STATISTICS WITH R
F test with var.test() to compare two variances
Hypothesis testing
Ttest to compare means
Hypothesis testing
Shapiro Wilk normality test
Hypothesis testing
Quantiles
Location measures
Box Cox transformation
Variance and standard deviation
Dispersion measures
Calculate the mean
Measures of central tendency
Calculate the median
Measures of central tendency
Mode estimation
Measures of central tendency
Binomial distribution
Distributions
Poisson distribution
Distributions
Continuous uniform distribution
Distributions
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.