
|
||||
|
||||
# kNN on first 2 PCs only
|
||||
Z2_train <- Z_train_full[, 1:2, drop = FALSE]
|
||||
Z2_test <- Z_test_full[, 1:2, drop = FALSE]
|
||||
n_train_2pc <- nrow(Z2_train)
|
||||
|
||||
folds_2pc <- sample(rep(1:Kfolds, length.out = n_train_2pc))
|
||||
cv_acc_2pc <- sapply(ks, function(k) {
|
||||
mean(sapply(1:Kfolds, function(f) {
|
||||
tr <- which(folds_2pc != f)
|
||||
va <- which(folds_2pc == f)
|
||||
pred_cv <- knn(train = Z2_train[tr, , drop = FALSE],
|
||||
test = Z2_train[va, , drop = FALSE],
|
||||
cl = y_train[tr], k = k)
|
||||
mean(pred_cv == y_train[va])
|
||||
}))
|
||||
})
|
||||
|
||||
best_k_2pc <- ks[which.max(cv_acc_2pc)]
|
||||
cat("[First 2 PCs] best k:", best_k_2pc, "cv acc:", max(cv_acc_2pc), "\n")
|
||||
|
||||
pred_2pc <- knn(train = Z2_train, test = Z2_test, cl = y_train, k = best_k_2pc)
|
||||
acc_2pc <- mean(pred_2pc == y_test)
|
||||
cm_2pc <- table(truth = y_test, pred = pred_2pc)
|
||||
|
||||
cat("[First 2 PCs] held-out accuracy:", round(acc_2pc, 4), "\n")
|
||||
print(cm_2pc)
|
||||
|
||||
metrics_2pc <- compute_metrics(cm_2pc)
|
||||
print(metrics_2pc$summary)
|
||||
print(metrics_2pc$per_class)
|
||||
|
||||
# ===========================================================================================
|
||||
outputs_dir <- "outputs"
|
||||
if (!dir.exists(outputs_dir)) dir.create(outputs_dir, recursive = TRUE, showWarnings = FALSE)
|
||||
|
||||
# plots
|
||||
if (exists("p_pc12") && inherits(p_pc12, "ggplot")) ggsave(filename = file.path(outputs_dir, "pc12_scatter.png"), plot = p_pc12, width = 8, height = 6, dpi = 300)
|
||||
if (exists("p_biplot") && inherits(p_biplot, "ggplot")) ggsave(filename = file.path(outputs_dir, "pc12_biplot.png"), plot = p_biplot, width = 8, height = 6, dpi = 300)
|
||||
if (exists("p_scree") && inherits(p_scree, "ggplot")) ggsave(filename = file.path(outputs_dir, "pca_scree.png"), plot = p_scree, width = 8, height = 6, dpi = 300)
|
||||
if (exists("p_cumvar") && inherits(p_cumvar, "ggplot")) ggsave(filename = file.path(outputs_dir, "pca_cumvar.png"), plot = p_cumvar, width = 8, height = 6, dpi = 300)
|
||||
|
||||
# top contributors/vars to PC1 and PC2
|
||||
write.csv(top_pc1, file = file.path(outputs_dir, "top_contributors_pc1.csv"), row.names = FALSE)
|
||||
write.csv(top_pc2, file = file.path(outputs_dir, "top_contributors_pc2.csv"), row.names = FALSE)
|
||||
|
||||
# confusion matrices as wide CSV and pretty text
|
||||
write.csv(as.matrix(cm_orig), file = file.path(outputs_dir, "confusion_original_wide.csv"))
|
||||
writeLines(capture.output(cm_orig), con = file.path(outputs_dir, "confusion_original.txt"))
|
||||
|
||||
write.csv(as.matrix(cm_2pc), file = file.path(outputs_dir, "confusion_2pc_wide.csv"))
|
||||
writeLines(capture.output(cm_2pc), con = file.path(outputs_dir, "confusion_2pc.txt"))
|
||||
|
||||
# metrics
|
||||
write.csv(metrics_orig$per_class, file = file.path(outputs_dir, "metrics_original_per_class.csv"), row.names = FALSE)
|
||||
write.csv(metrics_orig$summary, file = file.path(outputs_dir, "metrics_original_summary.csv"), row.names = FALSE)
|
||||
write.csv(metrics_2pc$per_class, file = file.path(outputs_dir, "metrics_2pc_per_class.csv"), row.names = FALSE)
|
||||
write.csv(metrics_2pc$summary, file = file.path(outputs_dir, "metrics_2pc_summary.csv"), row.names = FALSE)
|
||||
|
||||
# summary
|
||||
metrics_compare <- data.frame(
|
||||
model = c("original_variables", "first_2_pcs"),
|
||||
accuracy = c(metrics_orig$summary$accuracy, metrics_2pc$summary$accuracy),
|
||||
macro_precision = c(metrics_orig$summary$macro_precision, metrics_2pc$summary$macro_precision),
|
||||
macro_recall = c(metrics_orig$summary$macro_recall, metrics_2pc$summary$macro_recall),
|
||||
macro_f1 = c(metrics_orig$summary$macro_f1, metrics_2pc$summary$macro_f1)
|
||||
)
|
||||
write.csv(metrics_compare, file = file.path(outputs_dir, "metrics_comparison.csv"), row.names = FALSE)
|
||||
|
||||
# The below was made with help from ChatGPT because the psych package is confusing
|
||||
if (!interactive() && has_ggplot2) {
|
||||
pdf("Rplots_pca_fixed.pdf", width = 8, height = 6)
|
||||
if (has_psych) {
|
||||
psych::pairs.panels(
|
||||
wine[,-1],
|
||||
gap = 0,
|
||||
bg = c("red","gold","royalblue")[wine$Type],
|
||||
pch = 21,
|
||||
main = "wine (uci) – scatterplot matrix by class"
|
||||
)
|
||||
}
|
||||
|
||||
if (exists("p_scree") && inherits(p_scree, "ggplot")) print(p_scree)
|
||||
if (exists("p_pc12") && inherits(p_pc12, "ggplot")) print(p_pc12)
|
||||
dev.off()
|
||||
}
|
||||
@@ -0,0 +1,5 @@
|
||||
pred
|
||||
truth 1 2 3
|
||||
1 15 2 0
|
||||
2 1 19 1
|
||||
3 0 1 15
|
||||
@@ -0,0 +1,4 @@
|
||||
"","1","2","3"
|
||||
"1",15,2,0
|
||||
"2",1,19,1
|
||||
"3",0,1,15
|
||||
|
@@ -0,0 +1,5 @@
|
||||
pred
|
||||
truth 1 2 3
|
||||
1 17 0 0
|
||||
2 1 18 2
|
||||
3 0 0 16
|
||||
@@ -0,0 +1,4 @@
|
||||
"","1","2","3"
|
||||
"1",17,0,0
|
||||
"2",1,18,2
|
||||
"3",0,0,16
|
||||
|
@@ -0,0 +1,4 @@
|
||||
"class","precision","recall","f1"
|
||||
"1",0.9375,0.882352941176471,0.909090909090909
|
||||
"2",0.863636363636364,0.904761904761905,0.883720930232558
|
||||
"3",0.9375,0.9375,0.9375
|
||||
|
@@ -0,0 +1,2 @@
|
||||
"accuracy","macro_precision","macro_recall","macro_f1"
|
||||
0.907407407407407,0.912878787878788,0.908204948646125,0.910103946441156
|
||||
|
@@ -0,0 +1,3 @@
|
||||
"model","accuracy","macro_precision","macro_recall","macro_f1"
|
||||
"original_variables",0.944444444444444,0.944444444444444,0.952380952380952,0.94522732169791
|
||||
"first_2_pcs",0.907407407407407,0.912878787878788,0.908204948646125,0.910103946441156
|
||||
|
@@ -0,0 +1,4 @@
|
||||
"class","precision","recall","f1"
|
||||
"1",0.944444444444444,1,0.971428571428571
|
||||
"2",1,0.857142857142857,0.923076923076923
|
||||
"3",0.888888888888889,1,0.941176470588235
|
||||
|
@@ -0,0 +1,2 @@
|
||||
"accuracy","macro_precision","macro_recall","macro_f1"
|
||||
0.944444444444444,0.944444444444444,0.952380952380952,0.94522732169791
|
||||
|
Binary file not shown.
