The Grammar of Graphics

class: center, middle, title-slide

.title[
# The Grammar of Graphics
]
.author[
### Luke Tierney
]
.institute[
### University of Iowa
]
.date[
### 2023-05-06
]

---

layout: true

## Background
---

The _Grammar of Graphics_ is a language proposed by Leland Wilkinson
for describing statistical graphs.

> Wilkinson, L. (2005), _The Grammar of Graphics_, 2nd ed., Springer.

The grammar of graphics has served as the foundation for the graphics
frameworks in [SPSS](https://www.ibm.com/products/spss-statistics),
[Vega-Lite](https://vega.github.io/vega-lite/) and several other
systems.

`ggplot2` represents an implementation and extension of the grammar
of graphics for R.

> Wickham, H. (2016), _ggplot2: Elegant Graphics for Data Analysis_,
>  2nd ed., Springer. [3rd ed. in progress](https://ggplot2-book.org/).

> On line documentation: <https://ggplot2.tidyverse.org/reference/index.html>.

> Wickham. H., and Grolemund, G. (2016),
>  [_R for Data Science_](https://r4ds.had.co.nz/), O'Reilly.

> [Data visualization cheatsheet](https://raw.githubusercontent.com/rstudio/cheatsheets/main/data-visualization.pdf)

> Winston Chang (2018), [_R Graphics Cookbook_, 2nd
> edition](https://r-graphics.org/), O’Reilly. ([Book source on
> GitHub](https://github.com/wch/rgcookbook))

---

The idea is that any basic plot can be built out of a combination of

* a data set;

* one or more geometrical representation (_geoms_);

* mappings of values to _aesthetic_ features of the geom;

* a _stat_ to produce values to be mapped;

* position adjustments;

* a coordinate system;

* a scale specification;

* a faceting scheme.

`ggplot2` provides tools for specifying these components and adjusting
their features.

Many components and features are provided by default and do not need
to be specified explicitly unless the defaults are to be changed.

---
layout: true
## A Basic Template
---

The simplest graph needs a data set, a geom, and a mapping:

```r
ggplot(data = <DATA>) + <GEOM>(mapping = aes(<MAPPINGS>))
```

The appearance of geom objects is controlled by _aesthetic_ features.

Each geom has some required and some optional aesthetics.

For `geom_point` the required aesthetics are

* `x` position
* `y` position.

Optional aesthetics include

* `color`
* `fill`
* `shape`
* `size`

`geom_point` is used to produce a _scatter plot_.

---
layout: true
## Scatter Plots Using `geom_point`
---

The `mpg` data set included in the `ggpllot2` package includes EPA
fuel economy data from 1999 to 2008 for 38 popular models of cars.

```r
mpg
## # A tibble: 234 × 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a4 1.8 1999 4 auto… f 18 29 p comp…
## 2 audi a4 1.8 1999 4 manu… f 21 29 p comp…
## 3 audi a4 2 2008 4 manu… f 20 31 p comp…
## 4 audi a4 2 2008 4 auto… f 21 30 p comp…
## 5 audi a4 2.8 1999 6 auto… f 16 26 p comp…
## 6 audi a4 2.8 1999 6 manu… f 18 26 p comp…
## 7 audi a4 3.1 2008 6 auto… f 18 27 p comp…
## 8 audi a4 quattro 1.8 1999 4 manu… 4 18 26 p comp…
## 9 audi a4 quattro 1.8 1999 4 auto… 4 16 25 p comp…
## 10 audi a4 quattro 2 2008 4 manu… 4 20 28 p comp…
## # ℹ 224 more rows
```

---

.pull-left.width-45[
A simple scatter plot:

```r
ggplot(mpg) +
    geom_point(aes(x = displ,
                   y = hwy))
```
]

.pull-right.width-55[
<img src="ggplot_files/figure-html/mpg-plain-1.png" style="display: block; margin: auto auto auto 0;" />
]

---

.pull-left.width-45[
Map color to vehicle class:

```r
ggplot(mpg) +
    geom_point(aes(x = displ,
                   y = hwy,
*                  color = class))
```
]

.pull-right.width-55[
<img src="ggplot_files/figure-html/mpg-color-1.png" style="display: block; margin: auto auto auto 0;" />
]

---

.pull-left.width-45[
And map shape to number of cylinders:

```r
ggplot(mpg) +
    geom_point(aes(x = displ,
                   y = hwy,
                   color = class,
*                  shape = factor(cyl)))
```
]

.pull-right.width-55[
<img src="ggplot_files/figure-html/mpg-color-shape-1.png" style="display: block; margin: auto auto auto 0;" />
{{content}}
]
--

Perception:

* Too many colors;
* shapes are too small;
* interference between shapes and colors.

---

.pull-left.width-45[
Aesthetics can be mapped to a variable or set to a fixed common value.
{{content}}
]
--

This can be used to override default settings:

```r
ggplot(mpg) +
    geom_point(aes(x = displ,
                   y = hwy),
               color = "blue",
               shape = 1)
```
--

.pull-right.width-55[
<img src="ggplot_files/figure-html/mpg-fixed-1.png" style="display: block; margin: auto auto auto 0;" />
]

---

.pull-left.width-45[
Changing the `size` aesthetics makes shapes easier to recognize:

```r
ggplot(mpg) +
    geom_point(aes(x = displ,
                   y = hwy,
                   color = class,
                   shape = factor(cyl)),
*              size = 3)
```
]

.pull-right.width-55[
<img src="ggplot_files/figure-html/mpg-color-shape-large-1.png" style="display: block; margin: auto auto auto 0;" />
]

Perception: Still too many colors; still have interference.

---

.pull-left.width-60[
Available point shapes are specified by number:
{{content}}
]
--

<img src="ggplot_files/figure-html/unnamed-chunk-3-1.png" style="display: block; margin: auto;" />
--

.pull-right.width-40[
Shapes 1-20 have their color set by the `color` aesthetic and ignore
the `fill` aesthetic.
{{content}}
]
--

For shapes 21-25 the `color` aesthetic specifies the border color and
`fill` specifies the interior color.

---

Using `shape` 21 with `cyl` mapped to the `fill` aesthetic:

.pull-left.width-45[

```r
ggplot(mutate(mpg, cyl = factor(cyl))) +
 geom_point(aes(x = displ,
 y = hwy,
 fill = cyl),
 shape = 21,
 size = 4)
```
]
.pull-right.width-55[
<img src="ggplot_files/figure-html/mpg-fill-21-1.png" style="display: block; margin: auto;" />
]

Perception: Borders, larger symbols, fewer colors help.

---

Specifying a new default is very different from specifying a constant
value as an aesthetic.

