Tutorial: inline_text

Last Updated: January 16, 2020


Reproducible reports are an important part of good practices. We often need to report the results from a table in the text of an R markdown report. Inline reporting has been made simple with inline_text(). The inline_text() function reports statistics from gtsummary tables inline in an R markdown report.

This vignette will walk a reader through the inline_text() function, and the various functions available to modify and make additions. The inline.text() function works with tables made using tbl_summary(), tbl_regression(), tbl_uvregression(), and tbl_survival().


Before going through the tutorial, install {gtsummary} and {gt}.


Example data set

We’ll be using the trial data set throughout this example.

For brevity in the tutorial, let’s keep a subset of the variables from the trial data set.

trial2 =
  trial %>%
  select(trt, marker, stage)

Inline Results from tbl_summary()

First create a basic summary table using tbl_summary() (review tbl_summary() vignette for detailed overview of this function if needed).

tab1 <- tbl_summary(trial2, by = trt)
Characteristic Drug A, N = 981 Drug B, N = 1021
Marker Level, ng/mL 0.84 (0.24, 1.57) 0.52 (0.19, 1.20)
Unknown 6 4
T Stage
T1 28 (29%) 25 (25%)
T2 25 (26%) 29 (28%)
T3 22 (22%) 21 (21%)
T4 23 (23%) 27 (26%)

1 Statistics presented: median (IQR); n (%)

To report the median (IQR) of the marker levels in each group, use the following commands inline.

The median (IQR) marker level in the Drug A and Drug B groups are `r inline_text(tab1, variable = marker, column = "Drug A")` and `r inline_text(tab1, variable = marker, column = "Drug B")`, respectively.

Here’s how the line will appear in your report.

The median (IQR) marker level in the Drug A and Drug B groups are 0.84 (0.24, 1.57) and 0.52 (0.19, 1.20), respectively.

If you display a statistic from a categorical variable, include the level argument.

`r inline_text(tab1, variable = stage, level = "T1", column = "Drug B")` resolves to “25 (25%)”

Inline Results from tbl_regression()

Similar syntax is used to report results from tbl_regression(), tbl_uvregression(), and tbl_survival() tables. Refer to the tbl_regression() vignette if you need detailed guidance on using these functions.

Let’s first create a regression model.

# build logistic regression model
m1 = glm(response ~ age + stage, trial, family = binomial(link = "logit"))

Now summarize the results with tbl_regression(); exponentiate to get the odds ratios.

tbl_m1 <- tbl_regression(m1, exponentiate = TRUE)
Characteristic OR1 95% CI1 p-value
Age, yrs 1.02 1.00, 1.04 0.091
T Stage
T2 0.58 0.24, 1.37 0.2
T3 0.94 0.39, 2.28 0.9
T4 0.79 0.33, 1.90 0.6

1 OR = Odds Ratio, CI = Confidence Interval

To report the result for age, use the following commands inline.

`r inline_text(tbl_m1, variable = age)`

Here’s how the line will appear in your report.

1.02 (95% CI 1.00, 1.04; p=0.091)

It is reasonable that you’ll need to modify the text. To do this, use the pattern argument. The pattern argument syntax follows glue::glue() format with referenced R objects being inserted between curly brackets. The default is pattern = "{estimate} ({conf.level*100}% CI {conf.low}, {conf.high}; {p.value})". You have access the to following fields within the pattern argument.

{estimate}   primary estimate (e.g. model coefficient, odds ratio)
{conf.low}   lower limit of confidence interval
{conf.high}  upper limit of confidence interval
{p.value}    p-value
{conf.level} confidence level of interval
{N}          number of observations

Age was not significantly associated with tumor response `r inline_text(tbl_m1, variable = age, pattern = "(OR {estimate}; 95% CI {conf.low}, {conf.high}; {p.value})")`.

Age was not significantly associated with tumor response (OR 1.02; 95% CI 1.00, 1.04; p=0.091).

If you’re printing results from a categorical variable, include the level argument, e.g. inline_text(tbl_m1, variable = stage, level = "T3") resolves to “0.94 (95% CI 0.39, 2.28; p=0.9)”.

The inline_text function has arguments for rounding the p-value (pvalue_fun) and the coefficients and confidence interval (estimate_fun). These default to the same rounding performed in the table, but can be modified when reporting inline.

For more details about inline code, review to the RStudio documentation page.