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--- title: "Introduction to fixes" author: "Yosuke Abe" date: "`r Sys.setlocale('LC_TIME', 'C'); format(Sys.Date(), '%B %d, %Y')`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Introduction to fixes} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ``` ## Introduction The **fixes** package provides an easy-to-use toolkit for creating, estimating, and visualizing event study models using fixed effects regression. With **fixes**, you can automatically generate lead and lag dummy variables, flexibly estimate fixed effects event study regressions, and visualize the results with `ggplot2` using a single pipeline. This vignette introduces the core functions of the package through simple examples, including recent updates such as multiple confidence interval support and improved plotting options. ## Installation Install the released version from CRAN: ```r install.packages("fixes") ``` Or with **pak** (recommended for fast install): ```r pak::pak("fixes") ``` To install the latest development version from GitHub: ```r pak::pak("yo5uke/fixes") ``` or ```r devtools::install_github("yo5uke/fixes") ``` ## Minimal Example Below is a basic example simulating a panel dataset, running an event study, and visualizing the results. ```r library(fixes) library(dplyr) library(tibble) set.seed(2) n_firms <- 1000 n_states <- 50 T <- 36 firm_id <- 1:n_firms state_id <- sample(n_states, size = n_firms, replace = TRUE) year <- 1980:2015 fe_firm <- rnorm(n_firms, mean = 0, sd = .5) fe_year <- rnorm(T, mean = 0, sd = .5) error <- rnorm(n_firms * T, mean = 0, sd = .5) treated_1998 <- sample(c(1, 0), size = n_firms, replace = TRUE, prob = c(1/2, 1/2)) df <- tibble( firm_id = rep(firm_id, each = T), state_id = rep(state_id, each = T), year = rep(year, times = n_firms), fe_firm = rep(fe_firm, each = T), fe_year = rep(fe_year, times = n_firms), error = error, is_treated = rep(treated_1998, each = T), after_treat = if_else(is_treated == 1 & year >= 1998, 1, 0), x1 = runif(n_firms * T), x2 = rnorm(n_firms * T), y = case_when( after_treat == 1 ~ rnorm(n_firms * T, mean = .3, sd = .2) * (year - 1997) + fe_firm + fe_year + error, TRUE ~ fe_firm + fe_year + error ) ) # Run the event study (now supports multiple confidence levels) event_study <- run_es( data = df, outcome = y, treatment = is_treated, time = year, timing = 1998, lead_range = 18, lag_range = 17, covariates = ~ x1 + x2, fe = ~ firm_id + year, cluster = ~ state_id, baseline = -1, interval = 1, conf.level = c(0.90, 0.95, 0.99) # Multiple CIs now supported! ) # View results head(event_study) ``` ## Visualizing Event Study Results The **fixes** package provides `plot_es()` for flexible visualization. You can easily switch between ribbon-style or error bar CIs, select the displayed CI level, and customize appearance. ```r # Basic plot (default: ribbon, 95% CI) p1 <- plot_es(event_study) print(p1) # Plot with error bars and 99% CI p2 <- plot_es(event_study, type = "errorbar", ci_level = 0.99) print(p2) # Use a minimal theme and highlight the event period p3 <- plot_es(event_study, type = "ribbon", vline_val = 0, theme_style = "minimal") print(p3) # Customize further with ggplot2 library(ggplot2) p4 <- plot_es(event_study, type = "errorbar", ci_level = 0.9, theme_style = "classic") + scale_x_continuous(breaks = seq(-10, 10, by = 2)) + ggtitle("Event Study with 90% CI and Classic Theme") print(p4) ``` ## Handling Irregular Time Data If your panel data uses irregular time variables (e.g., Dates), or has gaps, set `time_transform = TRUE` and provide a `unit` variable. This will replace the time variable with a consecutive sequence within each unit, ensuring leads/lags are calculated correctly. ### Example using `Date` and `time_transform` ```r df_alt <- df |> mutate(date = as.Date(paste0(year, "-01-01"))) event_study_alt <- run_es( data = df_alt, outcome = y, treatment = is_treated, time = date, timing = 19, # 19th time point per unit (not the actual year!) lead_range = 3, lag_range = 3, fe = ~ firm_id + year, cluster = ~ state_id, baseline = -1, time_transform = TRUE, unit = firm_id ) head(event_study_alt) ``` **Note:** When `time_transform = TRUE`, use the index (1, 2, 3, ...) for `timing` rather than the actual value (e.g., "1998-01-01"), and always specify `unit`. ## Package Highlights - **`run_es()`**: - Fast, one-step event study for panel data. - Automatic creation of lead/lag dummies relative to treatment. - Supports both classic and staggered timing, covariates, clustering, weights, and flexible baseline normalization. - Multiple confidence interval levels supported (e.g., 90%, 95%, 99%). - Handles irregular time panels via `time_transform`. - **`plot_es()`**: - Intuitive event study plot with ribbon or errorbar CI display. - CI level and visual style are fully customizable. - ggplot2-based for further modification. ## Conclusion The **fixes** package streamlines event study estimation and visualization for panel data researchers. With a minimal API, multiple CI support, and robust visualization, it accelerates the workflow for dynamic treatment effect analysis. For further details and full argument documentation, see: ```r ?run_es ?plot_es ``` Happy analyzing!ð¥
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