.pull-left.small-code[
Constant aesthetic: Rarely what you want:

```r
ggplot(mpg) +
    geom_point(aes(x = displ,
                   y = hwy,
*                  color = "blue"))
```
{{content}}
]
--

.pull-right.small-code[
Default: Probably what you want:

```r
ggplot(mpg) +
    geom_point(aes(x = displ,
                   y = hwy),
*              color = "blue")
```
{{content}}
]
--

---
layout: true
## Geometric Objects
---

`ggplot2` provides a number of geoms:

.small-code[
```r
geom_abline             geom_area               geom_bar                geom_bin_2d
geom_bin2d              geom_blank              geom_boxplot            geom_col
geom_contour            geom_contour_filled     geom_count              geom_crossbar
geom_curve              geom_density            geom_density_2d         geom_density_2d_filled
geom_density2d          geom_density2d_filled   geom_dotplot            geom_errorbar
geom_errorbarh          geom_freqpoly           geom_function           geom_hex
geom_histogram          geom_hline              geom_jitter             geom_label
geom_line               geom_linerange          geom_map                geom_path
geom_point              geom_pointrange         geom_polygon            geom_qq
geom_qq_line            geom_quantile           geom_raster             geom_rect
geom_ribbon             geom_rug                geom_segment            geom_sf
geom_sf_label           geom_sf_text            geom_smooth             geom_spoke
geom_step               geom_text               geom_tile               geom_violin
geom_vline                                                              
```
]

Additional geoms are available in packages like `ggforce`, `ggridges`,
and others described on the [`ggplot2` extensions
site](https://exts.ggplot2.tidyverse.org/).

---

.pull-left.width-45[
Geoms can be added as _layers_ to a plot.
{{content}}
]
--

Mappings common to all, or most, geoms can be specified in the
`ggplot` call:
{{content}}
--

```r
ggplot(mpg,
       aes(x = displ,
           y = hwy)) +
    geom_smooth() +
    geom_point()
```
--

.pull-right.width-55[
<img src="ggplot_files/figure-html/mpg-smooth-1.png" style="display: block; margin: auto;" />
]

---

Geoms can also use different data sets.

.pull-left.small-code[
One way to highlight Europe in a plot of life expectancy against log
income for 2007 is to start with a plot of the full data:
{{content}}
]
--

```r
library(dplyr)
library(gapminder)
gm_2007 <- filter(gapminder, year == 2007)

(p <- ggplot(gm_2007, aes(x = gdpPercap,
 y = lifeExp)) +
 geom_point() +
 scale_x_log10())
```

.pull-right[
<img src="ggplot_files/figure-html/gm_2007-1.png" style="display: block; margin: auto;" />
]

---

Then add a layer showing only Europe:

.pull-left.small-code[

```r
gm_2007_eu <- filter(gm_2007, continent == "Europe")

p + geom_point(data = gm_2007_eu,
               color = "red",
               size = 3)
```
]

.pull-right[
<img src="ggplot_files/figure-html/gm_2007_eu-1.png" style="display: block; margin: auto;" />
]

---
layout: true
## Statistical Transformations
---

All geoms use a statistical transformation (_stat_) to convert raw
data to the values to be mapped to the object's features.

The available stats are

.small-code[
```r
stat_align              stat_bin                stat_bin_2d
stat_bin_hex            stat_bin2d              stat_binhex
stat_boxplot            stat_contour            stat_contour_filled
stat_count              stat_density            stat_density_2d
stat_density_2d_filled  stat_density2d          stat_density2d_filled
stat_ecdf               stat_ellipse            stat_function
stat_identity           stat_qq                 stat_qq_line
stat_quantile           stat_sf                 stat_sf_coordinates
stat_smooth             stat_spoke              stat_sum
stat_summary            stat_summary_2d         stat_summary_bin
stat_summary_hex        stat_summary2d          stat_unique
stat_ydensity                                   
```
]

Each geom has a default stat, and each stat has a default geom.

* For `geom_point` the default stat is `stat_identity`.

* For `geom_bar` the default stat is `stat_count`.

* For `geom_histogram` the default is `stat_bin`.

---

Stats can provide _computed variables_ that can be mapped to aesthetic
features.

For `stat_bin` some of the computed variables are

* `count`: number of points in bin
* `density`: density of points in bin, scaled to integrate to 1

The `density` variable can be accessed as `after_stat(dentity)`.

Older approaches that also work but are now discouraged:

* `stat(dentity)`
* `..density..`

---

By default, `geom_histogram` uses `y = after_stat(count)`.

.pull-left.small-code[

```r
ggplot(faithful) +
 geom_histogram(aes(x = eruptions),
 binwidth = 0.25,
 fill = "grey",
 color = "black")
```
]
.pull-right[
<img src="ggplot_files/figure-html/geyser-count-1.png" style="display: block; margin: auto;" />
]

---

Explicitly specifying `y = after_stat(count)` produces the same plot:

.pull-left.small-code[

```r
ggplot(faithful) +
 geom_histogram(aes(x = eruptions,
* y = after_stat(count)),
 binwidth = 0.25,
 fill = "grey",
 color = "black")
```
]
.pull-right[
<img src="ggplot_files/figure-html/geyser-count-exp-1.png" style="display: block; margin: auto;" />
]

---

Using `y = after_stat(density)` produces a density scaled axis.

.pull-left.small-code[

```r
(p <- ggplot(faithful) +
 geom_histogram(aes(x = eruptions,
* y = after_stat(density)),
 binwidth = 0.25,
 fill = "grey",
 color = "black"))
```
]
.pull-right[
<img src="ggplot_files/figure-html/geyser-dentity-1.png" style="display: block; margin: auto;" />
]

---

`stat_function` can be used to add a density curve specified as a
mixture of two normal densities:

.pull-left.small-code[

```r
(ms <- mutate(faithful,
 type = ifelse(eruptions < 3,
 "short",
 "long")) %>%
 group_by(type) %>%
 summarize(mean = mean(eruptions),
 sd = sd(eruptions),
 n = n()) %>%
 mutate(p = n / sum(n)))
## # A tibble: 2 × 5
## type mean sd n p
## <chr> <dbl> <dbl> <int> <dbl>
## 1 long 4.29 0.411 175 0.643
## 2 short 2.04 0.267 97 0.357
```

```r
f <- function(x)
 ms$p[1] * dnorm(x, ms$mean[1], ms$sd[1]) +
 ms$p[2] * dnorm(x, ms$mean[2], ms$sd[2])

p + stat_function(fun = f, color = "red")
```
]
.pull-right[
<img src="ggplot_files/figure-html/geyser-hist-dens-1.png" style="display: block; margin: auto;" />
]

---
layout: true
## Position Adjustments
---

The available position adjustments:

```r
position_dodge        position_dodge2       position_fill
position_identity     position_jitter       position_jitterdodge
position_nudge        position_stack        
```
---

