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Draws the ggplot2 equivalents of fixest::coefplot and fixest::iplot. These "gg" versions do their best to recycle the same arguments and plotting logic as their original base counterparts. But they also support additional features via the ggplot2 API and infrastructure. The overall goal remains the same as the original functions. To wit: ggcoefplot plots the results of estimations (coefficients and confidence intervals). The function ggiplot restricts the output to variables created with i, either interactions with factors or raw factors.


  geom_style = c("pointrange", "errorbar", "ribbon"),
  multi_style = c("dodge", "facet"),
  aggr_eff = c("none", "post", "pre", "both"),
  aggr_eff.par = list(col = "grey50", lwd = 1, lty = 1),
  facet_args = NULL,
  theme = NULL,

  geom_style = c("pointrange", "errorbar"),
  multi_style = c("dodge", "facet"),
  facet_args = NULL,
  theme = NULL,



A model object of class fixest or fixest_multi, or a list thereof.


Character string. One of c('pointrange', 'errorbar', 'ribbon') describing the preferred geometric representation of the coefficients. Note that ribbon plots not supported for ggcoefplot, since we cannot guarantee a continuous relationship among the coefficients.


Character string. One of c('dodge', 'facet'), defining how multi-model objects should be presented.


A character string indicating whether the aggregated mean post- (and/or pre-) treatment effect should be plotted alongside the individual period effects. Should be one of "none" (the default), "post", "pre", or "both".


List. Parameters of the aggregated treatment effect line, if plotted. The default values are col = 'gray50', lwd = 1, lty = 1.


A list of arguments passed down to ggplot::fact_wrap(). E.g. facet_args = list(ncol = 2, scales = 'free_y'). Only used if multi_style = 'facet'.


ggplot2 theme. Defaults to theme_linedraw() with some minor adjustments, such as centered plot title. Can also be defined on an existing ggiplot object to redefine theme elements. See examples.


Arguments passed down to, or equivalent to, the corresponding fixest::coefplot/fixest::iplot arguments. Note that some of these require list objects. Currently used are:

  • keep and drop for subsetting variables using regular expressions. The fixest::iplot help page includes more detailed examples, but these should generally work as you expect. One useful regexp trick worth mentioning briefly for event studies with many pre-/post-periods is drop = "[[:digit:]]{2}". This will cause the plot to zoom in around single digit pre-/post-periods.

  • group a list indicating variables to group over. Each element of the list reports the coefficients to be grouped while the name of the element is the group name. Each element of the list can be either: i) a character vector of length 1, ii) of length 2, or iii) a numeric vector. Special patterns such as "^^var_start" can be used to more appealing plotting, where group labels are separated from their subsidiary labels. This can be especially useful for plotting interaction terms. See the Details section of fixest::coefplot for more information.

  • Integer scalar, default is 1. In ggiplot, used to select which variable created with i() to select. Only used when there are several variables created with i. See the Details section of fixest::iplot for more information.

  • main, xlab, and ylab for setting the plot title, x- and y-axis labels, respectively.

  • zero and zero.par for defining or adjusting the zero line. For example, zero.par = list(col = 'orange').

  • ref.line and ref.line.par for defining or adjusting the vertical reference line. For example, ref.line.par = list(col = 'red', lty = 4).

  • pt.pch, pt.size, and pt.join for overriding the default point estimate shapes, size, and joining them, respectively.

  • col for manually defining line, point, and ribbon colours.

  • ci_level for changing the desired confidence level (default = 0.95). Note that multiple levels are allowed, e.g. ci_level = c(0.8, 0.95).

  • ci.width for changing the width of the extremities of the confidence intervals. Only used if geom_style = "errorbar" (or if multiple CI levels are requested for the default pointrange style). The default value is 0.2.

  • ci.fill.par for changing the confidence interval fill. Only used when geom_style = "ribbon" and currently only affects the alpha (transparency) channel. For example, we can make the CI band lighter with ci.fill.par = list(alpha = 0.2) (the default alpha is 0.3).

  • dict a dictionary for overriding coefficient names.


A ggplot2 object.


These functions generally try to mimic the functionality and (where appropriate) arguments of fixest::coefplot and fixest::iplot as closely as possible. However, by leveraging the ggplot2 API and infrastructure, they are able to support some more complex plot arrangements out-of-the-box that would be more difficult to achieve using the base coefplot/iplot alternatives.


  • ggcoefplot(): This function plots the results of estimations (coefficients and confidence intervals). The function ggiplot restricts the output to variables created with i, either interactions with factors or raw factors.


# We'll also load fixest to estimate the actual models that we're plotting.

# Author note: The examples that follow deliberately follow the original
#   examples from the coefplot/iplot help pages. A few "gg-" specific
#   features are sprinkled within, with the final set of examples in
#   particular highlighting unique features of this package.

