fixes

Overview

Note
By default, the fixes package assumes time is a regularly spaced numeric variable (e.g., year = 1995, 1996, …).
However, if your time variable is irregular or non-numeric (e.g., Date type), you can enable time_transform = TRUE to automatically convert it to a sequential index within each unit.
You can also specify unit-specific treatment timing by setting staggered = TRUE.

The fixes package is designed for conducting analysis and creating plots for event studies, a method used to verify the parallel trends assumption in two-way fixed effects (TWFE) difference-in-differences (DID) analysis.

The package includes two main functions:

1. run_es(): Accepts a data frame, generates lead and lag variables, and performs event study analysis. The function returns the results as a tidy data frame. Supports options for fixed effects, covariates, clustered standard errors, and staggered treatment timing.
2. plot_es(): Creates plots using ggplot2 based on the data frame generated by run_es(). Users can choose between a plot with geom_ribbon() or geom_errorbar() to visualize the results.

Installation

You can install the package like so:

# install.packages("pak")
pak::pak("fixes")

or

install.packages("fixes")

If you want to install development version, please install from GitHub repository:

pak::pak("yo5uke/fixes")

How to use

First, load the library.

library(fixes)

Data frame

The run_es() function is designed to work with panel data.
The data frame must include the following variables:

1. A unit identifier (e.g., individual, firm, region)
2. A treatment indicator variable (0/1 or TRUE/FALSE)
3. A time variable (numeric or Date)
4. An outcome variable (continuous)

In addition, if you use staggered = TRUE, you must provide a variable that indicates unit-specific treatment timing (e.g., the year treatment started for each unit).

To get started, you can use example data from widely used packages:

• did::sim_dt(): A simulated panel dataset commonly used in difference-in-differences tutorials.
• fixest::base_stagg: A built-in dataset designed for analyzing staggered adoption of treatment.

These datasets already contain the necessary structure and can be used directly with run_es().

# Load example data
df1 <- fixest::base_did      # Basic DID example
df2 <- fixest::base_stagg    # Staggered treatment example

run_es()

run_es() takes 14 arguments, including required variables and optional specifications like fixed effects, clustering, covariates, staggered treatment timing, and weights.

Example: Without Covariates

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

Note: The fe argument must be specified as a one-sided formula (e.g., ~ firm_id + year).
The cluster argument can be specified either as a one-sided formula (e.g., ~ state_id) or as a character vector (e.g., c("firm_id", "year")).

The run_es() function returns a tidy data frame that includes estimated event-study coefficients, confidence intervals, relative timing values, and an indicator for the omitted baseline period.
Estimation is performed using fast and flexible fixed effects regression.

Example: With Covariates

If your dataset includes additional covariates, you can include them in the regression by specifying a one-sided formula using the covariates argument, as shown below.

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

# Example using Date-type time variable and time_transform
df_alt <- df1 |>
  dplyr::mutate(
    year = rep(2001:2010, times = 108),  # 108 units × 10 periods
    date = as.Date(paste0(year, "-01-01"))
  )

event_study_alt <- run_es(
  data           = df_alt,
  outcome        = y,
  treatment      = treat,
  time           = date,
  timing         = 19,  # Corresponds to 19th time point in each unit
  lead_range     = 3,
  lag_range      = 3,
  fe             = ~ id + period,
  cluster        = ~ id,
  baseline       = -1,
  time_transform = TRUE,
  unit           = id
)

Note:
When time_transform = TRUE, the timing argument must be specified using the transformed index (e.g., timing = 19 for the 19th time point within each unit).
Support for specifying the original time values (e.g., a specific Date) directly as timing is planned for a future update.
Currently, time_transform = TRUE cannot be combined with staggered = TRUE. This combination is not yet supported, but may be implemented in a future release.

You can use this result to create custom plots, or take advantage of the built-in plot_es() function to visualize the estimates and confidence intervals with minimal code.

plot_es()

The plot_es() function creates a plot based on ggplot2.

plot_es() has 12 arguments.

If you don’t care about the details, you can just pass the data frame created with run_es() and the plot will be complete.

plot_es(event_study)

plot_es(event_study, type = "errorbar")

plot_es(event_study, type = "errorbar", vline_val = -.5)

Since it is created on a ggplot2 basis, it is possible to modify minor details.

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
  • Enable automatic alignment of treatment dates with transformed time indices, allowing analysis with irregular time variables (e.g., Date) in staggered adoption settings.
• Allow timing to accept original time values (e.g., specific Dates)
  • Instead of manually calculating the time index (e.g., timing = 19), users will be able to specify a Date or other original time value directly. This will simplify workflow when time_transform = TRUE.

Debugging

If you find an issue, please report it on the GitHub Issues page.