|
After Width: | Height: | Size: 344 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 227 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 101 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 105 KiB |
@@ -0,0 +1,6 @@
|
||||
"Variable","PC1"
|
||||
"Flavanoids",0.430570697054093
|
||||
"Total_phenols",0.388556731445086
|
||||
"OD280_OD315",0.379238757892512
|
||||
"Proanthocyanins",0.318149910146199
|
||||
"Nonflavanoid_phenols",-0.292569052362651
|
||||
|
@@ -0,0 +1,6 @@
|
||||
"Variable","PC2"
|
||||
"Color_intensity",-0.504116493512561
|
||||
"Alcohol",-0.480328824227057
|
||||
"Ash",-0.369020648548877
|
||||
"Proline",-0.3555672525193
|
||||
"Hue",0.300324646690879
|
||||
|
+178
@@ -0,0 +1,178 @@
|
||||
1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065
|
||||
1,13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050
|
||||
1,13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185
|
||||
1,14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480
|
||||
1,13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735
|
||||
1,14.2,1.76,2.45,15.2,112,3.27,3.39,.34,1.97,6.75,1.05,2.85,1450
|
||||
1,14.39,1.87,2.45,14.6,96,2.5,2.52,.3,1.98,5.25,1.02,3.58,1290
|
||||
1,14.06,2.15,2.61,17.6,121,2.6,2.51,.31,1.25,5.05,1.06,3.58,1295
|
||||
1,14.83,1.64,2.17,14,97,2.8,2.98,.29,1.98,5.2,1.08,2.85,1045
|
||||
1,13.86,1.35,2.27,16,98,2.98,3.15,.22,1.85,7.22,1.01,3.55,1045
|
||||
1,14.1,2.16,2.3,18,105,2.95,3.32,.22,2.38,5.75,1.25,3.17,1510
|
||||
1,14.12,1.48,2.32,16.8,95,2.2,2.43,.26,1.57,5,1.17,2.82,1280
|
||||
1,13.75,1.73,2.41,16,89,2.6,2.76,.29,1.81,5.6,1.15,2.9,1320
|
||||
1,14.75,1.73,2.39,11.4,91,3.1,3.69,.43,2.81,5.4,1.25,2.73,1150
|
||||
1,14.38,1.87,2.38,12,102,3.3,3.64,.29,2.96,7.5,1.2,3,1547
|
||||
1,13.63,1.81,2.7,17.2,112,2.85,2.91,.3,1.46,7.3,1.28,2.88,1310
|
||||
1,14.3,1.92,2.72,20,120,2.8,3.14,.33,1.97,6.2,1.07,2.65,1280
|
||||
1,13.83,1.57,2.62,20,115,2.95,3.4,.4,1.72,6.6,1.13,2.57,1130
|
||||
1,14.19,1.59,2.48,16.5,108,3.3,3.93,.32,1.86,8.7,1.23,2.82,1680
|
||||
1,13.64,3.1,2.56,15.2,116,2.7,3.03,.17,1.66,5.1,.96,3.36,845
|
||||
1,14.06,1.63,2.28,16,126,3,3.17,.24,2.1,5.65,1.09,3.71,780
|
||||
1,12.93,3.8,2.65,18.6,102,2.41,2.41,.25,1.98,4.5,1.03,3.52,770
|
||||
1,13.71,1.86,2.36,16.6,101,2.61,2.88,.27,1.69,3.8,1.11,4,1035
|
||||
1,12.85,1.6,2.52,17.8,95,2.48,2.37,.26,1.46,3.93,1.09,3.63,1015
|
||||
1,13.5,1.81,2.61,20,96,2.53,2.61,.28,1.66,3.52,1.12,3.82,845
|
||||
1,13.05,2.05,3.22,25,124,2.63,2.68,.47,1.92,3.58,1.13,3.2,830
|
||||
1,13.39,1.77,2.62,16.1,93,2.85,2.94,.34,1.45,4.8,.92,3.22,1195
|
||||
1,13.3,1.72,2.14,17,94,2.4,2.19,.27,1.35,3.95,1.02,2.77,1285
|
||||
1,13.87,1.9,2.8,19.4,107,2.95,2.97,.37,1.76,4.5,1.25,3.4,915
|
||||
1,14.02,1.68,2.21,16,96,2.65,2.33,.26,1.98,4.7,1.04,3.59,1035
|
||||
1,13.73,1.5,2.7,22.5,101,3,3.25,.29,2.38,5.7,1.19,2.71,1285
|
||||
1,13.58,1.66,2.36,19.1,106,2.86,3.19,.22,1.95,6.9,1.09,2.88,1515
|
||||
1,13.68,1.83,2.36,17.2,104,2.42,2.69,.42,1.97,3.84,1.23,2.87,990
|
||||
1,13.76,1.53,2.7,19.5,132,2.95,2.74,.5,1.35,5.4,1.25,3,1235
|
||||
1,13.51,1.8,2.65,19,110,2.35,2.53,.29,1.54,4.2,1.1,2.87,1095
|
||||
1,13.48,1.81,2.41,20.5,100,2.7,2.98,.26,1.86,5.1,1.04,3.47,920
|
||||
1,13.28,1.64,2.84,15.5,110,2.6,2.68,.34,1.36,4.6,1.09,2.78,880
|
||||
1,13.05,1.65,2.55,18,98,2.45,2.43,.29,1.44,4.25,1.12,2.51,1105
|
||||
1,13.07,1.5,2.1,15.5,98,2.4,2.64,.28,1.37,3.7,1.18,2.69,1020
|
||||
1,14.22,3.99,2.51,13.2,128,3,3.04,.2,2.08,5.1,.89,3.53,760
|
||||
1,13.56,1.71,2.31,16.2,117,3.15,3.29,.34,2.34,6.13,.95,3.38,795
|
||||
1,13.41,3.84,2.12,18.8,90,2.45,2.68,.27,1.48,4.28,.91,3,1035
|
||||
1,13.88,1.89,2.59,15,101,3.25,3.56,.17,1.7,5.43,.88,3.56,1095
|
||||
1,13.24,3.98,2.29,17.5,103,2.64,2.63,.32,1.66,4.36,.82,3,680
|
||||
1,13.05,1.77,2.1,17,107,3,3,.28,2.03,5.04,.88,3.35,885
|
||||
1,14.21,4.04,2.44,18.9,111,2.85,2.65,.3,1.25,5.24,.87,3.33,1080
|
||||
1,14.38,3.59,2.28,16,102,3.25,3.17,.27,2.19,4.9,1.04,3.44,1065
|
||||
1,13.9,1.68,2.12,16,101,3.1,3.39,.21,2.14,6.1,.91,3.33,985
|
||||
1,14.1,2.02,2.4,18.8,103,2.75,2.92,.32,2.38,6.2,1.07,2.75,1060
|
||||
1,13.94,1.73,2.27,17.4,108,2.88,3.54,.32,2.08,8.90,1.12,3.1,1260
|
||||
1,13.05,1.73,2.04,12.4,92,2.72,3.27,.17,2.91,7.2,1.12,2.91,1150
|
||||
1,13.83,1.65,2.6,17.2,94,2.45,2.99,.22,2.29,5.6,1.24,3.37,1265
|
||||
1,13.82,1.75,2.42,14,111,3.88,3.74,.32,1.87,7.05,1.01,3.26,1190
|
||||
1,13.77,1.9,2.68,17.1,115,3,2.79,.39,1.68,6.3,1.13,2.93,1375
|
||||
1,13.74,1.67,2.25,16.4,118,2.6,2.9,.21,1.62,5.85,.92,3.2,1060
|
||||
1,13.56,1.73,2.46,20.5,116,2.96,2.78,.2,2.45,6.25,.98,3.03,1120
|
||||
1,14.22,1.7,2.3,16.3,118,3.2,3,.26,2.03,6.38,.94,3.31,970
|
||||
1,13.29,1.97,2.68,16.8,102,3,3.23,.31,1.66,6,1.07,2.84,1270
|
||||
1,13.72,1.43,2.5,16.7,108,3.4,3.67,.19,2.04,6.8,.89,2.87,1285
|
||||
2,12.37,.94,1.36,10.6,88,1.98,.57,.28,.42,1.95,1.05,1.82,520
|
||||
2,12.33,1.1,2.28,16,101,2.05,1.09,.63,.41,3.27,1.25,1.67,680
|
||||
2,12.64,1.36,2.02,16.8,100,2.02,1.41,.53,.62,5.75,.98,1.59,450
|
||||
2,13.67,1.25,1.92,18,94,2.1,1.79,.32,.73,3.8,1.23,2.46,630
|
||||
2,12.37,1.13,2.16,19,87,3.5,3.1,.19,1.87,4.45,1.22,2.87,420
|
||||
2,12.17,1.45,2.53,19,104,1.89,1.75,.45,1.03,2.95,1.45,2.23,355
|
||||
2,12.37,1.21,2.56,18.1,98,2.42,2.65,.37,2.08,4.6,1.19,2.3,678
|
||||
2,13.11,1.01,1.7,15,78,2.98,3.18,.26,2.28,5.3,1.12,3.18,502
|
||||
2,12.37,1.17,1.92,19.6,78,2.11,2,.27,1.04,4.68,1.12,3.48,510
|
||||
2,13.34,.94,2.36,17,110,2.53,1.3,.55,.42,3.17,1.02,1.93,750
|
||||
2,12.21,1.19,1.75,16.8,151,1.85,1.28,.14,2.5,2.85,1.28,3.07,718
|
||||
2,12.29,1.61,2.21,20.4,103,1.1,1.02,.37,1.46,3.05,.906,1.82,870
|
||||
2,13.86,1.51,2.67,25,86,2.95,2.86,.21,1.87,3.38,1.36,3.16,410
|
||||
2,13.49,1.66,2.24,24,87,1.88,1.84,.27,1.03,3.74,.98,2.78,472
|
||||
2,12.99,1.67,2.6,30,139,3.3,2.89,.21,1.96,3.35,1.31,3.5,985
|
||||
2,11.96,1.09,2.3,21,101,3.38,2.14,.13,1.65,3.21,.99,3.13,886
|
||||
2,11.66,1.88,1.92,16,97,1.61,1.57,.34,1.15,3.8,1.23,2.14,428
|
||||
2,13.03,.9,1.71,16,86,1.95,2.03,.24,1.46,4.6,1.19,2.48,392
