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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
dpi = 300,
out.width = "100%"
)
```
# ggfixest
<!-- badges: start -->
[](https://CRAN.R-project.org/package=ggfixest)
[](https://grantmcdermott.r-universe.dev)
[](https://cran.r-project.org/web/checks/check_results_ggfixest.html)
[](https://github.com/grantmcdermott/ggfixest/actions/workflows/R-CMD-check.yaml)
[](https://grantmcdermott.com/ggfixest/index.html)
[](https://grantmcdermott.com/ggfixest/dev/index.html)
<!-- badges: end -->
The **ggfixest** package provides dedicated **ggplot2** plotting methods for
**fixest** objects. Specifically, it provides drop-in "gg" equivalents of the
latter's [`coefplot`](https://lrberge.github.io/fixest/reference/coefplot.html)
and [`iplot`](https://lrberge.github.io/fixest/reference/coefplot.html)
base plotting functions.
The goal of **ggfixest** is to produce nice looking coefficient plots and
interaction plots---including
[event study](https://theeffectbook.net/ch-EventStudies.html)
plots---with minimal effort and scope for further customization.
## Installation
The stable version of **ggfixest** is available on CRAN.
``` r
install.packages("ggfixest")
```
Or, you can grab the latest development version from R-universe.
``` r
install.packages("ggfixest", repos = "https://grantmcdermott.r-universe.dev")
```
## Quickstart
The [package website](https://grantmcdermott.com/ggfixest/)
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 **ggfixest** package.
```{r pkgload}
library(ggfixest)
```
Note that this automatically loads **ggplot2** and **fixest** as required
dependencies too. As the package name suggests, **ggfixest** _only_ supports
**fixest** model objects.^[For other model classes, a more generic visualization
package/tool like
[**see**](https://easystats.github.io/see/articles/parameters.html) or
[**modelsummary**](https://modelsummary.com/vignettes/modelplot.html)
would be more appropriate.]
### Coefficient plots
Use `ggcoefplot` to draw basic coefficient plots.
```{r coefplot1, message=FALSE}
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
to control the coefficient grouping logic.
```{r coefplot2, message=FALSE}
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`](https://lrberge.github.io/fixest/reference/i.html)
operator). This is especially useful for producing event study plots in a
difference-in-differences (DiD) setup.
```{r es1, message=FALSE}
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.
```{r es2}
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.
```{r es3}
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"
)
)
```
For more `ggiplot` examples and comparisons with its base counterpart, see the
detailed [vignette](http://grantmcdermott.com/ggfixest/articles/ggiplot.html) on
the package homepage (or, by typing `vignette("ggiplot")` in your R console).