chapter6.Rmd

---
title: 'Chapter 6: Data visualisation with R'
description:
  'This chapter will give you an introduction to the R graphics system and teach you how to get started with using the ggplot2 package for drawing all kind of plots.'
prev: /chapter5
next: /chapter7
type: chapter
id: 6
output:
  teachr::teachr_chapter:
  # html_document:
  #   self_contained: false
  #md_document
---

```{r setup, include = FALSE}
library(teachr)
knitr::opts_chunk$set(fig.path = "ch6/",
                      fig.retina = 2,
                      fig.width = 6, 
                      fig.height = 4,
                      fig.align = "center",
                      cache = FALSE,
                      cache.path = "cache/",
                      message = FALSE,
                      warning = FALSE)
                      #error = TRUE)
filter <- dplyr::filter
read_lines <- function(x) {
  if(file.exists(x)) {
    readLines(x)
  } else {
    ""
  }
}
  
```


<exercise id="1" title="R graphics" type="slides">

`r slides("chapter6_01_rgraphics")`

</exercise>

<exercise id="2" title="Test your knowledge of R graphics">

Which of the following is a contributed R package?

```{r, echo = FALSE, results = "asis"}
cat(mc_opts("ggplot2" = "That's correct! ggplot2 was developed by Hadley Wickham as part of his PhD.",
         "grid" = "No, this is a core package so it's already installed.",
         "graphics" = "No, this a core package and loads automatically when you launch R.",
         "grDevices" = "No, this a core package and loads automatically when you launch R.",
         correct = 1))
```

Which R package actually renders the graphics in R?

```{r, echo = FALSE, results = "asis"}
cat(mc_opts("ggplot2" = "No, that's incorrect.",
         "grid" = "No, that's incorrect.",
         "graphics" = "No, that's incorrect.",
         "grDevices" = "Yes, that's right!",
         correct = 4))
```

Remember that there are two primary graphic models in R: the base and grid graphics. Which one is ggplot2 using?

```{r, echo = FALSE, results = "asis"}
cat(mc_opts("base" = "No, that's incorrect.",
         "grid" = "Yes that's right! Well done!",
         correct = 2))
```


</exercise>



<exercise id="3" title="Getting started with ggplot2" type="slides">

`r slides("chapter6_02_ggplot2intro")`

</exercise>

<exercise id="4" title="Plotting two or more variables with ggplot2" type="slides">

`r slides("chapter6_03_ggplot2next")`

</exercise>


<exercise id="5" title="Make basic plots with ggplot2">

For the following questions we are going to use `BudgetFood` data from the `Ecdat` package which contains the budget share of food for Spanish households. You can load the dataset and see the structure of the data below.

```{r, message = FALSE, warning = FALSE}
library(Ecdat)
str(BudgetFood)
```
The meaning of the variables are described below:

* `wfood`: percentage of total expenditure which the household has spent on food
* `totexp`: total expenditure of the household
* `age`: age of reference person in the household
* `size`: size of the household
* `town`: size of the town where the household is placed categorized into 5 groups: 1 for small towns, 5 for big ones
* `sex`: sex of reference person (man,woman)

Try to recreate the scatter plot below. Is there anything unusual that you notice about the plot?

```{r plot-06-05A, code = read_lines("../exercises/solution_06_05A.R"), echo = FALSE}
```

```{teachr 06_05A, outdir = "../exercises"}
library(ggplot2)
data(BudgetFood, package = "Ecdat")

ggplot(data = {<BudgetFood>}, 
       mapping = aes(x = {<size>}, 
                     y = {<totexp>})) +
  geom_{<point>}()

---
  
Hint: The scatter plots are created using `geom_point`.

???

plot <- last_plot()
plot_has_point <- any(vapply(plot$layers, function(x) inherits(x$geom, "GeomPoint"), logical(1L)))
expect_true(
  plot_has_point, info = "Your plot is missing the appropriate geom for a scatter plot."
)
plot_mapping <- aes(x = size, y = totexp)
expect_equal(
  plot$mapping, plot_mapping, info = "The variables on your x and/or y axis are incorrect."
)
```

