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