Data Summarization: Intro

Nicky Wakim

Data Summarization

• There are many ways to summarize data in R
  • Each function can be useful in different scenarios!

 

• We may need different tools based on the type of measurement (categorical vs. numeric)
  • Categorical data (represented as factors in R) will focus on frequencies and counts
  • Numeric data (represented as numbers in R) will focus on summary metrics like mean, median, standard deviation, etc.

Summary functions we will cover

Package	Function	Categorical or Numeric?	Type	What it does
built-in	summary()	Both	Quick browsing	Base R’s simple way to get a quick stats of each variable
skimr	skim()	Both	Quick browsing, Grouped summaries	Overview of every variable in your dataset at once
rstatix	get_summary _stats()	Numeric	Quick browsing, Grouped summaries	Automatically generates common numeric summary statistics
dplyr	summarize()	Both	Pointed summaries, Grouped summaries	Calculates custom summary statistics you specify yourself
janitor	tabyl()	Categorical	Quick browsing	Builds frequency tables for one or more categorical variables
gtsummary	tbl_summary()	Both	Presentations, Grouped summaries	Creates a polished, publication-ready summary table

Helper functions we will cover

Package	Function	Type	What it does
dplyr	group_by()	Grouped summaries	Splits your data into groups so later functions operate per group
gt	gt()	Presentation	Creates a polished, publication-ready table

Make sure you have the following packages installed

• Remember, you can use pacman::p_load() to install and load packages in one step

• Needed packages:

  • dplyr
  • skimr
  • rstatix
  • janitor
  • gtsummary
  • gt