Editing 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()
** another useful function to clean up our messes: rm() or remove()
** 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 type (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()
* 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
* creating/saving files
** running things in the console
* installing new packages and loading new datasets:
** the simplest way is with load()
** install.packages("UsingR")
** install.packages("openintro")
*** library(UsingR) no quotes!

Second lecture on GitHub and saving files:

* creating/saving files
** creating saving R scripts in RStudio
** running things in the console (Ctrl-Enter)
** copying things from the console (and vice versa)
* github
** how version control, git, github works
*** working directors, the role of the github desktop client, and the github website!
*** just an interface between your working directory and the website
** walk through example of saving something and publishing it in github
* other sources of help:
** built in documentation
** StackOverflow
** R reference card