#### Introduction

Continuing my recent series on exploratory data analysis (EDA), today’s post focuses on histograms, which are very useful plots for visualizing the distribution of a data set. I will discuss how histograms are constructed and use histograms to assess the distribution of the “Ozone” data from the built-in “airquality” data set in R. In a later post, I will assess the distribution of the “Ozone” data in greater depth by combining histograms with various types of density plots.

Previous posts in this series on EDA include

- Descriptive statistics
- Box plots
- The conceptual foundations of kernel density estimation
- How to construct kernel density plots and rug plots in R
- Violin plots
- The conceptual foundations of empirical cumulative distribution functions (CDFs)
- 2 ways of plotting empirical CDFs in R

**Read the rest of this post to learn how to construct a histogram and get the R code for producing…**

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