# Exploratory Data Analysis – All Blog Posts on The Chemical Statistician

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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

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

Filed under: Applied Statistics, Data Analysis, Descriptive Statistics, Plots, R programming, Statistics Tagged: 5-number summary, applied statistics, box plot, data analysis, data visualization, ecdf(), empirical cumulative distribution function, exploratory data analysis, five-number summary, frequency table, histogram, kernel density estimation, kernel density plot, quantile, quantile-quantile plot, R, R programming, violin plot

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