---
title: "Exercises 1"
author: "Your Name Here"
date: "12/3/2017"
output: html_document
---

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

# Overview

Here is a [link](https://raw.githubusercontent.com/IDPT7810/practical-data-analysis/master/vignettes/problem-set-1.Rmd) to the text of these exercises.

# Question 1

Tidy the `mtcars` data set. Note that car names are rownames in the built-in data, so they need to be moved to their own column prior to tibble conversion.

The tidied data should look like:

```
# A tibble: 352 x 3
                name   var value
               <chr> <chr> <dbl>
 1         Mazda RX4   mpg  21.0
 2     Mazda RX4 Wag   mpg  21.0
 3        Datsun 710   mpg  22.8
 4    Hornet 4 Drive   mpg  21.4
 5 Hornet Sportabout   mpg  18.7
 6           Valiant   mpg  18.1
 7        Duster 360   mpg  14.3
 8         Merc 240D   mpg  24.4
 9          Merc 230   mpg  22.8
10          Merc 280   mpg  19.2
# ... with 342 more rows
```

## Strategy

## Interpretation

# Question 2

For each car in the tidy `mtcars` data set, calculate the mean (`mean()`) and variance (`var()`) for each variable. Try using `summarize()` and `summarize_at()` and `summarize_all()`.

## Strategy

## Interpretation

# Question 3

Plot `mpg` vs `cyl` for the `mtcars` data set. Which format should you use? The original data set, or the tidied one? Why? 

## Strategy

## Interpretation

# Question 4

Using the provided `qpcr` data, plot the changes in gene expression over time. Use **colors** to represent genotypes and **facets** for the different genes. If that's too easy, add error bars (`geom_errorbar()`) to the plot and connect each point with a line (`geom_line()`).

## Strategy

## Interpretation

