# Formula Interface for ggplot2

#### Daniel Kaplan and Randall Pruim

#### January, 2018

# Formula-driven graphics

There are several excellent graphics packages provided for R. The `ggformula` package currently builds on one of them, `ggplot2`, but provides a very different user interface for creating plots. The interface is based on formulas (much like the `lattice` interface) and the use of the chaining operator (`%>%`) to build more complex graphics from simpler components.

The `ggformula` graphics were designed with several user groups in mind:

- beginners who want to get started quickly and may find the syntax of `ggplot2()` a bit offputting,

- those familiar with `lattice` graphics, but wanting to be able to easily create multilayered plots,

- those who prefer a formula interface, perhaps because it is familiar from use with functions like `lm()` or from use of the `mosaic` package for numerical summaries.

## The basic formula template

The basic template for creating a plot with `ggformula` is

    gf_plottype(formula, data = mydata)

where

- `plottype` describes the type of plot (layer) desired (points, lines, a histogram, etc., etc.),

- `mydata` is a data frame containing the variables used in the plot, and

- `formula` describes how/where those variables are used.

For example, in a bivariate plot, `formula` will take the form `y ~ x`, where `y` is the name of a variable to be plotted on the y-axis and `x` is the name of a variable to be plotted on the x-axis. (It is also possible to use expressions that can be evaluated using variables in the data frame as well.)

Here is a simple example:

    library(ggformula)
    gf_point(mpg ~ hp, data = mtcars)

## Selecting the glyph type

The “kind of graphic” is specified by the name of the graphics function. All of the `ggformula` data graphics functions have names starting with `gf_`, which is intended to remind the user that they are formula-based interfaces to `ggplot2`: `g` for `ggplot2` and `f` for “formula.” Commonly used functions include

- `gf_point()` for scatter plots
- `gf_line()` for line plots (connecting dots in a scatter plot)
- `gf_density()` or `gf_dens()` or `gf_histogram()` or `gf_dhistogram()` or `gf_freqpoly()` to display distributions of a quantitative variable
- `gf_boxplot()` or `gf_violin()` for comparing distributions side-by-side
- `gf_counts()` for bar-graph style depictions of counts.
- `gf_bar()` for more general bar-graph style graphics

The function names generally match a corresponding function name from `ggplot2`, although

- `gf_counts()` is a simplified special case of `geom_bar()`,
- `gf_dens()` is an alternative to `gf_density()` that displays the density plot slightly differently
- `gf_dhistogram()` produces a density histogram rather than a count histogram.

Each of the `gf_` functions can create the coordinate axes and fill it in one operation. (In `ggplot2` nomenclature, `gf_` functions create a frame and add a layer, all in one operation.) This is what happens for the first `gf_` function in a chain. For subsequent `gf_` functions, new layers are added, each one “on top of” the previous layers.

## Attributes

Each of the marks in the plot is a glyph. Every glyph has graphical attributes (called aesthetics in `ggplot2`) that tell where and how to draw the glyph. In the above plot, the obvious attributes are x- and y-position:
We’ve told R to put `mpg` along the y-axis and `hp` along the x-asis, as is clear from the plot.

But each point also has other attributes, including color, shape, size, stroke, fill, and alpha (transparency). We didn’t specify those in our example, so `gf_point()` uses some default values for those – in this case smallish black filled-in circles.

### Specifying attributes

In the `gf_` functions, you specify the non-position graphical attributes using an extension of the basic formula. Attributes can be set to a constant value (e.g, set the color to “blue”; set the size to 2) or they can be mapped to a variable in the data or some expression involving the variables (e.g., map the color to `sex`, so sex determines the color groupings)

Attributes are set or mapped using additional arguments.

- adding an argument of the form `attribute = value` sets `attribute` to `value`.
- adding an argument of the form `attribute = ~ expression` maps `attribute` to `expression`

where `attribute` is one of `color`, `shape`, etc., `value` is a constant (e.g. `"red"` or `0.5`, as appropriate), and `expression` may be some more general expression that can be computed using the variables in `data` (although often is is better to create a new variable in the data and to use that variable instead of an on-the-fly calculation within the plot).

The following plot, for instance,

- We use `cyl` to determine the color and `carb` to determine the size of each dot. Color and size are mapped to `cyl` and `carb`. A legend is provided to show us how the mapping is being done. (Later, we can use scales to control precisely how the mapping is done – which colors and sizes are used to represent which values of `cyl` and `carb`.)

