ION606
187 lines
3.5 KiB
R
Executable File
187 lines
3.5 KiB
R
Executable File
library(readr)
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library(EnvStats)
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library(nortest)
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# install.packages(c("readr", "EnvStats"))
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# set working directory (relative path)
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setwd("~/Desktop/Data Analytics/Lab 1")
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pdf("all_plots.pdf", width = 8, height = 6)
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# read data
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epi.data <- read_csv("epi_results_2024_pop_gdp.csv")
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# view dataframe
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View(epi.data)
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# print summary of variables in dataframe
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summary(epi.data$epi_results_2024_pop_gdp.csv.new)
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# print values in variable
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epi.data$RLI.new
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# AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
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# attach dataframe
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attach(epi.data)
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# print values in variable
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RLI.new
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########################
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### Explore Variable ###
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RLI <- epi.data$RLI.new
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# find NAs in variable - outputs vector of logical values, true if NA, false otherwise
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NAs <- is.na(RLI)
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RLI[which(NAs)]
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# print values in variable
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PHL <- epi.data$PHL.new
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PHL
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# no NAs
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RLI_noNA <- epi.data$RLI.new[!is.na(epi.data$RLI.new)];
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PHL_noNA <- epi.data$PHL.new[!is.na(epi.data$PHL.new)];
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set.seed(1);
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RLI_sub <- sample(RLI_noNA, size = min(180, length(RLI_noNA)));
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RLI_new_sub <- RLI_sub; # only if you truly need a second alias
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# find NAs inv variavle - outputs vector of logical values, true if NA, false otherwise
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NAs <- is.na(PHL)
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# print NAs
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PHL[which(NAs)]
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# take subset of NOT NAs from variable
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PHL.noNA <- PHL[!NAs]
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PHL.noNA
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# filter for only values above 30
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PHL.above30 <- PHL.noNA[PHL.noNA>30]
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PHL.above30
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# stats
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summary(PHL.above30)
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# boxplot of variable(s)
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boxplot(RLI, PHL.above30, names = c("RHI","PHL"))
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### Histograms ###
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# histogram (frequency distribution)
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# hist(RLI)
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# define sequence of values over which to plot histogram
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x <- seq(0., 100., 10)
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# histogram (frequency distribution) over range
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hist(RLI, x, breaks=brks, prob=TRUE)
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# print estimated density curve for variable
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lines(density(RLI, na.rm=TRUE)) # or try bw=“SJ”
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# print rug
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rug(RLI)
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x <- seq(0., 100., 5)
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# histogram (frequency distribution) over rabge
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hist(RLI, breaks = "FD", prob=TRUE)
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# print estimated density curve for variable
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lines(density(RLI, na.rm=TRUE, bw="SJ"))
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# print rug
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rug(RLI)
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# histogram (frequency distribution) over rabge
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hist(RLI.new, breaks = "FD", prob=TRUE)
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# range
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x1<-seq(5,95,1)
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# generate probability density values for a normal distribution with given mean and sd
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d1 <- dnorm(x1,mean=45, sd=11,log=FALSE)
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# print density values
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lines(x1,d1)
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# generate probability density values for a normal distribution with given mean and sd
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d2 <- dnorm(x1,mean=64, sd=11,log=FALSE)
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# print density values
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lines(x1,d2)
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# print density values
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lines(x1,.5*d2)
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### Empirical Cumulative Distribution Function ###
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# plot ecdfs
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plot(ecdf(RLI), do.points=FALSE, verticals=TRUE)
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plot(ecdf(PHL), do.points=FALSE, verticals=TRUE)
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### Quantile-quantile Plots ###
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# print quantile-quantile plot for variable with theoretical normal distribuion
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qqnorm(RLI); qqline(RLI)
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# print quantile-quantile plot for random numbers from a normal distribution with theoretical normal distribution
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x <- rnorm(500)
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qqnorm(x); qqline(x)
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# print quantile-quantile plot for variable with any theoretical distribution
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qqplot(rnorm(180), RLI_sub, xlab = "Q-Q plot for norm dsn")
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qqline(RLI_sub)
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# print quantile-quantile plot for 2 variables
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qqplot(RLI, PHL, xlab = "Q-Q plot for RHI vs PHL")
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qqplot(x, RLI, xlab = "Q-Q plot for RHI vs PHL")
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qqline(RLI)
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y <- rnorm(500)
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qqplot(x, y, xlab = "Q-Q plot for RHI vs PHL")
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qqline(y)
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## Statistical Tests
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x <- rnorm(500)
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y <- rnorm(500)
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hist(x)
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hist(y)
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shapiro.test(x)
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shapiro.test(y)
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ad.test(x)
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ad.test(y)
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ks.test(x,y)
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wilcox.test(x,y)
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var.test(x,y)
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t.test(x,y)
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dev.off()
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