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

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

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

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

The CLI handles PBIR string escaping.

Step 4: Validate