This tutorial is on methods for detecting outliers in R.

This tutorial is on linear regression and ANOVA in R. It also shows steps to calculate point estimates for the mean and confidence intervals. Finally, this tutorial demonstrates how to plot point estimates for the mean and standard errors.

This tutorial shows how to set up a working directory in R, to read data from a .csv file in R, and to use algorithms to work on the wine data. Here we learn how to cluster based on cosine similarity (also called spherical k-means).

This tutorial shows how to perform exploratory and visualized analyses in R. Here I demonstrate different steps to do summary statistics and draw nice plots using packages such as car, mass, ggplot2, ggrepel.

This is only to teach data scientists how to do data science with R. All the examples in this notebook are from R for Data Science by Garrett Grolemund and Hadley Wickham.