A bar chart showing the counts for the different `cut` categories in
the `diamonds` data:

.pull-left.small-code[

```r
ggplot(diamonds, aes(x = cut)) +
 geom_bar()
```
]
.pull-right[
<img src="ggplot_files/figure-html/diamonds-cut-1.png" style="display: block; margin: auto;" />
]

---

Mapping `clarity` to `fill` shows the breakdown by both `cut` and
`clarity` in a _stacked bar chart_:

.pull-left.small-code[

```r
ggplot(diamonds, aes(x = cut,
* fill = clarity)) +
 geom_bar()
```
]
.pull-right[
<img src="ggplot_files/figure-html/diamonds-stack1-1.png" style="display: block; margin: auto;" />
]

---

The default `position` for bar charts is `position_stack`:

.pull-left.small-code[

```r
ggplot(diamonds, aes(x = cut,
 fill = clarity)) +
* geom_bar(position = "stack")
```
]
.pull-right[
<img src="ggplot_files/figure-html/diamonds-stack2-1.png" style="display: block; margin: auto;" />
]

---

`position_dodge` produces _side-by-side bar charts_:

.pull-left.small-code[

```r
ggplot(diamonds, aes(x = cut,
 fill = clarity)) +
* geom_bar(position = "dodge")
```
]
.pull-right[
<img src="ggplot_files/figure-html/diamonds-dodge-1.png" style="display: block; margin: auto;" />
]

---

`position_fill` rescales all bars to be equal height to help compare
proportions within bars.

.pull-left.small-code[

```r
ggplot(diamonds, aes(x = cut,
 fill = clarity)) +
* geom_bar(position = "fill")
```
]
.pull-right[
<img src="ggplot_files/figure-html/diamonds-fill-1.png" style="display: block; margin: auto;" />
]

---

Using the counts to scale the widths would produce a _spine plot_, a
variant of a _mosaic plot_.

This is easiest to do with the `ggmosaic` package.

`position_jitter` can be used with `geom_point` to avoid overplotting
or break up rounding artifacts.

---

Another version of the Old Faithful data available as `geyser` in
package `MASS` has some rounding in the `duration` variable:

.pull-left.small-code[

```r
data(geyser, package = "MASS")

## Adjust for different meaning of `waiting` variable
geyser2 <- na.omit(mutate(geyser,
 duration = lag(duration)))

p <- ggplot(geyser2, aes(x = duration, y = waiting))
p + geom_point()
```
]

.pull-right[
<img src="ggplot_files/figure-html/geyser2-1.png" style="display: block; margin: auto;" />
]

---

_Jittering_ can help break up the distracting _heaping_ of values on
durations of 2 and 4 minutes.

.pull-left.small-code[
The default amount of jittering isn't quite enough in this case:

```r
p + geom_point(position = "jitter")
```
]
.pull-right[
<img src="ggplot_files/figure-html/geyser2-jit-1.png" style="display: block; margin: auto;" />
]

---

To jitter only horizontally and by a larger amount you can use

.pull-left.small-code[

```r
p + geom_point(position =
 position_jitter(height = 0,
 width = 0.1))
```
]
.pull-right[
<img src="ggplot_files/figure-html/geyser2-jit2-1.png" style="display: block; margin: auto;" />
]

---
layout: true
## Coordinate Systems
---

Coordinate system functions include

```r
coord_cartesian  coord_equal      coord_fixed      coord_flip
coord_map        coord_munch      coord_polar      coord_quickmap
coord_sf         coord_trans                       
```
--

The default coordinate system is `coord_cartesian`.

---

### Cartesian Coordinates

`coord_cartesian` can be used to _zoom in_ on a particular regiion:

.pull-left.small-code[

```r
p + geom_point() +
 coord_cartesian(xlim = c(3, 4))
```
]
.pull-right[
<img src="ggplot_files/figure-html/geyser2-zoom-1.png" style="display: block; margin: auto;" />
]

---

`coord_fixed` and `coord_equal` fix the _aspect ratio_ for a cartesian
coordinate system.

The aspect ratio is the ratio of the number physical display units per
`y` unit to the number of physical display units per `x` unit.

The aspect ratio can be important for recognizing features and patterns.

.small-code[

```r
river <- scan("https://www.stat.uiowa.edu/~luke/data/river.dat")
r <- data.frame(flow = river, month = seq_along(river))
```
]

.pull-left.small-code.width-40[

```r
ggplot(r, aes(x = month, y = flow)) +
 geom_point() +
 coord_fixed(ratio = 4)
```
]
.pull-right.width-60[
<img src="ggplot_files/figure-html/river-flat-1.png" style="display: block; margin: auto;" />
]

---

.pull-left.small-code[
### Polar Coordinates

A filled bar chart

```r
(p <- ggplot(diamonds) +
 geom_bar(aes(x = 1, fill = cut),
 position = "fill"))
```
]
.pull-right[
<img src="ggplot_files/figure-html/diamonds-fill-1-1.png" style="display: block; margin: auto;" />
]

---

.pull-left.small-code[
is turned into a pie chart by changing to polar coordinates:

```r
p + coord_polar(theta = "y")
```
]
.pull-right[
<img src="ggplot_files/figure-html/diamonds-pie-1.png" style="display: block; margin: auto;" />
]

---

### Coordinate Systems for Maps

Coordinate systems are particularly important for maps.

Polygons for many political and geographic boundaries are available
through the `map_data` function.

Boundaries for the lower 48 US states can be obtained as

```r
usa <- map_data("state")
```

---

Polygon vertices are encoded by longitude and latitude.

Plotting these in the default cartesian coordinate system usually does
not work well:

.pull-left.small-code[

```r
usa <- map_data("state")
m <- ggplot(usa, aes(x = long,
 y = lat,
 group = group)) +
 geom_polygon(fill = "white",
 color = "black")
m
```
]
.pull-right[
<img src="ggplot_files/figure-html/usa-cart-1.png" style="display: block; margin: auto;" />
]

---

Using a fixed aspect ratio is better, but an aspect ratio of 1 does
not work well:

.pull-left.small-code[

```r
m + coord_equal()
```

<img src="ggplot_files/figure-html/unnamed-chunk-11-1.png" style="display: block; margin: auto;" />
]
--

.pull-right[
The problem is that away from the equator a one degree change in
latitude corresponds to a larger distance than a one degree change in
longitude.
{{content}}
]
--

The ratio of one degree longitude separation to one degree latitude
separation for the latitude at the middle of Iowa of 41 degrees is

```r
longlat <- cos(41 / 90 * pi / 2)
longlat
## [1] 0.7547096
```

---

A better map is obtained using the aspect ratio `1 / longlat`:

.pull-left.small-code[

```r
m + coord_fixed(1 / longlat)
```
]
.pull-right[
<img src="ggplot_files/figure-html/usa-fixed-1.png" style="display: block; margin: auto;" />
]

---

The best approach is to use a coordinate system designed specifically for
maps.

