This tutorial explains how to compute the main descriptive statistics in R and how to present them graphically.
Descriptive statistics (in the broad sense of the term) is a branch of statistics aiming at summarizing, describing and presenting a series of values or a dataset. Descriptive statistics is often the first step and an important part in any statistical analysis. It allows to check the quality of the data and it helps to “understand” the data by having a clear overview of it. If well presented, descriptive statistics is already a good starting point for further analyses. There exists many measures to summarize a dataset. They are divided into two types:
- location measures
- dispersion measures
Location measures give an understanding about the central tendency of the data, whereas dispersion measures give an understanding about the spread of the data. In this tutorial, we focus only on the implementation in R of the most common descriptive statistics and their visualizations (when deemed appropriate).
You'll learn how to compute and visualize the most common descriptive statistics in R. Ask any questions related to the content for free!