solutions.Rmd

---
title: "R Charting: Solutions"
author: ""
date: ""
output:
  slidy_presentation:
    highlight: tango
  ioslides_presentation:
    highlight: zenburn
    keep_md: no
    smaller: yes
    widescreen: yes
---

```{r, include=FALSE}
knitr::opts_chunk$set(message=FALSE, warning=FALSE, cache=FALSE)
import::from("ggplot2", "aes", "mpg")

```

## Solutions: Section 1

### 1.1

```{r}
ggplot2::ggplot(mpg, aes(x = displ, y = cty))
```

### 1.2
```{r, echo=TRUE}
?mpg
```

### 1.3 
Make a scatter plot of `hwy` vs. `cty`

```{r, fig.height=5}
ggplot2::ggplot(data = mpg, aes(x = hwy, y = cty)) +
  ggplot2::geom_point()
```

### 1.4
Make a scatter plot of urban fuel consumption vs engine size (displacement). 

```{r, fig.height=4}
ggplot2::ggplot(data = mpg, aes(x = displ, y = cty)) +
  ggplot2::geom_point()
```


## Solutions: Section 2

### 2.1

What's wrong with this code? Why aren't the points blue?
<div class="columns-2">
```{r, fig.height=3, fig.width=5}
ggplot2::ggplot(data = mpg, aes(x = displ, y = hwy, colour = "blue")) +
  ggplot2::geom_point()

ggplot2::ggplot(data = mpg, aes(x = displ, y = hwy)) +
  ggplot2::geom_point(colour = "blue")
```
</div>

### 2.2

Map a continuous variable to `colour`, `size` and `shape`. How do these aesthetics behave differently for categorical vs. continuous variables? Hint: use `dplyr::glimpse(mpg)` to identify variable types.


<div class="columns-2">

```{r, fig.height=3, fig.width=4}
ggplot2::ggplot(data = mpg, aes(x = displ, y = hwy, colour = cty)) +
  ggplot2::geom_point()
```


```{r, fig.height=3, fig.width=4}
ggplot2::ggplot(data = mpg, aes(x = displ, y = hwy, colour = drv)) +
  ggplot2::geom_point()
```

</div>

### 2.3

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

```{r,fig.height=5}
ggplot2::ggplot(data = mpg, aes(x = displ, y = hwy, colour = drv, shape = drv)) +
  ggplot2::geom_point()
```

### 2.4

What happens if you map an aesthetic to something other than a variable name, like a Boolean statement `aes(colour = displ < 5)`

```{r, fig.height=3}
ggplot2::ggplot(data = mpg, aes(x = displ, y = hwy, colour = displ < 5)) +
  ggplot2::geom_point() 
```

## Solutions: Section 3

### 3.1

Using the `mpg` dataset create a histogram of `cty`. What impact do different values for the `bins` argument have?

```{r, eval=TRUE}
ggplot2::ggplot(data = mpg, aes(x = cty)) +
  ggplot2::geom_histogram(bins = 100) 
```

```{r, eval=TRUE}
ggplot2::ggplot(data = mpg, aes(x = cty)) +
  ggplot2::geom_histogram(bins = 5) 
```

### 3.2 

Make a density plot of `cty` by `drv`. Use the `alpha` argument to make the overlapping regions visible.


```{r, fig.height = 2.7}
ggplot2::ggplot(data = mpg, aes(x = cty, fill = drv)) +
  ggplot2::geom_density(alpha = 0.7)
```

```{r, fig.height = 2.7}
ggplot2::ggplot(data = mpg, aes(x = cty, fill = drv)) +
  ggplot2::geom_density(alpha = 0.3)
```

### 3.3

Recreate the US unemployed population line graph. Add another line to show the total population. 

```{r, fig.height = 3}
ggplot2::economics |> 
  ggplot2::ggplot(aes(x = date, y = unemploy)) +
  ggplot2::geom_line() +
  ggplot2::geom_line(aes(x = date, y = pop))
```

## Solutions: Section 4

### 4.1

Create a basic scatter plot showing highway fuel efficiency vs city fuel efficiency. Add a 
theme_minimal; and colour the points based on the number of cylinders the car has.

```{r, fig.height = 3}

ggplot2::ggplot(data = mpg) +
  ggplot2::geom_point(mapping = aes(x = hwy, y = cty, colour = as.factor(cyl))) + 
  ggplot2::theme_minimal()


```

### 4.2

Change the title and axis labels to something sensible based on what the chart is showing.

```{r, fig.height = 3}

ggplot2::ggplot(data = mpg) +
  ggplot2::geom_point(mapping = aes(x = hwy, y = cty, colour = as.factor(cyl))) + 
  ggplot2::theme_minimal() +
  ggplot2::labs(title = "Fuel efficiency comparison between city and highway", 
                x = "Highway efficiency",
                y = "City efficiency")

```

### 4.3

Create a custom theme changing: major gridlines to grey, dashed; removing minor gridlines; x and y 
axis text to black, pt 10, italic; move the legend position to the bottom of the plot.

```{r, fig.height = 3}

ggplot2::ggplot(data = mpg) +
  ggplot2::geom_point(mapping = aes(x = hwy, y = cty, colour = as.factor(cyl))) + 
  ggplot2::labs(title = "Fuel efficiency comparison between city and highway", 
                x = "Highway efficiency", 
                y = "City efficiency") +
  ggplot2::theme(panel.grid.major = ggplot2::element_line(colour = "grey", linetype = "dashed"),
                 panel.grid.minor = ggplot2::element_blank(),
                 axis.text = ggplot2::element_text(colour = "black", size = 10, face = "italic"),
                 legend.position = "bottom")

```

### 4.4

Instead of the custom theme, use the gss theme from `mojchart` and one of the built-in colour schemes.

```{r, fig.height = 3}

ggplot2::ggplot(data = mpg) +
  ggplot2::geom_point(mapping = aes(x = hwy, y = cty, colour = as.factor(cyl))) + 
  ggplot2::labs(title = "Fuel efficiency comparison between city and highway", 
                x = "Highway efficiency", 
                y = "City efficiency") +
  mojchart::scale_colour_moj(n = 4, scheme = "govanal_bars") +
  mojchart::theme_gss(xlabel = TRUE)

```

```{r, fig.height = 3}

ggplot2::ggplot(data = mpg) +
  ggplot2::geom_point(mapping = aes(x = hwy, y = cty, colour = as.factor(cyl))) + 
  ggplot2::labs(title = "Fuel efficiency comparison between city and highway", 
                x = "Highway efficiency", 
                y = "City efficiency") +
  mojchart::scale_colour_moj(n = 4) +
  mojchart::theme_gss(xlabel = TRUE)

```