Exploratory Data Analysis: Useful R Functions for Exploring a Data Frame

Introduction

Data in R are often stored in data frames, because they can store multiple types of data.  (In R, data frames are more general than matrices, because matrices can only store one type of data.)  Today’s post highlights some common functions in R that I like to use to explore a data frame before I conduct any statistical analysis.  I will use the built-in data set “InsectSprays” to illustrate these functions, because it contains categorical (character) and continuous (numeric) data, and that allows me to show different ways of exploring these 2 types of data.

If you have a favourite command for exploring data frames that is not in this post, please share it in the comments!

This post continues a recent series on exploratory data analysis.  Previous posts in this series include

Useful Functions for Exploring Data Frames

Use dim() to obtain the dimensions of the data frame (number of rows and number of columns).  The output is a vector.

> dim(InsectSprays)
[1] 72 2

Use nrow() and ncol() to get the number of rows and number of columns, respectively.  You can get the same information by extracting the first and second element of the output vector from dim(). 

> nrow(InsectSprays) 
# same as dim(InsectSprays)[1]
[1] 72
> ncol(InsectSprays)
# same as dim(InsectSprays)[2]
[1] 2

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

Introduction

This is the first of a series of posts on exploratory data analysis (EDA).  This post will calculate the common summary statistics of a univariate continuous data set – the data on ozone pollution in New York City that is part of the built-in “airquality” data set in R.  This is a particularly good data set to work with, since it has missing values – a common problem in many real data sets.  In later posts, I will continue this series by exploring other methods in EDA, including box plots and kernel density plots.

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