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

```

For this homework, you will save the `R Markdown` (e.g., `.Rmd`) homework document and save it to your `homework` project directory on your computer. Save a new copy of this file so that its name has the prefix `2023_Lastname_FirstInitial`. Full name example: `2023_cookg_EX_08.Rmd`. Feel free to work with a partner but understand that the work submitted has to be your own.

This homework utilizes examples from these modules:

- **data frame manipulation and wrangling**
- **data subsets and summaries**
- **ggplot and the grammar of graphics**
- **visualizing uncertainty**
- **legends and arrangement**


**This homework is due on Tuesday by 2:30pm.**


*Enter your name in the author YAML code above*   


```{r}
source(here::here("r", "my_functions.R"))
```

# (1pt) **Load only used libraries**

```{r echo=TRUE}
library(dplyr)
library(ggplot2)
library(magrittr)
```


# (1 pt) **Using {here}, get your data**

```{r}


```


# (1 pt) **Create a point range plot**

Using data for your project, create a `geom_pointrange()` to visualize the uncertainty of the data. The point should be the mean and the range should be the a 95% confidence interval. Map a variable to `col` so that you have groups and dodge the ranges so that you can compare the two confidence ranges.  

```{r}

```


# (1 pt) **Do the ranges overlap?**

Describe the ranges and whether they overlap based on some meaningful comparison. An overlap would suggest that the groups are performing relatively the same. If they overlap, you can be 95% confident that the groups differ.

**ANSWER:**





# (1 pt) **Create another point range plot**

Create another point range plot that visualizes the point as the median and the range as the interquartile range. 

```{r}

```




# (1 pt) **Make a grob plot**

Take the two plots and arrange them side-by-side to facilitate comparison of the means and the medians. 

```{r}

```





# (1 pt) **Create a plot**

Create a point plot or box plot that visualizes a key performance variable from your project data. Map a categorical variable to an aesthetic so that you have a legend. 

```{r}


```



# (1 pt) **Legend interpretation**

Does the legend order map onto the ordering of the box plots?

**ANSWER:**




# (1 pt) **Change Legend Label Order**

Change the legend label order so that it reflects a better ordering. If the order makes sense, change the order so you have practice changing this legend characteristic. Whatever you do, make sure the changes you make do not assign aesthetics or labels in a way that does not reflect the data.

```{r}


```



# (1 pt) **Increase the size of the legend aesthetic**

For one of your plots, make the legend more prominent but not too big.

```{r}


```




**Please knit an `html` file and upload [here](https://claremontmckenna.app.box.com/f/140969a32dea44e9ab6c0c00146d66e3).** 