|
||||
2,11.84,2.89,2.23,18,112,1.72,1.32,.43,.95,2.65,.96,2.52,500
|
||||
2,12.33,.99,1.95,14.8,136,1.9,1.85,.35,2.76,3.4,1.06,2.31,750
|
||||
2,12.7,3.87,2.4,23,101,2.83,2.55,.43,1.95,2.57,1.19,3.13,463
|
||||
2,12,.92,2,19,86,2.42,2.26,.3,1.43,2.5,1.38,3.12,278
|
||||
2,12.72,1.81,2.2,18.8,86,2.2,2.53,.26,1.77,3.9,1.16,3.14,714
|
||||
2,12.08,1.13,2.51,24,78,2,1.58,.4,1.4,2.2,1.31,2.72,630
|
||||
2,13.05,3.86,2.32,22.5,85,1.65,1.59,.61,1.62,4.8,.84,2.01,515
|
||||
2,11.84,.89,2.58,18,94,2.2,2.21,.22,2.35,3.05,.79,3.08,520
|
||||
2,12.67,.98,2.24,18,99,2.2,1.94,.3,1.46,2.62,1.23,3.16,450
|
||||
2,12.16,1.61,2.31,22.8,90,1.78,1.69,.43,1.56,2.45,1.33,2.26,495
|
||||
2,11.65,1.67,2.62,26,88,1.92,1.61,.4,1.34,2.6,1.36,3.21,562
|
||||
2,11.64,2.06,2.46,21.6,84,1.95,1.69,.48,1.35,2.8,1,2.75,680
|
||||
2,12.08,1.33,2.3,23.6,70,2.2,1.59,.42,1.38,1.74,1.07,3.21,625
|
||||
2,12.08,1.83,2.32,18.5,81,1.6,1.5,.52,1.64,2.4,1.08,2.27,480
|
||||
2,12,1.51,2.42,22,86,1.45,1.25,.5,1.63,3.6,1.05,2.65,450
|
||||
2,12.69,1.53,2.26,20.7,80,1.38,1.46,.58,1.62,3.05,.96,2.06,495
|
||||
2,12.29,2.83,2.22,18,88,2.45,2.25,.25,1.99,2.15,1.15,3.3,290
|
||||
2,11.62,1.99,2.28,18,98,3.02,2.26,.17,1.35,3.25,1.16,2.96,345
|
||||
2,12.47,1.52,2.2,19,162,2.5,2.27,.32,3.28,2.6,1.16,2.63,937
|
||||
2,11.81,2.12,2.74,21.5,134,1.6,.99,.14,1.56,2.5,.95,2.26,625
|
||||
2,12.29,1.41,1.98,16,85,2.55,2.5,.29,1.77,2.9,1.23,2.74,428
|
||||
2,12.37,1.07,2.1,18.5,88,3.52,3.75,.24,1.95,4.5,1.04,2.77,660
|
||||
2,12.29,3.17,2.21,18,88,2.85,2.99,.45,2.81,2.3,1.42,2.83,406
|
||||
2,12.08,2.08,1.7,17.5,97,2.23,2.17,.26,1.4,3.3,1.27,2.96,710
|
||||
2,12.6,1.34,1.9,18.5,88,1.45,1.36,.29,1.35,2.45,1.04,2.77,562
|
||||
2,12.34,2.45,2.46,21,98,2.56,2.11,.34,1.31,2.8,.8,3.38,438
|
||||
2,11.82,1.72,1.88,19.5,86,2.5,1.64,.37,1.42,2.06,.94,2.44,415
|
||||
2,12.51,1.73,1.98,20.5,85,2.2,1.92,.32,1.48,2.94,1.04,3.57,672
|
||||
2,12.42,2.55,2.27,22,90,1.68,1.84,.66,1.42,2.7,.86,3.3,315
|
||||
2,12.25,1.73,2.12,19,80,1.65,2.03,.37,1.63,3.4,1,3.17,510
|
||||
2,12.72,1.75,2.28,22.5,84,1.38,1.76,.48,1.63,3.3,.88,2.42,488
|
||||
2,12.22,1.29,1.94,19,92,2.36,2.04,.39,2.08,2.7,.86,3.02,312
|
||||
2,11.61,1.35,2.7,20,94,2.74,2.92,.29,2.49,2.65,.96,3.26,680
|
||||
2,11.46,3.74,1.82,19.5,107,3.18,2.58,.24,3.58,2.9,.75,2.81,562
|
||||
2,12.52,2.43,2.17,21,88,2.55,2.27,.26,1.22,2,.9,2.78,325
|
||||
2,11.76,2.68,2.92,20,103,1.75,2.03,.6,1.05,3.8,1.23,2.5,607
|
||||
2,11.41,.74,2.5,21,88,2.48,2.01,.42,1.44,3.08,1.1,2.31,434
|
||||
2,12.08,1.39,2.5,22.5,84,2.56,2.29,.43,1.04,2.9,.93,3.19,385
|
||||
2,11.03,1.51,2.2,21.5,85,2.46,2.17,.52,2.01,1.9,1.71,2.87,407
|
||||
2,11.82,1.47,1.99,20.8,86,1.98,1.6,.3,1.53,1.95,.95,3.33,495
|
||||
2,12.42,1.61,2.19,22.5,108,2,2.09,.34,1.61,2.06,1.06,2.96,345
|
||||
2,12.77,3.43,1.98,16,80,1.63,1.25,.43,.83,3.4,.7,2.12,372
|
||||
2,12,3.43,2,19,87,2,1.64,.37,1.87,1.28,.93,3.05,564
|
||||
2,11.45,2.4,2.42,20,96,2.9,2.79,.32,1.83,3.25,.8,3.39,625
|
||||
2,11.56,2.05,3.23,28.5,119,3.18,5.08,.47,1.87,6,.93,3.69,465
|
||||
2,12.42,4.43,2.73,26.5,102,2.2,2.13,.43,1.71,2.08,.92,3.12,365
|
||||
2,13.05,5.8,2.13,21.5,86,2.62,2.65,.3,2.01,2.6,.73,3.1,380
|
||||
2,11.87,4.31,2.39,21,82,2.86,3.03,.21,2.91,2.8,.75,3.64,380
|
||||
2,12.07,2.16,2.17,21,85,2.6,2.65,.37,1.35,2.76,.86,3.28,378
|
||||
2,12.43,1.53,2.29,21.5,86,2.74,3.15,.39,1.77,3.94,.69,2.84,352
|
||||
2,11.79,2.13,2.78,28.5,92,2.13,2.24,.58,1.76,3,.97,2.44,466
|
||||
2,12.37,1.63,2.3,24.5,88,2.22,2.45,.4,1.9,2.12,.89,2.78,342
|
||||
2,12.04,4.3,2.38,22,80,2.1,1.75,.42,1.35,2.6,.79,2.57,580
|
||||
3,12.86,1.35,2.32,18,122,1.51,1.25,.21,.94,4.1,.76,1.29,630
|
||||
3,12.88,2.99,2.4,20,104,1.3,1.22,.24,.83,5.4,.74,1.42,530
|
||||
3,12.81,2.31,2.4,24,98,1.15,1.09,.27,.83,5.7,.66,1.36,560
|
||||
3,12.7,3.55,2.36,21.5,106,1.7,1.2,.17,.84,5,.78,1.29,600
|
||||
3,12.51,1.24,2.25,17.5,85,2,.58,.6,1.25,5.45,.75,1.51,650
|
||||
3,12.6,2.46,2.2,18.5,94,1.62,.66,.63,.94,7.1,.73,1.58,695
|
||||
3,12.25,4.72,2.54,21,89,1.38,.47,.53,.8,3.85,.75,1.27,720
|
||||
3,12.53,5.51,2.64,25,96,1.79,.6,.63,1.1,5,.82,1.69,515
|
||||
3,13.49,3.59,2.19,19.5,88,1.62,.48,.58,.88,5.7,.81,1.82,580
|
||||
3,12.84,2.96,2.61,24,101,2.32,.6,.53,.81,4.92,.89,2.15,590
|
||||
3,12.93,2.81,2.7,21,96,1.54,.5,.53,.75,4.6,.77,2.31,600
|
||||
3,13.36,2.56,2.35,20,89,1.4,.5,.37,.64,5.6,.7,2.47,780
|
||||
3,13.52,3.17,2.72,23.5,97,1.55,.52,.5,.55,4.35,.89,2.06,520
|
||||
3,13.62,4.95,2.35,20,92,2,.8,.47,1.02,4.4,.91,2.05,550
|
||||
3,12.25,3.88,2.2,18.5,112,1.38,.78,.29,1.14,8.21,.65,2,855
|
||||
3,13.16,3.57,2.15,21,102,1.5,.55,.43,1.3,4,.6,1.68,830
|
||||
3,13.88,5.04,2.23,20,80,.98,.34,.4,.68,4.9,.58,1.33,415
|
||||
3,12.87,4.61,2.48,21.5,86,1.7,.65,.47,.86,7.65,.54,1.86,625
|
||||
3,13.32,3.24,2.38,21.5,92,1.93,.76,.45,1.25,8.42,.55,1.62,650
|
||||
3,13.08,3.9,2.36,21.5,113,1.41,1.39,.34,1.14,9.40,.57,1.33,550
|
||||
3,13.5,3.12,2.62,24,123,1.4,1.57,.22,1.25,8.60,.59,1.3,500
|
||||
3,12.79,2.67,2.48,22,112,1.48,1.36,.24,1.26,10.8,.48,1.47,480
|
||||
3,13.11,1.9,2.75,25.5,116,2.2,1.28,.26,1.56,7.1,.61,1.33,425
|
||||
3,13.23,3.3,2.28,18.5,98,1.8,.83,.61,1.87,10.52,.56,1.51,675
|
||||
3,12.58,1.29,2.1,20,103,1.48,.58,.53,1.4,7.6,.58,1.55,640
|
||||
3,13.17,5.19,2.32,22,93,1.74,.63,.61,1.55,7.9,.6,1.48,725
|
||||
3,13.84,4.12,2.38,19.5,89,1.8,.83,.48,1.56,9.01,.57,1.64,480
|
||||
3,12.45,3.03,2.64,27,97,1.9,.58,.63,1.14,7.5,.67,1.73,880
|
||||
3,14.34,1.68,2.7,25,98,2.8,1.31,.53,2.7,13,.57,1.96,660
|
||||
3,13.48,1.67,2.64,22.5,89,2.6,1.1,.52,2.29,11.75,.57,1.78,620
|
||||
3,12.36,3.83,2.38,21,88,2.3,.92,.5,1.04,7.65,.56,1.58,520
|
||||
3,13.69,3.26,2.54,20,107,1.83,.56,.5,.8,5.88,.96,1.82,680
|
||||
3,12.85,3.27,2.58,22,106,1.65,.6,.6,.96,5.58,.87,2.11,570
|
||||
3,12.96,3.45,2.35,18.5,106,1.39,.7,.4,.94,5.28,.68,1.75,675
|
||||
3,13.78,2.76,2.3,22,90,1.35,.68,.41,1.03,9.58,.7,1.68,615
|
||||
3,13.73,4.36,2.26,22.5,88,1.28,.47,.52,1.15,6.62,.78,1.75,520
|
||||
3,13.45,3.7,2.6,23,111,1.7,.92,.43,1.46,10.68,.85,1.56,695
|
||||
