Organizing Public Health Data with gt Tables in R - Basics

Author

The Graph Network

Published

February 19, 2025

Introduction

Tables are a powerful tool for visualizing data in a clear and concise format. With R and the gt package, we can leverage the visual appeal of tables to efficiently communicate key information. In this lesson, we will learn how to build aesthetically pleasing, customizable tables that support data analysis goals.

Learning objectives

  1. Use the gt() function to create basic tables: Learn to initiate table creation in R using gt() for data display.

  2. Group columns under spanner headings: Master grouping related columns under shared headings for enhanced table organization.

  3. Add stubs: Implement stubs to provide row labels or descriptions in tables for better clarity.

  4. Relabel column names: Learn to rename column labels for improved readability and context in tables.

  5. Add summary rows for groups: Understand adding aggregate data rows like sums or averages per group for comprehensive analysis.

By the end, you will be able to generate polished, reproducible tables like this:

Example summary table

1 Packages

We will use these packages:

  • {gt} for creating tables
  • {tidyverse} for data wrangling
  • {here} for file paths
Code 
# Load packages
pacman::p_load(tidyverse, gt, here)

2 The dataset

Our data comes from the Malawi HIV Program and covers antenatal care and HIV treatment during 2019. We will focus on quarterly regional and facility-level aggregates (available here).

Code 
# Import data
hiv_malawi <- read_csv(here::here("data/clean/hiv_malawi.csv"))

Let’s explore the variables:

Code 
# First 6 rows
head(hiv_malawi)
Code 
# Variable names and types 
glimpse(hiv_malawi)
Rows: 17,235
Columns: 29
$ region                                 <chr> "Northern Region", "Northern Re…
$ zone                                   <chr> "Northern Zone", "Northern Zone…
$ district                               <chr> "Chitipa", "Chitipa", "Chitipa"…
$ traditional_authority                  <chr> "Senior TA Bulambya Songwe", "S…
$ facility_name                          <chr> "Kapenda Health Centre", "Kapen…
$ datim_code                             <chr> "K9u9BIAaJJT", "K9u9BIAaJJT", "…
$ system                                 <chr> "e-mastercard", "e-mastercard",…
$ hsector                                <chr> "Public", "Public", "Public", "…
$ period                                 <chr> "2019 Q1", "2019 Q1", "2019 Q1"…
$ reporting_period                       <chr> "1st month of quarter", "1st mo…
$ sub_groups                             <chr> "All patients (checked data)", …
$ new_women_registered                   <dbl> 45, NA, 40, NA, 43, NA, 32, NA,…
$ total_women_in_booking_cohort          <dbl> NA, 55, NA, 44, NA, 37, NA, 43,…
$ not_tested_for_syphilis                <dbl> NA, 45, NA, 19, NA, 5, NA, 43, …
$ syphilis_negative                      <dbl> NA, 10, NA, 25, NA, 32, NA, 0, …
$ syphilis_positive                      <dbl> NA, 0, NA, 0, NA, 0, NA, 0, NA,…
$ hiv_status_not_ascertained             <dbl> 4, 7, 9, 4, 9, 5, 3, 3, 4, 26, …
$ previous_negative                      <dbl> 0, 0, 0, 0, 0, 1, 3, 5, 1, 3, 0…
$ previous_positive                      <dbl> 0, 0, 0, 1, 1, 0, 1, 1, 1, 3, 2…
$ new_negative                           <dbl> 40, 47, 30, 38, 33, 31, 25, 34,…
$ new_positive                           <dbl> 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0…
$ not_on_cpt                             <dbl> NA, 0, NA, 0, NA, 0, NA, 0, NA,…
$ on_cpt                                 <dbl> NA, 1, NA, 2, NA, 0, NA, 1, NA,…
$ no_ar_vs                               <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ already_on_art_when_starting_anc       <dbl> 0, 1, 0, 1, 1, 0, 1, 1, 1, 3, 2…
$ started_art_at_0_27_weeks_of_pregnancy <dbl> 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0…
$ started_art_at_28_weeks_of_preg        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ no_ar_vs_dispensed_for_infant          <dbl> NA, 0, NA, 0, NA, 0, NA, 0, NA,…
$ ar_vs_dispensed_for_infant             <dbl> NA, 1, NA, 2, NA, 0, NA, 1, NA,…

The data covers geographic regions, healthcare facilities, time periods, patient demographics, test results, preventive therapies, antiretroviral drugs, and more. More information about the dataset is in the appendix section.

