Data Visualization

Create publication-ready figures using ggplot2 in R. Validate data and gather all visual requirements before generating the first plot.

When to Use

• •Creating any chart, figure, or plot
• •Refining an existing visualization (spacing, labels, colors, layout)
• •User says "plot", "graph", "visualize", "figure", "chart"

When NOT to Use

• •Quick exploratory plot() or hist() for debugging (just do it)
• •User explicitly asks for base R graphics

Core Rule

Ask about ALL visual elements upfront before generating the first plot. Do not produce a draft and iterate one tweak at a time.

Workflow

Step 1: Validate Data

Before any plotting code, confirm:

1. •Read the data and print column names, types, and 3-5 sample rows
2. •Confirm column mappings with the user: which columns map to x, y, color, facet, etc.
3. •Check for issues: NAs in plot variables, unexpected factor levels, wrong types

Do NOT guess column mappings silently. If ambiguous, ask.

Step 2: Gather Visual Requirements

Ask about ALL of these before the first plot:

Step 3: Build the Plot

Language: R with ggplot2. Always.

Theme baseline:

theme_minimal(base_size = 12) +
  theme(
    panel.grid.minor = element_blank(),
    strip.text = element_text(face = "bold"),
    legend.position = "bottom"
  )

Color palette: Colorblind-safe. No strong default - pick appropriate per chart:

• •Categorical (2-8 groups): scale_color_brewer(palette = "Set2") or similar
• •Sequential: scale_fill_viridis_c()
• •Diverging: scale_fill_distiller(palette = "RdBu")

Multi-panel approach:

• •Same chart type across panels: facet_wrap() / facet_grid()
• •Different chart types combined: patchwork package

Step 4: Chart-Specific Patterns

Bar Charts

ggplot(df, aes(x = group, y = estimate, fill = condition)) +
  geom_col(position = position_dodge(width = 0.8), width = 0.7) +
  geom_errorbar(aes(ymin = ci_low, ymax = ci_high),
                position = position_dodge(width = 0.8), width = 0.2) +
  # 95% CI error bars by default

Coefficient / Forest Plots

ggplot(df, aes(x = estimate, y = reorder(term, estimate))) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "gray50") +
  geom_point(size = 2) +
  geom_errorbarh(aes(xmin = ci_low, xmax = ci_high), height = 0.2)

Input sources (handle all three):

• •R model objects: Use broom::tidy(conf.int = TRUE) or marginaleffects::avg_slopes()
• •Stata margins output: Parse CSV/text from margins or esttab export
• •Pre-computed CSV: Expect columns: term, estimate, ci_low, ci_high

Line / Time Series

ggplot(df, aes(x = time, y = value, color = group)) +
  geom_line(linewidth = 0.8) +
  geom_ribbon(aes(ymin = ci_low, ymax = ci_high, fill = group), alpha = 0.15)

Network Graphs

Use igraph base plotting (not ggraph):

library(igraph)
plot(g,
     vertex.size = degree(g) * 2,
     vertex.label.cex = 0.7,
     vertex.color = V(g)$color,
     edge.arrow.size = 0.3,
     layout = layout_with_fr(g))

Step 5: Stata-to-R Pipeline

When combining Stata models with R visualization:

1. •In Stata: Export estimates with esttab using "estimates.csv", csv ci or: margins, post then matrix list e(b), matrix list r(table)
2. •In R: Parse the CSV, clean column names, build ggplot

# Parse Stata esttab CSV output
est <- read.csv("estimates.csv", skip = 1)  # skip header row
# Clean: remove significance stars, convert to numeric
est$estimate <- as.numeric(gsub("[*]", "", est$estimate))

Step 6: Save Output

Always save both formats:

ggsave("figure.png", width = 7, height = 5, dpi = 300)
ggsave("figure.pdf", width = 7, height = 5)

Adjust dimensions based on content:

• •Single panel: 7 x 5
• •Two panels side by side: 10 x 5
• •Tall coefficient plot (many terms): 7 x 8
• •Network graph: 7 x 7

Quick Reference

Common Mistakes