- We also set the transparency to 50%. The gives the same value of `alpha` to all glyphs in this layer.

    gf_point(mpg ~ hp, color = ~ cyl, size = ~ carb, alpha = 0.50, data = mtcars)

### On-the-fly calculations

`ggformula` allows for on-the-fly calculations of attributes, although the default labeling of the plot is often better if we create a new variable in our data frame. In the examples below, since there are only three values for `carb`, it is easier to read the graph if we tell R to treat `cyl` as a categorical variable by converting to a factor (or to a string). Except for the labeling of the legend, these two plots are the same.

    library(dplyr)
    gf_point(mpg ~ hp,  color = ~ factor(cyl), size = ~ carb, alpha = 0.75, data = mtcars)
    gf_point(mpg ~ hp,  color = ~ cylinders, size = ~ carb, alpha = 0.75,
             data = mtcars %>% mutate(cylinders = factor(cyl)))

## “One-variable” plots

For some plots, we only have to specify the x-position because the y-position is calculated from the x-values. Histograms, densityplots, and frequency polygons are examples. To illustrate, we’ll use density plots, but the same ideas apply to `gf_histogram()`, and `gf_freqpolygon()` as well. Note that in the one-variable density graphics, the variable whose density is to be calculated goes to the right of the tilde, in the position reserved for the x-axis variable.

    data(Runners, package = "mosaicModel")
    Runners <- Runners %>% filter( ! is.na(net))
    gf_density( ~ net, data = Runners)
    gf_density( ~ net,  fill = ~ sex,  alpha = 0.5, data = Runners)
    # gf_dens() is similar, but there is no line at bottom/sides, and it is not "fillable"
    gf_dens( ~ net, color = ~ sex, alpha = 0.7, data = Runners)

Several of the plotting functions include additional arguments that do not modify attributes of individual glyphs but control some other aspect of the plot. In this case, `adjust` can be used to increase or decrease the amount of smoothing.

    # less smoothing
    gf_dens( ~ net, color = ~ sex, alpha = 0.7, data = Runners, adjust = 0.25)
    # more smoothing
    gf_dens( ~ net, color = ~ sex, alpha = 0.7, data = Runners, adjust = 4)

## Position

When the `fill` or `color` or `group` aesthetics are mapped to a variable, the default behavior is to lay the group-wise densities on top of one another. Other behavior is also available by using `position` in the formula. Using the value `"stack"` causes the densities to be laid one on top of another, so that the overall height of the stack is the density across all groups. The value `"fill"` produces a conditional probability graphic.

    gf_density( ~ net, fill = ~ sex, color = NA, position = "stack", data = Runners)
    gf_density( ~ net, fill = ~ sex, color = NA, position = "fill", data = Runners, adjust = 2)

Similar commands can be constructed with `gf_histogram()` and `gf_freqpoly()`, but note that `color`, not `fill`, is the active attribute for frequency polygons. It’s also rarely good to overlay histograms on top of one another – better to use a density plot or a frequency polygon for that application.

## Faceting

The `ggplot2` system allows you to make subplots — called “facets” — based on the values of one or two categorical variables. This is done by chaining with `gf_facet_grid()` or `gf_facet_wrap()`. These functions use formulas to specify which variable(s) are to be used for faceting.

    gf_density_2d(net ~ age, data = Runners) %>% gf_facet_grid( ~ sex)
    # the dot here is a bit strange, but required to make a valid formula
    gf_density_2d(net ~ age, data = Runners) %>% gf_facet_grid( sex ~ .)
    gf_density_2d(net ~ age, data = Runners) %>% gf_facet_wrap( ~ year)
    gf_density_2d(net ~ age, data = Runners) %>% gf_facet_grid(start_position ~ sex)

An alternative syntax uses `|` to separate the faceting information from the main part of the formula.
Here is another example using our weather data. The redundant use of the `y` and `color` attributes for temperature makes it easier to compare across facets.

    gf_ribbon(low_temp + high_temp ~ date | city ~ year, data = Weather, alpha = 0.3)

    ## Warning: Detecting old grouped_df format, replacing `vars` attribute by `groups`

    gf_linerange(low_temp + high_temp ~ date | city ~ year, color = ~ avg_temp, data = Weather) %>%
      gf_refine(scale_colour_gradientn(colors = rev(rainbow(5))))

