# Exploring Data with TidyDensity: A Guide to Using `tidy_empirical()` and `tidy_four_autoplot()` in R

rtip
tidydensity
dplyr
purrr
Author

Steven P. Sanderson II, MPH

Published

May 24, 2023

# Introduction

Yesterday I had the need to see data that had a grouping column in it. I wanted to use the `tidy_four_autoplot()` function on it from the `{TidyDensity}` library on it. This post will explain how I did it. The data in my session was called `df_tbl`. In this blog post, we will explore the steps involved in using the tidy_empirical() and tidy_four_autoplot() functions from the R library TidyDensity. These functions are incredibly useful when working with data, as they allow us to analyze and visualize empirical distributions efficiently. We will walk through a code snippet that demonstrates how to use these functions within a map() function, enabling us to analyze multiple subsets of data simultaneously.

#Prerequisites

To follow along with this tutorial, it is assumed that you have a basic understanding of the R programming language, as well as familiarity with the dplyr, purrr, and TidyDensity libraries. Make sure you have these packages installed and loaded before proceeding.

Here is the code that I used, the explanation will follow:

``````library(dplyr) # to use group_split()
library(purrr) # to use map()
library(TidyDensity) # to use tidy_empirical() and tidy_four_plot()

df_tbl |>
group_split(SP_NAME) |>
map(\(run_time) pull(run_time) |>
tidy_empirical() |>
tidy_four_autoplot()
)``````

# Code Explanation

Let’s break down the code step by step:

Importing Required Libraries:

• To access the necessary functions, we need to load the required libraries. In this case, we use library(dplyr) to utilize the `group_split()` function from the dplyr package, library(purrr) to use the `map()` function from the purrr package, and library(TidyDensity) to access the `tidy_empirical()` and `tidy_four_autoplot()` functions from the TidyDensity package.

Grouping and Splitting the Data:

• The first line of the code snippet takes a dataframe named df_tbl and uses the `group_split()` function from the dplyr library to split it into multiple subsets based on a variable called SP_NAME. This creates a list of dataframes, each representing a unique group based on SP_NAME.

Applying Functions to Each Subset using map():

• The second line of code utilizes the `map()` function from the purrr library to iterate over each subset of data created in the previous step. The `map()` function takes two arguments: the object to iterate over (in this case, the list of dataframes) and a function to apply to each element.

Anonymous Function Inside map():

• Within the `map()` function, an anonymous function (denoted by (run_time)) is defined. This function takes a single argument named run_time, representing each individual subset of data. The purpose of this anonymous function is to perform the necessary computations and visualizations on each subset of data.

Data Manipulation and Visualization:

• Inside the anonymous function, the pull(run_time) function is used to extract the run_time column from each subset of data. This column is then passed to the `tidy_empirical()` function from the TidyDensity library, which calculates the empirical distribution of the data. The result is a tidy dataframe that contains information about the empirical distribution.

Tidy Four Autoplot:

• The output of `tidy_empirical()` is then piped (|>) into the `tidy_four_autoplot()` function from the TidyDensity library. This function generates a visualization called a “Tidy Four Plot,” which consists of four individual plots: empirical density, empirical cumulative density, QQ plot, and histogram.

Final Output:

• The result of the `tidy_four_autoplot()` function is the final output of the anonymous function within `map()`. This output represents the visualization of the empirical distribution for each subset of data.

Happy Coding!