Statistical Methods with R

Data Types & Packages

Unit A · Chapter 03 · Lecture 03a

Developed by Jeffrey M. Girard

Roadmap: More Programming

1. Strings

2. Factors

3. Packages

Strings

Strings

• When talking to R, we need a way to distinguish
  • Object/function names (e.g., the mean function)
  • Text/character data (e.g., the word mean)

• Strings are R’s way of storing text data
  • Strings can store any characters (no rules!)
  • Strings are created and displayed with quotes

• R has great tools for working with strings
  • Strings can be collected into vectors
  • Special functions can transform strings

name <- "John Doe"

Strings Live Coding

# USECASE: Strings are the main way to store character data in R
 
my_color <- red # error

my_color <- "red" # correct

# ==============================================================================

# USECASE: Strings can also store symbols not allowed in object names

dye <- "red#40"
dye

dyes <- c("red#40", "blue#02")
dyes

# ==============================================================================

# PITFALL: Many operations you can do to numbers won't work for strings

dyes + 1 # error

mean(dyes) # error

# ==============================================================================

# USECASE: But other operations work for both or even just for strings

length(dyes)

nchar(dyes)

dyes2 <- toupper(dyes)
dyes2

Factors

Factors

• Factors are used to represent categorical data
  • Factors have multiple possible levels
  • Levels are discrete and mutually-exclusive

• Sometimes levels are numeric “codes”
  • e.g., 1=Drama, 2=Action, 3=Comedy
  • R needs to know these aren’t numbers!
  • We can even give each level a descriptive label
  • Or we can just store the levels as strings

Factors Live Coding

# USECASE: Ask 10 kids to order 1: nuggets, 2: pizza, or 3: salad

food <- c(2, 2, 1, 2, 1, 2, 1, 1, 2, 2)
food

# ==============================================================================

# LESSON: We can turn this into a factor with the factor() function

food2 <- factor(food)
food

# ==============================================================================

# USECASE: We can quickly and easily count each level with table()

table(food2)

# ==============================================================================

# LESSON: We can let R know that level=3 is also possible by specifying levels

food3 <- factor(food, levels = c(1, 2, 3))
food3

table(food3)

# ==============================================================================

# LESSON: We can also give a label to each level so it is more readable

food4 <- factor(food, levels = c(1, 2, 3), 
                labels = c("nuggets", "pizza", "salad"))
food4

table(food4)

# ==============================================================================

# PITFALL: Don't confuse levels and labels

food5 <- factor(food, levels = c("nuggets", "pizza", "salad"),
                labels = c(1, 2, 3))
food5 # full of <NA> because it can't find these levels

# ==============================================================================

# USECASE: You can also just store the levels as strings (like self-labels)

genre <- c("pop", "metal", "pop", "rock", "rap", "rap", "pop", "rock")
genre

genre2 <- factor(genre) # observed levels will be assigned alphabetically
genre2

table(genre2)

Packages

Packages

• Cookbooks are a great way to learn to cook
  • They contain lots of recipes and instructions
  • Browse an online bookstore for a cookbook
  • Order it to add it to your personal bookshelf
  • To use, pull the cookbook off the shelf

• Packages are like cookbooks for R
  • They contain helpful functions and datasets
  • Browse an online repository for a package
  • Install it to add it to your personal library
  • To use, load the package from the library

library("pkg_name")

Packages Live Coding

# USECASE: The stringr package adds a function to fix capitalization

students <- c("mary anne", "BENjamin", "Lee")

# ==============================================================================

# PITFALL: But we can't use that function without installing the package

str_to_title(students) # error

# ==============================================================================

# LESSON: Installing a package using RStudio

# - RStudio > Extras pane > Packages tab > Install button

install.packages("stringr")

# PITFALL: Use this command in the console, not in your Quarto document!

# ==============================================================================

# PITFALL: We also need to load the package before we can use it

str_to_title(students) # error

# ==============================================================================

# LESSON: We load the package using library()

library("stringr")
str_to_title(students) # finally works!

# ==============================================================================

# LESSON: We can also keep our packages updated using RStudio

# RStudio > Extras pane > Packages tab > Update button

# ==============================================================================

# USECASE: We can see if a package contains any vignettes (articles)

browseVignettes("stringr")