Exploratory Data Analysis – All Blog Posts on The Chemical Statistician

This series of posts introduced various methods of exploratory data analysis, providing theoretical backgrounds and practical examples.  Fully commented and readily usable R scripts are available for all topics for you to copy and paste for your own analysis!  Most of these posts involve data visualization and plotting, and I include a lot of detail and comments on how to invoke specific plotting commands in R in these examples.

I will return to this blog post to add new links as I write more tutorials.

Useful R Functions for Exploring a Data Frame

The 5-Number Summary – Two Different Methods in R

Combining Histograms and Density Plots to Examine the Distribution of the Ozone Pollution Data from New York in R

Conceptual Foundations of Histograms – Illustrated with New York’s Ozone Pollution Data

Quantile-Quantile Plots for New York’s Ozone Pollution Data

Kernel Density Estimation and Rug Plots in R on Ozone Data in New York and Ozonopolis

2 Ways of Plotting Empirical Cumulative Distribution Functions in R

Conceptual Foundations of Empirical Cumulative Distribution Functions

Combining Box Plots and Kernel Density Plots into Violin Plots for Ozone Pollution Data

Kernel Density Estimation – Conceptual Foundations

Variations of Box Plots in R for Ozone Concentrations in New York City and Ozonopolis

Computing Descriptive Statistics in R for Data on Ozone Pollution in New York City

How to Get the Frequency Table of a Categorical Variable as a Data Frame in R

The advantages of using count() to get N-way frequency tables as data frames in R

Exploratory Data Analysis: Combining Box Plots and Kernel Density Plots into Violin Plots for Ozone Pollution Data

Introduction

Recently, I began a series on exploratory data analysis (EDA), and I have written about descriptive statistics, box plots, and kernel density plots so far.  As previously mentioned in my post on box plots, there is a way to combine box plots and kernel density plots.  This combination results in violin plots, and I will show how to create them in R today.

Continuing from my previous posts on EDA, I will use 2 univariate data sets.  One is the “ozone” data vector that is part of the “airquality” data set that is built into R; this data set contains data on New York’s air pollution.  The other is a simulated data set of ozone pollution in a fictitious city called “Ozonopolis”.  It is important to remember that the ozone data from New York has missing values, and this has created complications that needed to be addressed in previous posts; missing values need to be addressed for violin plots, too, and in a different way than before.  

The vioplot() command in the “vioplot” package creates violin plots; the plotting options in this function are different and less versatile than other plotting functions that I have used in R.  Thus, I needed to be more creative with the plot(), title(), and axis() functions to create the plots that I want.  Read the details carefully to understand and benefit fully from the code.

Read further to learn how to create these violin plots that combine box plots with kernel density plots!  Be careful – the syntax is more complicated than usual!