The key variables we will be considering are:

  • region: The geographical region or area where the data was collected or is being analyzed.

  • period: A specific time period associated with the data, often used for temporal analysis.

  • previous_negative: The number of patients who visited the healthcare facility in that quarter that had prior negative HIV tests.

  • previous_positive: The number of patients (as above) with prior positive HIV tests.

  • new_negative: The number of patients newly testing negative for HIV.

  • new_positive: The number of patients newly testing positive for HIV.

In this lesson, we will aggregate the data by quarter and summarize changes in HIV test results.

3 Creating simple tables with {gt}

{gt}’s flexibility, efficiency, and power make it a formidable package for creating tables in R. We’ll explore some of it’s core features in this lesson.

The {gt} package contains a set of functions that take raw data as input and output a nicely formatted table for further analysis and reporting.

To effectively leverage the {gt} package, we first need to wrangle our data into an appropriate summarized form.

In the code chunk below, we use {dplyr} functions to summarize HIV testing in select Malawi testing centers by quarter. We first group the data by time period, then sum case counts across multiple variables using across():

Code 
# Variables to summarize
cols <- c("new_positive", "previous_positive", "new_negative", "previous_negative")

# Create summary by quarter
hiv_malawi_summary <- hiv_malawi %>%  
  group_by(period) %>%
  summarize(
    across(all_of(cols), sum) # Summarize all columns
  )

hiv_malawi_summary

This aggregates the data nicely for passing to {gt} to generate a clean summary table.

To create a simple table from the aggregated data, we can then call the gt() function:

Code 
hiv_malawi_summary %>%  
  gt()
period new_positive previous_positive new_negative previous_negative
2019 Q1 6199 14816 284694 6595
2019 Q2 6132 15101 282249 5605
2019 Q3 5907 15799 300529 6491
2019 Q4 5646 15700 291622 6293

As you can see, the default table formatting is quite plain and unrefined. However, {gt} provides many options to customize and beautify the table output. We’ll delve into these in the next section.

4 Customizing {gt} tables

The {gt} package allows full customization of tables through its “grammar of tables” framework. This is similar to how {ggplot2}’s grammar of graphics works for plotting.

To take full advantage of {gt}, it helps to understand some key components of its grammar.

As seen in the figure from the package website, The main components of a {gt} table are:

  • Table Header: Contains an optional title and subtitle

  • Stub: Row labels that identify each row

  • Stub Head: Optional grouping and labels for stub rows

  • Column Labels: Headers for each column

  • Table Body: The main data cells of the table

  • Table Footer: Optional footnotes and source notes

Understanding this anatomy allows us to systematically construct {gt} tables using its grammar.

4.2 Stub

The stub is the left section of a table containing the row labels. These provide context for each row’s data.

This image displays the stub component of a {gt} table, marked with a red square.

In our HIV case table, the period column holds the row labels we want to use. To generate a stub, we specify this column in gt() using the rowname_col argument:

Code 
hiv_malawi_summary %>%
  gt(rowname_col = "period") %>%
  tab_header(
    title = "HIV Testing in Malawi",
    subtitle = "Q1 to Q2 2019"  
  ) %>%
  tab_source_note("Source: Malawi HIV Program")
HIV Testing in Malawi
Q1 to Q2 2019
new_positive previous_positive new_negative previous_negative
2019 Q1 6199 14816 284694 6595
2019 Q2 6132 15101 282249 5605
2019 Q3 5907 15799 300529 6491
2019 Q4 5646 15700 291622 6293
Source: Malawi HIV Program

Note that the column name passed to rowname_col should be in quotes.