# Example 1: Basic use and stacking two sets of results on the same graph

# Estimation on Iris data with one fixed-effect (Species)
est = feols(Petal.Length ~ Petal.Width + Sepal.Length + Sepal.Width | Species, iris)


# Show multiple CIs
ggcoefplot(est, ci_level = c(0.8, 0.95))

# By default, fixest model standard errors are clustered by the first fixed
# effect (here: Species).
# But we can easily switch to "regular" standard-errors
est_std = summary(est, se = "iid")

# You can plot both results at once in the same plot frame...
ggcoefplot(list("Clustered" = est, "IID" = est_std))

# ... or as separate facets
ggcoefplot(list("Clustered" = est, "IID" = est_std), multi_style = "facet") +
  theme(legend.position = "none")

# Example 2: Interactions

# Now we estimate and plot the "yearly" treatment effects

base_inter = base_did

# We interact the variable 'period' with the variable 'treat'
est_did = feols(y ~ x1 + i(period, treat, 5) | id + period, base_inter)

# In the estimation, the variable treat is interacted
#  with each value of period but 5, set as a reference

# ggcoefplot will show all the coefficients:

# Note that the grouping of the coefficients is due to 'group = "auto"'

# If you want to keep only the coefficients
# created with i() (ie the interactions), use ggiplot

# We can see that the graph is different from before:
#  - only interactions are shown,
#  - the reference is present,
# => this is fully flexible

ggiplot(est_did, ci_level = c(0.8, 0.95))

ggiplot(est_did, ref.line = FALSE, pt.join = TRUE, geom_style = "errorbar")

ggiplot(est_did, geom_style = "ribbon", col = "orange")
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.

# etc

# We can also use a dictionary to replace label values. The dicionary should
# take the form of a named vector or list, e.g. c("old_lab1" = "new_lab1", ...)

# Let's create a "month" variable
all_months = c("aug", "sept", "oct", "nov", "dec", "jan",
               "feb", "mar", "apr", "may", "jun", "jul")
# Turn into a dictionary by providing the old names
# Note the implication that treatment occured here in December (5 month in our series)
dict = all_months; names(dict) = 1:12
# Pass our new dictionary to our ggiplot call
ggiplot(est_did, pt.join = TRUE, geom_style = "errorbar", dict = dict)

# What if the interacted variable is not numeric?

# let's re-use our all_months vector from the previous example, but add it
# directly to the dataset
base_inter$period_month = all_months[base_inter$period]

# The new estimation
est = feols(y ~ x1 + i(period_month, treat, "oct") | id+period, base_inter)
# Since 'period_month' of type character, iplot/coefplot both sort it

# To respect a plotting order, use a factor
base_inter$month_factor = factor(base_inter$period_month, levels = all_months)
est     = feols(y ~ x1 + i(month_factor, treat, "oct") | id + period, base_inter)

# dict -> c("old_name" = "new_name")
dict = all_months; names(dict) = 1:12; dict
#>      1      2      3      4      5      6      7      8      9     10     11 
#>  "aug" "sept"  "oct"  "nov"  "dec"  "jan"  "feb"  "mar"  "apr"  "may"  "jun" 
#>     12 
#>  "jul" 
ggiplot(est_did, dict = dict)

# Example 3: Setting defaults

# The customization logic of ggcoefplot/ggiplot works differently than the
# original base fixest counterparts, so we don't have "gg" equivalents of
# setFixest_coefplot and setFixest_iplot. However, you can still invoke some
# global fixest settings like setFixest_dict(). SImple example:

base_inter$letter = letters[base_inter$period]
est_letters = feols(y ~ x1 + i(letter, treat, 'e') | id+letter, base_inter)

# Set global dictionary for capitalising the letters
dict = LETTERS[1:10]; names(dict) = letters[1:10]


setFixest_dict() # reset

# Example 4: group + cleaning

# You can use the argument group to group variables
# You can further use the special character "^^" to clean
#  the beginning of the coef. name: particularly useful for factors

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

# No grouping:

# now we group by Sepal and Species
ggcoefplot(est, group = list(Sepal = "Sepal", Species = "Species"))

# now we group + clean the beginning of the names using the special character ^^
ggcoefplot(est, group = list(Sepal = "^^Sepal.", Species = "^^Species"))

# Example 5: Some more ggcoefplot/ggiplot extras

# We'll demonstrate using the staggered treatment example from the
# introductory fixest vignette.

est_twfe = feols(
  y ~ x1 + i(time_to_treatment, treated, ref = c(-1, -1000)) | id + year,
est_sa20 = feols(
  y ~ x1 + sunab(year_treated, year) | id + year,
  data = base_stagg

# Plot both regressions in a faceted plot
  list('TWFE' = est_twfe, 'Sun & Abraham (2020)' = est_sa20),
  main = 'Staggered treatment', ref.line = -1, pt.join = TRUE

# So far that's no different than base iplot (automatic legend  aside). But an
# area where ggiplot shines is in complex multiple estimation cases, such as
# lists of fixest_multi objects. To illustrate, let's add a split variable
# (group) to our staggered dataset.
base_stagg_grp = base_stagg
base_stagg_grp$grp = ifelse(base_stagg_grp$id %% 2 == 0, 'Evens', 'Odds')

# Now re-run our two regressions from earlier, but splitting the sample to
# generate fixest_multi objects.
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 combines the list of multi-estimation objects without a problem...
ggiplot(list('TWFE' = est_twfe_grp, 'Sun & Abraham (2020)' = est_sa20_grp),
        ref.line = -1, main = 'Staggered treatment: Split multi-sample')

# ... but is even better when we use facets instead of dodged errorbars.
# Let's use this an opportunity to construct a fancy plot that invokes some
# additional arguments and ggplot theming.
  list('TWFE' = est_twfe_grp, 'Sun & Abraham (2020)' = est_sa20_grp),
  ref.line = -1,
  main = 'Staggered treatment: Split multi-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'

# Aside on theming and scale adjustments

# Setting the theme inside the `ggiplot()` call is optional and not strictly
# necessary, since the ggplot2 API allows programmatic updating of existing
# plots. E.g.
last_plot() +
  labs(caption = 'Note: Super fancy plot brought to you by ggiplot')

last_plot() +
  theme_grey() +
  theme(legend.position = 'none') +
  scale_fill_brewer(palette = 'Set1', aesthetics = c("colour", "fill"))
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.

# etc.