---
name: R Visuals
slug: r-visuals
category: Frontend
description: R Visuals creates static Power BI script visuals with R and ggplot2. Use it to add or edit R charts in PBIR reports when you need statistical visuals rendered as PNGs.
github: "https://github.com/data-goblin/power-bi-agentic-development/tree/main/plugins/custom-visuals/skills/r-visuals"
language: "C#"
stars: 872
forks: 129
install: "npx degit https://github.com/data-goblin/power-bi-agentic-development/tree/main/plugins/custom-visuals/skills/r-visuals ~/.claude/skills/r-visuals"
installs_to: ~/.claude/skills/r-visuals
source_path: plugins/custom-visuals/skills/r-visuals/SKILL.md
collection_size: 25
category_size: 567
collection_url: "https://dirskills.com/collections/data-goblin/power-bi-agentic-development"
added: 2026-08-22T05:21:24.341Z
last_synced: 2026-08-22T05:21:24.341Z
canonical_url: "https://dirskills.com/skills/r-visuals"
---

# R Visuals

R Visuals creates static Power BI script visuals with R and ggplot2. Use it to add or edit R charts in PBIR reports when you need statistical visuals rendered as PNGs.

**Install:**

```bash
npx degit https://github.com/data-goblin/power-bi-agentic-development/tree/main/plugins/custom-visuals/skills/r-visuals ~/.claude/skills/r-visuals
```

## README

# R Visuals in Power BI (PBIR)

> **Use `pbir` for every report mutation.** Read PBIR metadata only for diagnosis. If `pbir` is
> unavailable or lacks an operation, stop and report the gap; never edit report JSON directly.

R visuals execute R scripts (primarily ggplot2) to render static PNG images on the Power BI canvas. **ggplot2 is the preferred library** -- its grammar of graphics approach produces clean, publication-quality statistical visualizations with less code. R is particularly strong for statistical visualizations.

## Visual Identity

- **visualType:** `scriptVisual`
- **Data role:** `Values` (columns and measures, multiple allowed)
- **Data variable:** `dataset` (data.frame, auto-injected)
- **Row limit:** 150,000 rows
- **Output:** Static PNG at 72 DPI -- no interactivity

## Workflow: Creating an R Visual

### Step 1: Add the Visual

```bash
pbir add visual scriptVisual "Report.Report/Page.Page" --name RevenueByDateR \
  --data "Values:Sales.Date" --data "Values:Sales.Revenue"
```

### Step 2: Write the Script

```r
library(ggplot2)

p <- ggplot(dataset, aes(x=Date, y=Sales)) +
  geom_col(fill="#5B8DBE") +
  theme_minimal(base_size=12) +
  theme(panel.grid.major.x=element_blank())

print(p)  # MANDATORY for ggplot2
```

Critical rules:
- `print(p)` is **mandatory** for ggplot2 objects -- they do not auto-display in Power BI
- `dataset` is auto-injected as a data.frame; do not create it
- Access columns by index (`dataset[,1]`) to avoid name escaping issues
- Use backticks for column names with spaces: `` dataset$`Order Lines` ``

### Step 2b: Review

Before presenting the script to the user, dispatch the `r-reviewer` agent to validate correctness and provide design feedback.

### Step 3: Inject the Script

```bash
pbir visuals r "Report.Report/Page.Page/RevenueByDateR.Visual" --script-file chart.r
```

The CLI handles PBIR string escaping.

### Step 4: Validate

```bash
pbir visuals bind "Report.Report/Page.Page/RevenueByDateR.Visual" --show
pbir validate "Report.Report" --all
```

## PBIR Format

For read-only diagnosis, scripts are stored in `visual.objects.script[0].properties`:

```json
{
  "source": {"expr": {"Literal": {"Value": "'library(ggplot2)\\n...\\nprint(p)'"}}},
  "provider": {"expr": {"Literal": {"Value": "'R'"}}}
}
```

Identical structure to Python visuals except `visualType` is `scriptVisual` and `provider` is `'R'`.

## Supported Packages

### Power BI Service (R 4.3.3)

| Package | Version | Purpose |
|---------|---------|---------|
| ggplot2 | 3.5.1 | Grammar of graphics |
| dplyr | 1.1.4 | Data manipulation |
| tidyr | 1.3.1 | Data tidying |
| ggrepel | 0.9.5 | Non-overlapping labels |
| patchwork | 1.2.0 | Compose multiple plots |
| cowplot | 1.1.3 | Publication-quality plots |
| corrplot | 0.94 | Correlation matrices |
| viridis | 0.6.5 | Color scales |
| RColorBrewer | 1.1-3 | Color palettes |
| forecast | 8.23.0 | Time series forecasting |
| pheatmap | 1.0.12 | Heatmaps |
| treemap | 2.4-4 | Treemaps |
| lattice | 0.22-6 | Trellis graphics |

~1000 CRAN packages available. **Not supported:** packages requiring networking (RgoogleMaps, mailR).

Full package list: https://learn.microsoft.com/power-bi/connect-data/service-r-packages-support

### Desktop

Any locally installed R package works without restriction. R must be installed separately.

