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Showing content from https://github.com/yo5uke/fixes below:

GitHub - yo5uke/fixes

fixes

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:

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.
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:

For the latest development version from GitHub:

First, load the library.

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

y x1 id period post treat 2.8753063 0.5365377 1 1 0 1 1.8606527 -3.0431894 1 2 0 1 0.0941652 5.5768439 1 3 0 1 3.7814749 -2.8300587 1 4 0 1 -2.5581996 -5.0443544 1 5 0 1 1.7287324 -0.6363849 1 6 1 1 id year year_treated time_to_treatment treated treatment_effect_true x1 y 2 90 1 2 -1 1 0 -1.0947021 0.0172297 3 89 1 3 -2 1 0 -3.7100676 -4.5808453 4 88 1 4 -3 1 0 2.5274402 2.7381717 5 87 1 5 -4 1 0 -0.7204263 -0.6510307 6 86 1 6 -5 1 0 -3.6711678 -5.3338166 7 85 1 7 -6 1 0 -0.3152137 0.4956263

The main event study function. All key arguments below:

Argument Description data Data frame to be used. outcome Outcome variable. Can be specified as a raw variable or a transformation (e.g., log(y)). Provide it unquoted. treatment Dummy variable indicating the treated units. Provide it unquoted. Accepts both 0/1 and TRUE/FALSE. time Time variable. Provide it unquoted. timing The time at which the treatment occurs. If staggered = FALSE, this should be a scalar (e.g., 2005). If staggered = TRUE, provide a variable (column) indicating the treatment time for each unit. fe Fixed effects to control for unobserved heterogeneity. Must be a one-sided formula (e.g., ~ id + year). lead_range Number of pre-treatment periods to include (e.g., 3 = lead3, lead2, lead1). Default is NULL, which automatically uses the maximum available lead range. lag_range Number of post-treatment periods to include (e.g., 2 = lag0 (the treatment period), lag1, lag2). Default is NULL, which automatically uses the maximum available lag range. covariates Additional covariates to include in the regression. Must be a one-sided formula (e.g., ~ x1 + x2). cluster Specifies clustering for standard errors. Can be a character vector (e.g., c("id", "year")) or a formula (e.g., ~ id + year, ~ id^year). weights Optional weights to be used in the regression. Provide as a one-sided formula (e.g., ~ weight). baseline Relative time value to be used as the reference category. The corresponding dummy is excluded from the regression. Must be within the specified lead/lag range. interval Time interval between observations (e.g., 1 for yearly data, 5 for 5-year intervals). time_transform Logical. If TRUE, converts the time variable into a sequential index (1, 2, 3, …) within each unit. Useful for irregular time (e.g., Date). Default is FALSE. unit Required if time_transform = TRUE. Specifies the panel unit identifier (e.g., firm_id). staggered Logical. If TRUE, allows for unit-specific treatment timing (staggered adoption). Default is FALSE. conf.level Numeric vector of confidence levels (e.g., c(0.90, 0.95, 0.99); default: 0.95). 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.

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

Argument Description data Data frame from run_es() ci_level Confidence interval (default: 0.95) type “ribbon” (default) or “errorbar” vline_val X for vertical line (default: 0) vline_color Color for vline (default: “#000”) hline_val Y for horizontal line (default: 0) hline_color Color for hline (default: “#000”) linewidth Line width (default: 1) pointsize Point size (default: 2) alpha Ribbon transparency (default: 0.2) barwidth Errorbar width (default: 0.2) color Point/line color (default: “#B25D91FF”) fill Ribbon color (default: “#B25D91FF”) theme_style Theme: “bw” (default), “minimal”, “classic”

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

Debugging and Contributions

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

Happy analyzing!🥂

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