CH01.qmd

### Exercises (Chapter 1)

1.  How many rows are in `penguins`? How many columns?

    ::: {.callout-note icon="false" title="Answer"}
    ```{r}
    #| message: false
    #| warning: false
    library(tidyverse)
    library(palmerpenguins)
    # Your R Code here
    ```
    *Your answer here.*
    :::

2.  What does the `bill_depth_mm` variable in the `penguins` data frame describe? Read the help for `?penguins` to find out.

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

3.  Make a scatterplot of `bill_depth_mm` vs. `bill_length_mm`. That is, make a scatterplot with `bill_depth_mm` on the y-axis and `bill_length_mm` on the x-axis. Describe the relationship between these two variables.

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

4.  What happens if you make a scatterplot of `species` vs. `bill_depth_mm`? What might be a better choice of geom?

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

5.  Why does the following give an error and how would you fix it?

    ```{r}
    #| eval: false
    library(tidyverse)
    ggplot(data = penguins) + 
      geom_point()
    ```
    
    ::: {.callout-note icon="false" title="Answer"}
    *Your answer here.*
    ```{r}
    # Correct code here
    ```
    :::

6.  What does the `na.rm` argument do in `geom_point()`? What is the default value of the argument? Create a scatterplot where you successfully use this argument set to `TRUE`.

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

7.  Add the following caption to the plot you made in the previous exercise: "Data come from the `palmerpenguins` package." Hint: Take a look at the documentation for `labs()`.

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

8.  Recreate the following visualization. What aesthetic should `bill_depth_mm` be mapped to? And should it be mapped at the global level or at the geom level?

    ```{r}
    #| echo: false
    #| warning: false
    #| fig-alt: |
    #|   A scatterplot of body mass vs. flipper length of penguins, colored 
    #|   by bill depth. A smooth curve of the relationship between body mass 
    #|   and flipper length is overlaid. The relationship is positive, 
    #|   fairly linear, and moderately strong.
    library(tidyverse)
    library(palmerpenguins)
      ggplot(
        data = penguins,
        mapping = aes(x = flipper_length_mm, y = body_mass_g)
        ) +
         geom_point(aes(color = bill_depth_mm)) +
          geom_smooth()
    ```

    ::: {.callout-note icon="false" title="Answer"}

    ```{r}
    # Your R Code here
    ```
    
    *Your answer here.*
    
    :::

9.  Run this code in your head and predict what the output will look like. Then, run the code in R and check your predictions.

    ```{r}
    #| eval: false

    ggplot(
      data = penguins,
      mapping = aes(x = flipper_length_mm, y = body_mass_g, color = island)
    ) +
        geom_point() +
        geom_smooth(se = FALSE)
    ```
    
    ::: {.callout-note icon="false" title="Answer"}
    
    *Your answer here.*
     
    ```{r}
    # Your R code here
    ```
    
    :::

10. Will these two graphs look different? Why/why not?

    ```{r}
    #| eval: false

    ggplot(
      data = penguins,
      mapping = aes(x = flipper_length_mm, y = body_mass_g)
    ) +
      geom_point() +
      geom_smooth()

    ggplot() +
      geom_point(
        data = penguins,
        mapping = aes(x = flipper_length_mm, y = body_mass_g)
      ) +
      geom_smooth(
        data = penguins,
        mapping = aes(x = flipper_length_mm, y = body_mass_g)
      )
    ```
    
    ::: {.callout-note icon="false" title="Answer"}
    *Your answer here.*
    
    ```{r}
    #| warning: false
    library(patchwork)
    # Your R code here
    ```
    
    :::

11. Make a bar plot of `species` of `penguins`, where you assign `species` to the `y` aesthetic. How is this plot different?

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

12. How are the following two plots different? Which aesthetic, `color` or `fill`, is more useful for changing the color of bars?

    ```{r}
    #| eval: false

    ggplot(penguins, aes(x = species)) +
      geom_bar(color = "red")

    ggplot(penguins, aes(x = species)) +
      geom_bar(fill = "red")
    ```
    
    ::: {.callout-note icon="false" title="Answer"}
    ```{r}
    ggplot(penguins, aes(x = species)) +
        geom_bar(color = "red") -> p1

    ggplot(penguins, aes(x = species)) +
      geom_bar(fill = "red") -> p2
    p1 + p2
    ```
    *Your answer here.*
    :::

13. What does the `bins` argument in `geom_histogram()` do?

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

14. Make a histogram of the `carat` variable in the `diamonds` dataset that is available when you load the `tidyverse` package. Experiment with different binwidths. What binwidth reveals the most interesting patterns?

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

15. The `mpg` data frame that is bundled with the `ggplot2` package contains `r nrow(mpg)` observations collected by the US Environmental Protection Agency on `r mpg |> distinct(model) |> nrow()` car models. Which variables in `mpg` are categorical? Which variables are numerical? (Hint: Type `?mpg` to read the documentation for the dataset.) How can you see this information when you run `mpg`?

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

16. Make a scatterplot of `hwy` vs. `displ` using the `mpg` data frame. Next, map a third, numerical variable to `color`, then `size`, then both `color` and `size`, then `shape`. How do these aesthetics behave differently for categorical vs. numerical variables?

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

17. In the scatterplot of `hwy` vs. `displ`, what happens if you map a third variable to `linewidth`?

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

18. What happens if you map the same variable to multiple aesthetics?

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

19. Make a scatterplot of `bill_depth_mm` vs. `bill_length_mm` and color the points by `species`. What does adding coloring by species reveal about the relationship between these two variables? What about faceting by `species`?

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

20. Why does the following yield two separate legends? How would you fix it to combine the two legends?

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

    ggplot(
      data = penguins,
      mapping = aes(
        x = bill_length_mm, y = bill_depth_mm, 
        color = species, shape = species
      )
    ) +
      geom_point() +
      labs(color = "Species")
    ```
    
    ::: {.callout-note icon="false" title="Answer"}
    ```{r}
    # Your R fix here
    ```
    *Your answer here.*
    :::

21. Create the two following stacked bar plots. Which question can you answer with the first one? Which question can you answer with the second one?

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

    ggplot(penguins, aes(x = island, fill = species)) +
      geom_bar(position = "fill")
    ggplot(penguins, aes(x = species, fill = island)) +
      geom_bar(position = "fill")
    ```

    ::: {.callout-note icon="false" title="Answer"}
    
    ```{r}
    ggplot(penguins, aes(x = island, fill = species)) +
      geom_bar(position = "fill") -> p1
    ggplot(penguins, aes(x = species, fill = island)) +
      geom_bar(position = "fill") -> p2
    p1 / p2
    ```
    *Your answer here.*
    :::

22. Run the following lines of code. Which of the two plots is saved as `mpg-plot.png`? Why?

    ```{r}
    #| eval: false

    ggplot(mpg, aes(x = class)) +
      geom_bar()
    ggplot(mpg, aes(x = cty, y = hwy)) +
      geom_point()
    ggsave("mpg-plot.png")
    ```

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

23. What do you need to change in the code above to save the plot as a PDF instead of a PNG? How could you find out what types of image files would work in `ggsave()`?

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