3,12.82,3.37,2.3,19.5,88,1.48,.66,.4,.97,10.26,.72,1.75,685
|
||||
3,13.58,2.58,2.69,24.5,105,1.55,.84,.39,1.54,8.66,.74,1.8,750
|
||||
3,13.4,4.6,2.86,25,112,1.98,.96,.27,1.11,8.5,.67,1.92,630
|
||||
3,12.2,3.03,2.32,19,96,1.25,.49,.4,.73,5.5,.66,1.83,510
|
||||
3,12.77,2.39,2.28,19.5,86,1.39,.51,.48,.64,9.899999,.57,1.63,470
|
||||
3,14.16,2.51,2.48,20,91,1.68,.7,.44,1.24,9.7,.62,1.71,660
|
||||
3,13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740
|
||||
3,13.4,3.91,2.48,23,102,1.8,.75,.43,1.41,7.3,.7,1.56,750
|
||||
3,13.27,4.28,2.26,20,120,1.59,.69,.43,1.35,10.2,.59,1.56,835
|
||||
3,13.17,2.59,2.37,20,120,1.65,.68,.53,1.46,9.3,.6,1.62,840
|
||||
3,14.13,4.1,2.74,24.5,96,2.05,.76,.56,1.35,9.2,.61,1.6,560
|
||||
@@ -0,0 +1,100 @@
|
||||
1. Title of Database: Wine recognition data
|
||||
Updated Sept 21, 1998 by C.Blake : Added attribute information
|
||||
|
||||
2. Sources:
|
||||
(a) Forina, M. et al, PARVUS - An Extendible Package for Data
|
||||
Exploration, Classification and Correlation. Institute of Pharmaceutical
|
||||
and Food Analysis and Technologies, Via Brigata Salerno,
|
||||
16147 Genoa, Italy.
|
||||
|
||||
(b) Stefan Aeberhard, email: stefan@coral.cs.jcu.edu.au
|
||||
(c) July 1991
|
||||
3. Past Usage:
|
||||
|
||||
(1)
|
||||
S. Aeberhard, D. Coomans and O. de Vel,
|
||||
Comparison of Classifiers in High Dimensional Settings,
|
||||
Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
|
||||
Mathematics and Statistics, James Cook University of North Queensland.
|
||||
(Also submitted to Technometrics).
|
||||
|
||||
The data was used with many others for comparing various
|
||||
classifiers. The classes are separable, though only RDA
|
||||
has achieved 100% correct classification.
|
||||
(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))
|
||||
(All results using the leave-one-out technique)
|
||||
|
||||
In a classification context, this is a well posed problem
|
||||
with "well behaved" class structures. A good data set
|
||||
for first testing of a new classifier, but not very
|
||||
challenging.
|
||||
|
||||
(2)
|
||||
S. Aeberhard, D. Coomans and O. de Vel,
|
||||
"THE CLASSIFICATION PERFORMANCE OF RDA"
|
||||
Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
|
||||
Mathematics and Statistics, James Cook University of North Queensland.
|
||||
(Also submitted to Journal of Chemometrics).
|
||||
|
||||
Here, the data was used to illustrate the superior performance of
|
||||
the use of a new appreciation function with RDA.
|
||||
|
||||
4. Relevant Information:
|
||||
|
||||
-- These data are the results of a chemical analysis of
|
||||
wines grown in the same region in Italy but derived from three
|
||||
different cultivars.
|
||||
The analysis determined the quantities of 13 constituents
|
||||
found in each of the three types of wines.
|
||||
|
||||
-- I think that the initial data set had around 30 variables, but
|
||||
for some reason I only have the 13 dimensional version.
|
||||
I had a list of what the 30 or so variables were, but a.)
|
||||
I lost it, and b.), I would not know which 13 variables
|
||||
are included in the set.
|
||||
|
||||
-- The attributes are (dontated by Riccardo Leardi,
|
||||
riclea@anchem.unige.it )
|
||||
1) Alcohol
|
||||
2) Malic acid
|
||||
3) Ash
|
||||
4) Alcalinity of ash
|
||||
5) Magnesium
|
||||
6) Total phenols
|
||||
7) Flavanoids
|
||||
8) Nonflavanoid phenols
|
||||
9) Proanthocyanins
|
||||
10)Color intensity
|
||||
11)Hue
|
||||
12)OD280/OD315 of diluted wines
|
||||
13)Proline
|
||||
|
||||
5. Number of Instances
|
||||
|
||||
class 1 59
|
||||
class 2 71
|
||||
class 3 48
|
||||
|
||||
6. Number of Attributes
|
||||
|
||||
13
|
||||
|
||||
7. For Each Attribute:
|
||||
|
||||
All attributes are continuous
|
||||
|
||||
No statistics available, but suggest to standardise
|
||||
variables for certain uses (e.g. for us with classifiers
|
||||
which are NOT scale invariant)
|
||||
|
||||
NOTE: 1st attribute is class identifier (1-3)
|
||||
|
||||
8. Missing Attribute Values:
|
||||
|
||||
None
|
||||
|
||||
9. Class Distribution: number of instances per class
|
||||
|
||||
class 1 59
|
||||
class 2 71
|
||||
class 3 48
|
||||
Vendored
+9
@@ -0,0 +1,9 @@
|
||||
{
|
||||
"[r]": {
|
||||
// generated automatically? What even....
|
||||
"editor.wordSeparators": "`~!@#$%^&*()-=+[{]}\\|;:'\",<>/",
|
||||
"editor.indentSize": "tabSize",
|
||||
"editor.useTabStops": true,
|
||||
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,41 @@
|
||||
##########################################
|
||||
### Principal Component Analysis (PCA) ###
|
||||
##########################################
|
||||
|
||||
## load libraries
|
||||
library(ggplot2)
|
||||
library(ggfortify)
|
||||
library(GGally)
|
||||
library(e1071)
|
||||
library(class)
|
||||
library(psych)
|
||||
library(readr)
|
||||
|
||||
## set working directory so that files can be referenced without the full path
|
||||
setwd("~/Courses/Data Analytics/Fall25/labs/lab 4/")
|
||||
|
||||
## read dataset
|
||||
wine <- read_csv("wine.data", col_names = FALSE)
|
||||
|
||||
## set column names
|
||||
names(wine) <- c("Type","Alcohol","Malic acid","Ash","Alcalinity of ash","Magnesium","Total phenols","Flavanoids","Nonflavanoid Phenols","Proanthocyanins","Color Intensity","Hue","Od280/od315 of diluted wines","Proline")
|
||||
|
||||
## inspect data frame
|
||||
head(wine)
|
||||
|
||||
## change the data type of the "Type" column from character to factor
|
||||
####
|
||||
# Factors look like regular strings (characters) but with factors R knows
|
||||
# that the column is a categorical variable with finite possible values
|
||||
# e.g. "Type" in the Wine dataset can only be 1, 2, or 3
|
||||
####
|
||||
|
||||
wine$Type <- as.factor(wine$Type)
|
||||
|
||||
|
||||
## visualize variables
|
||||
pairs.panels(wine[,-1],gap = 0,bg = c("red", "yellow", "blue")[wine$Type],pch=21)
|
||||
|
||||
ggpairs(wine, ggplot2::aes(colour = Type))
|
||||
|
||||
###
|
||||
Binary file not shown.
