Create a Year-Trend Bar Chart

Shows how the values in a column distribute across publication years as a stacked bar chart. Returns a ggplot2::ggplot object.

Usage

reviewTrend(
  data,
  col,
  year_col = Year,
  sep = "\r\n",
  colors = PALETTE,
  base_size = 12,
  na.rm = TRUE,
  na_label = "Not reported",
  na_last = FALSE,
  labels = c("none", "count", "percent", "both", "studies"),
  study_id = StudyID
)

Arguments

data

A data frame with at least StudyID (or the column set by study_id), the column named by col, and a year column.

col

Column to visualize (quoted or unquoted).

year_col

Year column (quoted or unquoted). Defaults to Year.

sep

Character. Separator for multi-value cells. Defaults to "\r\n".

colors

Character vector. Fill colors cycled across categories. Defaults to PALETTE.

base_size

Numeric. Base font size in points. Defaults to 12.

na.rm

Logical. Drop missing values? Defaults to TRUE.

na_label

Character. Label for missing values when na.rm = FALSE. Defaults to "Not reported".

na_last

Logical. If TRUE (and na.rm = FALSE), place the missing category last in the stack and legend. Defaults to FALSE.

labels

Character. What to display on each bar segment. One of "none" (default), "count", "percent" (within-year), "both" (count and percent), or "studies" (comma-separated study IDs).

study_id

Column containing study identifiers (quoted or unquoted). Used when labels = "studies". Defaults to StudyID.

Value

A ggplot2::ggplot object.

Examples

df <- data.frame(
  StudyID = c("S1", "S2", "S3", "S4"),
  Year    = c(2020, 2021, 2021, 2022),
  Design  = c("RCT", "Cohort", "RCT", "Case-control"),
  stringsAsFactors = FALSE
)
reviewTrend(df, Design)

reviewTrend(df, Design, labels = "count")

reviewTrend(df, Design, labels = "percent")

reviewTrend(df, Design, labels = "studies")