class3solutions.Rmd

---
title: 'Class 3 In-Class Exercises: Solutions'
author: "Amy Allen & Dayne Filer"
date: "June 28, 2016"
output:
  pdf_document:
    highlight: tango
  html_document: null
---

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

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    background-color: #004c93;
    color: #FFFFFF; 
    width: 100px;
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    text-align: center;
    vertical-align: middle !important;
    border-radius: 8px;
    float:right;
    }

    .showopt:hover {
    background-color: #dfe4f2;
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</style>

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<script src="hideOutput.js"></script>

Load the data

```{r}
setwd("~/Documents/rclass")
data <- read.csv("yeastmutants.csv", header = TRUE)
```

1. Plot the average pheromone response for all three cell types. Plot them as lines, not points (remember: the argument `type` for `plot` and the function `lines` which is similar to `points`). Make each cell type a different color and create a legend to match. 

```{r}
plot(x = data$Time, y = data$sst2.avg, type = "l", main = "Yeast Response to Pheromone", 
     xlab = "Time (minutes)", ylab = "Mean pheromone response", col = "green")
lines(x = data$Time, y = data$gpa1.avg, col = "red")
lines(x = data$Time, y = data$wt.avg, col = "black")
legend("bottomright", c("sst2 mutant","gpa1 mutant","wild type"), lty = 1, 
       col = c("green","red","black"))
```

\pagebreak
2. Plot the standard deviation of the pheromone response for all three cell types. Plot them as points making each cell type a different color. Create a legend to match.

```{r}
plot(x = data$Time, y = data$sst2.stdev, type = "l", main = "Yeast Response to Pheromone", 
     xlab = "Time (minutes)", ylab = "Standard deviation of pheromone response", col = "green")
lines(x = data$Time, y = data$gpa1.stdev, col = "red")
lines(x = data$Time, y = data$wt.stdev, col = "black")
legend("bottomright", c("sst2 mutant","gpa1 mutant","wild type"), lty = 1, 
       col = c("green","red","black"))
```

\pagebreak
3. Noise in cells is often quantified by the coefficient of variation (CV) because it normalizes the standard deviation for differences in the mean. CV is defined as $CV=\sigma/\mu$ where $\sigma$ is the standard deviation and $\mu$ is mean. Create new variables in the data frame for the CV for each cell type. (Rememeber: you can use subsetting to assign new variables). Then create a plot of the CV for all cell types following the same guidelines as in questions 1 and 2.

```{r}
data$wt.cv <- data$wt.stdev/data$wt.avg 
data$gpa1.cv <- data$gpa1.stdev/data$gpa1.avg 
data$sst2.cv <- data$sst2.stdev/data$sst2.avg 
plot(x = data$Time, y = data$gpa1.cv, type = "l", main = "Yeast Response to Pheromone", 
     xlab = "Time (minutes)", ylab = "Standard deviation of pheromone response", col = "red")
lines(x = data$Time, y = data$sst2.cv, col = "green")
lines(x = data$Time, y = data$wt.cv, col = "black")
legend("bottomright", c("sst2 mutant","gpa1 mutant","wild type"), lty = 1, 
       col = c("green","red","black"))
```