powerbi-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`