---
title: "Getting Started with litReview"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting Started with litReview}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 8,
  fig.height = 5
)
has_ggalluvial <- requireNamespace("ggalluvial", quietly = TRUE)
has_treemapify <- requireNamespace("treemapify", quietly = TRUE)
has_maps       <- requireNamespace("maps", quietly = TRUE)
has_ggfittext  <- requireNamespace("ggfittext", quietly = TRUE)
has_ggupset    <- requireNamespace("ggupset", quietly = TRUE)
```

## Overview

`litReview` helps you summarize and visualize categorical data extracted during
a literature review. Every plot function returns a standard ggplot that you can
customize with `+`. Column names can be passed bare or quoted.

```{r setup}
library(litReview)
data(studies)
head(studies)
```

 

## Bar chart

`reviewBar()` produces a horizontal bar chart with frequency and percentage
labels.

```{r bar-design}
reviewBar(studies, Design)
```

Customize with `+` like any ggplot:

```{r bar-custom}
library(ggplot2)
reviewBar(studies, Design, fill = "#59a14f") +
  labs(title = "Study Designs", subtitle = "n = 12 studies")
```

### Study labels on bars

Set `studlabs = TRUE` to overlay the contributing study IDs on each bar:

```{r bar-studlabs, fig.height = 6, eval = has_ggfittext}
reviewBar(studies, Design, fill = PALETTE[2], studlabs = TRUE)
```

### Multi-value columns

The `Outcome` column contains multiple values per cell separated by `"\r\n"`:

```{r bar-outcome, fig.height=3}
reviewBar(studies, Outcome, fill = PALETTE[4], width = 0.6)
```

### Adjusting label space

If labels are clipped, increase `label_space` (default 1.6):

```{r bar-labelspace, fig.height=9}
reviewBar(studies, Country, fill = PALETTE[6], label_space = 2)
```

## Stacked / grouped bar chart

`reviewStackedBar()` cross-tabulates a primary category against a second
grouping variable, drawing one horizontal bar per category split by group. By
default (`position = "fill"`) each bar is scaled to 100%, so you can compare
composition across categories:

```{r stacked-fill, fig.height = 5}
reviewStackedBar(studies, Design, RiskOfBias)
```

Use `position = "stack"` to show raw counts instead:

```{r stacked-count, fig.height = 5}
reviewStackedBar(studies, Design, RiskOfBias, position = "stack")
```

Both `col` and `group` may contain multi-value cells, which are split before
counting. Hide the in-segment labels with `labels = FALSE`.

## Waffle chart

Each square represents one study occurrence:

```{r waffle-outcome, fig.height = 4}
reviewWaffle(studies, Outcome, ncol = 11)
```

## Donut / pie chart

```{r pie-design}
reviewPie(studies, Design)
```

Set `donut = FALSE` for a classic pie:

```{r pie-full}
reviewPie(studies, Design, donut = FALSE)
```

## Co-occurrence heatmap

`reviewOverlap()` shows how two columns co-occur across studies:

```{r overlap, fig.height = 4}
reviewOverlap(studies, Design, Outcome, fill = PALETTE[3])
```

## UpSet plot

`reviewUpset()` visualizes how the values of a multi-value column co-occur
across studies. Each study contributes the set of distinct values it reports,
and each bar counts the studies sharing that exact combination — a scalable
alternative to the pairwise heatmap when three or more values can co-occur.
Requires the `ggupset` package.

```{r upset, fig.height = 5, eval = has_ggupset, warning = FALSE}
reviewUpset(studies, Outcome)
```

Sort combinations by set size (`"degree"`) instead of frequency, and cap how
many are shown with `n_intersections`:

```{r upset-degree, fig.height = 5, eval = has_ggupset, warning = FALSE}
reviewUpset(studies, Intervention, sort_by = "degree", n_intersections = 10)
```

## Alluvial plot

`reviewAlluvial()` shows co-occurrence and flow between categories across
multiple columns. Each study traces a path through the strata. Requires the
`ggalluvial` package.