.pull-left.small-code[
There are many _projections_ used in map making.
{{content}}
]
--

The default projection used by `coord_map` is the
[Mercator](https://en.wikipedia.org/wiki/Mercator_projection)
projection.

```r
m + coord_map()
```
--

.pull-right[
<img src="ggplot_files/figure-html/usa-mercator-1.png" style="display: block; margin: auto;" />
]

---

Proper map projections are non-linear; this is easier to see with an
Albers projection:

.pull-left.small-code[

```r
m + coord_map("albers", 20, 50)
```
]
.pull-right[
<img src="ggplot_files/figure-html/usa-albers-1.png" style="display: block; margin: auto;" />
]

---
layout: true
## Scales
---

Scales are used for controlling the mapping of values to physical
representations such as colors, shapes, and positions.

Scale functions are also responsible for producing _guides_ for
translating physical representations back to values, such as

* axis labels and marks;

* color or shape legends.

There are currently 131
scale functions; some examples are

```r
scale_color_gradient      scale_shape_manual     scale_x_log10
scale_color_manual        scale_size_area        scale_y_log10
scale_fill_gradient                              scale_x_sqrt
scale_fill_manual                                scale_y_sqrt

```

An [experimental tool](https://ggplot2tor.com/scales/) to help
choosing scales has recently been introduced.

---

Start with a basic scatter plot:

.pull-left.small-code[

```r
(p <- ggplot(mpg, aes(x = displ,
 y = hwy)) +
 geom_point())
```
]
.pull-right[
<img src="ggplot_files/figure-html/mpg-basic-1.png" style="display: block; margin: auto;" />
]

---

Remove the `x` tick marks and labels (this can also be done with theme
settings):

.pull-left.small-code[

```r
p + scale_x_continuous(labels = NULL,
 breaks = NULL)
```
]
.pull-right[
<img src="ggplot_files/figure-html/mpg-no-ticks-labs-1.png" style="display: block; margin: auto;" />
]

---

Change the tick locations and labels:

.pull-left.small-code[

```r
p + scale_x_continuous(labels =
 paste(c(2, 4, 6), "ltr"),
 breaks = c(2, 4, 6))
```
]
.pull-right[
<img src="ggplot_files/figure-html/mpg-new-ticks-labs-1.png" style="display: block; margin: auto;" />
]

---

Use a logarithmic axis:

.pull-left.small-code[

```r
p + scale_x_log10(labels = paste(c(2, 4, 6), "ltr"),
 breaks = c(2, 4, 6),
 minor_breaks = c(3, 5, 7))
```
]
.pull-right[
<img src="ggplot_files/figure-html/mpg-log-x-1.png" style="display: block; margin: auto;" />
]
---

The
[Scales](https://r4ds.had.co.nz/graphics-for-communication.html#scales)
section in [R for Data Science](https://r4ds.had.co.nz/) provides some
more details.

Color assignment can also be controlled by scale functions.

For example, for some presidential approval ratings data

.pull-left.small-code[

```r
pr_appr
##         pres appr party year
## 1      Obama   79     D 2009
## 2     Carter   78     D 1977
## 3    Clinton   68     D 1993
## 4  G.W. Bush   65     R 2001
## 5     Reagan   58     R 1981
## 6 G.H.W Bush   56     R 1989
## 7      Trump   40     R 2017
```
{{content}}
]
--

the default color scale is not ideal:

```r
ggplot(pr_appr,
       aes(x = appr, y = pres, fill = party)) +
    geom_col()
```
--

.pull-right[
<img src="ggplot_files/figure-html/pr-appr0-1.png" style="display: block; margin: auto;" />
]

---

The common assignment of red for Republican and blue for Democrat can
be obtained by

.pull-left.small-code[

```r
ggplot(pr_appr,
       aes(x = appr, y = pres, fill = party)) +
    geom_col() +
*   scale_fill_manual(values
*                     = c(R = "red", D = "blue"))
```
]

.pull-right[
<img src="ggplot_files/figure-html/pr-appr-1.png" style="display: block; margin: auto;" />
]

---

A better choice is to use a well-designed [color
palette](https://hclwizard.org/#color-palettes):

.pull-left.small-code[

```r
ggplot(pr_appr,
       aes(x = appr, y = pres, fill = party)) +
    geom_col() +
*   colorspace::scale_fill_discrete_diverging(
*                   palette = "Blue-Red 2")
```
]

.pull-right[
<img src="ggplot_files/figure-html/pr-appr-2-1.png" style="display: block; margin: auto;" />
]

---
layout: true
## Facets
---

Faceting uses the _small multiples_ approach to introduce additional
variables.

For a single variable `facet_wrap` is usually used:

.pull-left.small-code[

```r
p <- ggplot(mpg) +
 geom_point(aes(x = displ,
 y = hwy))
p + facet_wrap(~ class)
```
]
.pull-right[
<img src="ggplot_files/figure-html/mpg-facet-wrap-1.png" style="display: block; margin: auto;" />
]

---

.pull-left.small-code[
For two variables, each with a modest number of categories,
`facet_grid` can be effective:

```r
p + facet_grid(factor(cyl) ~ drv)
```
]
.pull-right[
<img src="ggplot_files/figure-html/mpg-facet-grid-1.png" style="display: block; margin: auto;" />
]

---

.pull-left.small-code.width-40[
To show common data in all facets make sure the data does not contain the
faceting variable.
{{content}}
]
--

This was used to show muted views of the full data in faceted plots.
{{content}}
--

A faceted plot of the `gapminder` data:

```r
library(gapminder)

years_to_keep <- c(1977, 1987, 1997, 2007)
gd <- filter(gapminder,
 year %in% years_to_keep)

ggplot(gd,
       aes(x = gdpPercap,
           y = lifeExp,
           color = continent)) +
    geom_point(size = 2.5) +
    scale_x_log10() +
    facet_wrap(~ year)
```

.pull-right.width-60[
<img src="ggplot_files/figure-html/gapminder-not-muted-1.png" style="display: block; margin: auto;" />
]

---

.pull-left.small-code.width-40[
Add a muted version of the full data in the background of each panel:

```r
library(gapminder)

years_to_keep <- c(1977, 1987, 1997, 2007)
gd <- filter(gapminder,
 year %in% years_to_keep)
*gd_no_year <- mutate(gd, year = NULL)

ggplot(gd,
       aes(x = gdpPercap,
           y = lifeExp,
           color = continent)) +
*   geom_point(data = gd_no_year,
*              color = "grey80") +
    geom_point(size = 2.5) +
    scale_x_log10() +
    facet_wrap(~ year)
```
]

.pull-right.width-60[
<img src="ggplot_files/figure-html/gapminder-muted-1.png" style="display: block; margin: auto;" />
]

---

Usually facets use common axis scales, but one or both can be allowed
to vary.