You may have noticed that there is _overplotting_ in the previous plot for the bottom left region. Let's try to create a **hex plot** like below this time.

```{r plot-06-05B, code = read_lines("../exercises/solution_06_05B.R"), echo = FALSE}
```

```{teachr 06_05B, outdir = "../exercises"}
ggplot(data = {<BudgetFood>}, 
       mapping = aes(x = {<size>}, 
                     y = {<totexp>})) +
  geom_{<hex>}()

---
  
Hint: The hex plots are created using `geom_hex`.

???

plot <- last_plot()
plot_has_hex <- any(vapply(plot$layers, function(x) inherits(x$geom, "GeomHex"), logical(1L)))
expect_true(
  plot_has_hex, info = "Your plot is missing the appropriate geom for a hex plot."
)
plot_mapping <- aes(x = size, y = totexp)
expect_equal(
  plot$mapping, plot_mapping, info = "The variables on your x and/or y axis are incorrect."
)  
```

This time let's have a look at some other variables in the `BudgetFood` data. Let's check the distribution of the age by sex using **boxplot** like below.


```{r plot-06-05C, code = read_lines("../exercises/solution_06_05C.R"), echo = FALSE}
```

```{teachr 06_05C, outdir = "../exercises"}
ggplot(data = {<BudgetFood>}, 
       mapping = aes(x = {<sex>}, 
                     y = {<age>})) +
  geom_{<boxplot>}()

---
  
Hint: The five number summary is often graphically depicted by a boxplot. 

???

plot <- last_plot()
plot_has_boxplot <- any(vapply(plot$layers, function(x) inherits(x$geom, "GeomBoxplot"), logical(1L)))
expect_true(
  plot_has_boxplot, info = "Your plot is missing the appropriate geom for a boxplot."
)
plot_mapping <- aes(x = sex, y = age)
expect_equal(
  plot$mapping, plot_mapping, info = "The variables on your x and/or y axis are incorrect."
)  
```

Next study the distribution of the total expenditure of the household by size of the town using a **violin plot** like below.


```{r plot-06-05D, code = read_lines("../exercises/solution_06_05D.R"), echo = FALSE}
```

```{teachr 06_05D, outdir = "../exercises"}
ggplot(data = {<BudgetFood>}, 
       mapping = aes(x = {<factor(town)>}, 
                     y = {<totexp>})) +
  geom_{<violin>}()

---
  
Hint: what do you notice about the x-axis scale? Do you perhaps need to convert `town`?
  
???
  
plot <- last_plot()
plot_has_violin <- any(vapply(plot$layers, function(x) inherits(x$geom, "GeomViolin"), logical(1L)))
expect_true(
  plot_has_violin, info = "Your plot is missing the appropriate geom for a violin plot."
)
plot_mapping <- aes(x = factor(town), y = totexp)
expect_equal(
  plot$mapping, plot_mapping, info = "The variables on your x and/or y axis are incorrect."
)
```

Let's have a look at the distribution of the percentage of total expenditure which the household has spent on food (`wfood`). We'll adjust the bin width of the **histogram** to 0.001. Do you notice the peak at 0?

```{r plot-06-05E, code = read_lines("../exercises/solution_06_05E.R"), echo = FALSE}
```

```{teachr 06_05E, outdir = "../exercises"}
ggplot(data = {<BudgetFood>}, 
       mapping = aes(x = {<wfood>})) +
  geom_{<histogram>}(binwidth = 0.001)

---
  
Hint: this plot is called a histogram.

???
  
plot <- last_plot()
plot_has_hist <- any(vapply(plot$layers, function(x) inherits(x$geom, "GeomBar"), logical(1L)))
expect_true(
  plot_has_hist, info = "Your plot is missing the appropriate geom for a scatter plot."
)
plot_mapping <- aes(x = wfood)
expect_equal(
  plot$mapping, plot_mapping, info = "The variables on your x axis are incorrect."
)
```

