---
title: "Plotting and data visualization in base R Answer Key"
author:
- Will Gammerdinger
date: "2025-12-05"
---
```{r}
#| label: load_data_libraries
#| echo: false
# Load the purrr package
library(purrr)
# Load data
metadata <- read.csv(file="data/mouse_exp_design.csv")
rpkm_ordered <- read.csv("data/ordered_counts_rpkm.csv",
header=T, row.names = 1)
samplemeans <- map_dbl(rpkm_ordered, mean)
age_in_days <- c(40, 32, 38, 35, 41, 32, 34, 26, 28, 28, 30, 32)
new_metadata <- data.frame(metadata, samplemeans, age_in_days)
```
# Exercise 1
1. Change the color scheme in the scatterplot, such that it reflects the `genotype` of samples rather than `celltype`.
```{r}
#| label: color_by_genotype
# Create factor of genotype
new_metadata$genotype <- factor(new_metadata$genotype)
# Create plot
plot(samplemeans ~ age_in_days, data=new_metadata,
main="Expression changes with age",
xlab="Age (days)",
ylab="Mean expression",
pch="*",
cex=2.0,
col=c("blue", "green")[genotype])
# Add legend
legend("topleft",
pch="*",
col=c("blue", "green"),
c("A", "B"),
cex=0.8,
title="Genotype")
```
2. Use R help to find out how to increase the size of the text on the axis labels.
```{r}
#| label: par_help
#| eval: false
# Open graphical parameters help page
?par
```