A useful approach for showing time series data with a good aspect
ratio can be to split the data into facets for non-overlapping
portions of the time axis.

.pull-left.small-code[

```r
pd <- rep(paste(seq(1, by = 32, length.out = 4),
 seq(32, by = 32, length.out = 4),
 sep = " - "),
 each = 32)
rd <- data.frame(month = seq_along(river),
 flow = river,
 panel = pd)
ggplot(rd, aes(x = month,
 y = flow)) +
 geom_point() +
 facet_wrap(~ panel,
 scale = "free_x",
 ncol = 1)
```
]
.pull-right[
<img src="ggplot_files/figure-html/river-facet-1.png" style="display: block; margin: auto;" />
]

---

Facet arrangement can also be used to convey other information, such
as geographic location.

The [`geofacet` package](https://hafen.github.io/geofacet/) allows
facets to be placed in approximate locations of different geographic
regions.

.pull-left.small-code[
An example for data from US states:

```r
library(geofacet)
ggplot(state_unemp, aes(year, rate)) +
 geom_line() +
 facet_geo(~ state,
 grid = "us_state_grid2",
 label = "code") +
 scale_x_continuous(labels =
 function(x) paste0("'", substr(x, 3, 4))) +
 labs(title = "Seasonally Adjusted US Unemployment Rate 2000-2016",
 caption = "Data Source: bls.gov",
 x = "Year",
 y = "Unemployment Rate (%)") +
 theme(strip.text.x = element_text(size = 6),
 axis.text = element_text(size = 5))
```
]
.pull-right[
<img src="ggplot_files/figure-html/geofacet-1.png" style="display: block; margin: auto;" />
]

Arrangement according to a calendar can also be useful.

---
layout: true
## Themes
---

`ggplot2` supports the notion of _themes_ for adjusting non-data
appearance aspects of a plot, such as

* plot titles

* axis and legend placement and titles

* background colors

* guide line placement

Theme elements can be customized in several ways:

* `theme()` can be used to adjust individual elements in a plot.

* `theme_set()` adjusts default settings for a session;

* pre-defined theme functions allow consistent style changes.

The
[full documentation](https://ggplot2.tidyverse.org/reference/theme.html)
of the `theme` function lists many customizable elements.

---

One simple example:

.pull-left.small-code[

```r
ggplot(mutate(mpg, cyl = factor(cyl))) +
 geom_point(aes(x = displ,
 y = hwy,
 fill = cyl),
 shape = 21,
 size = 3) +
* theme(legend.position = "top",
* axis.text = element_text(size = 12),
* axis.title = element_text(size = 14,
* face = "bold"))
```
]
.pull-right[
<img src="ggplot_files/figure-html/theme-simple-1.png" style="display: block; margin: auto;" />
]

---

Another example:

.pull-left.small-code[

```r
gthm <-
 theme(plot.background =
 element_rect(fill = "lightblue",
 color = NA),
 panel.background =
 element_rect(fill = "pink"))
p + gthm
```
]
.pull-right[
<img src="ggplot_files/figure-html/theme-simple-2-1.png" style="display: block; margin: auto;" />
]

---

Some alternate complete themes provided by `ggplot2` are

```r
theme_bw        theme_gray      theme_minimal   theme_void
theme_classic   theme_grey      theme_dark      theme_light
```

.pull-left.small-code[
Some examples:

```r
p_bw <- p + theme_bw() + ggtitle("BW")

p_classic <- p + theme_classic() + ggtitle("Classic")

p_min <- p + theme_minimal() + ggtitle("Minimal")

p_void <- p + theme_void() + ggtitle("Void")

library(patchwork)
(p_bw + p_classic) / (p_min + p_void)
```
]
.pull-right[
<img src="ggplot_files/figure-html/alt-themes-1.png" style="display: block; margin: auto;" />
]

---

The
[`ggthemes`](http://www.rpubs.com/Mentors_Ubiqum/ggthemes_1)
package provides some additional themes.

.pull-left.small-code[
Some examples:

```r
library(ggthemes)

p_econ <- p + theme_economist() + ggtitle("Economist")

p_wsj <- p + theme_wsj() + ggtitle("WSJ")

p_tufte <- p + theme_tufte() + ggtitle("Tufte")

p_few <- p + theme_few() + ggtitle("Few")

(p_econ + p_wsj) / (p_tufte + p_few)
```
]
.pull-right[
<img src="ggplot_files/figure-html/ggthemes-examples-1.png" style="display: block; margin: auto;" />
]

---

`ggthemes` also provides `theme_map` that removes unnecessary elements
from maps:

```r
m + coord_map() + theme_map()
```

The
[Themes](https://r4ds.had.co.nz/graphics-for-communication.html#themes)
section in [R for Data Science](https://r4ds.had.co.nz/) provides some
more details.

---
layout: false
## A More Complete Template

```r
ggplot(data = <DATA>) +
 <GEOM>(mapping = aes(<MAPPINGS>),
 stat = <STAT>,
 position = <POSITION>) +
 < ... MORE GEOMS ... > +
 <COORDINATE_ADJUSTMENT> +
 <SCALE_ADJUSTMENT> +
 <FACETING> +
 <THEME_ADJUSTMENT>
```

---
layout: true
## Labels and Annotations
---

A basic plot:

.pull-left.small-code[

```r
p <- ggplot(mpg, aes(x = displ,
 y = hwy))
p1 <- p + geom_point(aes(color = factor(cyl)),
 size = 2.5)
p1
```
]
.pull-right[
<img src="ggplot_files/figure-html/mpg-ann-1.png" style="display: block; margin: auto;" />
]

Axis labels are based on the expressions given to `aes`.

This is convenient for exploration but usually not ideal for a report.

---

The `labs()` function can be used to change axis and legend labels:

.pull-left.small-code[

```r
p1 + labs(x = "Displacement (Liters)",
 y = "Highway Miles Per Gallon",
 color = "Cylinders")
```
]
.pull-right[
<img src="ggplot_files/figure-html/mpg-ann-labs-1.png" style="display: block; margin: auto;" />
]

---

The `labs()` function can also add a title, subtitle, and caption:

.pull-left.small-code[

```r
p2 <- p1 +
 labs(x = "Displacement (Liters)",
 y = "Highway Miles Per Gallon",
 color = "Cylinders",
 title = "Gas Mileage and Displacement",
 subtitle = paste("For models which had a new release every year",
 "between 1999 and 2008"),
 caption = "Data Source: https://fueleconomy.gov/")
p2
```
]
.pull-right[
<img src="ggplot_files/figure-html/mpg-ann-labs-2-1.png" style="display: block; margin: auto;" />
]

---

Annotations can be used to provide popout that draws a viewer's
attention to particular features.