Finally, let's also draw a density plot but display the y-axis as counts instead of density.


```{r plot-06-05F, code = read_lines("../exercises/solution_06_05F.R"), echo = FALSE}
```

```{teachr 06_05F, outdir = "../exercises"}
ggplot(data = {<BudgetFood>}, 
       mapping = aes(x = {<wfood>}))  +
  geom_density(aes(y = {<after_stat(count)>}))

---

Hint: this is a density plot but the y-axis is showing counts. What did `after_stat` function do?
  
???
plot <- last_plot()
plot_has_density <- vapply(plot$layers, function(x) inherits(x$geom, "GeomDensity"), logical(1L))
expect_true(
  any(plot_has_density), info = "Your plot is missing the appropriate geom for a density plot."
)
plot_mapping <- aes(x = wfood)
expect_equal(
  plot$mapping, plot_mapping, info = "The variables on your x axis is incorrect."
)
density_mapping <- plot$layers[[which(plot_has_density)[1]]]$mapping
geom_mapping <- aes(y = after_stat(count))
expect_equal(
  density_mapping, geom_mapping, info = "The variable on your y axis is incorrect. Have you tried using the after_stat() function to show the computed count variable?"
)
```

</exercise>


<exercise id="6" title="Multiple layers and attributes in ggplot2"  type="slides">
`r slides("chapter6_04_ggplot2layers")`
</exercise>


<exercise id="7" title="Practice creating multiple layers">

We are again going to use the `BudgetFood` data from the `Ecdat` package to make the plots.


The plot below shows the histogram with the density plot overlaid on top of it. Try to recreate this figure.

```{r plot-06-07A, code = read_lines("../exercises/solution_06_07A.R"), echo = FALSE}
```

```{teachr 06_07A, outdir = "../exercises"}
ggplot(data = {<BudgetFood>}, 
       mapping = aes(x = {<totexp>})) +
  geom_{<histogram>}(aes(y = after_stat(density))) +
  geom_{<density>}(colour = "red")

---

Hint: this is a density plot but the y-axis is showing counts. What did `after_stat` function do?
  
???
  
plot <- last_plot()
plot_has_density <- vapply(plot$layers, function(x) inherits(x$geom, "GeomDensity"), logical(1L))
expect_true(
  any(plot_has_density), info = "Your plot is missing the appropriate geom for a density plot."
)
plot_has_hist <- vapply(plot$layers, function(x) inherits(x$geom, "GeomBar"), logical(1L))
expect_true(
  any(plot_has_hist), info = "Your plot is missing the appropriate geom for a histogram plot."
)
plot_mapping <- aes(x = totexp)
expect_equal(
  plot$mapping, plot_mapping, info = "The variables on your x axis is incorrect."
)
hist_mapping <- plot$layers[[which(plot_has_hist)[1]]]$mapping
geom_mapping <- aes(y = after_stat(density))
expect_equal(
  hist_mapping, geom_mapping, info = "The variable on your y axis is incorrect. Have you tried using the after_stat() function to show the computed density variable for the histogram?"
)

density_aes <- plot$layers[[which(plot_has_density)[[1]]]]$aes_params
density_colour <- rlang::`%||%`(density_aes$color, density_aes$colour)
expect_equal(
  density_colour, "red", info = "The colour of the density line geometry is not red."
)
```

In the next target plot, we are going to look at the distribution of the `age` by `sex`. There are not that many observations where sex information is missing so let's focus on `sex` levels for "man" and "woman".

```{r plot-06-7B, code = read_lines("../exercises/solution_06_07B.R"), echo = FALSE}
```

```{teachr 06_07B, outdir = "../exercises"}
library(tidyverse)
BudgetFood %>%
  {<filter>}({<!is.na(sex)>}) %>%
  ggplot(
    mapping = aes(x = {<sex>}, y = {<age>})
  ) +
  geom_{<violin>}({<aes(fill = sex)>}) +
  geom_{<boxplot>}({<width>} = 0.1) 

---
  
Hint: Use the `width` argument to change the width of the boxplot.

???

plot <- last_plot()

solution_data <- dplyr::filter(BudgetFood, !is.na(sex))
expect_equal(plot$data, solution_data, info = "The data has not been correctly filtered to look at only data where `sex` is 'man' or 'woman'.")

plot_has_violin <- vapply(plot$layers, function(x) inherits(x$geom, "GeomViolin"), logical(1L))
expect_true(
  any(plot_has_violin), info = "Your plot is missing the appropriate geom for a violin plot."
)
plot_has_boxplot <- vapply(plot$layers, function(x) inherits(x$geom, "GeomBoxplot"), logical(1L))
expect_true(
  any(plot_has_boxplot), info = "Your plot is missing the appropriate geom for a boxplot."
)
plot_mapping <- aes(x = sex, y = age)
expect_equal(
  plot$mapping, plot_mapping, info = "The variables on your x and/or y axis is incorrect."
)
violin_mapping <- plot$layers[[which(plot_has_violin)[1]]]$mapping
geom_mapping <- aes(fill = sex)
expect_equal(
  violin_mapping, geom_mapping, info = "The variable used to fill the violins is incorrect."
)

boxplot_width <- plot$layers[[which(plot_has_boxplot)[[1]]]]$geom_params$width
expect_equal(
  boxplot_width, 0.1, info = "The boxplot width has not been correctly specified."
)
```

