From reports
R visual creation and ggplot2 patterns for PBIR reports. Automatically invoke when the user mentions "R visual", "ggplot2", "ggplot in Power BI", or asks to "create an R visual", "add an R chart", "write an R visual script", "inject an R script into Power BI".
npx claudepluginhub data-goblin/power-bi-agentic-development --plugin reportsThis skill uses the workspace's default tool permissions.
> **Report modification requires tooling.** Two paths exist:
Searches, retrieves, and installs Agent Skills from prompts.chat registry using MCP tools like search_skills and get_skill. Activates for finding skills, browsing catalogs, or extending Claude.
Searches prompts.chat for AI prompt templates by keyword or category, retrieves by ID with variable handling, and improves prompts via AI. Use for discovering or enhancing prompts.
Guides MCP server integration in Claude Code plugins via .mcp.json or plugin.json configs for stdio, SSE, HTTP types, enabling external services as tools.
Report modification requires tooling. Two paths exist:
pbirCLI (preferred) -- use thepbircommand and thepbir-cliskill. Install withuv tool install pbir-cliorpip install pbir-cli. Check availability withpbir --version.- Direct JSON modification -- if
pbiris not available, use thepbir-formatskill (pbip plugin) for PBIR JSON structure and patterns. Validate every change withjq empty <file.json>.If neither the
pbir-cliskill nor thepbir-formatskill 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.
scriptVisualValues (columns and measures, multiple allowed)dataset (data.frame, auto-injected)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.
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 BIdataset is auto-injected as a data.frame; do not create itdataset[,1]) to avoid name escaping issuesdataset$`Order Lines`Before presenting the script to the user, dispatch the r-reviewer agent to validate correctness and provide design feedback.
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:
\n in the JSON string"#5B8DBE") become \" in the JSON string'library(ggplot2)\n...\nprint(p)'examples/visual/ for a complete real-world visual.json showing this encodingValidate JSON syntax with jq empty <visual.json> and inspect the visual.json to confirm script content and field bindings.
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'.
| 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
Any locally installed R package works without restriction. R must be installed separately.
print(p) -- ggplot2 objects require explicit printingif (nrow(dataset) == 0) { plot.new(); text(0.5, 0.5, "No data") }dataset[,1] avoids name escaping issuestheme_minimal() -- clean aesthetic that works well with Power BIfactor()plot.margin=margin(t, r, b, l) to prevent clipping| 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) | Ending May 2026 | Ending May 2026 |
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)
}
| Aspect | R (scriptVisual) | Python (pythonVisual) |
|---|---|---|
| Primary library | ggplot2 | matplotlib |
| 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 control | factor(x, levels=...) | pd.Categorical(x, categories=...) |
| Runtime (Service) | R 4.3.3 | Python 3.11 |
R visuals are the preferred choice for statistical and analytical visualizations, particularly where R's statistical ecosystem excels. Use R visuals when you need:
Output is static PNG -- no cross-filtering FROM the visual, no hover/tooltip interactivity. Use Deneb instead for interactive custom visuals. Use SVG measures for simple inline graphics in tables/cards.
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 escapingexamples/visual/bullet-chart.json -- PBIR visual.json: bullet chart with conditional coloring, error handling, and extensive escapingexamples/visual/bar-chart.json -- PBIR visual.json: horizontal bar with PY comparison lines and colored account labelsexamples/visual/trend-line.json -- PBIR visual.json: area chart with ribbon plot and month factor handlingTo 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.
pbi-report-design -- Layout and design best practicespython-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