```{r load-packages, include=FALSE}
library(tidyverse)
```

### Glimpse at the starwars data frame.

```{r glimpse-starwars}
glimpse(starwars)
```

### Modify the following plot to change the color of all points to `"pink"`.

```{r scatterplot}
ggplot(starwars, 
       aes(x = height, y = mass, color = gender, size = birth_year)) +
  geom_point(color = "#30509C")
```

### Add labels for title, x and y axes, and size of points. Uncomment to see the effect.

```{r scatterplot-labels}
ggplot(starwars, 
       aes(x = height, y = mass, color = gender, size = birth_year)) +
  geom_point(color = "#30509C") +
  labs(
    #title = "___",
    #x = "___", 
    #y = "___",
    #___
    )
```

### Pick a single categorical variable from the data set and make a bar plot of its distribution.

(A little bit of starter code is provided below, and the code chunk is set to not be evaluated with `eval = FALSE` because the current code in there is not valid code and hence the document wouldn't knit. Once you replace the code with valid code, set the chunk option to `eval = TRUE`, or remove the `eval` option altogether since it's set to `TRUE` by default.)

```{r barplot, eval = FALSE}
ggplot(starwars, aes(___)) +
  geom___
```

### Pick a single numerical variable and make a histogram of it.

(This time no starter code is provided, you're on your own!)

```{r histogram}

```

### Pick a numerical variable and a categorical variable and make a visualisation (you pick the type!) to visualise the relationship between the two variables. Along with your code and output, provide an interpretation of the visualisation.

```{r num-cat}

```

### Pick two categorical variables and make a visualisation to visualise the relationship between the two variables. Along with your code and output, provide an interpretation of the visualisation.

```{r cat-cat}

```

### Pick two numerical variables and two categorical variables and make a visualisation that incorportes all of them and provide an interpretation with your answer.

```{r multi}

```
