In higgi13425/medicaldata: Data Package for Medical Datasets

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Getting Started

First, set up your working environment by loading these 4 packages using the library() function. Note that you can copy any of the code blocks below by hovering over the top right corner, clicking on the copy icon that appears, and them paste these into your local version of R to run them.

library(medicaldata)
suppressMessages(library(tidyverse))
suppressMessages(library(janitor))
suppressMessages(library(flextable))

Counting Data in Tables

It is common to count data, particularly in categories, to summarize characteristics or outcomes. The tabyl function in the {janitor} package is helpful for this.

In this vignette, we will look at how to use this function to make simple tables of counts of your data.

First, we will read in the data from strep_tb and indo_rct to use for our tables. Load the libraries in the setup chunk above. If you then run the code chunk below, you should have two new data objects in your Environment tab.

strep_tb <- medicaldata::strep_tb
indo_rct <- medicaldata::indo_rct

Table 2 from Streptomycin for Tuberculosis

We will now try to reproduce Table 2 from the Streptomycin for Tuberculosis manuscript, which can be found here on page 771. These are the primary endpoint results, summarized in a 6 rows x 2 columns table (with an added totals row). Let's walk through how to do this with the tabyl() function in the {janitor} package.

First Table

The tabyl() function allows you to pipe data into tables, and add 'adornments' like total rows and percentages. Let's start with a basic one-variable tabyl using this ordinal endpoint. You can pipe the dataset into the tabyl function, with your desired variable (radiologic_6m) as the only argument to the function.

strep_tb %>% 
  tabyl(radiologic_6m)

This gives us the n and proportion of each level of the primary outcome.

Two Variable Table

If we add the treatment arm variable as a 2nd argument, we can come closer to the original table. Note that the levels of the first argument make up the rows of the table, and that the levels of the 2nd argument make up the columns (standard R x C order). Also note that with 2 variables, we get the counts by default, but not proportions of each level, as you might want proportions that are column-wise, or row-wise.

strep_tb %>% 
  tabyl(radiologic_6m, arm)

Add A Total Row

This is closer, but lacks the total row, and the percentages. In order to have numbers to calculate totals, we have to start with the total row first. We will 'adorn' the table with a totals row. We have to specify that we want an additional row of totals at the bottom (not a column of row-wise totals in a new column on the right), with the where argument to the adorn_totals function.

strep_tb %>% 
  tabyl(radiologic_6m, arm) %>% 
  adorn_totals(where = "row") # add a total row

Add Percentages and formatting

This is closer. Now we need to add the percentages, and percentage formatting. We need to specify that we want column-wise, rather than row-wise percentages. We then have to adorn_ns to add the counts, so that we have both counts and percentages. We can specify that we want the counts to be listed first, with the argument position = "front" in the adorn_ns function.

strep_tb %>% 
  tabyl(radiologic_6m, arm) %>% #2 dimensional table, RxC
  adorn_totals(where = "row") %>% # add totals row
  adorn_percentages("col") %>%  # column-wise percentages
  adorn_pct_formatting() %>% 
  adorn_ns(position = "front")  # put n first

Making it Pretty

You can pipe this table into a flextable() object, which makes it easy to add fancy formatting. There are many formatting options in the flextable package, which you can learn about here. You can control column width, fonts, colors, and much more once you are in flextable format. Flextables can be output to MS Word, powerpoint, HTML, and PDF, through Rmarkdown.

strep_tb %>% 
  tabyl(radiologic_6m, arm) %>% 
  adorn_totals(where = "row") %>% 
  adorn_percentages("col") %>%  # column-wise percentages
  adorn_pct_formatting() %>% 
  adorn_ns(position = "front") %>%   # put n first
  flextable::flextable()

Try this Yourself

Now try this yourself, but instead of using the ordinal radiologic_6m outcome, use the improved dichotomous outcome in its place. Copy the code block below and add the piping and additional lines to produce a 2 x 2 table of outcomes. You can websearch for janitor tabyl adorn_title to learn how to add a title to your table.

strep_tb

Challenge

Now try to do this with the indo_rct dataset, using the treatment variable group and the outcome variable of outcome. Add a total row, percentages, and a title.