# Pie Chart in Statistics: What is it used for?

## What is a Pie Chart?

A Pie Chart is a type of graph that displays data in a circular graph. The pieces of the graph are proportional to the fraction of the whole in each category. In other words, each slice of the pie is relative to the size of that category in the group as a whole. The following chart shows water usage (image courtesy of the EPA).

Pie charts give you a snapshot of how a group is broken down into smaller pieces. The following chart shows what New Yorkers throw in their trash cans. You could read that New Yorkers (perhaps surprisingly) throw a lot of recyclables into their trash, but a pie graph gives a clear picture of the large percentage of recyclables that find their way into the trash.

In order to make a pie chart, you must have a list of categorical variables (descriptions of your categories) as well as numerical variables. In the above graph, percentages are the numerical variables and the type of trash are the categorical variables.

While it’s possible to draw one by hand, it isn’t really necessary with the wide variety of computer programs that can make pie charts for you. Two of the most popular programs for making charts in elementary statistics or AP statistics classes are Microsoft Excel and IBM SPSS Statistics:

Note: When you make a pie chart, you must make sure that your categories don’t overlap, otherwise you’ll have a meaningless chart. For example, if you have a chart of what breeds of dog U.S. pet owners own, you can’t put a husky-German shepherd mix in two categories, otherwise it will create larger pieces of the “pie” for those categories.

Reference:
What’s in NYC’s Waste? Retrieved February 2, 2016 from: http://www.nyc.gov/html/nycwasteless/html/resources/wcs_charts.shtml

------------------------------------------------------------------------------

If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.