In the next target plot, we want to draw a scatter plot between the size of the household and the total expenditure of the household. Then we want to overlay this with a loess curve in red colour. You may not have seen how to fit a curve on `ggplot` yet so check out the hint if you don't know.


```{r plot-06-07C, code = read_lines("../exercises/solution_06_07C.R"), echo = FALSE}
```


```{teachr 06_07C, outdir = "../exercises"}
BudgetFood %>%
  filter({<size < 30>}) %>%
  ggplot(
    mapping = aes(x = {<size>}, y = {<totexp>})
  ) +
  geom_{<point>}() +
  geom_{<smooth>}(colour = "red",
                  method = loess,
                  formula = y ~ x)

---
  
Hint: `geom_smooth` can draw curves from a fitted model.

???
  
plot <- last_plot()

solution_data <- dplyr::filter(BudgetFood, size < 30)
expect_equal(plot$data, solution_data, info = "The data has not been correctly filtered to look at only data size is less than 30.")

plot_has_point <- vapply(plot$layers, function(x) inherits(x$geom, "GeomPoint"), logical(1L))
expect_true(
  any(plot_has_point), info = "Your plot is missing the appropriate geom for a scatter plot."
)
plot_has_smooth <- vapply(plot$layers, function(x) inherits(x$geom, "GeomSmooth"), logical(1L))
expect_true(
  any(plot_has_smooth), info = "Your plot is missing the appropriate geom for a smooth fitted model through the data."
)
plot_mapping <- aes(x = size, y = totexp)
expect_equal(
  plot$mapping, plot_mapping, info = "The variables on your x and/or y axis is incorrect."
)

smooth_aes <- plot$layers[[which(plot_has_smooth)[[1]]]]$aes_params
smooth_colour <- rlang::`%||%`(smooth_aes$color, smooth_aes$colour)
expect_equal(
  smooth_colour, "red", info = "The colour of the smooth line geometry is not red."
)
```

In the next plot, try drawing a **barplot** by the different sexes with the total count written on top of the bar.

```{r plot-06-07D, code = read_lines("../exercises/solution_06_07D.R"), echo = FALSE, cache = FALSE}
```


```{teachr 06_07D, outdir = "../exercises"}
BudgetFood %>% 
  filter({<!is.na(sex)>}) %>% 
  ggplot(mapping = aes(x = {<sex>})) +
  geom_{<bar>}() +
  geom_{<text>}(
    aes(label = {<after_stat(count)>}),
    vjust = -0.3,
    stat = {<"count">}
  )

---
  
Hint: change the stat of one of the geometry used to write the count to compute the total count to be used on the y axis.

???
  
plot <- last_plot()

solution_data <- dplyr::filter(BudgetFood, !is.na(sex))
expect_equal(plot$data[colnames(solution_data)], solution_data, info = "The data has not been correctly filtered to look at only data where `sex` is 'man' or 'woman'.")

plot_has_bar <- vapply(plot$layers, function(x) inherits(x$geom, "GeomBar"), logical(1L))
expect_true(
  any(plot_has_bar), info = "Your plot is missing the appropriate geom for a bar plot."
)
plot_has_text <- vapply(plot$layers, function(x) inherits(x$geom, "GeomText"), logical(1L))
expect_true(
  any(plot_has_text), info = "Your plot is missing the appropriate geom for a text shown above the bars."
)
plot_mapping <- aes(x = sex)
expect_equal(
  plot$mapping, plot_mapping, info = "The variable on your x axis is incorrect."
)

text_aes <- plot$layers[[which(plot_has_text)[[1]]]]$mapping
text_aes_solution <- aes(label = after_stat(count))
expect_equal(
  text_aes, text_aes_solution, info = "The aesthetics for your text labels are incorrect. Hint: this can be computed with after_stat()."
)

has_text_stat <- inherits(plot$layers[[which(plot_has_text)[[1]]]]$stat, "StatCount")
expect_true(
  has_text_stat, info = "Your text geometry is missng the stat used to compute the appropriate position on the y-axis."
)
```