For convenience, let’s save the table to a variable t1:

Code 
t1 <- hiv_malawi_summary %>%
  gt(rowname_col = "period") %>%
  tab_header(
    title = "HIV Testing in Malawi",
    subtitle = "Q1 to Q2 2019"
  ) %>% 
  tab_source_note("Source: Malawi HIV Program")

t1
HIV Testing in Malawi
Q1 to Q2 2019
new_positive previous_positive new_negative previous_negative
2019 Q1 6199 14816 284694 6595
2019 Q2 6132 15101 282249 5605
2019 Q3 5907 15799 300529 6491
2019 Q4 5646 15700 291622 6293
Source: Malawi HIV Program

4.3 Spanner columns & sub columns

To better structure our table, we can group related columns under “spanners”. Spanners are headings that span multiple columns, providing a higher-level categorical organization. We can do this with the tab_spanner() function.

Let’s create two spanner columns for new and Previous tests. We’ll start with the “New tests” spanner so you can observe the syntax:

Code 
t1 %>%  
  tab_spanner(
    label = "New tests",
    columns = starts_with("new") # selects columns starting with "new"
  )
HIV Testing in Malawi
Q1 to Q2 2019
New tests
previous_positive previous_negative
new_positive new_negative
2019 Q1 6199 284694 14816 6595
2019 Q2 6132 282249 15101 5605
2019 Q3 5907 300529 15799 6491
2019 Q4 5646 291622 15700 6293
Source: Malawi HIV Program

The columns argument lets us select the relevant columns, and the label argument takes in the span label.

Let’s now add both spanners:

Code 
# Save table to t2 for easy access  
t2 <- t1 %>%  
  # First spanner for "New tests"   
  tab_spanner(
    label = "New tests",
    columns = starts_with("new") 
  ) %>%
  # Second spanner for "Previous tests"
  tab_spanner(
    label = "Previous tests",
    columns = starts_with("prev")
  )

t2
HIV Testing in Malawi
Q1 to Q2 2019
New tests
Previous tests
new_positive new_negative previous_positive previous_negative
2019 Q1 6199 284694 14816 6595
2019 Q2 6132 282249 15101 5605
2019 Q3 5907 300529 15799 6491
2019 Q4 5646 291622 15700 6293
Source: Malawi HIV Program

Note that the tab_spanner function automatically rearranged the columns in an appropriate way.

Question 1: The Purpose of Spanners

What is the purpose of using “spanner columns” in a {gt} table?

A. To apply custom CSS styles to specific columns.

B. To create group columns and increase readability.

C. To format the font size of all columns uniformly.

D. To sort the data in ascending order.

Question 2: Spanner Creation

Using the hiv_malawi data frame, create a {gt} table that displays a summary of the sum of new_positive and previous_positive cases for each region. Create spanner headers to label these two summary columns. To achieve this, fill in the missing parts of the code below:

Code 
region_summary <- hiv_malawi %>%
  group_by(region) %>%
  summarize(
    _________(
      c(new_positive, previous_positive),
      ______
    )
  )

# Create a gt table with spanner headers
summary_table_spanners <- region_summary %>%
  _____________ %>%
  ___________(
    label = "Positive cases",
    ________ = c(new_positive, previous_positive)
  )

5 Renaming Table Columns

The column names currently contain unneeded prefixes like “new_” and “previous_”. For better readability, we can rename these using cols_label().

cols_label() takes a set of old names to match (on the left side of a tilde, ~) and new names to replace them with (on the right side of the tilde). We can use contains() to select columns with “positive” or “negative”:

Code 
t3 <- t2 %>%
  cols_label(
    contains("positive") ~ "Positive", 
    contains("negative") ~ "Negative"
  )

t3
HIV Testing in Malawi
Q1 to Q2 2019
New tests
Previous tests
Positive Negative Positive Negative
2019 Q1 6199 284694 14816 6595
2019 Q2 6132 282249 15101 5605
2019 Q3 5907 300529 15799 6491
2019 Q4 5646 291622 15700 6293
Source: Malawi HIV Program

This relabels the columns in a cleaner way.

cols_label() accepts several column selection helpers like contains(), starts_with(), ends_with() etc. These come from the tidyselect package and provide flexibility in renaming.

cols_label() has more identification function like contains() that work in a similar manner that are identical to the tidyselect helpers, these also include :

  • starts_with(): Starts with an exact prefix.

  • ends_with(): Ends with an exact suffix.

