```{r global_options, include=FALSE}

library(knitr)
library(ggplot2)
opts_chunk$set(fig.align = "center", fig.height = 4, fig.width = 6)

```
##Homework 2
*Enter your name and EID here*

**This homework is due on Feb. 3, 2020 at 12:00pm. Please submit as a PDF file on Canvas.**

This homework uses the `Cars93` data set. Each observation in the data frame contains information on passenger cars from 1993. This is a big data frame with 27 columns. We are interested in the information on manufacturer (`Manufacturer`), car model (`Model`), type of car (`Type`), midrange price in $1000 (`Price`), maximum horsepower (`Horsepower`), drivetrain (`DriveTrain`), and city MPG (miles per US gallon, `MPG.city`). 

```{r}

Cars93 <- read.csv("http://wilkelab.org/classes/SDS348/data_sets/Cars93.csv")
head(Cars93)

```

**Problem 1: (3 pts)** Use ggplot2 to create a scatter plot of maximum horse power versus car price. In the same plot, fit a curve to these data using `geom_smooth()`. In 1-2 sentences, what broad trend do you observe in horsepower for different car prices? **HINT**: Plot maximum horse power on the y-axis and price on the x-axis.

```{r}

# R code goes here

```


**Problem 2: (3 pts)** Next, create a scatter plot of horsepower against city MPG, facetted by drivetrain type. In 1-2 sentences, make one observation about the data from this plot. 

```{r}

# R code goes here

```


**Problem 3: (2 pts)** Plot the distribution of car price, once using `geom_histogram()` and once using `geom_density()`. 

```{r}

# R code goes here

```


**Problem 4: (2 pts)** What does the y-axis in your histogram represent? In your density plot, what is the *total* area under the curve? Please give a single number as your answer. **HINT**: You do not need to do any additional calculations to determine the area under the curve. Use Google to find the answer. 