The next one might be tricky. You want to draw a bar plot showing the average total expenditure of by sex and town but the top blue bars show the average for women, while the bottom red bars show the average for men. 

```{r plot-06-07E, code = read_lines("../exercises/solution_06_07E.R"), echo = FALSE, cache = FALSE}
```

```{teachr 06_07E, outdir = "../exercises"}
library(tidyverse)
dat <- BudgetFood %>% 
  filter(!is.na(sex)) %>% 
  group_by(town, sex) %>% 
  summarise(totexp = mean(totexp))

{<ggplot()>} +
  geom_{<col>}(data = filter(dat, sex=="woman"),
               aes(x = {<factor(town)>},
                   y = {<totexp>},
                  {<fill = "woman">})) +
  geom_{<col>}(data = subset(dat, sex=="man"),
               aes(x = {<factor(town)>},
                   y = {<-totexp>},
                  {<fill = "man">}))

---
  
Hint: The legend shows the `fill` with a label "woman" and "man". In this case should `fill` be an attribute or aesthetic?

???
  
plot <- last_plot()

plot_has_cols <- vapply(plot$layers, function(x) inherits(x$geom, "GeomCol"), logical(1L))
expect_true(
  all(plot_has_cols), info = "Your is using the incorrect geometry type."
)

col_aes <- plot$layers[[which(plot_has_cols)[[1]]]]$mapping
col_aes_solution <- aes(x = factor(town), y = totexp, fill = "woman")
expect_equal(
  col_aes, col_aes_solution, info = "The aesthetics for your geometry plotting `sex==\"woman\"` is incorrect. Have you set the fill aethetic correctly?"
)
col_aes <- plot$layers[[which(plot_has_cols)[[2]]]]$mapping
col_aes_solution <- aes(x = factor(town), y = -totexp, fill = "man")
expect_equal(
  col_aes, col_aes_solution, info = "The aesthetics for your geometry plotting `sex==\"man\"` is incorrect. Have you set the fill aethetic correctly?"
)
```


</exercise>

<exercise id="8" title="Scales and guides in ggplot2"  type="slides">
`r slides("chapter6_05_ggplot2scales")`
</exercise>

<exercise id="9" title="Practice changing scales">

In the plot below, we show the age distribution by man and woman. Change the colour so the violin plot for the man  coloured as "violet" and woman as "royalblue".


```{r plot-06-09A, code = read_lines("../exercises/solution_06_09A.R"), echo = FALSE, cache = FALSE}
```


```{teachr 06_09A, outdir = "../exercises"}
ggplot(data = subset(BudgetFood, !is.na(sex)), 
       mapping = aes(x = sex,
                     y = age)) +
  geom_violin(aes(fill = sex)) + 
  {<scale_fill>}_manual(values = c("violet", "royalblue"))
  

---
  
Hint: Recall that scales are controlled by functions beginning with `scale_`. The second "word" in the scale function is the aesthetic. 

???
  
plot <- last_plot()
plot_scale <- vapply(plot$scales$scales, function(x) x$aesthetics == "fill", logical(1L))
expect_true(plot_scale, info = "The scale function has been incorrectly specified.")
```


