How do I….

This page is dedicated to showing example code for selected tasks.

pen <- palmerpenguins::penguins
library(gtsummary)
library(tidyverse)
library(performance) # for check_model. You will also need to install the `see` package
# webshot2 -- You will have to install this package to use gtsave but don't need to load it

Create Nice variable names

Select the variables of interest and rename them nicely using select. Note any name with a space in it has to be surrounded by a back tick (to the left of the 1 key)

analysis.data <- 
  pen %>% 
  select(`Bill Depth (mm)` = bill_depth_mm, 
         `Bill Length (mm)` = bill_length_mm, 
         Sex = sex) %>%
  na.omit()

Summarize a continuous variable across a categorical variable with many levels?

Problem:

Sometimes the categorical variable has so many levels, or the level names are so long that the table wraps off the edge of the PDF and is unreadable.

pen |> 
  tbl_summary(
  include = bill_length_mm,
  by = species
)

Characteristic	Adelie N = 152¹	Chinstrap N = 68¹	Gentoo N = 124¹
bill_length_mm	38.8 (36.7, 40.8)	49.6 (46.3, 51.2)	47.3 (45.3, 49.6)
Unknown	1	0	1
¹ Median (Q1, Q3)

Solution

There is no easy way to rotate or “transpose” a gtsummary object. One approach is to manually use a “split-apply-combine” method.

we “map” a function \() across all levels of pen$species indexing by s
For species s, we create a tbl_summary
where we modify the header to have a common name of the numeric variable in question
And change the label of the row to the species level s
Then we stack the tables together, and replace the header with the name of the Categorical variable

purrr::map(levels(pen$species), \(s)
  pen |>
    filter(species == s) |>
    tbl_summary(include = bill_length_mm, missing = "no") |>
    modify_header(all_stat_cols() ~ "**Bill length (mm)**") |>
    modify_table_body(~ mutate(.x, label = s))
  ) |>
  tbl_stack(group_header = NULL) |>
  modify_header(label ~ "**Species**")

Species	Bill length (mm)¹
Adelie	38.80 (36.70, 40.80)
Chinstrap	49.6 (46.3, 51.2)
Gentoo	47.30 (45.30, 49.60)
¹ Median (Q1, Q3)

Another approach is to use dplyr to group_by and summarize, then pass the data frame into kable from the knitr package to make a nicer table.

Create your summary statistics using group_by and summarize.
Create a new variable as the column header, using back ticks to allow for a “non standard” variable name.
Create this new variable by paste’ing the value of the mean and sd together with parenthesis, after rounding to 2 digits.
select the variables you want to display
Pass it to kable()

pen |>
  group_by(species) |>
  summarize(mean=mean(bill_length_mm, na.rm=TRUE), 
            sd = sd(bill_length_mm, na.rm=TRUE)) |>
  mutate(`Bill Length(mm)` = paste0(round(mean,2), " (", round(sd,2), ")")) |>
  select(Species= species, `Bill Length(mm)`) |>
  knitr::kable()

Species	Bill Length(mm)
Adelie	38.79 (2.66)
Chinstrap	48.83 (3.34)
Gentoo	47.5 (3.08)

See the kableExtra vignette for additional references on how to prettify this table.

Plots

Export to a file

Make it look good first.

ggplot(analysis.data, aes(x=`Bill Length (mm)`, 
                          y = `Bill Depth (mm)`, 
                          color = Sex)) + 
  geom_point() + 
  theme_bw(base_size=18)

Then save. Import this into your poster file and adjust the width and height as needed.

ggplot(analysis.data, aes(x=`Bill Length (mm)`, 
                          y = `Bill Depth (mm)`, 
                          color = Sex)) + 
  geom_point() + 
  theme_bw(base_size=18) 
  ggsave(filename = "myplot.png",  plot = get_last_plot(),
         width = 6, height = 6, units = "in")

Remove a legend

ggplot(pen, aes(x=island, fill = island)) + geom_bar() + 
  theme(legend.position="none")

Regression model results

Make it look good first.

model <- lm(`Bill Depth (mm)` ~ `Bill Length (mm)` + Sex, data=analysis.data)

model %>% 
  tbl_regression() %>% 
  add_glance_table(include = c(adj.r.squared, nobs))

Characteristic	Beta	95% CI	p-value
Bill Length (mm)	-0.15	-0.18, -0.11	<0.001
Sex
female	—	—
male	2.0	1.6, 2.4	<0.001
Adjusted R²	0.279
No. Obs.	333
Abbreviation: CI = Confidence Interval

Then add the code to export it as an image.

model %>%
  tbl_regression() %>% 
  add_glance_table(include = c(adj.r.squared, nobs)) %>% 
  as_gt() %>%
  gt::gtsave(filename = "model_results.png", expand = 10)

Resizing your output

Necessary to not make certain plots super squished.

Without resizing.

check_model(model) # from the performance package

Apply a code chunk option (#|) to resize the height

```{r}
#| fig-height: 10
check_model(model)
```

Log linear model + gtsummary

If you fit a log-linear model using lm(log(y)~x), tbl_regression doesn’t want to auto-exponentiate your coefficients and CI for you. Here is the workaround

# not run
tbl <-
  lm(log(mpg) ~ am, mtcars) |> 
  tbl_regression()

# run this function to see the underlying column names in `tbl$table_body`
# show_header_names(tbl)

tbl |>
  # remove character version of 95% CI
  modify_column_hide(ci) |> 
  # exponentiate the regression estimates
  modify_table_body(
    \(x) x |> mutate(across(c(estimate, conf.low, conf.high), exp))
  ) |> 
  # merge numeric LB and UB together to display in table
  modify_column_merge(pattern = "{conf.low}, {conf.high}", rows = !is.na(estimate)) |> 
  modify_header(conf.low = "**95% CI**") |> 
  as_kable()

ref: Stack Overflow