## Best Practices

1. **Always call `print(p)`** -- ggplot2 objects require explicit printing
2. **Guard against empty data** -- `if (nrow(dataset) == 0) { plot.new(); text(0.5, 0.5, "No data") }`
3. **Use index-based column access** -- `dataset[,1]` avoids name escaping issues
4. **Use `theme_minimal()`** -- clean aesthetic that works well with Power BI
5. **Factor categorical variables** -- control sort order explicitly with `factor()`
6. **Use hex colors** matching the report theme
7. **Set margins** -- `plot.margin=margin(t, r, b, l)` to prevent clipping
8. **Keep scripts concise** -- 5-min timeout Desktop, 1-min Service

## Limitations

| Constraint | Desktop | Service |
|------------|---------|---------|
| Output | Static PNG, 72 DPI | Static PNG, 72 DPI |
| Timeout | 5 minutes | 1 minute |
| Row limit | 150,000 | 150,000 |
| Output size | 2 MB | 30 MB |
| Networking | Unrestricted | Blocked |
| Gateway | Personal only | Personal only |
| Cross-filter FROM | Not supported | Not supported |
| Receive cross-filter | Yes | Yes |
| Publish to web | Not supported | Not supported |
| Embed (app-owns-data) | Not supported | Not supported |

## Script Structure Template

```r
library(ggplot2)

# 1. Guard against empty data
if (nrow(dataset) == 0) {
  plot.new()
  text(0.5, 0.5, "No data available", cex=1.5)
} else {
  # 2. Data preparation (index-based access)
  df <- data.frame(
    category = dataset[,1],
    value = dataset[,2]
  )

  # 3. Create visualization
  p <- ggplot(df, aes(x=reorder(category, -value), y=value)) +
    geom_col(fill="#5B8DBE", width=0.7) +
    theme_minimal(base_size=12) +
    theme(
      panel.grid.major.x = element_blank(),
      axis.title = element_blank()
    )

  # 4. Render
  print(p)
}
```

## R vs Python Syntax Reference

For the language-choice decision, see the "When to Use a Script Visual" section above. This table covers only mechanical syntax differences for scripts already committed to R:

| Aspect | R (`scriptVisual`) | Python (`pythonVisual`) |
|--------|-------|--------|
| Render call | `print(p)` | `plt.show()` |
| Column access | `dataset[,1]` or `dataset$col` | `dataset.iloc[:,0]` or `dataset["col"]` |
| Empty guard | `if (nrow(dataset) == 0)` | `if len(dataset) == 0:` |
| Factor/category order | `factor(x, levels=...)` | `pd.Categorical(x, categories=...)` |
| Runtime (Service) | R 4.3.3 | Python 3.11 |

## When to Use a Script Visual

Reach for an R visual only when **all** of the following hold:

- The chart has no native equivalent and no reasonable Deneb spec
- The value is in a statistical computation that must run at render time (model fit, kernel density, forecast band), not just a shape Vega could draw
- The visual does not need to be a cross-filter source, hover tooltips, publish-to-web, or app-owns-data embed
- The report is served in a Pro/PPU or higher capacity with a Fabric-enabled region

If interactivity or cross-filtering matters, use **Deneb** (a static PNG cannot be a selection source). If the need is a small inline mark (sparkline, bar, status pill), use an **SVG measure** (no row cap, no timeout, no licensing/region gate, renders under publish-to-web). The script visual's niche is narrow: compute-at-render statistical plots for internal or org consumption.

**R vs Python once a script visual is the right call:** use R for publication-quality statistical defaults and packages with no Python peer (`forecast`, `corrplot`, `pheatmap`, ridgeline/violin). Use Python when the computation leans on scikit-learn, statsmodels, or scipy, or when surrounding report logic is already Python. Where equal, default to whichever language the report's other scripts use; mixing doubles the publish-time package surface to validate.

Do not default to a script visual because a chart type "looks statistical." A box plot, lollipop, or dumbbell is an SVG-measure or Deneb job; reserve scripts for charts that genuinely compute.

## References

- **`references/data-model.md`** -- `dataset` grouping mechanic, row/byte caps, forcing per-row input, and R-specific traps (Time type, text rendering flags, CJK fonts)
- **`references/community-examples.md`** -- R Graph Gallery examples organized by chart type (distribution, correlation, ranking, evolution, flow)
- **`references/ggplot2-patterns.md`** -- Common ggplot2 chart patterns (bar, donut, line, heatmap, bullet)
- **`examples/script/`** -- Standalone R scripts (bar-chart, trend-line) -- ready to inject into visual.json after escaping
- **`examples/visual/bullet-chart.json`** -- PBIR visual.json: bullet chart with conditional coloring, error handling, and extensive escaping
- **`examples/visual/bar-chart.json`** -- PBIR visual.json: horizontal bar with PY comparison lines and colored account labels
- **`examples/visual/trend-line.json`** -- PBIR visual.json: area chart with ribbon plot and month factor handling

## Fetching Docs

To retrieve current R visual / package support docs, use `microsoft_docs_search` + `microsoft_docs_fetch` (MCP) if available, otherwise `mslearn search` + `mslearn fetch` (CLI). Search based on the user's request and run multiple searches as needed to ensure sufficient context before proceeding.

## Related Skills

- **`pbi-report-design`** -- Layout and design best practices
- **`python-visuals`** -- Python Script visuals (same concept, different language)
- **`deneb-visuals`** -- Vega/Vega-Lite visuals (interactive, vector-based alternative)
- **`svg-visuals`** -- SVG via DAX measures (lightweight inline graphics)
- **`pbir-format`** (pbip plugin) -- PBIR JSON format reference