Current version: 0.6.0 (development)

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.

Install from CRAN:

install.packages("fixes")

Or with pak:

pak::pak("fixes")

For the latest development version from GitHub:

pak::pak("yo5uke/fixes")

First, load the library.

library(fixes)

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”).

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

The main event study function. All key arguments below:

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.

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
)

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

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")

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

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

Happy analyzing!🥂