The `annotate()` function is one option:

.pull-left.small-code[

```r
p2 +
 annotate("label", x = 2.8, y = 43,
 label = "Volkswagens") +
 annotate("rect",
 xmin = 1.7, xmax = 2.1,
 ymin = 40, ymax = 45,
 fill = NA, color = "black")
```
]
.pull-right[
<img src="ggplot_files/figure-html/mpg-ann-popout-1.png" style="display: block; margin: auto;" />
]

---

Often more convenient are some `geom_mark` objects provided by the
`ggforce` package:

.pull-left.small-code[

```r
library(ggforce)
p2 +
 geom_mark_hull(aes(filter = class == "2seater"),
 description =
 paste("2-Seaters have high displacement",
 "values, but also high fuel efficiency",
 "for their displacement.")) +
 geom_mark_rect(aes(filter = hwy > 40),
 description =
 "These are Volkswagens") +
 geom_mark_circle(aes(filter = hwy == 12),
 description =
 "Three pickups and an SUV.")
```
]
.pull-right[
<img src="ggplot_files/figure-html/mpg-ann-popout-2-1.png" style="display: block; margin: auto;" />
]

These annotations can be customized in a number of ways.

---
layout: false
## Arranging Plots

There are several tools available for assembling ensemble plots.

The [`patchwork`](https://patchwork.data-imaginist.com/) package is a
good choice.

.pull-left.small-code[
A simple example:

```r
p1 <- ggplot(mpg, aes(x = displ,
 y = hwy)) +
 geom_point()
p2 <- ggplot(mpg, aes(x = cyl,
 y = hwy,
 group = cyl)) +
 geom_boxplot()
p3 <- ggplot(mpg, aes(x = cyl)) +
 geom_bar()

library(patchwork)
(p1 + p2) / p3
```
]
.pull-right[
<img src="ggplot_files/figure-html/mpg-patchwork-1.png" style="display: block; margin: auto;" />
]

---
layout: true
## Interaction
---

### Plotly

The `ggplotly` function in the [`plotly` package](https://plotly.com/r/)
can be used to add some interactive features to a plot created with
`ggplot2`.

* In an R session a call to `ggplotly()` opens a browser window with the
  interactive plot.

* In an Rmarkdown document the interactive plot is embedded in the
  `html` file.

Another interactive plotting approach that can be used from R is
described in an [Infoworld
article](https://www.infoworld.com/article/3607068/plot-in-r-with-echarts4r.html).

---

A simple example using `ggplotly()`:

.pull-left.small-code[

```r
library(ggplot2)
library(plotly)
p <- ggplot(mutate(mpg, cyl = factor(cyl))) +
 geom_point(aes(x = displ,
 y = hwy,
 fill = cyl),
 shape = 21,
 size = 3)
ggplotly(p)
```
]
.pull-right[
<div class="plotly html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-db97395c4a5fd701554a" style="width:432px;height:360px;"></div>
<script type="application/json" data-for="htmlwidget-db97395c4a5fd701554a">{"x":{"data":[{"x":[1.8,1.8,2,2,1.8,1.8,2,2,2.4,2.4,2.4,1.6,1.6,1.6,1.6,1.6,1.8,1.8,1.8,2,2.4,2.4,2.4,2.4,2,2,2,2,2.4,2.4,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.2,2.2,2.5,2.5,2.5,2.5,2.5,2.5,2.7,2.7,2.2,2.2,2.4,2.4,2.2,2.2,2.4,2.4,1.8,1.8,1.8,1.8,1.8,2.7,2.7,2.7,2,2,2,2,1.9,2,2,2,2,1.9,1.9,2,2,1.8,1.8,2,2],"y":[29,29,31,30,26,25,28,27,27,30,24,33,32,32,29,32,34,36,36,29,26,27,30,31,26,29,28,27,29,27,31,32,25,24,27,25,26,23,26,26,26,26,25,27,25,27,20,20,29,27,31,31,27,29,31,31,30,33,35,37,35,20,20,22,29,26,29,29,44,29,26,29,29,44,41,29,26,29,29,28,29],"text":["displ: 1.8 hwy: 29 cyl: 4","displ: 1.8 hwy: 29 cyl: 4","displ: 2.0 hwy: 31 cyl: 4","displ: 2.0 hwy: 30 cyl: 4","displ: 1.8 hwy: 26 cyl: 4","displ: 1.8 hwy: 25 cyl: 4","displ: 2.0 hwy: 28 cyl: 4","displ: 2.0 hwy: 27 cyl: 4","displ: 2.4 hwy: 27 cyl: 4","displ: 2.4 hwy: 30 cyl: 4","displ: 2.4 hwy: 24 cyl: 4","displ: 1.6 hwy: 33 cyl: 4","displ: 1.6 hwy: 32 cyl: 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4"],"type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(248,118,109,1)","opacity":1,"size":11.3385826771654,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(0,0,0,1)"}},"hoveron":"points","name":"4","legendgroup":"4","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[2.5,2.5,2.5,2.5],"y":[29,29,28,29],"text":["displ: 2.5 hwy: 29 cyl: 5","displ: 2.5 hwy: 29 cyl: 5","displ: 2.5 hwy: 28 cyl: 5","displ: 2.5 hwy: 29 cyl: 5"],"type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(124,174,0,1)","opacity":1,"size":11.3385826771654,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(0,0,0,1)"}},"hoveron":"points","name":"5","legendgroup":"5","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[2.8,2.8,3.1,2.8,2.8,3.1,3.1,2.8,3.1,3.1,3.5,3.6,3,3.3,3.3,3.3,3.3,3.3,3.8,3.8,3.8,4,3.7,3.7,3.9,3.9,3.9,4,4,4,4,4.2,4.2,3.8,3.8,4,4,2.5,2.5,3.3,2.7,2.7,2.7,3,3.7,4,4,4,3.5,3.5,3,3,3.5,3.3,3.3,4,3.1,3.8,3.8,3.8,3.4,3.4,4,3,3,3.5,3,3,3.3,3.4,3.4,4,4,2.8,2.8,2.8,2.8,2.8,3.6],"y":[26,26,27,25,25,25,25,24,25,26,29,26,24,22,22,24,24,17,22,21,23,23,19,18,17,17,17,17,19,17,19,17,17,26,25,26,24,26,26,28,24,24,24,22,19,20,17,19,27,26,26,25,25,17,17,20,26,26,27,28,19,17,20,26,26,28,26,26,27,17,19,18,20,24,23,24,26,26,26],"text":["displ: 2.8 hwy: 26 cyl: 6","displ: 2.8 hwy: 26 cyl: 6","displ: 3.1 hwy: 27 cyl: 6","displ: 2.8 hwy: 25 cyl: 6","displ: 2.8 hwy: 25 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hwy: 24 cyl: 6","displ: 2.7 hwy: 24 cyl: 6","displ: 2.7 hwy: 24 cyl: 6","displ: 3.0 hwy: 22 cyl: 6","displ: 3.7 hwy: 19 cyl: 6","displ: 4.0 hwy: 20 cyl: 6","displ: 4.0 hwy: 17 cyl: 6","displ: 4.0 hwy: 19 cyl: 6","displ: 3.5 hwy: 27 cyl: 6","displ: 3.5 hwy: 26 cyl: 6","displ: 3.0 hwy: 26 cyl: 6","displ: 3.0 hwy: 25 cyl: 6","displ: 3.5 hwy: 25 cyl: 6","displ: 3.3 hwy: 17 cyl: 6","displ: 3.3 hwy: 17 cyl: 6","displ: 4.0 hwy: 20 cyl: 6","displ: 3.1 hwy: 26 cyl: 6","displ: 3.8 hwy: 26 cyl: 6","displ: 3.8 hwy: 27 cyl: 6","displ: 3.8 hwy: 28 cyl: 6","displ: 3.4 hwy: 19 cyl: 6","displ: 3.4 hwy: 17 cyl: 6","displ: 4.0 hwy: 20 cyl: 6","displ: 3.0 hwy: 26 cyl: 6","displ: 3.0 hwy: 26 cyl: 6","displ: 3.5 hwy: 28 cyl: 6","displ: 3.0 hwy: 26 cyl: 6","displ: 3.0 hwy: 26 cyl: 6","displ: 3.3 hwy: 27 cyl: 6","displ: 3.4 hwy: 17 cyl: 6","displ: 3.4 hwy: 19 cyl: 6","displ: 4.0 hwy: 18 cyl: 6","displ: 4.0 hwy: 20 cyl: 6","displ: 2.8 hwy: 24 cyl: 6","displ: 2.8 hwy: 23 cyl: 6","displ: 2.8 hwy: 24 cyl: 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]

