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Gmisc

CRAN status CRAN downloads R-CMD-check

Gmisc collects utilities for the graphics and tables that recur in medical research papers — built so they compose with the native R pipe (|>):

Installation

# From CRAN
install.packages("Gmisc")

# Development version from GitHub
# install.packages("remotes")
remotes::install_github("gforge/Gmisc")

Flowcharts & CONSORT diagrams

Build a flowchart as a named list of boxes, position the columns with spread()/move(), then add the connectors. Parallel arms are just lists, and phaseLabel() drops a CONSORT phase heading (“Allocation”, “Follow-up”, …) between the arms — centred, slightly overlapping, and drawn on top.

library(Gmisc)
library(grid)

main_gp <- gpar(fill = "white", col = "black", lwd = 1)
head_gp <- gpar(fill = "#c8daf7", col = "#2f5f9f", lwd = 1)
con_gp  <- gpar(col = "#4f86c6", fill = "#4f86c6", lwd = 1.8)
sw      <- unit(70, "mm")

flowchart(
  rando = boxGrob("Randomised\nN = 100", box_gp = main_gp),
  groups = list(
    boxGrob("Allocated to intervention\nn = 50", width = sw, box_gp = main_gp),
    boxGrob("Allocated to control\nn = 50", width = sw, box_gp = main_gp)
  ),
  followup = list(
    boxGrob("Lost to follow-up\nn = 1", width = sw, box_gp = main_gp),
    boxGrob("Lost to follow-up\nn = 2", width = sw, box_gp = main_gp)
  ),
  analysis = list(
    boxGrob("Analysed\nn = 49", width = sw, box_gp = main_gp),
    boxGrob("Analysed\nn = 48", width = sw, box_gp = main_gp)
  )
) |>
  spread(axis = "y", margin = unit(0.04, "npc")) |>
  move(subelement = list(c("groups", 1), c("followup", 1), c("analysis", 1)), x = 0.27) |>
  move(subelement = list(c("groups", 2), c("followup", 2), c("analysis", 2)), x = 0.73) |>
  phaseLabel("groups",   "Allocation", box_gp = head_gp) |>
  phaseLabel("followup", "Follow-up",  box_gp = head_gp) |>
  phaseLabel("analysis", "Analysis",   box_gp = head_gp) |>
  connect("rando",    "groups",   type = "N", lty_gp = con_gp, arrow_size = 3, smooth = TRUE) |>
  connect("groups",   "followup", type = "v", lty_gp = con_gp, arrow_size = 3) |>
  connect("followup", "analysis", type = "v", lty_gp = con_gp, arrow_size = 3)

See vignette("Grid-based_flowcharts", package = "Gmisc") for the full API.

Descriptive “Table 1”

getDescriptionStatsBy() summarises variables split by a grouping column. It is often used with mergeDesc() to group related variables into sections, and pipes straight into htmlTable() for a publication-ready table.

library(dplyr)
library(Gmisc)

# A custom wrapper to keep statistics and formatting consistent
# (e.g., same digits, p-values, and header count)
get_stats <- function(data, ...) {
  res <- data |>
    getDescriptionStatsBy(...,
                          by = am,
                          statistics = TRUE,
                          digits = 1,
                          header_count = TRUE)
  if (is.list(res)) {
    return(do.call(rbind, res))
  }
  return(res)
}

mtcars_prep <- mtcars |>
  mutate(am = factor(am, labels = c("Automatic", "Manual")),
         gear = factor(gear),
         cyl = factor(cyl)) |>
  set_column_labels(mpg = "Gas",
                    wt = "Weight",
                    hp = "Horsepower",
                    cyl = "Cylinders",
                    gear = "Gears") |>
  set_column_units(mpg = "Miles/gallon",
                   wt = "10<sup>3</sup> lbs",
                   hp = "hp")

# Group variables and merge them into a single table
mergeDesc(
  "Main" = mtcars_prep |> get_stats(mpg, wt),
  "Engine" = mtcars_prep |> get_stats(hp, cyl),
  "Transmission" = mtcars_prep |> get_stats(gear)
) |>
  htmlTable(caption = "Baseline characteristics by transmission",
            tfoot = "&dagger; Statistics: Mean (SD) for continuous; n (%) for categorical")

See vignette("Descriptives", package = "Gmisc") for the many formatting options.

Transition plots

The Transition class shows how observations move between classes over time; a third dimension can be encoded as a colour split within each box.

set.seed(1)
n <- 100
sex    <- sample(c("Male", "Female"), n, replace = TRUE)
before <- sample(1:3, n, replace = TRUE)
# Most cases improve one class, some stay, a few worsen
after  <- pmin(pmax(before - sample(c(-1, 0, 1), n, replace = TRUE, prob = c(.15, .35, .5)), 1), 3)

lbl <- c("A", "B", "C")
tbl <- table(factor(before, 1:3, lbl), factor(after, 1:3, lbl), sex)

transitions <- getRefClass("Transition")$new(tbl, label = c("Before surgery", "1 year after"))
transitions$title   <- "Charnley class before vs. after surgery"
transitions$clr_bar <- "bottom"
transitions$render()

See vignette("Transition-class", package = "Gmisc") for customisation.

Learn more

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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