fixes

CRAN status R-CMD-check

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:

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

Example data

Widely used panel datasets include:

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

run_es()

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)
)

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.

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”

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

Debugging and Contributions

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


Happy analyzing!🥂