CH09.qmd

```{r}
#| include: false
library(tidyverse)
library(nycflights13)
```

### Exercises (Chapter 9)

1.  Create a scatterplot of `hwy` vs. `displ` where the points are pink filled in triangles.

    ::: {.callout-note icon="false" title="Answer"}
    ```{r}
    # Your R code here
    ```

    :::

2.  Why did the following code not result in a plot with blue points?

    ```{r}
    #| fig-show: hide
    #| fig-alt: |
    #|   Scatterplot of highway fuel efficiency versus engine size of cars  
    #|   that shows a negative association. All points are red and 
    #|   the legend shows a red point that is mapped to the word blue.

    ggplot(mpg) + 
      geom_point(aes(x = displ, y = hwy, color = "blue"))
    ```
    
    ::: {.callout-note icon="false" title="Answer"}
    ```{r}
    # Proper R code here
    ```
    *Your text answer here.*
    :::

3.  What does the `stroke` aesthetic do? What shapes does it work with? (Hint: use `?geom_point`)

    ::: {.callout-note icon="false" title="Answer"}
    ```{r}
    mpg |>
      ggplot(aes(x = displ, y = hwy)) +
        geom_point(shape = 21, stroke = 0.5) -> p1
    mpg |>
      ggplot(aes(x = displ, y = hwy)) +
        geom_point(shape = 21, stroke = 1) -> p2
    mpg |>
      ggplot(aes(x = displ, y = hwy)) +
        geom_point(shape = 21, stroke = 2) -> p3
    library(patchwork)
    p1 / p2 / p3
    ```
    *Your text answer here.*
    :::

4.  What happens if you map an aesthetic to something other than a variable name, like `aes(color = displ < 5)`? Note, you'll also need to specify x and y.

    ::: {.callout-note icon="false" title="Answer"}
    ```{r}
    mpg |>
      ggplot(aes(x = displ, y = hwy, color = displ < 5)) +
        geom_point()
    ```
    *Your text answer here.*
    :::

5.  What geom would you use to draw a line chart? A boxplot? A histogram? An area chart?

    ::: {.callout-note icon="false" title="Answer"}
    *Your text answer here.*
    ```{r}
    # R Code here
    ```
    :::
    
    ::: {.callout-note icon="false" title="Answer"}
    *Your text answer here.*
    ```{r}
    # R code here
    ```
    :::
    
    ::: {.callout-note icon="false" title="Answer"}
    *Your text answer here.*
    ```{r}
    # R code here
    ```
    :::
    
    ::: {.callout-note icon="false" title="Answer"}
    *Your text answer here.*
    ```{r}
    # Youe R code here
    ```
    :::

6.  Earlier in this chapter we used `show.legend` without explaining it:

    ```{r}
    #| fig-show: hide
    #| message: false

    ggplot(mpg, aes(x = displ, y = hwy)) +
      geom_smooth(aes(color = drv), show.legend = FALSE)
    ```

    What does `show.legend = FALSE` do here? What happens if you remove it? Why do you think we used it earlier?
    
    ::: {.callout-note icon="false" title="Answer"}
    *Your text answer here.*
    
    ```{r}
    #| warning: false
    ggplot(mpg, aes(x = displ, y = hwy)) +
      geom_smooth(aes(color = drv), show.legend = FALSE) -> p1
    ggplot(mpg, aes(x = displ, y = hwy)) +
      geom_smooth(aes(color = drv), show.legend = TRUE) -> p2
    p1 / p2
    ```
    
    :::

7.  What does the `se` argument to `geom_smooth()` do?

    ::: {.callout-note icon="false" title="Answer"}
    *Your text answer here.*
    
    ```{r}
    #| warning: false
    ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
      geom_smooth(se = FALSE)
    ```
    :::

8.  Recreate the R code necessary to generate the following graphs. Note that wherever a categorical variable is used in the plot, it's `drv`.

