solutions.Rmd

---
title: "Introduction to R for Biologists - Solutions"
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
  html_notebook:
    toc: yes
    toc_depth: 3
    toc_float: yes
---

#### Exercise 
You can easily make different types of plots with ggplotby using different geoms. If you type "geom" in RStudio, RStudio will show you the different types of geoms you can use. Using the same data (same x and y values)
1) Make a violin plot (geom_violin)
2) Make a jitter plot (geom_jitter)
3) Make a boxplot with a jitter plot overlaid (Hint: you can add multiple geoms with + )

#### Solution
```{r}
ggplot(data=allinfo, mapping=aes(x=Sample, y=log2(Count))) + 
  geom_violin()
ggplot(data=allinfo, mapping=aes(x=Sample, y=log2(Count))) + 
  geom_jitter()
ggplot(data=allinfo, mapping=aes(x=Sample, y=log2(Count))) + 
  geom_boxplot() +
  geom_jitter()
```


#### Exercise 
Colour the plots by other variables (columns) in the metadata file
1) characteristics
2) immunophenotype
2) `developmental stage` (Check what happens if you don't use backticks)

#### Solution
```{r}
ggplot(data=allinfo, mapping=aes(x=Sample, y=log2(Count), fill=immunophenotype)) + 
  geom_boxplot() 
ggplot(data=allinfo, mapping=aes(x=Sample, y=log2(Count), fill=`developmental stage`)) + 
  geom_boxplot() 
```


#### Exercise
Make a colourblind friendly plot. Hint there are colourblind friendly palettes [here](http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/#a-colorblind-friendly-palette)

#### Solution
```{r}
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2")

ggplot(data=allinfo, mapping=aes(x=Sample, y=log2(Count), fill=characteristics)) + 
  geom_boxplot() +
  scale_fill_manual(values=cbPalette)
```