ggiplot

This package provides a ggplot2 equivalent of the base fixest::iplot() function. The goal of ggiplot is to produce nice event study plots with minimal effort, but with lots of scope for further customization.

Installation

The package is not yet on CRAN, but can be installed from R-universe:

install.packages("ggiplot", repos = "https://grantmcdermott.r-universe.dev")

Quickstart

A detailed introductory vignette with many examples is provided on the package homepage (or, by typing vignette("ggiplot") in your R console). But here are a few quickstart examples to whet your appetite. First, a basic event study plot.

library(ggiplot)
library(fixest)

est_did = feols(y ~ x1 + i(period, treat, 5) | id+period, base_did)

# iplot(est_did) ## base version
ggiplot(est_did) ## this package

The above plot call and output should look very familiar to regular fixest users. But note that ggiplot() supports several features that are not available in the base iplot() version. For example, plotting multiple confidence intervals and aggregate treatments effects.

ggiplot(
    est_did,
    ci_level = c(.8, .95),
    aggr_eff = "post", aggr_eff.par = list(col = "orange")
)

And you can get quite fancy, combining lists of complex multiple estimation objects with custom themes, and so on.

base_stagg_grp = base_stagg
base_stagg_grp$grp = ifelse(base_stagg_grp$id %% 2 == 0, 'Evens', 'Odds')

est_twfe_grp = feols(
    y ~ x1 + i(time_to_treatment, treated, ref = c(-1, -1000)) | id + year,
    data = base_stagg_grp, split = ~grp
)

est_sa20_grp = feols(
    y ~ x1 + sunab(year_treated, year) | id + year,
    data = base_stagg_grp, split = ~grp
)

ggiplot(
    list("TWFE" = est_twfe_grp, "Sun & Abraham (2020)" = est_sa20_grp),
    ref.line = -1,
    main = "Staggered treatment: Split mutli-sample",
    xlab = "Time to treatment",
    multi_style = "facet",
    geom_style = "ribbon",
    facet_args = list(labeller = labeller(id = \(x) gsub(".*: ", "", x))),
    theme = theme_minimal() +
        theme(
            text = element_text(family = "HersheySans"),
            plot.title = element_text(hjust = 0.5),
            legend.position = "none"
        )
)