library(dplyr)
library(ggplot2)
library(readr)
library(palmerpenguins)
library(gtsummary)
library(knitr)Data summaries with “gtsummary” package
This tutorial introduces the gtsummary package, a tool for producing a polished, presentation-quality summary table.
The glimpse and summary functions you’ve used elsewhere in these tutorials are great for quickly checking a dataset’s structure and contents while you’re working, but their plain, console-style output isn’t designed to be dropped directly into a written report or assignment answer. The gtsummary package fills that gap: it produces a clean, nicely formatted summary table suitable for presenting your results.
If you haven’t already, install the gtsummary package by typing this in your console (do this only once):
install.packages("gtsummary")Let’s load packages …
The key function in the gtsummary package is the tbl_summary function:
?tbl_summaryTake note of the default settings for the “statistic” argument… by default, the function will return the median and IQR for numeric variables, and the sample size and relative frequency (expressed as percentage) for categorical variables.
For details on this function, along with a tutorial, see this webpage.
We’ll get an overview of the data using the tbl_summary function.
penguins %>%
tbl_summary()| Characteristic | N = 3441 |
|---|---|
| species | |
| Adelie | 152 (44%) |
| Chinstrap | 68 (20%) |
| Gentoo | 124 (36%) |
| island | |
| Biscoe | 168 (49%) |
| Dream | 124 (36%) |
| Torgersen | 52 (15%) |
| bill_length_mm | 44.5 (39.2, 48.5) |
| Unknown | 2 |
| bill_depth_mm | 17.30 (15.60, 18.70) |
| Unknown | 2 |
| flipper_length_mm | 197 (190, 213) |
| Unknown | 2 |
| body_mass_g | 4,050 (3,550, 4,750) |
| Unknown | 2 |
| sex | |
| female | 165 (50%) |
| male | 168 (50%) |
| Unknown | 11 |
| year | |
| 2007 | 110 (32%) |
| 2008 | 114 (33%) |
| 2009 | 120 (35%) |
| 1 n (%); Median (Q1, Q3) | |
We could select just some numeric variables, and ask for the mean and standard deviation. Note the syntax for the “statistic” argument… we have to provide a “list”, as follows:
penguins %>%
select(bill_length_mm, bill_depth_mm) %>%
tbl_summary(statistic = list(all_continuous() ~ "{mean} ({sd})"))| Characteristic | N = 3441 |
|---|---|
| bill_length_mm | 43.9 (5.5) |
| Unknown | 2 |
| bill_depth_mm | 17.15 (1.97) |
| Unknown | 2 |
| 1 Mean (SD) | |
So, whenever you want to present a dataset overview as a polished table in a report — rather than just checking it out for yourself while working — reach for the gtsummary package!