In this case, we should either not facet by year, or allows the x-scale to be freely adjusted in each column so that we don’t have so much unnecessary white space. We can do the latter using the `scales` argument to `gf_facet_grid()`.

    gf_ribbon(low_temp + high_temp ~ date | city ~ ., data = Weather, alpha = 0.3)

    gf_linerange(low_temp + high_temp ~ date, color = ~ avg_temp, data = Weather) %>%
      gf_refine(scale_colour_gradientn(colors = rev(rainbow(5)))) %>%
      gf_facet_grid(city ~ year, scales = "free_x")

## More 2-variable plots

### Using jitter and transparency to handle overlapping cases

Sometimes you have so many points in a scatter plot that they obscure one another. The `ggplot2` system provides two easy ways to deal with this: translucency and jittering.

Use `alpha = 0.5` to make the points semi-translucent. If there are many points overlapping at one point, a much smaller value of alpha, say `alpha = 0.01`. We’ve already seen this above.

Using `gf_jitter()` in place of `gf_point()` will move the plotted points to reduce overlap. Jitter and transparency can be used together as well.

    gf_point(age ~ sex, alpha = 0.05, data = Runners)
    gf_jitter(age ~ sex, alpha = 0.05, data = Runners)

### Box and Whisker plots

Box and whisker plots show the distribution of a quantitative variable as a function of a categorical variable. The formula used in `gf_boxplot()` should have the quantitative variable to the left of the tilde. (To make horizontal boxplots using `ggplot2` you have to make vertical boxplots and then flip the coordinates with `coord_flip()`.)

    gf_boxplot(net ~ sex, color = "red", data = Runners)
    gf_boxplot(net ~ sex, color = ~ start_position, data = Runners)

This plot may surprise you.

    gf_boxplot(net ~ year, data = Runners)

    ## Warning: Continuous x aesthetic -- did you forget aes(group=...)?

This plot is placing a single box and whisker plot at the mean value of `year`. The warning message suggests that we need to tell R how to form the groups when using a quantitative variable for `x`. It suggests using the `group` aesthetic, and sometimes, this is just what we want.

    gf_boxplot(net ~ year, group = ~ year, data = Runners)

But often, is is better to convert a discrete quantitative variable used for grouping into a categorical variable (a factor or character vector). This can be done in several ways:

    # add a new variable to the data
    Runners$the_year <- as.character(Runners$year)               # in base R
    Runners <- Runners %>% mutate(the_year = as.character(year)) # in dplyr
    gf_boxplot(net ~ the_year, color = ~ sex, data = Runners)

    # or do it on the fly
    gf_boxplot(net ~ factor(year), color = ~ sex, data = Runners)

### 2-dimensional density plots

Two-dimensional plots of density also have both a left and right component to the formula.

    gf_density_2d(net ~ age, data = Runners)
    gf_hex(net ~ age, data = Runners)

### Paths and lines

The `ggplot2` system offers two ways to connect points. `gf_line()` ignores the order of the points in the data, and draws the line going from left to right. `gf_path()` goes from point to point according to the order in the data. Both forms can use a `color` or `group` aesthetic to indicate which groups of points are connected.

Here’s an example where `gf_line()` is useful. We begin with a scatter plot showing the number of live births in the US for each day of 1978.

    library(mosaicData)
    gf_point(births ~ date, data = Births78)

Can this interesting pattern be explained by a weekday/weekend effect?
Converting to a line plot and coloring by day of week highlights the pattern and makes it easy to spot the unusual days.

    gf_line(births ~ date, color = ~ wday, data = Births78)

The example above would look the same if we used `gf_path()` because the data set is already sorted by date. But in general, `gf_path()` and `gf_line()` produce different results. In the plots below, the first connects days chronologically (because the data are sorted by date) and the second in order of their low temperature.

    Weather %>%
      filter(month == 5, year == 2017, city == "Chicago") %>%
      gf_path(high_temp ~ low_temp | city ~ year, color = ~ day) %>%
      gf_refine(scale_color_viridis_c(option = "C", begin = 0.2, end = 0.8))
    Weather %>%
      filter(month == 5, year == 2017, city == "Chicago") %>%
      gf_line(high_temp ~ low_temp | city ~ year, color = ~ day) %>%
      gf_refine(scale_color_viridis_c(option = "C", begin = 0.2, end = 0.8))

## Plots with more than 2 positional attributes

Some layers require more than two attributes. Typically this happens when the glyphs of a layer are complex objects that could have been made using multiple layers, but belong together conceptually. Examples include

- `gf_pointrange()` – plots a dot flanked by a line segment.
- `gf_linrange()` – like `gf_pointrange()` but without the point.
- `gf_errorbar()` – vertical error bars.
- `gf_errorbarh()` – horizontal error bars.
- `gf_ribbon()` – a band between a line above and line below.

