---
title: "HW04 Part 1: Complete Chapter 3"
author: "your name"
date: "`r format(Sys.time(), '%d %B %Y')`"
output: html_notebook
editor_options: 
  chunk_output_type: inline
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Chapter 3

- Change "your name" in the YAML header above to your name.

### Section 3.1

Load the tidyverse packages. I've entered the first code chunk for you. **You must enter all subsequent code chunks and run the code.** 

```{r}
library("tidyverse")
```

If you get an error, then you did not install the `tidyverse` package like you were supposed to in the previous assignment. Go back to that assignment and complete that step. (This should not be the case, though.)

### Section 3.2: First steps

Enter your code chunks for Section 3.2 here. **Do not enter the graphing template shown in 3.2.3.**

Get in the habit of entering a brief description of what each chunk of code does. Enter this above the chunk, like I did in Section 3.1.

#### Section 3.2 Questions

Answer the questions *completely.* Some answers will require you to write code in a code chunk and also to type text before or after the chunk. Other answers will require only code or only text. 

**1:** Run ggplot(data = mpg). What do you see?


**2:** How many rows are in `mpg`? How many columns? **Hint:** Use the `dim()` function. Type `dim()` in the console to learn what `dim()` does.


**3:** What does the `drv` variable describe? Read the help for `?mpg` to find out.


**4:** Make a scatterplot of `hwy` vs `cyl`.


**5:** What happens if you make a scatterplot of `class` vs `drv`? Why is the plot not useful?


### Section 3.3: Aesthetic mappings


#### Section 3.3 questions

**1:** What’s gone wrong with this code? Why are the points not blue?


**2:** Which variables in `mpg` are categorical? Which variables are continuous? (*Hint:* type `?mpg` to read the documentation for the dataset). How can you see this information when you run mpg?


**3:** Map a continuous variable to color, size, and shape. How do these aesthetics behave differently for categorical vs. continuous variables? **Note:** You need several code chunks here to answer this question.


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


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



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


### Section 3.5: Facets



#### Section 3.5 questions

**1:** What happens if you facet on a continuous variable? **Note:** Write a prediction, then make and run your code chunk.



**2:** What do the empty cells in plot with `facet_grid(drv ~ cyl)` mean? How do they relate to this plot?



**3:** What plots does the following code make? What does `.` do?



**4:** Take the first faceted plot below. What are the advantages to using faceting instead of the colour aesthetic? What are the disadvantages? How might the balance change if you had a larger dataset?



**5:** 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 argument?


**6:** When using `facet_grid()` you should usually put the variable with more unique levels in the columns. Why?

### Section 3.6: Geometric objects



#### Section 3.6.1 Questions

**1:** What geom would you use to draw a line chart? A boxplot? A histogram? An area chart? **Note:** You will have to make some types of these charts in Part 2 of this assignment.


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



**3:** What does `show.legend = FALSE` do? What happens if you remove it?
**Note:** Skip the "Why do you think I used it earlier in the chapter?" question.


**4:** What does the `se` argument to `geom_smooth()` do?


**5:** Will these two graphs look different? Why/why not?


**6:** Recreate the R code necessary to generate the following graphs. **Note:** Enter a separate code chunk for each graph, numbered 1 to 6, starting row-wise at upper left. 

1: upper left.


2: upper right.


3: middle left.


4: middle right.


5: lower left.


6: lower right. *Hint:* Read the help file for `geom_point`. Use `shape = 21`, `stroke = 2`, and `size = 3`.

### Section 3.7: Statistical transformations.

**Skip this section.** We may return to it later.


### Section 3.8 Position adjustments


#### Section 3.8 Questions

**1:** What is the problem with this plot? How could you improve it. *Hint:* Remember the size of the `mpg` data set from earlier in this exercise. Does the number of points seem correct? **Note:** Write the code to improve it. 


**2:** What parameters to `geom_jitter()` control the amount of jittering?


**3:** Skip this question.


**4:** Skip this question.


### Section 3.9: Coordinate systems

**Note:** Read about and do only the code chunk associated with `coord_flip()`. `coord_flip()` is a great layer to use when you have categorical data with longish names as it makes the text easier to read.

#### Section 3.9 Questions

**2:** What does `labs()` do? Read the documentation. Include a code chunk where you use `labs()` to change the name of the variables from the default `hwy` to "Highway MPG" and and the default `class` to "Car Class" for the flipped coordinate boxplot you just made in 3.9. 


### Section 3.10: The layered grammar of graphics

Just read this section as a contextual review of everything you just learned.


