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Pie and donut charts

Source: vignettes/articles/pie.Rmd
pie.Rmd

reviewPie() summarizes a single column as a donut (default) or pie chart, with each slice annotated by its count and percentage. It is the categorical counterpart to reviewBar(). This article walks through every argument of

reviewPie(data, col, sep = "\r\n", colors = PALETTE, donut = TRUE,
          study_id = StudyID, base_size = 12, na.rm = TRUE,
          na_label = "Not reported", na_in_percent = TRUE, na_last = FALSE)

Default

Pass the data frame and a column. Column names may be bare or quoted. Slices are sized by frequency and labelled with their count and percentage. By default you get a donut (a pie with a hollow center).

reviewPie(studies, Design)

col: the column to summarize

Any categorical column works. Multi-value columns (cells holding several values) are split first — see sep below.

reviewPie(studies, Outcome)

sep: multi-value separator

Some cells hold several values. In studies, Outcome uses newline separators ("\r\n", the default), so each value is counted independently. If your data uses a different delimiter, set sep. Here we rebuild a semicolon-separated column to demonstrate:

studies_semi <- studies
studies_semi$Outcome <- gsub("\r\n", "; ", studies_semi$Outcome)
reviewPie(studies_semi, Outcome, sep = "; ")

colors: slice fill colors

By default slices are filled from the package PALETTE, cycled across categories:

reviewPie(studies, Design, colors = PALETTE)

Passing a custom vector of colors maps one color per slice (recycled if shorter than the number of categories):

reviewPie(studies, Design, colors = c("#59a14f", "#f28e2b", "#4e79a7"))

A named vector pins specific colors to specific categories, regardless of slice order:

reviewPie(studies, Design,
          colors = c("RCT" = "#f16769", "Cohort" = "#7ea9c7",
                     "Case-control" = "#59a14f"))

donut: donut vs. full pie

donut = TRUE (the default) leaves a hollow center. Set donut = FALSE for a solid, filled pie:

reviewPie(studies, Design, donut = FALSE)

The donut form again for contrast:

reviewPie(studies, Design, donut = TRUE)

study_id: the identifier column

study_id names the column of study identifiers used when counting rows (default StudyID). It rarely needs changing, but if your identifiers live in a differently named column, point reviewPie() at it. Here Author serves as the identifier:

reviewPie(studies, Design, study_id = Author)

base_size: overall text and element scaling

A single knob scales all text and spacing proportionally. Smaller, for multi-panel figures:

reviewPie(studies, Design, base_size = 9)

Larger, for slides or posters:

reviewPie(studies, Design, base_size = 18)

Missing data: na.rm, na_label, na_in_percent, na_last

These four arguments control how NA (and empty) cells are treated. We use a column that actually has missing values — FundingSource.

By default na.rm = TRUE drops missing rows, but the percentage denominator is still the full sample, so the shown percentages need not sum to 100%:

reviewPie(studies, FundingSource)

Keep the missing rows as their own slice with na.rm = FALSE; na_label sets its name:

reviewPie(studies, FundingSource, na.rm = FALSE, na_label = "Not reported")

With na_in_percent = FALSE the denominator excludes missing rows, so the reported categories sum to 100%:

reviewPie(studies, FundingSource, na.rm = FALSE, na_in_percent = FALSE)

na_last = TRUE forces the missing-value slice to the end regardless of its frequency:

reviewPie(studies, FundingSource, na.rm = FALSE, na_last = TRUE)

Composing with ggplot2

Every review*() function returns a plain ggplot, so you can keep adding layers, scales, and labels with +:

reviewPie(studies, Design, colors = PALETTE) +
  labs(title = "Study designs", subtitle = "n = 50 studies",
       caption = "Source: example dataset") +
  theme(plot.title.position = "plot")