r-visuals

R Visuals in Power BI (PBIR)

Report modification requires tooling. Two paths exist:

1. pbir CLI (preferred) -- use the pbir command and the pbir-cli skill. Install with uv tool install pbir-cli or pip install pbir-cli. Check availability with pbir --version.
2. Direct JSON modification -- if pbir is not available, use the pbir-format skill (pbip plugin) for PBIR JSON structure and patterns. Validate every change with jq empty <file.json>.

If neither the pbir-cli skill nor the pbir-format skill is loaded, ask the user to install the appropriate plugin before proceeding with report modifications.

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

Create the visual.json file manually (see pbir-format skill in the pbip plugin for JSON structure) with visualType: scriptVisual, field bindings for the columns and measures you need (use Values:Table.Column or Values:Table.Measure format), and position/size as required.

Step 2: Write the Script

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

Set the script content in the visual's objects.script[0].properties.source literal value (see PBIR Format section below).

Escaping rules for visual.json injection:

The script must be encoded as a single-quoted DAX literal string inside expr.Literal.Value:

• Newlines in the script become \n in the JSON string
• Double quotes inside the script (e.g., "#5B8DBE") become \" in the JSON string
• The entire script is wrapped in single quotes: 'library(ggplot2)\n...\nprint(p)'
• See examples/visual/ for a complete real-world visual.json showing this encoding

Step 4: Validate

Validate JSON syntax with jq empty <visual.json> and inspect the visual.json to confirm script content and field bindings.

PBIR Format

Scripts are stored in visual.objects.script[0].properties:

{
  "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

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