STATISTICS WITH R
Wilcoxon tests with wilcox.test()
Hypothesis testing
KolmogorovSmirnov test with ks.test()
Hypothesis testing
Coefficient of variation
Dispersion measures
Range of values
Dispersion measures
Pearson's Chisquared test with chisq.test()
Hypothesis testing
Random samples and permutations
Simulation
Interquartile range
Dispersion measures
Median absolute deviation
Dispersion measures
Lilliefors normality test
Hypothesis testing
Kruskal Wallis rank sum test (H test)
Hypothesis testing
Covariance and correlation
Association measures
Test for proportions with prop.test()
Hypothesis testing
WHAT ARE THE KEY ADVANTAGES OF USING R FOR STATISTICAL ANALYSIS?
Statistics in R encompass a broad spectrum of functionalities and packages designed to perform various statistical analyses, data exploration, hypothesis testing and modeling tasks. R is extensively used in data analysis, academia, industry, scientific research and statistical computing due to its rich set of statistical tools and packages. Some key statistical functionalities in R include:

Descriptive Statistics
R offers functions to compute basic descriptive statistics such as mean, median, standard deviation, variance, range, quartiles, percentiles, and summary statistics for data exploration (summary
function). 
Hypothesis testing
R provides functions for conducting various statistical tests, including ttests (t.test
), chisquare tests (chisq.test
), ANOVA (aov
), Ftests (var.test
), and nonparametric tests (such aswilcox.test
orkruskal.test
). 
Probability distributions
R includes a wide array of functions to work with probability distributions (e.g., normal, uniform, binomial, Poisson) for generating random numbers, calculating probabilities, quantiles, and density functions.