  • contains(): Contains a literal string.

  • matches(): Matches a regular expression.

  • num_range(): Matches a numerical range like x01, x02, x03.

These helpers are useful especially in the case of multiple columns selection.

More on the cols_label() function can be found here: https://gt.rstudio.com/reference/cols_label.html

Question 3: Column labels

Which function is used to change the labels or names of columns in a {gt} table?

A. `tab_header()`

B. `tab_style()`

C. `tab_options()`

D. `tab_relabel()`

6 Summary Rows

Let’s take the same data we started with in the beginning of this lesson and instead of only grouping by period(quarters), let’s group by both period and region. We will do this to illustrate the power of summarization features in {gt}: summary tables.

{gt} reminder - Summary Rows This image shows the summary rows component of a {gt} table, clearly indicated within a red square. Summary rows, provide aggregated data or statistical summaries of the data contained in the corresponding columns.

First let’s recreate the data:

Code 
summary_data_2 <- hiv_malawi %>% 
  group_by(
    # Note the order of the variables we group by.
    region,
    period
  ) %>% 
  summarise(
    across(all_of(cols), sum) 
    ) 
`summarise()` has grouped output by 'region'. You can override using the
`.groups` argument.
Code 
summary_data_2

The order in the group_by() function affects the row groups in the {gt} table.

Second, let’s re-incorporate all the changes we’ve done previously into this table:

Code 
# saving the progress to the t4 object

t4 <- summary_data_2 %>% 
  gt(rowname_col = "period", groupname_col = "region") %>% 
  tab_header(
    title = "Sum of HIV Tests in Malawi",
    subtitle = "from Q1 2019 to Q4 2019"
  ) %>% 
  tab_source_note("Data source: Malawi HIV Program") %>% tab_spanner(
    label = "New tests",
    columns = starts_with("new") # selects columns starting with "new"
  ) %>% 
   # creating the first spanner for the Previous tests
  tab_spanner(
    label = "Previous tests",
    columns = starts_with("prev") # selects columns starting with "prev"
  ) %>% 
  cols_label(
    # locate ### assign 
    contains("positive") ~ "Positive",
    contains("negative") ~ "Negative"
  )

t4
Sum of HIV Tests in Malawi
from Q1 2019 to Q4 2019
New tests
Previous tests
Positive Negative Positive Negative
Central Region
2019 Q1 2004 123018 3682 2562
2019 Q2 1913 116443 3603 1839
2019 Q3 1916 127799 4002 2645
2019 Q4 1691 124728 3754 1052
Northern Region
2019 Q1 664 36196 1197 675
2019 Q2 582 35315 1084 590
2019 Q3 570 36850 1191 542
2019 Q4 519 34322 1132 346
Southern Region
2019 Q1 3531 125480 9937 3358
2019 Q2 3637 130491 10414 3176
2019 Q3 3421 135880 10606 3304
2019 Q4 3436 132572 10814 4895
Data source: Malawi HIV Program

Now, what if we want to visualize on the table a summary of each variable for every region group? More precisely we want to see the sum and the mean for the 4 columns we have for each region.

Remember that our 4 columns of interest are : new_positive, previous_positive, new_negative, and previous_negative. We only changed the labels of these columns in the {gt} table and not in the data.frame itself, so we can use the names of these columns to tell {gt} where to apply the summary function. Additionally, we already stored the names of these 4 columns in the object cols so we will use it again here.

In order to achieve this we will use the handy function summary_rows where we explicitly provide the columns that we want summarized, and the functions we want to summarize with, in our case it’s sum and mean. Note that we assign the name of the new row(unquoted) a function name (“quoted”).

