R Empirical Finance

R Empirical Finance Code Standards

Package stack

Always prefer these packages unless the user specifies otherwise:

• Data wrangling: dplyr or tidyverse (only data.table when required)
• Regressions: fixest (feols, feglm) — never lm() for panel data
• Large data I/O: arrow for Parquet files; never read.csv() for large data
• Plotting: ggplot2 with clean minimal themes
• Tables: etable() from fixest, or modelsummary
• String handling: data.table patterns or base R; not stringr

Data loading

# Parquet — preferred format for large datasets
library(arrow)
library(data.table)

dt <- as.data.table(read_parquet("data/panel.parquet"))

# For partitioned Hive-style datasets (e.g., WellDatabase county files)
ds <- open_dataset("data/partitioned/", format = "parquet")
dt <- as.data.table(ds |> filter(year >= 2010) |> collect())

# CSV — only for small files, and always with data.table
dt <- fread("data/small_file.csv")

Never use read.csv() or read_csv(). Use fread() for CSV, read_parquet() for Parquet.

Panel data conventions

# Always set keys for panel data
setkey(dt, firm_id, year)

# Check for duplicates immediately after loading
stopifnot(!anyDuplicated(dt, by = c("firm_id", "year")))

# Or if duplicates are expected (e.g., multi-formation reporting), document why:
dupes <- dt[duplicated(dt, by = c("well_id", "month")), ]
message(sprintf("%d duplicate well-month observations (multi-formation)", nrow(dupes)))

Always verify:

• Whether the panel is balanced or unbalanced
• Whether duplicates exist and why
• Whether merges are many-to-one or one-to-one

Regressions with fixest

library(fixest)

# Two-way fixed effects with clustered SEs
est <- feols(y ~ x1 + x2 | firm_id + year,
             data = dt, vcov = ~firm_id)

# Multiple outcomes in one call
est_multi <- feols(c(roa, tobinq) ~ treatment + controls | firm_id + year,
                   data = dt, vcov = ~firm_id)

# Staggered DiD with Sun & Abraham (2021)
est_sa <- feols(y ~ sunab(treatment_year, year) | firm_id + year,
                data = dt, vcov = ~firm_id)

# IV / 2SLS
est_iv <- feols(y ~ x_control | firm_id + year | x_endog ~ z_instrument,
                data = dt, vcov = ~firm_id)

# Event study
est_es <- feols(y ~ i(rel_year, ref = -1) | firm_id + year,
                data = dt, vcov = ~firm_id)
iplot(est_es, main = "Event Study")

Always cluster standard errors. The default for panel data is clustering at the unit level (firm_id). Two-way clustering (firm + year) is sometimes appropriate for short panels — flag this choice explicitly.

Results output

# Quick inspection
etable(est1, est2, est3, vcov = "cluster")

# Publication-quality table
etable(est1, est2, est3,
       vcov = "cluster",
       dict = c(x1 = "Treatment", x2 = "Size", x3 = "Leverage"),
       tex = TRUE,
       file = "output/tables/main_results.tex")

# Summary statistics
dt[, .(mean = mean(y, na.rm = TRUE),
       sd = sd(y, na.rm = TRUE),
       p25 = quantile(y, 0.25, na.rm = TRUE),
       median = median(y, na.rm = TRUE),
       p75 = quantile(y, 0.75, na.rm = TRUE),
       n = .N),
   by = group_var]

Plotting

library(ggplot2)

# Preferred theme
theme_clean <- theme_minimal() +
  theme(
    panel.grid.minor = element_blank(),
    legend.position = "bottom",
    plot.title = element_text(face = "bold", size = 12),
    axis.title = element_text(size = 10)
  )

# Always label axes, cite data sources in captions
ggplot(dt, aes(x = year, y = mean_y)) +
  geom_line() +
  labs(title = "Title Here",
       x = "Year", y = "Outcome Variable",
       caption = "Source: WRDS/Compustat") +
  theme_clean

Common gotchas — always flag these

• Using lm() on panel data (no fixed effects absorption, slow)
• Missing vcov = ~firm_id on panel regressions (defaults to iid)
• Using read.csv() on files > 50MB (suggest fread() or arrow)
• Hardcoded file paths (should be relative to project root)
• Missing duplicate checks after merges or data loading
• na.rm = TRUE missing on summary statistics
• For DiD: not checking parallel trends pre-treatment
• For IV: not reporting the first stage F-statistic

File and folder conventions

project/
├── data/
│   ├── raw/          # Never modify raw data
│   └── processed/    # Cleaned, analysis-ready datasets
├── code/
│   ├── 01_clean.R
│   ├── 02_merge.R
│   ├── 03_analysis.R
│   └── 04_figures.R
├── output/
│   ├── tables/
│   └── figures/
└── README.md

Number scripts in execution order. Never modify raw data in place — always write cleaned data to a separate location.

For more detailed fixest patterns and advanced usage, see references/fixest-patterns.md.