01-Visualize-Data.Rmd

---
title: "Visualize Data"
output: html_notebook
---

```{r setup}
library(tidyverse)
library(unienrollment)
```

```{r}
income
```


## Your Turn 1

Run the code on the slide to make a graph. Pay strict attention to spelling, capitalization, and parentheses!

```{r}

```

## Your Turn 2

Add `color`, `size`, `alpha`, and `shape` aesthetics to your graph. Experiment.  

```{r}
ggplot(data = income) +
  geom_point(mapping = aes(x = income_2yr, y = income_5yr))
```

## Your Turn 3

Replace this scatterplot with one that draws boxplots. Use the cheatsheet. Try your best guess.

```{r}
ggplot(income) + 
  geom_point(aes(degree_type, income_5yr))
```

## Your Turn 4

Make a histogram of the `income_5yr` variable from `income`.

```{r}

```

## Your Turn 5

Make a density plot of `income_5yr` colored by `degree_type`.

```{r}

```

## Your Turn 6

Make a bar chart of `degree_type` colored by `degree_type`.

```{r}

```

## Your Turn 7

Predict what this code will do. Then run it.

```{r}
ggplot(income) + 
  geom_point(aes(income_2yr, income_5yr)) +
  geom_smooth(aes(income_2yr, income_5yr))
```

## Your Turn 8

What does `getwd()` return?

```{r}

```

## Your Turn 9

Save the last plot and then locate it in the files pane.

```{r}

```

***

# Take aways

You can use this code template to make thousands of graphs with **ggplot2**.

```{r eval = FALSE}
ggplot(data = <DATA>) +
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))
```

# More practice

- The [Data Visualization](https://r4ds.had.co.nz/data-visualisation.html) chapter of [R for Data Science](https://r4ds.had.co.nz/)
- The [Data Visualization Primers](https://rstudio.cloud/learn/primers/3) from [RStudio Cloud](http://rstudio.cloud)