---
title: "Explore and Visualize"
author: "Prof. Eric A. Suess"
output:
  word_document: default
  html_document:
    df_print: paged
  pdf_document: default
  html_notebook: default
---

# Chapter 3 Data Visualization

1. ggplot2
2. scatterplots, points, color, shape
3. faceting

Today we are going to try some of the code from Chapter 3 Data Visualization.

To start we will load the tidyverse.  Note that *ggplot2* is the first package loaded!

```{r message=FALSE}
library(tidyverse)
```

We will start the *mpg* dataset that is in the ggplot2 package.

```{r}
mpg
```


Make a scatterplot of highway miles per gallon (hwy) and engine size (displ).

Start with an empty graph and add to it.

```{r}
ggplot(data = mpg)
```

Add points using geom_point mapping and aesthetic.  

```{r}
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))
```

Add color.

```{r}
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))
```

Change size of the points.

```{r}
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, size = class))
```

Change the  transparentcy or shape.

```{r}
# Left
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, alpha = class))

# Right
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, shape = class))
```

Specify color.

```{r}
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue")
```

Faceting, spliting a plot into separate plots for different levels of a categorical variable.

```{r}
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)
```