Often these are used to depict some sort of estimate of uncertainty in a measurement or a prediction, but they can be used to represent any data of the correct form. Here we will use `gf_linerange()` and `gf_ribbon()` to indicate the high and low temperatures in New York for the first few months of 2013.

    Temps <-
      Weather %>%
      filter(month <= 4, year <= 2016, city == "Chicago")

    gf_pointrange(avg_temp + low_temp + high_temp  ~ date, color = ~ avg_temp, data = Temps) %>%
        gf_refine(scale_color_gradientn(colors = c("blue", "green", "orange", "red")))

    gf_ribbon(low_temp + high_temp  ~ date, color = "navy", alpha = 0.3, data = Temps)

## Positions and Stats

### Positions

`position_dodge()`, `position_jitter()`, and `position_jitterdodge()` can be used to adjust the positions at which glyphs are placed. Jittering adds some random noise and can be useful when many observations have the same value. Dodging moves groups of glyphs a fixed difference to make it easier to distinguish the groups.

    gf_point(length ~ sex, color = ~ domhand, data = KidsFeet,
             position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.4))

### Stats

A stat is a transformation that is applied to the data before a plot is generated. Several of the plots we have seen have made use of stats.

- `gf_histogram()` uses `stat_bin()` to bin the data and count the number of observations in each bin. It is equivalent to

    gf_bar( ~ age, data = HELPrct, stat = "bin")

    ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

- `gf_boxplot()` uses `stat_boxplot()` to compute the five-number summary on which the boxplot is based.

- `gf_violin()`, `gf_density()`, and `gf_density2d()` use stats to compute a sequence of points along an estimated density. It is these points, and the not the raw data that are used to create the plot. `gf_sina()` uses the estimated density to jitter points.

    gf_boxplot(age ~ substance, data = HELPrct)
    gf_violin(age ~ substance, data = HELPrct) %>%
      gf_sina(alpha = 0.3)

There are also a number of stats that can be used to show a functional relationship between two variables.

- `gf_smooth()` plots a model fit using `lm()`, `glm()`, `gam()`, `loess()`, `MASS::rlm()`.

- `gf_lm()` is `gf_smooth()` with the method set to `lm()` so it plots a least squares regression line.

- `gf_spline()` plots a spline fit to the data.

    gf_point(length ~ width, data = KidsFeet, color = ~ sex) %>%
      gf_lm()
    gf_point(births ~ date, color = ~wday,
             data = mosaicData::Births, alpha = 0.25) %>%
      gf_smooth()

    ## `geom_smooth()` using method = 'gam'

Confidence or prediction bands can be added to these as well.

    gf_point(length ~ width | sex, data = KidsFeet, color = ~ sex) %>%
      gf_lm(interval = "prediction", fill = "red") %>%
      gf_lm(interval = "confidence", fill = "navy")

Mostly, the stats selected by default are just the ones you need. But sometimes it is useful to select a different stat. The `stat_summary()` and `stat_summary_bin()` stats are particularly useful in this respect. These stats use a function to aggregate over unique values of `x` or over bins of `x` values and save the user needing to do that data transformation manually.

The default function applied in each group is `mean_se()`, which computes the mean (and the mean plus and minus one standard error) This makes it simple to create an “interaction plot”.

    gf_jitter(length ~ sex, color = ~ domhand, data = KidsFeet,
              width = 0.1, height = 0) %>%
      gf_line(length ~ sex, color = ~ domhand, data = KidsFeet,
              group = ~ domhand, stat="summary")

    ## No summary function supplied, defaulting to `mean_se()`

The other two values computed by `mean_se()` are available (starting with `ggplot2` version 2.3) as `stat(ymin)` and `stat(ymax)`.

    gf_jitter(length ~ sex, color = ~ domhand, data = KidsFeet,
              width = 0.1, height = 0, alpha = 0.3) %>%
      gf_pointrange(length + stat(ymin) + stat(ymax) ~ sex,
                    color = ~ domhand, data = KidsFeet,
                    group = ~ domhand, stat="summary")