In the next plot, let's have a look at the distribution of the total household expenditure by sex using histograms. Since the total household expenditure skewed to the right, take a log with base 10 transformation for the scale and change the colours like the target plot below.



```{r plot-06-09B, code = read_lines("../exercises/solution_06_09B.R"), echo = FALSE, cache = FALSE}
```


```{teachr 06_09B, outdir = "../exercises"}
ggplot(data = subset(BudgetFood, !is.na(sex)), 
       mapping = aes(x = totexp,
                     fill = sex)) +
  geom_histogram(bins = 40,
                 aes(colour = sex)) + 
  {<scale_x>}_log10() +
  {<scale_colour>}_discrete() +
  {<scale_fill>}_brewer(palette = 10)
  

---

Hint: Recall that scales are controlled by functions beginning with `scale_`. The second "word" in the scale function is the aesthetic. 

???

plot <- last_plot()
log10_scale <- vapply(plot$scales$scales, function(x) "x" %in% x$aesthetics, logical(1L))
discrete_scale <- vapply(plot$scales$scales, function(x) any(c("color", "colour") %in% x$aesthetics), logical(1L))
brewer_scale <- vapply(plot$scales$scales, function(x) "fill" %in% x$aesthetics, logical(1L))
log10_answer <- which(log10_scale) == 1L
discrete_answer <- which(discrete_scale) == 2L
brewer_answer <- which(brewer_scale) == 3L
expect_true(log10_answer, info = "The answer for the *_log10() scale is incorrect.")
expect_true(discrete_answer, info = "The answer for the *_discrete() scale is incorrect.")
expect_true(brewer_answer, info = "The answer for the *_brewer() scale is incorrect.")
```


</exercise>

<exercise id="10" title="Multiple plots in one figure"  type="slides">
`r slides("chapter6_06_ggplot2facet")`
</exercise>

<exercise id="11" title="Practice making multiple subplots">

In the following section, we won't give you much hints! Try to reach the target plots by seeing what's drawn in the target plot and complete the code needed to get to the target plot.

```{r plot-06-11A, code = read_lines("../exercises/solution_06_11A.R"), echo = FALSE, cache = FALSE}
```

```{teachr 06_11A, outdir = "../exercises"}
ggplot(BudgetFood, aes(x = {<age>})) + 
  geom_histogram(data = {<select(BudgetFood, -town)>},
                 fill = "grey", binwidth = 1,
                 aes(y = after_stat(density))) +
  geom_histogram(aes(y = after_stat(density)),
                 binwidth = 1) +
  geom_density(colour = "red") +
  facet_{<wrap(~town)>}
  
---
  
Hint: what variable is the data facetted by? If you want to see the grey shadow of the overall distribution for each panel, remember that the variable needs to be removed from the layer data.  

???
  
  
plot <- last_plot()
answer_mapping <- aes(x = age)
expect_equal(plot$mapping, answer_mapping, info = "The plot's mapping variables are incorrect.")
facet_type <- inherits(plot$facet, "FacetWrap")
expect_true(facet_type, info = "The facet type is incorrect.")
facet_vars <- identical(names(plot$facet$params$facets), "town")
expect_true(facet_type, info = "The variable facetting the plot is incorrect.")
```



```{r plot-06-11B, code = read_lines("../exercises/solution_06_11B.R"), echo = FALSE, cache = FALSE}
```


```{teachr 06_11B, outdir = "../exercises"}
ggplot(subset(BudgetFood, !is.na(sex)), aes(age)) +
  geom_histogram(aes(y = {<after_stat(density)>})) +
  geom_text(x = 35, y = 0.035, 
            data = function(.data) .data %>% 
              group_by(sex, town) %>% 
              count(),
            aes(label = n)) +
  facet_{<grid(sex ~ town)>}

---

Hint: A different style of facet is required to arrange the subplots in both x and y directions.

???

plot <- last_plot()
answer_mapping <- aes(x = age)
expect_equal(plot$mapping, answer_mapping, info = "The plot's mapping variables are incorrect.")
facet_type <- inherits(plot$facet, "FacetGrid")
expect_true(facet_type, info = "The facet type is incorrect.")
facet_rows <- identical(names(plot$facet$params$rows), "sex")
facet_cols <- identical(names(plot$facet$params$cols), "town")
expect_true(facet_rows, info = "The variable row facetting the plot is incorrect.")  
expect_true(facet_cols, info = "The variable column facetting the plot is incorrect.")  

plot_has_hist <- vapply(plot$layers, function(x) inherits(x$geom, "GeomBar"), logical(1L))

hist_aes <- plot$layers[[which(plot_has_hist)[[1]]]]$mapping
hist_aes_solution <- aes(y = after_stat(density))
expect_equal(
  hist_aes, hist_aes_solution, info = "The aesthetics for your histogram are incorrect. Note that you'll need to use after_stat() to use the variables computed by the histogram stat."
)
```



```{r plot-06-11C, code = read_lines("../exercises/solution_06_11C.R"), echo = FALSE, cache = FALSE}
```


```{teachr 06_11C, outdir = "../exercises"}
library(tidyverse)
library({<patchwork>})


g1 <- ggplot(BudgetFood, 
             aes(factor(size), wfood, colour = sex)) +
  geom_boxplot() 

g2 <- ggplot(BudgetFood, 
             aes(factor(size), totexp, colour = sex)) +
  geom_boxplot() + 
  scale_y_log10()

# combine the plot as one figure 
{<g1 / g2>} + plot_layout(guides = "collect")

---
  
Hint: The patchwork package allows you to arbitrarily combine multiple separate plots.

???
  
  
expect_true(
  "patchwork" %in% loadedNamespaces(),
  info = "You haven't loaded a package which allows you to combine g1 and g2."
)

plot <- last_plot()
expect_true(inherits(plot, "patchwork"), info = "The plot outputted does not use the patchwork package to produce it.")
```


