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


##Instructions
Complete this assignment in RStudio. Run all of the chunks, then Knit the markdown file to html. Upload that HTML file to the HW6 assignment on Sakai.

###Load the tidyverse package(s)
```{r}
#install.packages("tidyverse")
library(tidyverse)
```


### Load the data
If you have not already loaded the MLB salary data into R, run the following code chunk. WARNING: If you run this chunk and you already have a dataframe called "MLBPayrolls" then this will overwrite it. That probably won't be a big deal, but I just wanted to make you aware for future cases where it might be a big deal.

```{r}
library(readxl)
#download.file("https://mjwebster.github.io/DataJ/spreadsheets/MLBpayrolls.xls", "MLBPayrolls.xls")
MLBpayrolls <- read_excel("../data/MLBpayrolls.xls", sheet = "MLB Payrolls", na = "n/a", .name_repair = "universal")
MLBpayrolls
```


### 1. Sort the teams so they appear in reverse alphabetical order.
```{r}

```


### 2. Did any teams pay see their payroll jump from less than $100 million in 2004 to more than $200 million by 2016?
```{r}

```


### 3. What teams saw their payrolls at least double between 2004 and 2016?
```{r}

```

### 4. How many teams in 2016 have a payroll that is higher than the top payroll in 2004?
```{r}

```



### 5. Write an R question that tells us the 2016 payrolls for just the teams in California. 
```{r}

```

### 6. Save the 2016 payrolls for just the California teams to a new dataframe called CA2016.

```{r}

```

### 7. Come up with your own question for the MLBpayrolls data. Then ask that question in R and write a sentence that translates the answer you get back into English.

```{r}


# Replace this comment with your sentence.
```