Binary file not shown.
+128
@@ -0,0 +1,128 @@
|
||||
install.packages(
|
||||
c("e1071", "caret", "randomForest", "ggplot2", "pROC"),
|
||||
repos = c("https://cloud.r-project.org/"),
|
||||
dependencies = TRUE
|
||||
)
|
||||
|
||||
suppressPackageStartupMessages({
|
||||
library(e1071) # for svm/tune.svm
|
||||
library(caret) # for metrics
|
||||
library(randomForest) # alternative classifier
|
||||
library(ggplot2)
|
||||
})
|
||||
|
||||
set.seed(42)
|
||||
|
||||
read_wine <- function() {
|
||||
df <- read.csv("wine.data", header = FALSE)
|
||||
colnames(df) <- c(
|
||||
"Class",
|
||||
"Alcohol", "Malic.acid", "Ash", "Alcalinity.of.ash", "Magnesium",
|
||||
"Total.phenols", "Flavanoids", "Nonflavanoid.phenols", "Proanthocyanins",
|
||||
"Color.intensity", "Hue", "OD280.OD315", "Proline"
|
||||
)
|
||||
df$Class <- factor(df$Class)
|
||||
df
|
||||
}
|
||||
|
||||
df <- read_wine()
|
||||
|
||||
# split into train/test
|
||||
idx <- createDataPartition(df$Class, p = 0.8, list = FALSE)
|
||||
train <- df[idx, ]
|
||||
test <- df[-idx, ]
|
||||
|
||||
# choose a subset of features based on ANOVA F-test
|
||||
# I picked this sbuset before the runs:
|
||||
# alcohol, flavanoids, color intensity, od280/od315, proline, total phenols
|
||||
features <- c("Alcohol", "Flavanoids", "Color.intensity", "OD280.OD315", "Proline", "Total.phenols")
|
||||
x_train <- train[, features]
|
||||
y_train <- train$Class
|
||||
x_test <- test[, features]
|
||||
y_test <- test$Class
|
||||
|
||||
# scale features
|
||||
pp <- preProcess(x_train, method = c("center", "scale"))
|
||||
x_train_s <- predict(pp, x_train)
|
||||
x_test_s <- predict(pp, x_test)
|
||||
|
||||
# linear kernel svm with hyperparameter tuning (C)
|
||||
set.seed(42)
|
||||
lin_grid <- data.frame(cost = c(0.1, 1, 10, 100))
|
||||
tune_lin <- tune.svm(
|
||||
x = x_train_s, y = y_train,
|
||||
kernel = "linear",
|
||||
cost = lin_grid$cost,
|
||||
tunecontrol = tune.control(cross = 5)
|
||||
)
|
||||
lin_best <- tune_lin$best.model
|
||||
|
||||
# rbf kernel svm with tuning (C, gamma)
|
||||
set.seed(42)
|
||||
rbf_grid_cost <- c(0.1, 1, 10, 100, 1000)
|
||||
rbf_grid_gamma <- c(0.001, 0.01, 0.1, 1)
|
||||
tune_rbf <- tune.svm(
|
||||
x = x_train_s, y = y_train,
|
||||
kernel = "radial",
|
||||
cost = rbf_grid_cost,
|
||||
gamma = rbf_grid_gamma,
|
||||
tunecontrol = tune.control(cross = 5)
|
||||
)
|
||||
rbf_best <- tune_rbf$best.model
|
||||
|
||||
# alt classifier: random forest (same features)
|
||||
set.seed(42)
|
||||
rf_fit <- randomForest(x = x_train, y = y_train, ntree = 500, mtry = 2, importance = TRUE)
|
||||
|
||||
# evaluation helper
|
||||
eval_model <- function(model, x_test_s, y_test, name) {
|
||||
pred <- predict(model, x_test_s)
|
||||
cm <- confusionMatrix(pred, y_test)
|
||||
pr <- data.frame(
|
||||
model = name,
|
||||
accuracy = cm$overall["Accuracy"],
|
||||
precision_macro = mean(cm$byClass[, "Precision"], na.rm = TRUE),
|
||||
recall_macro = mean(cm$byClass[, "Recall"], na.rm = TRUE),
|
||||
f1_macro = mean(cm$byClass[, "F1"], na.rm = TRUE)
|
||||
)
|
||||
list(cm = cm, pr = pr)
|
||||
}
|
||||
|
||||
# eval svm models (use scaled features)
|
||||
lin_eval <- eval_model(lin_best, x_test_s, y_test, "svm_linear")
|
||||
rbf_eval <- eval_model(rbf_best, x_test_s, y_test, "svm_rbf")
|
||||
|
||||
# evaluate random forest (no scaling)
|
||||
rf_pred <- predict(rf_fit, x_test)
|
||||
rf_cm <- confusionMatrix(rf_pred, y_test)
|
||||
|
||||
rf_pr <- data.frame(
|
||||
model = "random_forest",
|
||||
accuracy = rf_cm$overall["Accuracy"],
|
||||
precision_macro = mean(rf_cm$byClass[, "Precision"], na.rm = TRUE),
|
||||
recall_macro = mean(rf_cm$byClass[, "Recall"], na.rm = TRUE),
|
||||
f1_macro = mean(rf_cm$byClass[, "F1"], na.rm = TRUE)
|
||||
)
|
||||
|
||||
perf <- rbind(lin_eval$pr, rbf_eval$pr, rf_pr)
|
||||
|
||||
# print
|
||||
cat("best params (linear svm): C =", lin_best$cost, "\n")
|
||||
cat("best params (rbf svm): C =", rbf_best$cost, " gamma =", rbf_best$gamma, "\n\n")
|
||||
print(perf)
|
||||
|
||||
# macro-f1 comparison
|
||||
ggplot(perf, aes(x = model, y = f1_macro)) +
|
||||
geom_col() +
|
||||
labs(title = "macro-F1 by model (wine test set)")
|
||||
|
||||
# save outputs
|
||||
write.table(perf, file = "lab5_performance_table.txt", sep = "\t", row.names = FALSE, quote = FALSE)
|
||||
sink("lab5_confusion_matrices.txt")
|
||||
cat("=== svm linear ===\n")
|
||||
print(lin_eval$cm)
|
||||
cat("\n=== svm rbf ===\n")
|
||||
print(rbf_eval$cm)
|
||||
cat("\n=== random forest ===\n")
|
||||
print(rf_cm)
|
||||
sink()
|
||||
@@ -0,0 +1,95 @@
|
||||
=== svm linear ===
|
||||
Confusion Matrix and Statistics
|
||||
|
||||
Reference
|
||||
Prediction 1 2 3
|
||||
1 11 1 0
|
||||
2 0 13 0
|
||||
3 0 0 9
|
||||
|
||||
Overall Statistics
|
||||
|
||||
Accuracy : 0.9706
|
||||
95% CI : (0.8467, 0.9993)
|
||||
No Information Rate : 0.4118
|
||||
P-Value [Acc > NIR] : 3.92e-12
|
||||
|
||||
Kappa : 0.9553
|
||||
|
||||
Mcnemar's Test P-Value : NA
|
||||
|
||||
Statistics by Class:
|
||||
|
||||
Class: 1 Class: 2 Class: 3
|
||||
Sensitivity 1.0000 0.9286 1.0000
|
||||
Specificity 0.9565 1.0000 1.0000
|
||||
Pos Pred Value 0.9167 1.0000 1.0000
|
||||
Neg Pred Value 1.0000 0.9524 1.0000
|
||||
Prevalence 0.3235 0.4118 0.2647
|
||||
Detection Rate 0.3235 0.3824 0.2647
|
||||
Detection Prevalence 0.3529 0.3824 0.2647
|
||||
Balanced Accuracy 0.9783 0.9643 1.0000
|
||||
|
||||
=== svm rbf ===
|
||||
Confusion Matrix and Statistics
|
||||
|
||||
Reference
|
||||
Prediction 1 2 3
|
||||
1 11 1 0
|
||||
2 0 13 0
|
||||
3 0 0 9
|
||||
|
||||
Overall Statistics
|
||||
|
||||
Accuracy : 0.9706
|
||||
95% CI : (0.8467, 0.9993)
|
||||
No Information Rate : 0.4118
|
||||
P-Value [Acc > NIR] : 3.92e-12