    ## No summary function supplied, defaulting to `mean_se()`

Custom functions can be used by defining `fun.y`, `fun.ymin`, and `fun.ymax`, or a single function `fun.data` that returns a data frame with variables named `y`, `ymin`, and `ymax`.

    gf_point(length ~ sex, color = ~ domhand, data = KidsFeet,
              width = 0.1, height = 0, alpha = 0.5,
              position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.3)) %>%
      gf_pointrange(length + stat(ymin) + stat(ymax) ~ sex,
                    color = ~ domhand, data = KidsFeet,
                    group = ~ domhand, stat="summary",
                    fun.y = median, fun.ymin = min, fun.ymax = max,
                    position = position_dodge(width = 0.6))

    ## No summary function supplied, defaulting to `mean_se()`

### Plotting functions

`ggformula` provides two way to plot functions: `gf_function()` and `gf_fun()`. They differ primarily in how one specifies the function to be plotted.

- `gf_function()` requires a (vectorized) function of one variable.
- `gf_fun()` requires a formula describing how the y-values are computed from the x-values.

If these are used as a first layer, the limits for the x-axis must be specified.

    gf_function(fun = sqrt, xlim = c(0, 10)) %>%
      gf_fun(2 + 3 * cos(10 * x) ~ x, color = "red", n = 800)
    f <- makeFun(lm(totalbill ~ poly(month, 2), data = mosaicData::Utilities))
    gf_point(totalbill ~ month, data = mosaicData::Utilities, alpha = 0.6) %>%
      gf_fun(f(m) ~ m, color = "red")

## Chaining to create complex plots

### Multiple layers

Often it is useful to overlay multiple layers onto a single plot. This can be done by chaining them with `%>%`, the “then” operator from `magrittr`. The `data` argument can be omitted if the new layers uses the same data as the first layer in the chain.

The following plot illustrates how histograms and frequency polygons are related.

    gf_histogram( ~ age, data = Runners, alpha = 0.3, fill = "navy") %>%
      gf_freqpoly( ~ age)

    ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

A 2-d density plot can be augmented with a scatterplot.

    gf_density_2d(net ~ age, data = Runners) %>%
      gf_point(net ~ age, alpha = 0.01)

### Maps

Prior to `ggplot2` version 2.3, basic maps could be created using `gf_polygon()`.

    if (require(maps) && require(dplyr)) {
      US <- map_data("state") %>%
        dplyr::mutate(name_length = nchar(region))
      States <- US %>%
        dplyr::group_by(region) %>%
        dplyr::summarise(lat = mean(range(lat)), long = mean(range(long))) %>%
        dplyr::mutate(name = abbreviate(region, 3))

      gf_polygon(lat ~ long, data = US, group = ~ group,
                 fill = ~ name_length, color = "white") %>%
      gf_text(lat ~ long, label = ~ name, data = States,
        color = "gray70", inherit = FALSE) %>%
      gf_refine(mosaic::theme_map())
    }

Starting with `ggplot2` version 2.3, there is direct support for shape files. This allows each row of a data frame to contain all the geometry information for a region in a single column. There is also better support for overlaying information from multiple shape files.

    if (require(maps) && require(dplyr) && require(sf) && require(purrr)) {
      USshape <-
        sf::st_as_sf(maps::map('state', plot = FALSE, fill = TRUE)) %>%
        dplyr::mutate(
          name_length = nchar(as.character(ID)),
          centroid = purrr::map(geom, st_centroid),
          coords_x = purrr::map_dbl(centroid, 1),
          coords_y = purrr::map_dbl(centroid, 2)
        )

      gf_sf(fill = ~ factor(name_length), color = "white", data = USshape, alpha = 0.5) %>%
        gf_sf(data = sf::st_centroid(USshape), color = "white", alpha = 0.5, size = 3) %>%
        gf_text(coords_y ~ coords_x, label = ~ ID, color = "gray20", size = 2) %>%
        gf_labs(x = "", y = "") %>%
        gf_refine(mosaic::theme_map(), theme_bw()) %>%
        gf_labs(fill = "name length")
    }

