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This package provides ggplot2 equivalents of the (base) fixest::coefplot and fixest::iplot functions. The goal of ggiplot is to produce nice coefficient and interaction plots (including event study plots) with minimal effort, but with lots of scope for further customization.


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

install.packages("ggiplot", repos = "")


The package website provides a number of examples in the help documentation. (Also available by typing ?ggcoefplot or ?ggiplot in your R console.) But here are a few quickstart examples to whet your appetite.

Start by loading the ggiplot and fixest packages together. Note that ggiplot only supports fixest model objects, so the latter must be loaded alongside the former.

Coefficient plots

Use ggcoefplot to draw basic coefficient plots.

est = feols(
  Petal.Length ~ Petal.Width + Sepal.Length + Sepal.Width + Species, 
  data = iris

# coefplot(est) ## base version
ggcoefplot(est) ## this package

The above plot call and output should look very familiar to regular fixest users. Like its base equivalent, ggcoefplot can be heavily customized and contains various shortcuts for common operations. For example, we can use regex the control the coefficient grouping logic.

ggcoefplot(est, group = list(Sepal = "^^Sepal.", Species = "^^Species"))

Event study plots

The ggiplot function is a special case of ggocoefplot that only plots coefficients with factor levels or interactions (specifically, those created with the i() operator). This is especially useful for producing event study plots in a difference-in-differences (DiD) setup.

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

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

Again, 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.

    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

    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() +
            text = element_text(family = "HersheySans"),
            plot.title = element_text(hjust = 0.5),
            legend.position = "none"

For more ggiplot examples and comparisons with its base counterpart, see the detailed vignette on the package homepage (or, by typing vignette("ggiplot") in your R console).