## 14.3 Exercise 8: Base plots

Create the script “exercise8.R” and save it to the “Rcourse/Module3” directory: you will save all the commands of exercise 8 in that script.
Remember you can comment the code using #.

``````getwd()
setwd("~/Rcourse/Module3")``````

### 14.3.1 Exercise 8a- scatter plot

1- Create the following data frame

``````genes <- data.frame(sample1=rnorm(300),
sample2=rnorm(300))``````

2- Create a scatter plot showing sample1 (x-axis) vs sample2 (y-axis) of genes.

``plot(genes\$sample1, genes\$sample2)``

3- Change the point type and color.

``````plot(genes\$sample1,
genes\$sample2,
col="lightblue",
pch=3)``````

4- Change x-axis and y-axis labels to “Sample 1” and “Sample 2,” respectively.

``````plot(genes\$sample1,
genes\$sample2,
col="lightblue",
pch=3,
xlab="Sample 1",
ylab="Sample 2")``````

5- Add a title to the plot.

``````plot(genes\$sample1,
genes\$sample2,
col="lightblue",
pch=3,
xlab="Sample 1",
ylab="Sample 2",
main="scatter plot")``````

6- Add a vertical red line that shows the median expression value of sample 1. Do it in two steps:
a. calculate the median expression of genes in sample 1.
b. plot a vertical line using abline().

``````# median expression of sample1
med1 <- median(genes\$sample1)

# plot
plot(genes\$sample1,
genes\$sample2,
col="lightblue",
pch=3,
xlab="Sample 1",
ylab="Sample 2",
main="scatter plot")

# vertical line
abline(v=med1, col="red")``````

### 14.3.2 Exercise 8b- bar plot + pie chart

1- Create the following vector

``genes_significance <- rep(c("enriched", "depleted", "none"), c(20, 32, 248))``

2- The vector describes whether a gene is up- (enriched) or down- (depleted) regulated, or not regulated (none).
Produce a barplot that displays this information: how many genes are enriched, depleted, or not regulated.

``barplot(table(genes_significance))``

3- Color the bars of the boxplot, each in a different color (3 colors of your choice)

``````barplot(table(genes_significance),
col=c("blue", "red", "grey"))

# If you want to order the bars not by the default (alphabetical) order, you need to create an ordered factor!
genes_factor <- factor(genes_significance, ordered=TRUE, levels=c("enriched", "none", "depleted"))
barplot(table(genes_factor),
col=c("blue", "red", "grey"))``````

4- Use the argument “names.arg” in barplot() to rename the bars: Change depleted to “Down,” enriched to “Up,” none to “Not significant”

``````barplot(table(genes_significance),
col=c("blue", "red", "grey"),
names.arg=c("Down", "Up", "Not significant"))``````

5- The “las” argument allows to rotate the x-axis labels for a better readability. Try value 2 for las: what happens?

``````barplot(table(genes_significance),
col=c("blue", "red", "grey"),
names.arg=c("Down", "Up", "Not significant"),
las=2)``````

6- Create a pie chart of the same information (Enriched, Depleted, None)

``pie(table(genes_significance))``

Change the color of the slices, modify the labels, and add a title.

``````pie(table(genes_significance),
col=c("blue", "red", "grey"),
main="pie chart",
labels=c("Down", "Up", "Not significant"))``````

### 14.3.3 Exercise 8c- histogram

1- Use genes object from exercise 11a to create a histogram of the gene expression distribution of sample 1.

``hist(genes\$sample1)``

2- Repeat the histogram but change argument “breaks” to 50.
What is the difference ?

``````hist(genes\$sample1,
breaks=50)``````

3- Color this histogram in light blue.

``````hist(genes\$sample1,
breaks=50,
col="lightblue")``````

4- “Zoom in” the histogram: show only the distribution of expression values from 0 to 2 (x-axis) using the xlim argument.

``````hist(genes\$sample1,
breaks=50,
col="lightblue",
xlim=c(0, 2))``````

5- Save the histogram in a pdf file.

``````pdf("myhistogram.pdf")