</exercise>

<exercise id="12" title="Customise the look with themes in ggplot2"  type="slides">
`r slides("chapter6_07_ggplot2theme")`
</exercise>

<exercise id="13" title="Make pretty plots">

Well done getting to the last practice exercise! 

The next plot you're going to draw is the plot below -- or make the plot as pretty as you can!

```{r plot-06-13A, code = read_lines("../exercises/solution_06_13A.R"), echo = FALSE, cache = FALSE}
```
```{teachr 06_13A, outdir = "../exercises"}
ggplot(BudgetFood, aes(x = age)) + 
  geom_histogram(aes(y = after_stat(density)),
                 binwidth = 1) +
  geom_density(colour = "red") +
  facet_wrap(~town, labeller = label_both) +
  labs(x = "Age", y = "Density", title = "Age distribution by town size") +
  theme(text = {<element_text>}(size = 14, family = "mono"),
        plot.background = {<element_rect>}(fill = "grey80"),
        {<strip.background>} = element_rect(fill = "black"),
        {<strip.text>} = element_text(color = "white"),
        panel.background = {<element_rect>}(fill = "white",
                                        color = "black"),
        panel.grid = element_blank())

---
  
Hint: Theme options for ggplot2 are customised in the theme() function. You specify how a particular target (say the text) is shown using the appropriate `element_*()` function.

???
  
plot <- last_plot()
theme_answer <- theme(text = element_text(size = 14, family = "mono"),
        plot.background = element_rect(fill = "grey80"),
        strip.background = element_rect(fill = "black"),
        strip.text = element_text(color = "white"),
        panel.background = element_rect(fill = "white",
                                        color = "black"),
        panel.grid = element_blank())

expect_equivalent(plot$theme, theme_answer, info = "The theme specified does not exactly match the plot shown above. Check that you are targeting the correct element for each customisation.")
```

</exercise>


<exercise id="14" title="Resources">

* [ggplot2: elegant graphics for data analysis](https://ggplot2-book.org/)
* [R Graphics Cookbook](https://r-graphics.org/)
* [R for data science slack community](https://rfordatascience.slack.com/join/shared_invite/zt-13ohm2as3-k3Vl31J54zTOjw1h3wpAuQ#/shared-invite/email) has a channel on `ggplot2`
* [Download](https://raw.githubusercontent.com/rstudio/cheatsheets/main/data-visualization.pdf) the `ggplot2` cheat sheet
* Ask or search questions at [StackOverflow](https://stackoverflow.com/questions/tagged/ggplot2) or [RStudio Community](https://community.rstudio.com/tag/ggplot2) with `ggplot2` tag.
* [The R Graph Gallery](https://www.r-graph-gallery.com/)
* [Fundamentals of Data Visualization](https://clauswilke.com/dataviz/) -- see code for this book [here](https://github.com/clauswilke/dataviz).


</exercise>