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litReview: An R package for plotting literature review results

litReview provides functions to summarize and visualize categorical data from literature reviews. All plot functions return standard ggplot objects you can customize with +.

Installation

Install the released version from CRAN:

install.packages("litReview")

Or the development version from GitHub:

# install.packages("remotes")
remotes::install_github("sonsoleslp/litReview")

Usage

Bar chart 

reviewBar(studies, Design)

reviewBar(studies, Design, fill = "#59a14f") +
  ggplot2::labs(title = "Study Designs")

Study labels on bars 

reviewBar(studies, Design, fill = PALETTE[2], studlabs = TRUE)

Stacked bar chart

reviewStackedBar() compares the composition of one category across another. By default each bar is scaled to 100% to compare proportions:

reviewStackedBar(studies, Design, RiskOfBias)

Use position = "stack" for raw counts:

reviewStackedBar(studies, Design, RiskOfBias, position = "stack")

Histogram

reviewHistogram() bins a numeric column; add fill_by to stack by a group.

reviewHistogram(studies, SampleSize, bins = 15)

Waffle chart 

reviewWaffle(studies, Design, ncol = 10)

Donut chart 

reviewPie(studies, Design)

Co-occurrence heatmap 

reviewOverlap(studies, Design, Outcome, fill = "#b07aa1")

UpSet plot

reviewUpset() shows how the values of a multi-value column co-occur across studies — a scalable alternative to the pairwise heatmap. Requires the ggupset package.

reviewUpset(studies, Outcome,  fill = "#f16769")

Alluvial plot 

reviewAlluvial(studies, c("Design", "Outcome"), labels = "prop")

Year trend 

reviewTrend(studies, Design)

World map 

reviewMap(studies)

Treemap 

reviewTreemap(studies, Design)

reviewTreemap(studies, Intervention, color_by = InterventionType)

Coding matrix

reviewMatrix() shows a study-by-criteria evidence matrix: a tile wherever a study addresses a criterion, coloured by a study attribute with the coding level inside.

criteria <- c("Randomization", "Blinding", "SampleJustification",
              "AttritionReported", "EthicsApproval", "EffectSize")
reviewMatrix(studies[1:20, ], criteria, color_by = "PubType",
             levels = c(F = "Full", P = "Partial", M = "Mention"))

Tree diagram

reviewTree() draws a left-to-right hierarchy from columns given in order, listing the studies at each leaf.

reviewTree(studies, c("InterventionType", "Intervention"), study_id = Author)

Summary table 

reviewTable(studies, Design, study_id = "Author")

Handling missing data 

df_na <- data.frame(
  StudyID = paste0("S", 1:8),
  Design  = c("RCT", "Cohort", NA, "RCT", "Case-control", NA, "RCT", "Cohort"),
  stringsAsFactors = FALSE
)
reviewBar(df_na, Design, na.rm = FALSE, na_label = "Missing", na_last = TRUE)