---

Adding a `text` aesthetic allows the tooltip display to be customized:

.pull-left.small-code[

```r
p <- ggplot(mutate(mpg, cyl = factor(cyl))) +
 geom_point(aes(x = displ,
 y = hwy,
 fill = cyl,
* text = paste(year,
* manufacturer,
* model)),
 shape = 21,
 size = 3)
ggplotly(p, tooltip = "text") %>%
 style(hoverlabel = list(bgcolor = "white"))
```
]
.pull-right[
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]

---

### Ggiraph

.pull-left.small-code[
The [`ggiraph` package](https://davidgohel.github.io/ggiraph/)
provides another approach.

```r
library(ggplot2)
library(ggiraph)
p <- ggplot(mutate(mpg, cyl = factor(cyl))) +
* geom_point_interactive(aes(x = displ,
 y = hwy,
 fill = cyl,
* tooltip = paste(year,
* manufacturer,
* model)),
 shape = 21,
 size = 3)
*girafe(ggobj = p)
```
]

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]

---

### Grammar of Interactive Graphics

There have been several efforts to develop a grammar of interactive
graphics, including [`ggvis`](https://ggvis.rstudio.com/) and
[`animint`](https://tdhock.github.io/animint/); neither seems to be
under active development at this time.

A promising approach is
[Vega-Lite](https://vega.github.io/vega-lite/), with a Python
interface [Altair](https://altair-viz.github.io/) and an R interface
[altair](https://vegawidget.github.io/altair/) to the Python
interface.

---

An example using the `altair` package:

.small-code[

```r
rub <- read.csv(here::here("rubber.csv"))

library(altair)

chartTH <- alt$Chart(rub)$
 mark_point()$
 encode(x = alt$X("H:Q", scale = alt$Scale(domain = range(rub$H))),
 y = alt$Y("T:Q", scale = alt$Scale(domain = range(rub$T))))

brush <- alt$selection_interval()

chartTH_brush <- chartTH$add_selection(brush)

chartTH_selection <-
 chartTH_brush$encode(color = alt$condition(brush,
 "Origin:N",
 alt$value("lightgray")))

chartAT <- chartTH_selection$
 encode(x = alt$X("T:Q", scale = alt$Scale(domain = range(rub$T))),
 y = alt$Y("A:Q", scale = alt$Scale(domain = range(rub$A))))

chartAT | chartTH_selection
```
]

---

The resulting linked plots:

<div id="htmlwidget-bf9369760f4f3ddf61f3" style="width:auto;height:auto;" class="vegawidget-vl4 html-widget "></div>
<script type="application/json" data-for="htmlwidget-bf9369760f4f3ddf61f3">{"x":{
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 "continuousHeight": 300,
 "continuousWidth": 400
 }
 },
 "data": {
 "name": "data-73865040e655670a533f736fa31e5d65"
 },
 "datasets": {
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 {
 "A": 372,
 "H": 45,
 "T": 162
 },
 {
 "A": 206,
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 "T": 233
 },
 {
 "A": 175,
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 "T": 232
 },
 {
 "A": 154,
 "H": 66,
 "T": 231
 },
 {
 "A": 136,
 "H": 71,
 "T": 231
 },
 {
 "A": 112,
 "H": 71,
 "T": 237
 },
 {
 "A": 55,
 "H": 81,
 "T": 224
 },
 {
 "A": 45,
 "H": 86,
 "T": 219
 },
 {
 "A": 221,
 "H": 53,
 "T": 203
 },
 {
 "A": 166,
 "H": 60,
 "T": 189
 },
 {
 "A": 164,
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 {
 "A": 113,
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 {
 "A": 82,
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 {
 "A": 32,
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 {
 "A": 228,
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 {
 "A": 196,
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 {
 "A": 128,
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 {
 "A": 97,
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 {
 "A": 155,
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 {
 "A": 114,
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 },
 {
 "A": 341,
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 "T": 161
 },
 {
 "A": 340,
 "H": 59,
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 },
 {
 "A": 283,
 "H": 65,
 "T": 148
 },
 {
 "A": 267,
 "H": 74,
 "T": 144
 },
 {
 "A": 215,
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 "T": 134
 },
 {
 "A": 148,
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 "T": 127
 }
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 "selection": "selector001",
 "type": "nominal"
 },
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 },
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 "scale": {
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 ]
 },
 "type": "quantitative"
 },
 "y": {
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 ]
 },
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 }
 },
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 }
 }
 },
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 "selection": "selector001",
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 },
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 ]
 },
 "type": "quantitative"
 },
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 "field": "T",
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 "domain": [
 119,
 237
 ]
 },
 "type": "quantitative"
 }
 },
 "mark": "point",
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 "type": "interval"
 }
 }
 }
 ]
 },
 "embed_options": {
 "renderer": "canvas",
 "defaultStyle": true
 }
},"evals":[],"jsHooks":[]}</script>

---
layout: false
## Notes

* A recent project
  [`gganimate`](https://github.com/thomasp85/gganimate) to add
  animation to `ggplot` looks very promising.