```{r alluvial, fig.height = 5, eval = has_ggalluvial}
reviewAlluvial(studies, c("Design", "Outcome"))
```

Add proportion or count labels on each stratum:

```{r alluvial-prop, fig.height = 5, eval = has_ggalluvial}
reviewAlluvial(studies, c("Design", "Outcome"), labels = "prop")
```

Show flow counts between strata:

```{r alluvial-flow, fig.height = 5, eval = has_ggalluvial}
reviewAlluvial(studies, c("Design", "Outcome"), labels = "none",
               flow_labels = TRUE)
```

Custom axis labels:

```{r alluvial-labels, fig.height = 5, eval = has_ggalluvial}
reviewAlluvial(studies, c("Design", "Outcome","AgeGroup"),
               axis_labels = c("Study Design", "Reported Outcome", "Age group"))
```

## Treemap

`reviewTreemap()` displays category frequencies as nested rectangles whose area
is proportional to the count. Requires the `treemapify` package.

```{r treemap, fig.height = 4, eval = has_treemapify}
reviewTreemap(studies, Design)
```

Use `color_by` to add a hierarchical grouping. Here we show interventions
colored by their higher-order type:

```{r treemap-color, fig.height = 5, eval = has_treemapify}
reviewTreemap(studies, Intervention, color_by = InterventionType)
```

Show study IDs inside each rectangle:

```{r treemap-studlabs, fig.height = 5, eval = has_treemapify}
reviewTreemap(studies, Design, studlabs = TRUE)
```

## Year trend

`reviewTrend()` shows how categories distribute across publication years:

```{r trend}
reviewTrend(studies, Design)
```

Add counts, within-year percentages, or both on each segment:

```{r trend-count}
reviewTrend(studies, Design, labels = "count")
```

```{r trend-percent}
reviewTrend(studies, Design, labels = "percent")
```

```{r trend-both}
reviewTrend(studies, Design, labels = "both")
```

Or overlay study IDs:

```{r trend-studies}
reviewTrend(studies, Design, labels = "studies")
```

## World map

`reviewMap()` shades countries by the number of studies. Common aliases like
"United States" or "United Kingdom" are resolved automatically.
Requires the `maps` package (`install.packages("maps")`):

```{r map, fig.width = 10, fig.height = 5, eval = has_maps}
reviewMap(studies)
```

## Summary table

`reviewTable()` returns a formatted `gt` table:

```{r table-design}
reviewTable(studies, Design)
```

## Handling missing data

Literature review datasets often have missing values. All functions accept
`na.rm`, `na_label`, and `na_in_percent` to control how NAs are handled.

Let's create example data with some missing values:

```{r na-data}
df_na <- data.frame(
  StudyID = paste0("S", 1:10),
  Design  = c("RCT", "Cohort", NA, "RCT", "Case-control",
              NA, "RCT", "Cohort", NA, "RCT"),
  stringsAsFactors = FALSE
)
```

### Drop NAs, percentages of total (default)

NAs are dropped, but the denominator is all 10 studies. Percentages reflect the
share of the full sample, so they do not sum to 100%:

```{r na-default}
summarize_data(df_na, Design)
```

### Drop NAs, percentages of reported only

Set `na_in_percent = FALSE` so that the denominator only counts the 7 studies
that reported a design. Percentages sum to 100%:

```{r na-pct-reported}
summarize_data(df_na, Design, na_in_percent = FALSE)
```

### Keep NAs, percentages of total

Set `na.rm = FALSE` to include a "Not reported" category. All percentages sum
to 100%:

```{r na-keep}
summarize_data(df_na, Design, na.rm = FALSE)
```

### Keep NAs with a custom label, percentages of reported

Combine all three parameters. Here "Missing" replaces `NA`, and the denominator
excludes missing rows:

```{r na-custom}
summarize_data(df_na, Design, na.rm = FALSE, na_label = "Missing",
               na_in_percent = FALSE)
```

### Using NA options in plots

The same parameters work in every plot function:

```{r na-bar, fig.height = 4}
reviewBar(df_na, Design, na.rm = FALSE, na_label = "Missing", na_in_percent = FALSE)
```

```{r na-pie}
reviewPie(df_na, Design, na.rm = FALSE)
```

## Custom study ID column

All functions default to `study_id = StudyID`. If your data uses a different
column, pass it:

```{r custom-id}
df <- data.frame(
  ID = paste0("A", 1:5),
  Type = c("X", "Y", "X", "Z", "X"),
  stringsAsFactors = FALSE
)
reviewBar(df, Type, study_id = ID)
```

## Using `summarize_data()` directly

If you need the raw summary data frame (e.g. for further processing), use
`summarize_data()`. Note that `Percent` is numeric:

```{r summarize}
summarize_data(studies, Design)
```

## Palette

`PALETTE` provides 8 colors you can cycle through:

```{r palette}
PALETTE
```