|
||||
|
||||
Kappa : 0.9553
|
||||
|
||||
Mcnemar's Test P-Value : NA
|
||||
|
||||
Statistics by Class:
|
||||
|
||||
Class: 1 Class: 2 Class: 3
|
||||
Sensitivity 1.0000 0.9286 1.0000
|
||||
Specificity 0.9565 1.0000 1.0000
|
||||
Pos Pred Value 0.9167 1.0000 1.0000
|
||||
Neg Pred Value 1.0000 0.9524 1.0000
|
||||
Prevalence 0.3235 0.4118 0.2647
|
||||
Detection Rate 0.3235 0.3824 0.2647
|
||||
Detection Prevalence 0.3529 0.3824 0.2647
|
||||
Balanced Accuracy 0.9783 0.9643 1.0000
|
||||
|
||||
=== random forest ===
|
||||
Confusion Matrix and Statistics
|
||||
|
||||
Reference
|
||||
Prediction 1 2 3
|
||||
1 11 1 0
|
||||
2 0 13 0
|
||||
3 0 0 9
|
||||
|
||||
Overall Statistics
|
||||
|
||||
Accuracy : 0.9706
|
||||
95% CI : (0.8467, 0.9993)
|
||||
No Information Rate : 0.4118
|
||||
P-Value [Acc > NIR] : 3.92e-12
|
||||
|
||||
Kappa : 0.9553
|
||||
|
||||
Mcnemar's Test P-Value : NA
|
||||
|
||||
Statistics by Class:
|
||||
|
||||
Class: 1 Class: 2 Class: 3
|
||||
Sensitivity 1.0000 0.9286 1.0000
|
||||
Specificity 0.9565 1.0000 1.0000
|
||||
Pos Pred Value 0.9167 1.0000 1.0000
|
||||
Neg Pred Value 1.0000 0.9524 1.0000
|
||||
Prevalence 0.3235 0.4118 0.2647
|
||||
Detection Rate 0.3235 0.3824 0.2647
|
||||
Detection Prevalence 0.3529 0.3824 0.2647
|
||||
Balanced Accuracy 0.9783 0.9643 1.0000
|
||||
@@ -0,0 +1,4 @@
|
||||
model accuracy precision_macro recall_macro f1_macro
|
||||
svm_linear 0.970588235294118 0.972222222222222 0.976190476190476 0.973161567364466
|
||||
svm_rbf 0.970588235294118 0.972222222222222 0.976190476190476 0.973161567364466
|
||||
random_forest 0.970588235294118 0.972222222222222 0.976190476190476 0.973161567364466
|
||||
+178
@@ -0,0 +1,178 @@
|
||||
1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065
|
||||
1,13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050
|
||||
1,13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185
|
||||
1,14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480
|
||||
1,13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735
|
||||
1,14.2,1.76,2.45,15.2,112,3.27,3.39,.34,1.97,6.75,1.05,2.85,1450
|
||||
1,14.39,1.87,2.45,14.6,96,2.5,2.52,.3,1.98,5.25,1.02,3.58,1290
|
||||
1,14.06,2.15,2.61,17.6,121,2.6,2.51,.31,1.25,5.05,1.06,3.58,1295
|
||||
1,14.83,1.64,2.17,14,97,2.8,2.98,.29,1.98,5.2,1.08,2.85,1045
|
||||
1,13.86,1.35,2.27,16,98,2.98,3.15,.22,1.85,7.22,1.01,3.55,1045
|
||||
1,14.1,2.16,2.3,18,105,2.95,3.32,.22,2.38,5.75,1.25,3.17,1510
|
||||
1,14.12,1.48,2.32,16.8,95,2.2,2.43,.26,1.57,5,1.17,2.82,1280
|
||||
1,13.75,1.73,2.41,16,89,2.6,2.76,.29,1.81,5.6,1.15,2.9,1320
|
||||
1,14.75,1.73,2.39,11.4,91,3.1,3.69,.43,2.81,5.4,1.25,2.73,1150
|
||||
1,14.38,1.87,2.38,12,102,3.3,3.64,.29,2.96,7.5,1.2,3,1547
|
||||
1,13.63,1.81,2.7,17.2,112,2.85,2.91,.3,1.46,7.3,1.28,2.88,1310
|
||||
1,14.3,1.92,2.72,20,120,2.8,3.14,.33,1.97,6.2,1.07,2.65,1280
|
||||
1,13.83,1.57,2.62,20,115,2.95,3.4,.4,1.72,6.6,1.13,2.57,1130
|
||||
1,14.19,1.59,2.48,16.5,108,3.3,3.93,.32,1.86,8.7,1.23,2.82,1680
|
||||
1,13.64,3.1,2.56,15.2,116,2.7,3.03,.17,1.66,5.1,.96,3.36,845
|
||||
1,14.06,1.63,2.28,16,126,3,3.17,.24,2.1,5.65,1.09,3.71,780
|
||||
1,12.93,3.8,2.65,18.6,102,2.41,2.41,.25,1.98,4.5,1.03,3.52,770
|
||||
1,13.71,1.86,2.36,16.6,101,2.61,2.88,.27,1.69,3.8,1.11,4,1035
|
||||
1,12.85,1.6,2.52,17.8,95,2.48,2.37,.26,1.46,3.93,1.09,3.63,1015
|
||||
1,13.5,1.81,2.61,20,96,2.53,2.61,.28,1.66,3.52,1.12,3.82,845
|
||||
1,13.05,2.05,3.22,25,124,2.63,2.68,.47,1.92,3.58,1.13,3.2,830
|
||||
1,13.39,1.77,2.62,16.1,93,2.85,2.94,.34,1.45,4.8,.92,3.22,1195
|
||||
1,13.3,1.72,2.14,17,94,2.4,2.19,.27,1.35,3.95,1.02,2.77,1285
|
||||
1,13.87,1.9,2.8,19.4,107,2.95,2.97,.37,1.76,4.5,1.25,3.4,915
|
||||
1,14.02,1.68,2.21,16,96,2.65,2.33,.26,1.98,4.7,1.04,3.59,1035
|
||||
1,13.73,1.5,2.7,22.5,101,3,3.25,.29,2.38,5.7,1.19,2.71,1285
|
||||
1,13.58,1.66,2.36,19.1,106,2.86,3.19,.22,1.95,6.9,1.09,2.88,1515
|
||||
1,13.68,1.83,2.36,17.2,104,2.42,2.69,.42,1.97,3.84,1.23,2.87,990
|
||||
1,13.76,1.53,2.7,19.5,132,2.95,2.74,.5,1.35,5.4,1.25,3,1235
|
||||
1,13.51,1.8,2.65,19,110,2.35,2.53,.29,1.54,4.2,1.1,2.87,1095
|
||||
1,13.48,1.81,2.41,20.5,100,2.7,2.98,.26,1.86,5.1,1.04,3.47,920
|
||||
1,13.28,1.64,2.84,15.5,110,2.6,2.68,.34,1.36,4.6,1.09,2.78,880
|
||||
1,13.05,1.65,2.55,18,98,2.45,2.43,.29,1.44,4.25,1.12,2.51,1105
|
||||
1,13.07,1.5,2.1,15.5,98,2.4,2.64,.28,1.37,3.7,1.18,2.69,1020
|
||||
1,14.22,3.99,2.51,13.2,128,3,3.04,.2,2.08,5.1,.89,3.53,760
|
||||
1,13.56,1.71,2.31,16.2,117,3.15,3.29,.34,2.34,6.13,.95,3.38,795
|
||||
1,13.41,3.84,2.12,18.8,90,2.45,2.68,.27,1.48,4.28,.91,3,1035
|
||||
1,13.88,1.89,2.59,15,101,3.25,3.56,.17,1.7,5.43,.88,3.56,1095
|
||||
1,13.24,3.98,2.29,17.5,103,2.64,2.63,.32,1.66,4.36,.82,3,680
|
||||
1,13.05,1.77,2.1,17,107,3,3,.28,2.03,5.04,.88,3.35,885
|
||||
1,14.21,4.04,2.44,18.9,111,2.85,2.65,.3,1.25,5.24,.87,3.33,1080
|
||||
1,14.38,3.59,2.28,16,102,3.25,3.17,.27,2.19,4.9,1.04,3.44,1065
|
||||
1,13.9,1.68,2.12,16,101,3.1,3.39,.21,2.14,6.1,.91,3.33,985
|
||||
1,14.1,2.02,2.4,18.8,103,2.75,2.92,.32,2.38,6.2,1.07,2.75,1060
|
||||
1,13.94,1.73,2.27,17.4,108,2.88,3.54,.32,2.08,8.90,1.12,3.1,1260
|
||||
1,13.05,1.73,2.04,12.4,92,2.72,3.27,.17,2.91,7.2,1.12,2.91,1150
|
||||
1,13.83,1.65,2.6,17.2,94,2.45,2.99,.22,2.29,5.6,1.24,3.37,1265
|
||||
1,13.82,1.75,2.42,14,111,3.88,3.74,.32,1.87,7.05,1.01,3.26,1190
|
||||
1,13.77,1.9,2.68,17.1,115,3,2.79,.39,1.68,6.3,1.13,2.93,1375
|
||||
1,13.74,1.67,2.25,16.4,118,2.6,2.9,.21,1.62,5.85,.92,3.2,1060
|
||||
1,13.56,1.73,2.46,20.5,116,2.96,2.78,.2,2.45,6.25,.98,3.03,1120
|
||||
1,14.22,1.7,2.3,16.3,118,3.2,3,.26,2.03,6.38,.94,3.31,970
|
||||
1,13.29,1.97,2.68,16.8,102,3,3.23,.31,1.66,6,1.07,2.84,1270
|
||||
1,13.72,1.43,2.5,16.7,108,3.4,3.67,.19,2.04,6.8,.89,2.87,1285
