Here is Assignment 2. It will be due on April 16th by 8 PM.

Assignment_2.txt

---
title: "Assignment 2"
author: "YOUR NAME"
date: "April 10, 2017"
output: html_document
---


```{r message=FALSE, warning=FALSE}
library(ggplot2)
library(dplyr)
library(car)
library(ggmap)
library(leaflet)
library(ggcorrplot)
library(dotwhisker)
library(plotly)
library(highcharter)
library(readr)
```



1. Use the following syntax to load a dataset of profit and loss statements for EIU's Academic Departments

```{r message=FALSE, warning=FALSE}
profit <- read.csv("https://raw.githubusercontent.com/ryanburge/profitloss/master/all.csv")
```

What department had the highest personnel expenses in 2012-2013?
Visualize personnel expenses across all departments in 2012-2013. 

```{r message=FALSE, warning=FALSE}
PUT YOUR SYNTAX HERE
```

Now, tell me what department posted the largest profit across the entire time period? 
Visualize that. 

```{r message=FALSE, warning=FALSE}
PUT YOUR SYNTAX HERE
```


2. Let's take a look at doing some correlations. Read in the following dataset:
Here's the codebook: https://dataverse.harvard.edu/file.xhtml?fileId=3004423&version=1.2

```{r message=FALSE, warning=FALSE}
cces <- read.csv(url("https://raw.githubusercontent.com/ryanburge/pls2003_sp17/master/cces.csv"))
```

Find the variable that reports personal income and education level and run a simple correlation of the two. 
Then put together a scatterplot. Is there a relationship between the two variables in this scatterplot? 

```{r message=FALSE, warning=FALSE}
PUT YOUR SYNTAX HERE
```

Now, pick six variables from your dataset that might be related to each other in a correlation. 
Create a smaller dataset of just those variables. Then use ggcorrplot to visualize the correlation coefficients. 
What do you see? What is related? 

```{r message=FALSE, warning=FALSE}
PUT YOUR SYNTAX HERE
```

3. Here we will look at mean, median, and standard deviation. Load in this dataset of ACT scores in Wisconsin.  

```{r message=FALSE, warning=FALSE}
act <- read.csv(url("https://raw.githubusercontent.com/ryanburge/pls2003_sp17/master/act.csv"))
```

What's the mean? What's the median? Why the big difference? Visualize the distribution. 

```{r message=FALSE, warning=FALSE}
PUT YOUR SYNTAX HERE
```

Now, find the standard deviation. 
Remember the 68-95-99 rule? If I said that a random student's ACT score was 30, how rare is that? Show your work!

```{r message=FALSE, warning=FALSE}
PUT YOUR SYNTAX HERE
```

4. Regression Time. Load up the Simon dataset that we started this all with. 
Here's the link to the codebook: http://opensiuc.lib.siu.edu/cgi/viewcontent.cgi?article=1010&context=ppi_statepolls

```{r message=FALSE, warning=FALSE}
simon <- read.csv(url("http://goo.gl/exQA14"))
```

There's a question in there about expanded gambling in the state. That's going to be our DV. Make sure to clean this variable first!!

```{r message=FALSE, warning=FALSE}
PUT YOUR SYNTAX HERE
```

Now, I will let you pick four other variables in the dataset that could potentially predict support or opposition for expanded gambling. Clean those variables. 

```{r message=FALSE, warning=FALSE}
PUT YOUR SYNTAX HERE
```

Now, it is time to regress. Do the regression analysis. Then visualize that with the dotwhisker package. 

```{r message=FALSE, warning=FALSE}
PUT YOUR SYNTAX HERE
```

Interpret your output. 

5. For BONUS POINTS (20 pts.), find a table of locations on wikipedia. Scrape it. Map the locations using leaflet. 


When you are done, upload your html files to this website: 
https://panthershare.eiu.edu/sites/aa/cos/polsci/burge/_layouts/15/start.aspx#/DropOffLibrary/Forms/AllItems.aspx