```{r global_options, include=FALSE, message=FALSE}

library(knitr)
opts_chunk$set(fig.align="center", fig.height=3, fig.width=4)
library(ggplot2)
theme_set(theme_bw(base_size=12))
library(grid) # for `arrow()`
library(tidyverse)
library(plotROC)
library(ggthemes)

```
##Lab Worksheet 7

In 1898, Hermon Bumpus, an American biologist working at Brown University, collected data on one of the first examples of natural selection directly observed in nature. Immediately following a bad winter storm, he collected 136 English house sparrows, *Passer domesticus*, and brought them indoors. Of these birds, 64 had died during the storm, but 72 recovered and survived. By comparing measurements of physical traits, Bumpus demonstrated physical differences between the dead and living birds. He interpreted this finding as evidence for natural selection as a result of this storm:

```{r}

bumpus <- read.csv("http://wilkelab.org/classes/SDS348/data_sets/bumpus_full.csv")
head(bumpus)

```

The data set has three categorical variables (`Sex` with levels `Male` and `Female`; `Age` with levels `Adult` and `Young`; and `Survival`, with levels `Alive` and `Dead`) and nine numerical variables that hold various aspects of the birds' anatomy, such as wingspread, weight, etc.

Split the `bumpus` data set into a random training and test set. Use 70% of the data as a training set. 
```{r}

set.seed(13)  # set the seed to make your partition reproductible

# your R code here

```

Fit a logistic regression model on the training data set, then predict the survival on the test data set, and plot the resulting ROC curves. 
```{r}
# model to use: 
# Survival ~ Sex + Length + Weight + Humerus_Length + Sternum_Length

# your R code here

```

# 2. Area under the ROC curves

Calculate the area under the training and test curve for the following model.
```{r}
# model to use:
# Survival ~ Weight + Humerus_Length

set.seed(13)  # set the seed to make your partition reproductible

# your R code here

```

# 3. What happens if we don't use `set.seed()` to create reproducibility in the way we partition the dataset?

```{r }

# your R code here (if the above was easy)

```

*Your answer here.*