|
||||
2,12.37,.94,1.36,10.6,88,1.98,.57,.28,.42,1.95,1.05,1.82,520
|
||||
2,12.33,1.1,2.28,16,101,2.05,1.09,.63,.41,3.27,1.25,1.67,680
|
||||
2,12.64,1.36,2.02,16.8,100,2.02,1.41,.53,.62,5.75,.98,1.59,450
|
||||
2,13.67,1.25,1.92,18,94,2.1,1.79,.32,.73,3.8,1.23,2.46,630
|
||||
2,12.37,1.13,2.16,19,87,3.5,3.1,.19,1.87,4.45,1.22,2.87,420
|
||||
2,12.17,1.45,2.53,19,104,1.89,1.75,.45,1.03,2.95,1.45,2.23,355
|
||||
2,12.37,1.21,2.56,18.1,98,2.42,2.65,.37,2.08,4.6,1.19,2.3,678
|
||||
2,13.11,1.01,1.7,15,78,2.98,3.18,.26,2.28,5.3,1.12,3.18,502
|
||||
2,12.37,1.17,1.92,19.6,78,2.11,2,.27,1.04,4.68,1.12,3.48,510
|
||||
2,13.34,.94,2.36,17,110,2.53,1.3,.55,.42,3.17,1.02,1.93,750
|
||||
2,12.21,1.19,1.75,16.8,151,1.85,1.28,.14,2.5,2.85,1.28,3.07,718
|
||||
2,12.29,1.61,2.21,20.4,103,1.1,1.02,.37,1.46,3.05,.906,1.82,870
|
||||
2,13.86,1.51,2.67,25,86,2.95,2.86,.21,1.87,3.38,1.36,3.16,410
|
||||
2,13.49,1.66,2.24,24,87,1.88,1.84,.27,1.03,3.74,.98,2.78,472
|
||||
2,12.99,1.67,2.6,30,139,3.3,2.89,.21,1.96,3.35,1.31,3.5,985
|
||||
2,11.96,1.09,2.3,21,101,3.38,2.14,.13,1.65,3.21,.99,3.13,886
|
||||
2,11.66,1.88,1.92,16,97,1.61,1.57,.34,1.15,3.8,1.23,2.14,428
|
||||
2,13.03,.9,1.71,16,86,1.95,2.03,.24,1.46,4.6,1.19,2.48,392
|
||||
2,11.84,2.89,2.23,18,112,1.72,1.32,.43,.95,2.65,.96,2.52,500
|
||||
2,12.33,.99,1.95,14.8,136,1.9,1.85,.35,2.76,3.4,1.06,2.31,750
|
||||
2,12.7,3.87,2.4,23,101,2.83,2.55,.43,1.95,2.57,1.19,3.13,463
|
||||
2,12,.92,2,19,86,2.42,2.26,.3,1.43,2.5,1.38,3.12,278
|
||||
2,12.72,1.81,2.2,18.8,86,2.2,2.53,.26,1.77,3.9,1.16,3.14,714
|
||||
2,12.08,1.13,2.51,24,78,2,1.58,.4,1.4,2.2,1.31,2.72,630
|
||||
2,13.05,3.86,2.32,22.5,85,1.65,1.59,.61,1.62,4.8,.84,2.01,515
|
||||
2,11.84,.89,2.58,18,94,2.2,2.21,.22,2.35,3.05,.79,3.08,520
|
||||
2,12.67,.98,2.24,18,99,2.2,1.94,.3,1.46,2.62,1.23,3.16,450
|
||||
2,12.16,1.61,2.31,22.8,90,1.78,1.69,.43,1.56,2.45,1.33,2.26,495
|
||||
2,11.65,1.67,2.62,26,88,1.92,1.61,.4,1.34,2.6,1.36,3.21,562
|
||||
2,11.64,2.06,2.46,21.6,84,1.95,1.69,.48,1.35,2.8,1,2.75,680
|
||||
2,12.08,1.33,2.3,23.6,70,2.2,1.59,.42,1.38,1.74,1.07,3.21,625
|
||||
2,12.08,1.83,2.32,18.5,81,1.6,1.5,.52,1.64,2.4,1.08,2.27,480
|
||||
2,12,1.51,2.42,22,86,1.45,1.25,.5,1.63,3.6,1.05,2.65,450
|
||||
2,12.69,1.53,2.26,20.7,80,1.38,1.46,.58,1.62,3.05,.96,2.06,495
|
||||
2,12.29,2.83,2.22,18,88,2.45,2.25,.25,1.99,2.15,1.15,3.3,290
|
||||
2,11.62,1.99,2.28,18,98,3.02,2.26,.17,1.35,3.25,1.16,2.96,345
|
||||
2,12.47,1.52,2.2,19,162,2.5,2.27,.32,3.28,2.6,1.16,2.63,937
|
||||
2,11.81,2.12,2.74,21.5,134,1.6,.99,.14,1.56,2.5,.95,2.26,625
|
||||
2,12.29,1.41,1.98,16,85,2.55,2.5,.29,1.77,2.9,1.23,2.74,428
|
||||
2,12.37,1.07,2.1,18.5,88,3.52,3.75,.24,1.95,4.5,1.04,2.77,660
|
||||
2,12.29,3.17,2.21,18,88,2.85,2.99,.45,2.81,2.3,1.42,2.83,406
|
||||
2,12.08,2.08,1.7,17.5,97,2.23,2.17,.26,1.4,3.3,1.27,2.96,710
|
||||
2,12.6,1.34,1.9,18.5,88,1.45,1.36,.29,1.35,2.45,1.04,2.77,562
|
||||
2,12.34,2.45,2.46,21,98,2.56,2.11,.34,1.31,2.8,.8,3.38,438
|
||||
2,11.82,1.72,1.88,19.5,86,2.5,1.64,.37,1.42,2.06,.94,2.44,415
|
||||
2,12.51,1.73,1.98,20.5,85,2.2,1.92,.32,1.48,2.94,1.04,3.57,672
|
||||
2,12.42,2.55,2.27,22,90,1.68,1.84,.66,1.42,2.7,.86,3.3,315
|
||||
2,12.25,1.73,2.12,19,80,1.65,2.03,.37,1.63,3.4,1,3.17,510
|
||||
2,12.72,1.75,2.28,22.5,84,1.38,1.76,.48,1.63,3.3,.88,2.42,488
|
||||
2,12.22,1.29,1.94,19,92,2.36,2.04,.39,2.08,2.7,.86,3.02,312
|
||||
2,11.61,1.35,2.7,20,94,2.74,2.92,.29,2.49,2.65,.96,3.26,680
|
||||
2,11.46,3.74,1.82,19.5,107,3.18,2.58,.24,3.58,2.9,.75,2.81,562
|
||||
2,12.52,2.43,2.17,21,88,2.55,2.27,.26,1.22,2,.9,2.78,325
|
||||
2,11.76,2.68,2.92,20,103,1.75,2.03,.6,1.05,3.8,1.23,2.5,607
|
||||
2,11.41,.74,2.5,21,88,2.48,2.01,.42,1.44,3.08,1.1,2.31,434
|
||||
2,12.08,1.39,2.5,22.5,84,2.56,2.29,.43,1.04,2.9,.93,3.19,385
|
||||
2,11.03,1.51,2.2,21.5,85,2.46,2.17,.52,2.01,1.9,1.71,2.87,407
|
||||
2,11.82,1.47,1.99,20.8,86,1.98,1.6,.3,1.53,1.95,.95,3.33,495
|
||||
2,12.42,1.61,2.19,22.5,108,2,2.09,.34,1.61,2.06,1.06,2.96,345
|
||||
2,12.77,3.43,1.98,16,80,1.63,1.25,.43,.83,3.4,.7,2.12,372
|
||||
2,12,3.43,2,19,87,2,1.64,.37,1.87,1.28,.93,3.05,564
|
||||
2,11.45,2.4,2.42,20,96,2.9,2.79,.32,1.83,3.25,.8,3.39,625
|
||||
2,11.56,2.05,3.23,28.5,119,3.18,5.08,.47,1.87,6,.93,3.69,465
|
||||
2,12.42,4.43,2.73,26.5,102,2.2,2.13,.43,1.71,2.08,.92,3.12,365
|
||||
2,13.05,5.8,2.13,21.5,86,2.62,2.65,.3,2.01,2.6,.73,3.1,380
|
||||
2,11.87,4.31,2.39,21,82,2.86,3.03,.21,2.91,2.8,.75,3.64,380
|
||||
2,12.07,2.16,2.17,21,85,2.6,2.65,.37,1.35,2.76,.86,3.28,378
|
||||
2,12.43,1.53,2.29,21.5,86,2.74,3.15,.39,1.77,3.94,.69,2.84,352
|
||||
2,11.79,2.13,2.78,28.5,92,2.13,2.24,.58,1.76,3,.97,2.44,466
|
||||
2,12.37,1.63,2.3,24.5,88,2.22,2.45,.4,1.9,2.12,.89,2.78,342
|
||||
2,12.04,4.3,2.38,22,80,2.1,1.75,.42,1.35,2.6,.79,2.57,580
|
||||
3,12.86,1.35,2.32,18,122,1.51,1.25,.21,.94,4.1,.76,1.29,630
|
||||
3,12.88,2.99,2.4,20,104,1.3,1.22,.24,.83,5.4,.74,1.42,530
|
||||
3,12.81,2.31,2.4,24,98,1.15,1.09,.27,.83,5.7,.66,1.36,560
|
||||
3,12.7,3.55,2.36,21.5,106,1.7,1.2,.17,.84,5,.78,1.29,600
|
||||
3,12.51,1.24,2.25,17.5,85,2,.58,.6,1.25,5.45,.75,1.51,650
|
||||
3,12.6,2.46,2.2,18.5,94,1.62,.66,.63,.94,7.1,.73,1.58,695
|
||||
3,12.25,4.72,2.54,21,89,1.38,.47,.53,.8,3.85,.75,1.27,720
|
||||
3,12.53,5.51,2.64,25,96,1.79,.6,.63,1.1,5,.82,1.69,515
|
||||
3,13.49,3.59,2.19,19.5,88,1.62,.48,.58,.88,5.7,.81,1.82,580
|
||||
3,12.84,2.96,2.61,24,101,2.32,.6,.53,.81,4.92,.89,2.15,590
|
||||
3,12.93,2.81,2.7,21,96,1.54,.5,.53,.75,4.6,.77,2.31,600
|
||||
3,13.36,2.56,2.35,20,89,1.4,.5,.37,.64,5.6,.7,2.47,780
|
||||
3,13.52,3.17,2.72,23.5,97,1.55,.52,.5,.55,4.35,.89,2.06,520
|
||||
3,13.62,4.95,2.35,20,92,2,.8,.47,1.02,4.4,.91,2.05,550
|
||||
