A Descriptive Table 1

In rifttable: Results Tables to Bridge the Rift Between Epidemiologists and Their Data

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

Loading the package

After installation (using remotes::install_github("stopsack/rifttable")), load the package with:

library(rifttable)

Data handling

This example uses the cancer dataset from the survival package.

library(dplyr) # for data handling
data(cancer, package = "survival")
cancer <- cancer |>
  tibble::as_tibble() |>
  dplyr::mutate(
    sex = factor(
      sex,
      levels = 1:2,
      labels = c("Male", "Female")
    ),
    ph.ecog = factor(ph.ecog)
  )

Generate a table design

rifttable's table1_design() generates the design table that can then be passed on to rifttable().

design <- cancer |>
  table1_design(
    age, ph.ecog, ph.karno, pat.karno, # leave empty to include all variables
    by = sex
  )

# Print the design for illustration purposes
design

Obtain Table 1

design |>
  rifttable() |>
  rt_gt() # obtain formatted output

Of note, the dataset is passed along silently together with the design. However, the same design can be applied to different data, if it contains the same variable names, e.g., rifttable(design = design, data = otherdata).

Customize the table

Label variables, set custom rounding, change estimands, and add "Overall" column.

# Alternative: set_variable_labels() from the {labelled} package
attr(cancer$age, "label") <- "Age, years" # At diagnosis? Unclear (Loprinzi 94)
attr(cancer$ph.ecog, "label") <- "Physician-rated ECOG score"
attr(cancer$ph.karno, "label") <- "Physician-rated Karnofsky score"
attr(cancer$pat.karno, "label") <- "Patient-rated Karnofsky score"
attr(cancer$sex, "label") <- "Sex"

design <- cancer |>
  table1_design(
    age, ph.ecog, ph.karno, pat.karno,
    by = sex,
    continuous_type = "mean (sd)"
  ) |> # default: "median (iqr)"
  mutate( # rounding specifically for the "age" variable
    digits = if_else(
      outcome == "age",
      true = 1,
      false = NA
    )
  )

design |>
  rifttable(
    diff_digits = 0, # rounding for continuous variables other than age
    overall = TRUE
  ) |> # add unstratified "overall" column
  rt_gt() |> # obtain formatted output
  gt::tab_footnote(
    footnote = "Data shown are count (percent) or mean (standard deviation)."
  )

Add more statistics per variable

For the age variable, do not just display mean and standard deviation, but also the range. To this end, edit the design just like a regular dataset.

design_new <- design |>
  mutate( # create three rows, not one, for "age"
    copy = if_else(
      outcome == "age",
      true = 3,
      false = 1
    )
  ) |>
  tidyr::uncount(copy, .id = "copy") |>
  mutate( # set new labels and types for the three rows on age
    label = case_when(
      outcome == "age" & copy == 2 ~ "  Mean (SD)",
      outcome == "age" & copy == 3 ~ "  Range",
      TRUE ~ label
    ),
    type = case_when(
      outcome == "age" & copy == 1 ~ "",
      outcome == "age" & copy == 2 ~ "mean (sd)",
      outcome == "age" & copy == 3 ~ "range",
      TRUE ~ type
    )
  )
design_new
design_new |>
  rifttable(
    diff_digits = 0,
    overall = TRUE
  ) |>
  rt_gt() |>
  gt::tab_footnote(
    footnote = "Data shown are count (percent), unless indicated otherwise."
  )

rifttable documentation built on June 8, 2025, 1:52 p.m.