fixes

Overview

Note
By default, the fixes package assumes time is a regularly spaced numeric variable (e.g., year = 1995, 1996, …).
If your time variable is irregular or non-numeric (e.g., Date type), set time_transform = TRUE to automatically convert it to a sequential index within each unit.
For unit-specific treatment timing, set staggered = TRUE.

The fixes package is designed for convenient event study analysis and plotting, particularly useful for visualizing parallel trends and dynamic effects in two-way fixed effects (TWFE) difference-in-differences (DID) research.

Key Functions:

1. run_es() — Takes a data frame, generates lead/lag dummies, and fits the event study regression. Supports fixed effects, covariates, clustering, staggered timing, weights, custom baseline, and multiple confidence intervals.
2. plot_es() — Plots event study results using ggplot2 with flexible options: ribbon or error bars, choice of CI level, and theme customization.

Installation

Install from CRAN:

install.packages("fixes")

Or with pak:

pak::pak("fixes")

For the latest development version from GitHub:

pak::pak("yo5uke/fixes")

How to use

First, load the library.

library(fixes)

Data frame requirements

run_es() expects a panel data frame with at least:

• Unit identifier (e.g., individual, firm, region)
• Treatment indicator (0/1 or TRUE/FALSE)
• Time variable (numeric or Date)
• Outcome variable (continuous)

For staggered adoption (staggered = TRUE), include a variable specifying unit-specific treatment timing (e.g., “treatment_year”).

Example data

Widely used panel datasets include:

• did::sim_dt(): simulated panel for DiD tutorials
• fixest::base_stagg: a built-in dataset for staggered adoption

df1 <- fixest::base_did      # Basic DiD
df2 <- fixest::base_stagg    # Staggered treatment

run_es()

The main event study function. All key arguments below:

Example: basic event study

event_study <- run_es(
  data       = df1,
  outcome    = y,
  treatment  = treat,
  time       = period,
  timing     = 6,
  fe         = ~ id + period,
  lead_range = 5,
  lag_range  = 4,
  cluster    = ~ id,
  baseline   = -1,
  interval   = 1,
  conf.level = c(0.90, 0.95, 0.99)
)

• fe must be a one-sided formula (e.g., ~ firm_id + year).
• cluster can be a one-sided formula or a character vector.

With covariates

event_study <- run_es(
  data       = df1,
  outcome    = y,
  treatment  = treat,
  time       = period,
  timing     = 6,
  fe         = ~ id + period,
  lead_range = 5,
  lag_range  = 4,
  covariates = ~ cov1 + cov2 + cov3,
  cluster    = ~ id,
  baseline   = -1,
  interval   = 1
)

Using irregular time data (Date), with time_transform

df_alt <- df1 |>
  dplyr::mutate(
    year = rep(2001:2010, times = 108),
    date = as.Date(paste0(year, "-01-01"))
  )

event_study_alt <- run_es(
  data           = df_alt,
  outcome        = y,
  treatment      = treat,
  time           = date,
  timing         = 9,  # Use index, not the original Date
  fe             = ~ id + period,
  lead_range     = 3,
  lag_range      = 3,
  cluster        = ~ id,
  baseline       = -1,
  time_transform = TRUE,
  unit           = id
)

Note:
When time_transform = TRUE, specify timing as an index (e.g., 9 = 9th observation in unit).
Currently, time_transform = TRUE cannot be combined with staggered = TRUE (future versions may support this).

plot_es()

plot_es() visualizes results using ggplot2. By default, it plots a ribbon for the 95% CI, but supports error bars, CI level selection, and multiple themes.

Example usage

plot_es(event_study)
plot_es(event_study, type = "errorbar")
plot_es(event_study, type = "ribbon", ci_level = 0.9, theme_style = "minimal")
plot_es(event_study, type = "errorbar", ci_level = 0.99) + ggplot2::ggtitle("Event Study, 99% CI")

Further customization with ggplot2 is fully supported:

plot_es(event_study, type = "errorbar") + 
  ggplot2::scale_x_continuous(breaks = seq(-5, 5, by = 1)) + 
  ggplot2::ggtitle("Result of Event Study")

Planned Features

• Support for staggered = TRUE with time_transform = TRUE
• Allow timing to accept original time values (e.g., Date), not just index

Debugging and Contributions

If you find an issue or want to contribute, please use the GitHub Issues page.

Happy analyzing!🥂