---
title: "Lab 6"
author: "Your Name Here"
date: ""
output: html_document
---

##### Remember to change the `author: ` field on this Rmd file to your own name.

### Learning objectives

> In today's Lab you will gain practice with the following concepts from today's class:

>- Using the `qplot` and `ggplot` commands from the `ggplot2` library
- Specifying `shape` and `color` attributes
- Using `facet_grid` to create plots that show the data broken down by various subgroups
- Constructing geographic heatmaps 


### Problems

We'll begin by loading all the required packages.
```{r}
library(tidyverse)
Cars93 <- as_tibble(MASS::Cars93)
```

#### 1. facet_grid

Using the `diamonds` data set and the `facet_grid` command, create a figure that shows a scatterplot of `price` against `carat` for each combination of `cut` and `clarity`.   

There are `r length(levels(diamonds$clarity))` levels of clarity, and `r length(levels(diamonds$cut))` levels of cut.  Your figure should therefore contain `r length(levels(diamonds$clarity)) * length(levels(diamonds$cut))` scatterplots.

```{r fig.width=10, fig.height=10, dpi=70}
# Edit me
```

#### 2. Plotting the Cars93 data

This problem uses the Cars93 dataset from the MASS package.

**(a)** Use `qplot` to create a scatterplot with Price on the y-axis and EngineSize on the `x-axis`.  

```{r, fig.align='center', fig.height=4, fig.width=5}
# Edit me
```

**Describe the relationship between Price and EngineSize.**

> Replace this text with your solution.


**(b)** Repeat part (a) using the `ggplot` function and `geom_point()` layer.

```{r, fig.align='center', fig.height=4, fig.width=5}
# Edit me
```

**(c)** Repeat part (b), but this time specifying that the `color` mapping should depend on `Type` and the `shape` mapping should depend on `DriveTrain`.  

```{r, fig.align='center', fig.height=4, fig.width=5}
# Edit me
```

**Do you see any obvious patterns in how the different Types of cars cluster in the plot?  Describe any clear patterns that you see.**

> Replace this text with your solution.

**Do you see any obvious patterns in how the different DriveTrains of cars cluster in the plot?  Describe any clear patterns that you see.**

> Replace this text with your solution.

**(d)** Construct boxplots showing Price on the y-axis and AirBags on the x-axis.  (Hint: `boxplot` is a valid ggplot2 geometry)

```{r}
# Edit me
```

**Do you observe any association between AirBag type and Price?  Explain.**

> Replace this text with your solution.

#### 3. Plotting a map

At the end of lecture we used the following code to generate a headmap of murder rates in the US.  

```{r, fig.width = 7, fig.height = 4, fig.align='center'}
library(maps)
# Create data frame for map data (US states)
states <- map_data("state")

# Here's what the states data frame looks like
str(states)

# Make a copy of the data frame to manipulate
arrests <- USArrests

# Convert everything to lower case
names(arrests) <- tolower(names(arrests))
arrests$region <- tolower(rownames(USArrests))

# Merge the map data with the arrests data based on region
choro <- merge(states, arrests, sort = FALSE, by = "region")
choro <- choro[order(choro$order), ]

# Plot a map, filling in the states based on murder rate
qplot(long, lat, data = choro, group = group, fill = murder,
  geom = "polygon") + scale_fill_gradient(low = "#56B1F7", high = "#132B43")
```

Modify the code above to produce a heatmap of `assault` rates instead, with **orange colours** instead of blue colours for the gradient.  

Here's a document that may help you pick colors: [Hex colour picker](http://www.w3schools.com/tags/ref_colorpicker.asp)