    ```{r}
    #| echo: false
    #| message: false
    #| layout-ncol: 2
    #| fig-width: 2.5
    #| fig-height: 2.5
    ggplot(mpg, aes(x = displ, y = hwy)) + 
      geom_point() + 
      geom_smooth(se = FALSE)
    ggplot(mpg, aes(x = displ, y = hwy)) + 
      geom_smooth(aes(group = drv), se = FALSE) +
      geom_point()
    ggplot(mpg, aes(x = displ, y = hwy, color = drv)) + 
      geom_point() + 
      geom_smooth(se = FALSE)
    ggplot(mpg, aes(x = displ, y = hwy)) + 
      geom_point(aes(color = drv)) + 
      geom_smooth(se = FALSE)
    ggplot(mpg, aes(x = displ, y = hwy)) + 
      geom_point(aes(color = drv)) +
      geom_smooth(aes(linetype = drv), se = FALSE)
    ggplot(mpg, aes(x = displ, y = hwy)) + 
      geom_point(size = 4, color = "white") + 
      geom_point(aes(color = drv))
    ```

    ::: {.callout-note icon="false" title="Answer"}
    The code for each of the plots is given below.
    ```{r}
    #| echo: true
    #| eval: false
    # Your R code here
    ```
    :::
    
    
9.  What happens if you facet on a continuous variable?

    ::: {.callout-note icon="false" title="Answer"}
    *Your text answer here.*
    
    ```{r}
    mpg |> 
      ggplot(aes(x = drv, y = cyl)) + 
      geom_point() + 
      facet_wrap(~hwy)
    ```
    :::

10. What do the empty cells in the plot above with `facet_grid(drv ~ cyl)` mean? Run the following code. How do they relate to the resulting plot?

    ```{r}
    #| fig-show: hide
    ggplot(mpg) + 
      geom_point(aes(x = drv, y = cyl))
    ```

    ::: {.callout-note icon="false" title="Answer"}
    ```{r}
    ggplot(mpg) + 
      geom_point(aes(x = drv, y = cyl)) +
      facet_grid(drv ~ cyl)
    ```
    *Your text answer here.*
    :::
    
11. What plots does the following code make? What does `.` do?

    ```{r}
    #| fig-show: hide
    ggplot(mpg) + 
      geom_point(aes(x = displ, y = hwy)) +
      facet_grid(drv ~ .)

    ggplot(mpg) + 
      geom_point(aes(x = displ, y = hwy)) +
      facet_grid(. ~ cyl)
    ```
    

    ::: {.callout-note icon="false" title="Answer"}
    ```{r}
    ggplot(mpg) + 
      geom_point(aes(x = displ, y = hwy)) +
      facet_grid(drv ~ .)
    ```
    *Your text answer here.*
    
    ```{r}
    ggplot(mpg) + 
      geom_point(aes(x = displ, y = hwy)) +
      facet_grid(. ~ cyl)
    ```
    *Your text answer here.*
    
    ::: 
    
12. Take the first faceted plot in this section:

    ```{r}
    #| fig-show: hide

    ggplot(mpg) + 
      geom_point(aes(x = displ, y = hwy)) + 
      facet_wrap(~ cyl, nrow = 2)
    ```

    What are the advantages to using faceting instead of the color aesthetic? What are the disadvantages? How might the balance change if you had a larger dataset?
    
    ::: {.callout-note icon="false" title="Answer"}
    *Your text answer here.*
    
    ```{r}
    # facet
    ggplot(mpg) + 
     geom_point(aes(x = displ, y = hwy)) + 
      facet_wrap(~ class, nrow = 2)

    # color
    ggplot(mpg) + 
      geom_point(aes(x = displ, y = hwy, color = class))

    # both
    ggplot(mpg) + 
      geom_point(
        aes(x = displ, y = hwy, color = class), 
        show.legend = FALSE) + 
     facet_wrap(~ class, nrow = 2)

    # highlighting
    ggplot(mpg, aes(x = displ, y = hwy)) + 
      geom_point(color = "gray") +
      geom_point(
        data = mpg |> filter(class == "compact"),
        color = "pink"
      )
    ```
    
    :::  
    
13. Read `?facet_wrap`. What does `nrow` do? What does `ncol` do? What other options control the layout of the individual panels? Why doesn't `facet_grid()` have `nrow` and `ncol` arguments?

    ::: {.callout-note icon="false" title="Answer"}
    *Your text answer here.*
    ::: 
    
14. Which of the following plots makes it easier to compare engine size (`displ`) across cars with different drive trains? What does this say about when to place a faceting variable across rows or columns?