Code 
t5 <- t4 %>% 
  summary_rows(
    columns = cols, #using columns = 3:6 also works 
    fns = list( 
      TOTAL = "sum",
      AVERAGE = "mean"
    )
  )

t5
Sum of HIV Tests in Malawi
from Q1 2019 to Q4 2019
New tests
Previous tests
Positive Negative Positive Negative
Central Region
2019 Q1 2004 123018 3682 2562
2019 Q2 1913 116443 3603 1839
2019 Q3 1916 127799 4002 2645
2019 Q4 1691 124728 3754 1052
sum 7524.00 491988.00 15041.00 8098.00
mean 1881.00 122997.00 3760.25 2024.50
Northern Region
2019 Q1 664 36196 1197 675
2019 Q2 582 35315 1084 590
2019 Q3 570 36850 1191 542
2019 Q4 519 34322 1132 346
sum 2335.00 142683.00 4604.00 2153.00
mean 583.75 35670.75 1151.00 538.25
Southern Region
2019 Q1 3531 125480 9937 3358
2019 Q2 3637 130491 10414 3176
2019 Q3 3421 135880 10606 3304
2019 Q4 3436 132572 10814 4895
sum 14025.00 524423.00 41771.00 14733.00
mean 3506.25 131105.75 10442.75 3683.25
Data source: Malawi HIV Program

Question 4 : Summary Rows

What is the correct answer (or answers) if you had to summarize the standard deviation of the rows of columns new_positive and previous_negative only?

A. Use summary_rows() with the columns argument set to new_positive and previous_negative and fns argument set to “sd”.

Code 
# Option A 
your_data %>%   
  summary_rows(
    columns = c("new_positive", "previous_negative"),     
    fns = "sd" 
  )

B. Use summary_rows() with the columns argument set to new_positive and previous_negative and fns argument set to “summarize(sd)”.

Code 
# Option B 

your_data %>%   
  summary_rows(
    columns = c("new_positive", "previous_negative"),     
    fns = summarize(sd) 
  )

C. Use summary_rows() with the columns argument set to new_positive and previous_negative and fns argument set to list(SD = "sd").

Code 
# Option C 
your_data %>%   
  summary_rows(
    columns = c("new_positive", "previous_negative"),     
    fns = list(SD = "sd")   
  )

D. Use summary_rows() with the columns argument set to new_positive and previous_negative and fns argument set to “standard_deviation”.

Code 
# Option D
your_data %>%
  summary_rows(
    columns = c("new_positive", "previous_negative"),
    fns = "standard_deviation"
  )

Wrap-up

In today’s lesson, we got down to business with data tables in R using {gt}. We started by setting some clear goals, introduced the packages we’ll be using, and met our dataset. Then, we got our hands dirty by creating straightforward tables. We learned how to organize our data neatly using spanner columns and tweaking column labels to make things crystal clear and coherent. Then wrapped up with some nifty table summaries. These are the nuts and bolts of table-making in R and {gt}, and they’ll be super handy as we continue our journey on creating engaging and informative tables in R.

Answer Key

  1. Question 1: The Purpose of Spanners
    • B
  2. Question 2: Spanners Creation
Code 
# Solutions are where the numbered lines are

# summarize data first
district_summary <- hiv_malawi %>%
  group_by(region) %>%
  summarize(
    across(  #1
      c(new_positive, previous_positive),
      sum #2
    )
  )

# Create a gt table with spanner headers
summary_table_spanners <- district_summary %>%
  gt() %>% #3
  tab_spanner( #4
    label = "Positive cases",
    columns = c(new_positive, previous_positive) #5
  )
  1. Question 3: Column labels
    • D
  2. Question 4: Summary rows
    • C

Contributors

The following team members contributed to this lesson:

References

  1. Tom Mock, “The Definite Cookbook of {gt}” (2021), The Mockup Blog, https://themockup.blog/static/resources/gt-cookbook.html#introduction.

  2. Tom Mock, “The Grammar of Tables” (May 16, 2020), The Mockup Blog, https://themockup.blog/posts/2020-05-16-gt-a-grammar-of-tables/#add-titles.

  3. RStudio, “Introduction to Creating gt Tables,” Official {gt} Documentation, https://gt.rstudio.com/articles/intro-creating-gt-tables.html.

  4. Fleming, Jessica A., Alister Munthali, Bagrey Ngwira, John Kadzandira, Monica Jamili-Phiri, Justin R. Ortiz, Philipp Lambach, et al. 2019. “Maternal Immunization in Malawi: A Mixed Methods Study of Community Perceptions, Programmatic Considerations, and Recommendations for Future Planning.” Vaccine 37 (32): 4568–75. https://doi.org/10.1016/j.vaccine.2019.06.020.

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