3,12.25,3.88,2.2,18.5,112,1.38,.78,.29,1.14,8.21,.65,2,855
|
||||
3,13.16,3.57,2.15,21,102,1.5,.55,.43,1.3,4,.6,1.68,830
|
||||
3,13.88,5.04,2.23,20,80,.98,.34,.4,.68,4.9,.58,1.33,415
|
||||
3,12.87,4.61,2.48,21.5,86,1.7,.65,.47,.86,7.65,.54,1.86,625
|
||||
3,13.32,3.24,2.38,21.5,92,1.93,.76,.45,1.25,8.42,.55,1.62,650
|
||||
3,13.08,3.9,2.36,21.5,113,1.41,1.39,.34,1.14,9.40,.57,1.33,550
|
||||
3,13.5,3.12,2.62,24,123,1.4,1.57,.22,1.25,8.60,.59,1.3,500
|
||||
3,12.79,2.67,2.48,22,112,1.48,1.36,.24,1.26,10.8,.48,1.47,480
|
||||
3,13.11,1.9,2.75,25.5,116,2.2,1.28,.26,1.56,7.1,.61,1.33,425
|
||||
3,13.23,3.3,2.28,18.5,98,1.8,.83,.61,1.87,10.52,.56,1.51,675
|
||||
3,12.58,1.29,2.1,20,103,1.48,.58,.53,1.4,7.6,.58,1.55,640
|
||||
3,13.17,5.19,2.32,22,93,1.74,.63,.61,1.55,7.9,.6,1.48,725
|
||||
3,13.84,4.12,2.38,19.5,89,1.8,.83,.48,1.56,9.01,.57,1.64,480
|
||||
3,12.45,3.03,2.64,27,97,1.9,.58,.63,1.14,7.5,.67,1.73,880
|
||||
3,14.34,1.68,2.7,25,98,2.8,1.31,.53,2.7,13,.57,1.96,660
|
||||
3,13.48,1.67,2.64,22.5,89,2.6,1.1,.52,2.29,11.75,.57,1.78,620
|
||||
3,12.36,3.83,2.38,21,88,2.3,.92,.5,1.04,7.65,.56,1.58,520
|
||||
3,13.69,3.26,2.54,20,107,1.83,.56,.5,.8,5.88,.96,1.82,680
|
||||
3,12.85,3.27,2.58,22,106,1.65,.6,.6,.96,5.58,.87,2.11,570
|
||||
3,12.96,3.45,2.35,18.5,106,1.39,.7,.4,.94,5.28,.68,1.75,675
|
||||
3,13.78,2.76,2.3,22,90,1.35,.68,.41,1.03,9.58,.7,1.68,615
|
||||
3,13.73,4.36,2.26,22.5,88,1.28,.47,.52,1.15,6.62,.78,1.75,520
|
||||
3,13.45,3.7,2.6,23,111,1.7,.92,.43,1.46,10.68,.85,1.56,695
|
||||
3,12.82,3.37,2.3,19.5,88,1.48,.66,.4,.97,10.26,.72,1.75,685
|
||||
3,13.58,2.58,2.69,24.5,105,1.55,.84,.39,1.54,8.66,.74,1.8,750
|
||||
3,13.4,4.6,2.86,25,112,1.98,.96,.27,1.11,8.5,.67,1.92,630
|
||||
3,12.2,3.03,2.32,19,96,1.25,.49,.4,.73,5.5,.66,1.83,510
|
||||
3,12.77,2.39,2.28,19.5,86,1.39,.51,.48,.64,9.899999,.57,1.63,470
|
||||
3,14.16,2.51,2.48,20,91,1.68,.7,.44,1.24,9.7,.62,1.71,660
|
||||
3,13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740
|
||||
3,13.4,3.91,2.48,23,102,1.8,.75,.43,1.41,7.3,.7,1.56,750
|
||||
3,13.27,4.28,2.26,20,120,1.59,.69,.43,1.35,10.2,.59,1.56,835
|
||||
3,13.17,2.59,2.37,20,120,1.65,.68,.53,1.46,9.3,.6,1.62,840
|
||||
3,14.13,4.1,2.74,24.5,96,2.05,.76,.56,1.35,9.2,.61,1.6,560
|
||||
@@ -0,0 +1,100 @@
|
||||
1. Title of Database: Wine recognition data
|
||||
Updated Sept 21, 1998 by C.Blake : Added attribute information
|
||||
|
||||
2. Sources:
|
||||
(a) Forina, M. et al, PARVUS - An Extendible Package for Data
|
||||
Exploration, Classification and Correlation. Institute of Pharmaceutical
|
||||
and Food Analysis and Technologies, Via Brigata Salerno,
|
||||
16147 Genoa, Italy.
|
||||
|
||||
(b) Stefan Aeberhard, email: stefan@coral.cs.jcu.edu.au
|
||||
(c) July 1991
|
||||
3. Past Usage:
|
||||
|
||||
(1)
|
||||
S. Aeberhard, D. Coomans and O. de Vel,
|
||||
Comparison of Classifiers in High Dimensional Settings,
|
||||
Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
|
||||
Mathematics and Statistics, James Cook University of North Queensland.
|
||||
(Also submitted to Technometrics).
|
||||
|
||||
The data was used with many others for comparing various
|
||||
classifiers. The classes are separable, though only RDA
|
||||
has achieved 100% correct classification.
|
||||
(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))
|
||||
(All results using the leave-one-out technique)
|
||||
|
||||
In a classification context, this is a well posed problem
|
||||
with "well behaved" class structures. A good data set
|
||||
for first testing of a new classifier, but not very
|
||||
challenging.
|
||||
|
||||
(2)
|
||||
S. Aeberhard, D. Coomans and O. de Vel,
|
||||
"THE CLASSIFICATION PERFORMANCE OF RDA"
|
||||
Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
|
||||
Mathematics and Statistics, James Cook University of North Queensland.
|
||||
(Also submitted to Journal of Chemometrics).
|
||||
|
||||
Here, the data was used to illustrate the superior performance of
|
||||
the use of a new appreciation function with RDA.
|
||||
|
||||
4. Relevant Information:
|
||||
|
||||
-- These data are the results of a chemical analysis of
|
||||
wines grown in the same region in Italy but derived from three
|
||||
different cultivars.
|
||||
The analysis determined the quantities of 13 constituents
|
||||
found in each of the three types of wines.
|
||||
|
||||
-- I think that the initial data set had around 30 variables, but
|
||||
for some reason I only have the 13 dimensional version.
|
||||
I had a list of what the 30 or so variables were, but a.)
|
||||
I lost it, and b.), I would not know which 13 variables
|
||||
are included in the set.
|
||||
|
||||
-- The attributes are (dontated by Riccardo Leardi,
|
||||
riclea@anchem.unige.it )
|
||||
1) Alcohol
|
||||
2) Malic acid
|
||||
3) Ash
|
||||
4) Alcalinity of ash
|
||||
5) Magnesium
|
||||
6) Total phenols
|
||||
7) Flavanoids
|
||||
8) Nonflavanoid phenols
|
||||
9) Proanthocyanins
|
||||
10)Color intensity
|
||||
11)Hue
|
||||
12)OD280/OD315 of diluted wines
|
||||
13)Proline
|
||||
|
||||
5. Number of Instances
|
||||
|
||||
class 1 59
|
||||
class 2 71
|
||||
class 3 48
|
||||
|
||||
6. Number of Attributes
|
||||
|
||||
13
|
||||
|
||||
7. For Each Attribute:
|
||||
|
||||
All attributes are continuous
|
||||
|
||||
No statistics available, but suggest to standardise
|
||||
variables for certain uses (e.g. for us with classifiers
|
||||
which are NOT scale invariant)
|
||||
|
||||
NOTE: 1st attribute is class identifier (1-3)
|
||||
|
||||
8. Missing Attribute Values:
|
||||
|
||||
None
|
||||
|
||||
9. Class Distribution: number of instances per class
|
||||
|
||||
class 1 59
|
||||
class 2 71
|
||||
class 3 48
|
||||
Binary file not shown.
Reference in New Issue
Block a user