    ```{r}
    #| fig-show: hide
    #| message: false

    ggplot(mpg, aes(x = displ)) + 
      geom_histogram() + 
      facet_grid(drv ~ .)

    ggplot(mpg, aes(x = displ)) + 
      geom_histogram() +
      facet_grid(. ~ drv)
    ```
    
    ::: {.callout-note icon="false" title="Answer"}
    ```{r}
    ggplot(mpg, aes(x = displ)) + 
      geom_histogram() + 
      facet_grid(drv ~ .)
    ggplot(mpg, aes(x = displ)) + 
      geom_histogram() +
      facet_grid(. ~ drv)
    ```
    *Your text answer here.*
    ::: 
    
15. Recreate the following plot using `facet_wrap()` instead of `facet_grid()`. How do the positions of the facet labels change?

    ```{r}
    #| fig-show: hide

    ggplot(mpg) + 
      geom_point(aes(x = displ, y = hwy)) +
      facet_grid(drv ~ .)
    ```
    
    ::: {.callout-note icon="false" title="Answer"}
    ```{r}
    ggplot(mpg) + 
      geom_point(aes(x = displ, y = hwy)) +
      facet_grid(drv ~ .) -> p1
    ggplot(mpg) + 
      geom_point(aes(x = displ, y = hwy)) +
      facet_wrap(~drv, nrow = 3) -> p2
    p1 + p2
    ```
    *Your text answer here.*
    :::

16. What is the default geom associated with `stat_summary()`? How could you rewrite the previous plot to use that geom function instead of the stat function?

    ```{r}
    ggplot(diamonds) + 
      stat_summary(
        aes(x = cut, y = depth),
        fun.min = min,
        fun.max = max,
        fun = median
      )
    ```
    ::: {.callout-note icon="false" title="Answer"}
    *Your text answer here.*
    
    
    ```{r}
    diamonds |>
      group_by(cut) |>
      summarize(
        lower = min(depth),
        upper = max(depth),
        midpoint = median(depth)
      ) |>
      ggplot(aes(x = cut, y = midpoint)) +
      geom_pointrange(aes(ymin = lower, ymax = upper))
    ```
    :::

17. What does `geom_col()` do? How is it different from `geom_bar()`?

    ::: {.callout-note icon="false" title="Answer"}
    *Your text answer here.*
    
    :::
    
18. Most geoms and stats come in pairs that are almost always used in concert. Make a list of all the pairs. What do they have in common? (Hint: Read through the documentation.)

    ::: {.callout-note icon="false" title="Answer"}
    Geoms and stats that are almost always used in concert are listed below:
    :::
    


    | **geom**                | **stat**                |
    |-------------------------|-------------------------|
    | `geom_bar()`            | `stat_count()`          |
    | `geom_bin2d()`          | `stat_bin_2d()`         |
    | `geom_boxplot()`        | `stat_boxplot()`        |
    | `geom_contour_filled()` | `stat_contour_filled()` |
    | `geom_contour()`        | `stat_contour()`        |
    | `geom_count()`          | `stat_sum()`            |
    | `geom_density_2d()`     | `stat_density_2d()`     |
    | `geom_density()`        | `stat_density()`        |
    | `geom_dotplot()`        | `stat_bindot()`         |
    | `geom_function()`       | `stat_function()`       |
    | `geom_sf()`             | `stat_sf()`             |
    | `geom_sf()`             | `stat_sf()`             |
    | `geom_smooth()`         | `stat_smooth()`         |
    | `geom_violin()`         | `stat_ydensity()`       |
    | `geom_hex()`            | `stat_bin_hex()`        |
    | `geom_qq_line()`        | `stat_qq_line()`        |
    | `geom_qq()`             | `stat_qq()`             |
    | `geom_quantile()`       | `stat_quantile()`       | 
    
19. What variables does `stat_smooth()` compute? What arguments control its behavior?

    ::: {.callout-note icon="false" title="Answer"}
    `stat_smooth()` computes the following variables:

    -   `y` or `x`: Predicted value
    -   `ymin` or `xmin`: Lower pointwise confidence interval around the mean
    -   `ymax` or `xmax`: Upper pointwise confidence interval around the mean
    -   `se`: Standard error
    :::

20. In our proportion bar chart, we needed to set `group = 1`. Why? In other words, what is the problem with these two graphs?

