Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 1

Lecture Outline

Intro to R and basic variables types:

• using R as a calculator:
  • addition: 2 + 2
  • subtraction: 2 - 3
  • multiplication: 5 * 4
  • division: 5/2
  • more complicated stuff: use parentheses!
  • powers: 2^2; 2^3
• variables
  • the basic concept and how they work
  • syntax for assignment: use <- (although = equals too, it's not idiomatic R)
  • what makes a valid variables name: starts with a letter, contains letters and numbers; case is important; instead of spaces, use "." (not _ as in Python, although _ will usually work too)
  • saving numbers to variables: cups.of.flour <- 2
  • special variables built in: pi (we'll see many more)
  • variables can be set to anything!
  • there's also one special thing: NA (no quotes!) which means missing
• types of variables
  • numeric: we've already seen, with or without the decimal point
  • character: name <- "mako" (uses single or double quotes)
  • logical: TRUE or FALSE (all caps)
• functions: contains parentheses right after the variable name
  • functions take some input (called an argument) and provide some output (called the output or something the return value) — both are optional!
    • some arguments are named (meaning that they have "foo=" or similar before them. mostly names are optional)
  • the most important function: help()
  • there are many built in functions including:
    • sqrt()
    • log()
    • log1p() — super useful!
    • class() — tells you what type of variable you have
    • ls()
    • check your reference card for many, many more
• vectors: you can think of a vector as like a list of things that are all the same time (lists, which will come to letter, actually refer to lists of things that might be of different types!)
  • in R, all variables are vectors! although many have just one thing in them! that's why it prints out [1] next to every numbers
  • you can make vectors with a special function: c(), like ages <- c(36, 4, 35)
  • vectors can be of any type but they have to one type: c("mako", "mika")
  • if you mix vectors together, they will be "coerced"(!)
  • slicing or indexing:
    • basic syntax: ages[1]; ages[2]
    • more complex: ages[1:2]
    • assignment through indexing: ages[1] <- 20
  • most math operators operate on vectors with recycling: ages * 2; ages - 3
  • vectors can names for elements! we can set those with names():
    • names(ages)
    • names(ages) <- c("mako", "atom", "mika")
    • once we do that, we can index with names: ages["mako"]
  • many functions are particularly useful on vectors with multiple elements:
    • some functions return a single item: sum(); mean(); sd(); median(); var(); length()
    • some return vectors: sort(); head(); range();
    • some functions return other things: table(); summary()
• more advanced variables types:
  • factors: for categorical data
    • make with factor("mako", "mika", "mako")
    • you can create factors from characters with as.factor()
  • also think about: dates with POSIXct(), ordered() — really just a type of factor for ordinal data
• using logical vectors to index and recode data:
  • comparison operators will return logical variables: rivers > 300; rivers < 300; rivers <= 320; rivers == 210; rivers != 210
  • indexing with logicals: rivers[rivers > 300]
  • recoding data: my.rivers <- rivers; rivers[rivers < 300] <- NA
• basic plotting and visualization:
  • boxplot() — boxplots
  • hist() — draw histograms
  • density() — density plots
• comments: lines that start with # (or anything after a #)
• installing new pacakges and loading new datasets:
  • the simplest way is with load()
  • install.packages("UsingR")
  • install.packages("openintro")
    • library(UsingR) no quotes!
• other sources of help:
  • built in documentation
  • StackOverflow
  • R reference card