    ## Loading required package: sf

    ## Linking to GEOS 3.7.2, GDAL 2.4.2, PROJ 5.2.0

    ## Loading required package: purrr

    ##
    ## Attaching package: 'purrr'

    ## The following object is masked from 'package:maps':
    ##
    ##     map

    ## Warning in st_centroid.sf(USshape): st_centroid assumes attributes are constant
    ## over geometries of x

    ## Warning in st_centroid.sfc(st_geometry(x), of_largest_polygon =
    ## of_largest_polygon): st_centroid does not give correct centroids for longitude/
    ## latitude data

### New types of plots

`ggformula` adds some additional plot options to `ggplot2`

#### ASH plots

Average shifted histograms can be created with `gf_ash()`. These plots average the height over all histograms with the same bin width. ASH plots often work well with larger bin widths than you might use for an individual histogram.

    gf_ash( ~ age, data = HELPrct, binwidth = 2) %>%
      gf_dhistogram( ~ age, data = HELPrct, binwidth = 2, alpha = 0.3)
    gf_ash( ~ age, data = HELPrct, binwidth = 10) %>%
      gf_dhistogram( ~ age, data = HELPrct, binwidth = 2, alpha = 0.3)

#### Distribution plots

`gf_dist()` can be used to create plots of discrete and continuous distributions.

    gf_dist("pois", lambda = 5)
    gf_dist("pois", lambda = 5, kind = "cdf")
    gf_dist("gamma", shape = 3, rate = 4, geom = "area")
    gf_dist("gamma", shape = 3, rate = 4, geom = "area", fill = ~ (x <= 1))
    gf_dist("gamma", shape = 3, rate = 4, kind = "cdf")

When distribution parameters and plot attributes have the same name, there are two ways to avoid the name colision:

    # size is used by the binomial distribution functions and when plotting
    gf_dist("binom", size = 20, prob = 0.25, plot_size = 3)
    gf_dist("binom", params = list(size = 20, prob = 0.25), size = 3)

`ggformula` also provides an interface to `MASS::fitdistr()` for fitting distributions to data and displaying the resulting pdf.

    x <- rgamma(1000, shape = 2, rate = 5)
    gf_dhistogram( ~ x, alpha = 0.3) %>%
      gf_fitdistr(dist = "dnorm", color = ~ "Normal") %>%
      gf_fitdistr(dist = "dgamma", color = ~ "Gamma") %>%
      gf_fitdistr(dist = "dweibull", color = ~ "Weibull")

    ## Warning in densfun(x, parm[1], parm[2], ...): NaNs produced

    ## Warning in densfun(x, parm[1], parm[2], ...): NaNs produced

    ## Warning in densfun(x, parm[1], parm[2], ...): NaNs produced

    ## Warning in densfun(x, parm[1], parm[2], ...): NaNs produced

    ## Warning in densfun(x, parm[1], parm[2], ...): NaNs produced

### Global plot adjustments

There are a number of things we may want to do to the entire plot – adjusting labels, colors, fonts, etc. `ggformula` provides wrappers to the `ggplot2` functions for this so that the chaining syntax can be used.

    gf_histogram( ~ age, data = Runners, alpha = 0.2, fill = "navy",
                  binwidth = 5) %>%
      gf_freqpoly( ~ age, binwidth = 5) %>%
      gf_labs(x = "age (years)", title = "Age of runners") %>%
      gf_lims(x = c(20, 80)) %>%
      gf_theme(theme = theme_minimal())

    ## Warning: Removed 84 rows containing non-finite values (stat_bin).

    ## Warning: Removed 84 rows containing non-finite values (stat_bin).

    ## Warning: Removed 2 rows containing missing values (geom_bar).

    gf_histogram( ~ age, data = Runners, alpha = 0.5, fill = "white",
                  binwidth = 5) %>%
      gf_freqpoly( ~ age, color = "skyblue", binwidth = 5, size = 1.5) %>%
      gf_labs(x = "age (years)", title = "Age of runners") %>%
      gf_lims(x = c(20, 80)) %>%
      gf_theme(theme = theme_dark())

    ## Warning: Removed 84 rows containing non-finite values (stat_bin).

    ## Warning: Removed 84 rows containing non-finite values (stat_bin).

    ## Warning: Removed 2 rows containing missing values (geom_bar).

For convenience, a few modifications can also be made directly in the original function call:

    gf_histogram( ~ age, data = Runners, alpha = 0.5, fill = "skyblue", color = "navy",
                  binwidth = 5,
                  xlab = "age (years)", title = "Age of runners"
                  )