    ```{r}
    #| fig-show: hide

    ggplot(diamonds, aes(x = cut, y = after_stat(prop))) + 
      geom_bar()
    ggplot(diamonds, aes(x = cut, fill = color, y = after_stat(prop))) + 
      geom_bar()
    ```
    
    ::: {.callout-note icon="false" title="Answer"}
    *Your text answer here.*
    :::
    
    ```{r}
    #| layout-ncol: 2

    # one variable
    ggplot(diamonds, aes(x = cut, 
                         y = after_stat(prop))) + 
      geom_bar()
    ggplot(diamonds, aes(x = cut, 
                         y = after_stat(prop), 
                         group = 1)) + 
      geom_bar()

    # two variables
    ggplot(diamonds, aes(x = cut, 
                         fill = color, 
                         y = after_stat(prop))) + 
      geom_bar()
    ggplot(diamonds, aes(x = cut, 
                         fill = color, 
                         y = after_stat(prop), 
                         group = color)) + 
      geom_bar()
    ```
    
21. What is the problem with the following plot? How could you improve it?

    ```{r}
    #| fig-show: hide

    ggplot(mpg, aes(x = cty, y = hwy)) + 
      geom_point()
    ```
    
    ::: {.callout-note icon="false" title="Answer"}
    *Your text answer here.*
    :::
    
    ```{r}
    #| layout-ncol: 2

    ggplot(mpg, aes(x = cty, y = hwy)) + 
      geom_point()
    ggplot(mpg, aes(x = cty, y = hwy)) + 
      geom_jitter()
    ```

22. What, if anything, is the difference between the two plots? Why?

    ```{r}
    #| fig-show: hide
    ggplot(mpg, aes(x = displ, y = hwy)) +
      geom_point()
    ggplot(mpg, aes(x = displ, y = hwy)) +
      geom_point(position = "identity")
    ```
    
    ::: {.callout-note icon="false" title="Answer"}
    *Your text answer here.*
    :::
    ```{r}
    #| layout-ncol: 2
    # Your R code here
    ```
    
23. What parameters to `geom_jitter()` control the amount of jittering?

    ::: {.callout-note icon="false" title="Answer"}
    *Your text answer here.*
    :::
    
    ```{r}
    #| layout-ncol: 3
    set.seed(321)
    ggplot(mpg, aes(x = displ, y = hwy)) +
      geom_point(color = "gray") +
      geom_jitter(height = 1, width = 1)
    ggplot(mpg, aes(x = displ, y = hwy)) +
      geom_point(color = "gray") +
      geom_jitter(height = 1, width = 5)
    ggplot(mpg, aes(x = displ, y = hwy)) +
      geom_point(color = "gray") +
      geom_jitter(height = 5, width = 1)
    ```
    
24. Compare and contrast `geom_jitter()` with `geom_count()`.

    ::: {.callout-note icon="false" title="Answer"}
    *Your text answer here.*
    :::
    
    ```{r}
    #| layout-ncol: 2

    ggplot(mpg, aes(x = displ, y = hwy)) +
      geom_jitter()
    ggplot(mpg, aes(x = displ, y = hwy)) +
      geom_count()
    ```


25. What's the default position adjustment for `geom_boxplot()`? Create a visualization of the `mpg` dataset that demonstrates it.

    ::: {.callout-note icon="false" title="Answer"}
    *Your text answer here.*
    :::
    
    ```{r}
    #| layout-ncol: 2

    ggplot(mpg, aes(x = drv, y = displ)) +
      geom_boxplot()
    ggplot(mpg, aes(x = drv, y = displ)) +
      geom_boxplot(position = "dodge2")
    ```

26. Turn a stacked bar chart into a pie chart using `coord_polar()`.

    ::: {.callout-note icon="false" title="Answer"}
    *Your text answer here.*
    :::
    
    ```{r}
    #| layout-ncol: 2

    # Your R code here  
    ```

27. What's the difference between `coord_quickmap()` and `coord_map()`?

    ::: {.callout-note icon="false" title="Answer"}
    *Your text answer here.*
    :::

28. What does the following plot tell you about the relationship between city and highway mpg? Why is `coord_fixed()` important? What does `geom_abline()` do?

    ```{r}
    #| fig-show: hide
    ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
      geom_point() + 
      geom_abline() +
      coord_fixed()
    ```

    ::: {.callout-note icon="false" title="Answer"}
    *Your text answer here.*

    ```{r}
    ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
      geom_point() + 
      geom_abline() +
      coord_fixed()
    ```
    :::