* A number of other [`ggplot`
  extensions](https://exts.ggplot2.tidyverse.org/) are available.

* A [blog
  post](https://medium.com/bbc-visual-and-data-journalism/how-the-bbc-visual-and-data-journalism-team-works-with-graphics-in-r-ed0b35693535)
  explains how the [BBC Visual and Data
  Journalism](https://medium.com/bbc-visual-and-data-journalism) team
  creates their graphics. More details are provided in an [_R cook
  book_](https://bbc.github.io/rcookbook/).

* A [blog
  post](https://blog.revolutionanalytics.com/2016/07/data-journalism-with-r-at-538.html)
  on use of R and `ggplot` by
  [FiveThirtyEight](https://fivethirtyeight.com/).  The `ggthemes`
  packages includes `theme_fivethirtyeight` to emulate their style.

---
## Reading

Chapters [_Data
visualization_](https://r4ds.had.co.nz/data-visualisation.html) and
[_Graphics for
communication_](https://r4ds.had.co.nz/graphics-for-communication.html)
in [_R for Data Science_](https://r4ds.had.co.nz/), O'Reilly.

Chapter [_Make a plot_](https://socviz.co/makeplot.html) in [_Data
Visualization_](https://socviz.co/).

Chapter [_ggplot2_](https://rafalab.dfci.harvard.edu/dsbook/ggplot2.html) in
[_Introduction to Data Science Data Analysis and Prediction Algorithms
with R_](https://rafalab.dfci.harvard.edu/dsbook/).

---
layout: false

## Interactive Tutorial

An interactive [`learnr`](https://rstudio.github.io/learnr/) tutorial
for these notes is [available](../tutorials/ggplot.Rmd).

You can run the tutorial with

```r
STAT4580::runTutorial("ggplot")
```

You can install the current version of the `STAT4580` package with

```r
remotes::install_gitlab("luke-tierney/STAT4580")
```

You may need to install the `remotes` package from CRAN first.

---
layout: true
## Exercises
---

1) In the following expression, which value of the `shape` aesthetic
   produces a plot with points represented as triangles outlined in
   black colored according to the number of cylinders?

```r
library(ggplot2)
ggplot(mpg, aes(x = displ, y = hwy, fill = factor(cyl))) +
 geom_point(size = 4, shape = ---)
```

*   a. 15
*   b. 17
*   c. 21
*   d. 24

---

2) It can sometimes be useful to plot text labels in a scatterplot
   instead of points. Consider the plot set up as

```r
library(ggplot2)
library(dplyr)
data(gapminder, package = "gapminder")
p <- filter(gapminder, year == 2007) %>%
 group_by(continent) %>%
 summarize(gdpPercap = mean(gdpPercap), lifeExp = mean(lifeExp)) %>%
 ggplot(aes(x = gdpPercap, y = lifeExp))
```
 Which of the following produces a plot with continent
 names on white rectangles?

*   a. `p + geom_text(aes(label = continent))`
*   b. `p + geom_label(aes(label = continent))`
*   c. `p + geom_label(label = continent)`
*   d. `p + geom_text(text = continent)`

---

3) The following code plots a _kernel density estimate_ for the
   `eruptions` variable in the `faithful` data set:

```r
library(ggplot2)
ggplot(faithful, aes(x = eruptions)) + geom_density(bw = 0.1)
```
  Look at the help page for `geom_density`. Which of the following best
  describes what specifying a value for `bw` does:

*   a. Changes the _kernel_ used to construct the estimate.
*   b. Changes the _smoothing bandwidth_ to make the result more or less smooth.
*   c. Changes the `stat` used to `stat_bw`.
*   d. Has no effect on the retult.

---

4) This code creates a map of Iowa counties.

```r
library(ggplot2)
p <- ggplot(map_data("county", "iowa"),
 aes(x = long, y = lat, group = group)) +
 geom_polygon(, fill = "White", color = "black")
```

Which of these produces a plot with an aspect ratio that best
  matches the map on [this
  page](https://en.wikipedia.org/w/index.php?title=List_of_counties_in_Iowa&oldid=1001171082)?

*   a. `p`
*   b. `p + coord_fixed(0.75)`
*   c. `p + coord_fixed(1.25)`
*   d. `p + coord_fixed(2)`

---

5) Consider the two plots created by this code (print the values of
   `p1` and `p2` to see the plots):

```r
library(ggplot2)
data(gapminder, package = "gapminder")
p1 <- ggplot(gapminder, aes(x = log(gdpPercap), y = lifeExp)) +
 geom_point() +
 scale_x_continuous(name = "")
p2 <- ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) +
 geom_point() +
 scale_x_log10(labels = scales::comma, name = "")
```

Which of these statements is true?

*   a. The `x` axis labels are identical in both plots.
*   b. The `x` axis labels in `p2` are in dollars; the labels in `p1`
       are in log dollars.
*   c. The `x` axis labels in `p1` are in dollars; the labels in `p2`
       are in log dollars.
*   d. There are no labels on the `x` axis in `p2`.

---

6) Consider the plot created by

```r
library(ggplot2)
data(gapminder, package = "gapminder")
p <- ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) +
 geom_point() +
 scale_x_log10(labels = scales::comma)
```

Which of these expressions produces a plot with a white background?

*   a. `p`
*   b. `p + theme_grey()`
*   c. `p + theme_classic()`
*   d. `p + ggthemes::theme_economist()`

---

7) There are many different ways to change the `x` axis label in
   `ggplot`.  Consider the plot created by

```r
library(ggplot2)
p <- ggplot(mpg, aes(x = displ, y = hwy)) +
 geom_point()
```

Which of the following does **not** change the `x` axis label to
  _Displacement_?

*   a. `p + labs(x = "Displacement")`
*   b. `p + scale_x_continuous("Displacement")`
*   c. `p + xlab("Displacement")`
*   d. `p + theme(axis.title.x = "Displacement")`

//adapted from Emi Tanaka's gist at //https://gist.github.com/emitanaka/eaa258bb8471c041797ff377704c8505