R CODER

STATISTICS WITH R

Descriptive Statistics Distributions Hypothesis testing Simulation
Learn how to use the dexp, pexp, qexp and rexp functions of the exponential distribution in R

Exponential distribution

Distributions

Create and plot Normal distributions in R

Normal distribution

Distributions

Create a contingency table in R and calculate association measures

Contingency table

Association of categorical variables

Learn what is and how to set seed in R

Set seed

Simulation

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:

  1. 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).

  2. Hypothesis testing

    R provides functions for conducting various statistical tests, including t-tests (t.test), chi-square tests (chisq.test), ANOVA (aov), F-tests (var.test), and non-parametric tests (such as wilcox.test or kruskal.test).

  3. 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.