Exploratory Data Analysis: Variations of Box Plots in R for Ozone Concentrations in New York City and Ozonopolis


Last week, I wrote the first post in a series on exploratory data analysis (EDA).  I began by calculating summary statistics on a univariate data set of ozone concentration in New York City in the built-in data set “airquality” in R.  In particular, I talked about how to calculate those statistics when the data set has missing values.  Today, I continue this series by creating box plots in R and showing different variations and extensions that can be added; be sure to examine the details of this post’s R code for some valuable details.  I learned many of these tricks from Robert Kabacoff’s “R in Action” (2011).  Robert also has a nice blog called Quick-R that I consult often.

Recall that I the “Ozone” vector in the data set “airquality” has missing values.  Let’s remove those missing values first before constructing the box plots.

# abstract the raw data vector
ozone0 = airquality$Ozone

# remove the missing values
ozone = ozone0[!is.na(ozone)] 

Box Plots – What They Represent

The simplest box plot can be obtained by using the basic settings in the boxplot() command.  As usual, I use png() and dev.off() to print the image to a local folder on my computer.

png('INSERT YOUR DIRECTORY HERE/box plot ozone.png')
boxplot(ozone, ylab = 'Ozone (ppb)', main = 'Box Plot of Ozone in New York')

box plot ozone

What do the different parts of this box plot mean?

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