Slides

Here is a link to the lecture slides for this session: LINK

Overview

In this practical you’ll get started with R. By the end of this practical you will:

1. Know your way around R Studio.
2. Know how to run code.
3. Have an impression of R’s basic functionality.

For this practical you will go through an existing analysis script chunk by chunk to experience how programming and analyzing in R works. The idea is that you go through the code, copy the code chunks to the script editor, send the code to the console, and evaluate what happens.

While at it, try to practice two very useful shortcuts: (1) cmd + enter (MAC) or cntrl + enter (Windows) for running the current line in the console. (2) cmd + shift + p (MAC) or cntrl + shift + p (Windows) for running the same block of code again in the console. The latter is really helpful because you can rerun the a chunk of code after you have made changes to it.

Also, try to take a look at the help-files of a functions used in the code below using ?function, e.g., ?mean to inspect the help-file of the mean()-function and try to understand why they work. You may also already try to evaluate the type of the various objects using typeof(object), e.g., typeof(pirates) or typeof(mean), to learn about the different types (of objects) in R.

All of the code used in this tutorial is based on base-R functions. While they are already powerful, we will later in the course introduce you to more modern, better options for most of the steps.

Install and load the yarrr package

1. First we’ll install and load the yarrr package. The yarrr package contains many data sets and functions.
# Install and load the yarrr package
# 1install.packages('yarrr')
library(yarrr)

Explore the pirates dataset

1. The pirates data set contains data from a survey of 1,000 pirates. Inspect it one-by-one using the following functions.
# Get help for pirates data - included in package yarrr
?pirates

# Print the first few rows of the dataset

# Show the structure of the dataset
str(pirates)

# Show the entire dataset in a new window
View(pirates)
1. Descriptive statistics for numeric data and categorical data.
# What is the mean age?
mean(pirates$age) # What was the height of the tallest pirate? max(pirates$height)

# How many pirates are there of each sex?
table(pirates$sex) 1. Descriptive statistics as a function of another categorical variable. # What was the mean age for each sex? aggregate(formula = age ~ sex, data = pirates, FUN = mean) # What is the median number of tattoos of pirates for each combination of sex and headband? aggregate(formula = tattoos ~ sex + headband, data = pirates, FUN = median) Base plotting 1. Creating a histograms of numeric variables. # --- A default histogram of pirate ages hist(x = pirates$age)

# --- A customized histogram of pirate ages
hist(x = pirates$age, main = "Distribution of pirate ages", col = "skyblue", border = "white", xlab = "Age", ylim = c(0, 400)) # Add mean label text(x = mean(pirates$age), y = 375,
labels = paste("Mean = ", round(mean(pirates$age), 2))) # Add dashed line at mean segments(x0 = mean(pirates$age), y0 = 0,
x1 = mean(pirates$age), y1 = 360, col = gray(.2, .2), lty = 2) # ---- Overlapping histograms of pirate ages for females and males # Start with the female data hist(x = pirates$age[pirates$sex == "female"], main = "Distribution of pirate ages by sex", col = transparent("red", .2), border = "white", xlab = "Age", breaks = seq(0, 50, 2), probability = T, ylab = "", yaxt = "n") # Add male data hist(x = pirates$age[pirates$sex == "male"], add = T, probability = T, border = "white", breaks = seq(0, 50, 2), col = transparent("skyblue", .5)) # Add the legend legend(x = 40, y = .05, col = c("red", "skyblue"), legend = c("Female", "Male"), pch = 16, bty = "n") 1. Creating scatterplots of two numerical variables. # --- A simple scatterplot of pirate height and weight plot(x = pirates$height,
y = pirates$weight, xlab = "Height (cm)", ylab = "Weight (kg)") # --- A fancier scatterplot of the same data with some additional arguments # Create main plot plot(x = pirates$height,
y = pirates$weight, main = 'My first scatterplot of pirate data!', xlab = 'Height (in cm)', ylab = 'Weight (in kg)', pch = 16, # Filled circles col = gray(0, .2)) # Transparent gray # Add gridlines grid() # Create a linear regression model model <- lm(formula = weight ~ height, data = pirates) # Add regression to plot abline(model, col = 'skyblue', lwd = 3) 1. Changing colors # --- Look at all the palettes from piratepal() piratepal() # Look at the basel palette in detail piratepal(palette = "basel", plot.result = TRUE) # --- Scatterplot of pirate height and weight using the pony palette my.cols <- piratepal(palette = "pony", trans = .2, length.out = nrow(pirates)) # Create the plot plot(x = pirates$height, y = pirates$weight, main = "Random scatterplot with My Little Pony Colors", xlab = "Pony height", ylab = "Pony weight", pch = 21, # Round symbols with borders col = "white", # White border bg = my.cols, # Random colors bty = "n" # No plot border ) # Add gridlines grid() 1. Create a barplot for stratified data # --- Barpot of mean height by favorite.pirate # Calculate mean height for each favorite.pirate pirate_heights <- aggregate(height ~ favorite.pirate, data = pirates, FUN = mean) # Do barplot barplot(pirate_heights$height,
main = "Barplot of mean height by favorite pirate",
names.arg = pirate_heights$favorite.pirate) # --- Same, but with customizations barplot(pirate_heights$height,
ylim = c(0, 200),
ylab = "Pirate Height (in cm)",
main = "Barplot of mean height by favorite pirate",
names.arg = pirate_heights\$favorite.pirate,
col = piratepal("basel", trans = .2))

abline(h = seq(0, 200, 25), lty = 3, lwd = c(1, .5))

Hypothesis testing

1. Run a group comparisons.
# Do pirates with eyepatches have longer beards than those without eyepatches?
t.test(formula = beard.length ~ eyepatch,
data = pirates,
alternative = 'two.sided')

# ANOVA on beard.length as a function of sex and college

# Run the ANOVA
beard_aov <- aov(formula = beard.length ~ sex + college,
data = pirates)

# Print summary results
summary(beard_aov)
1. Run a regression analysis.
# regression analysis showing if age, weight, and tattoos predict how many treasure chests a pirate has found

# Run the regression
chests_lm <- lm(formula = tchests ~ age + weight + tattoos,
data = pirates)

# Print summary results
summary(chests_lm)

# Plot regression (continue with <Return>)
plot(chests_lm)