Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 2

material not covered last week we'll want to cover this time:

• adding comments: lines that start with # (or anything after a #)
• 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
• matrix: lets create the table from the homework as a matrix
  • create from vectors: start with 1:9, then add real numbers: matrix(x, ncol=3)
• lists: like vecotrs but can contains objects of any kind
  • lets say we have two vectors: short.rivers (rivers * 0.5) and normal.rivers (rivers)
  • construct lists: rivers.list <- list(normal.rivers, short.rivers)
  • named lists: list(foo=foo, bar=bar), or add names with names()
  • index into lists: use double square brackets like rivers.list1, otherwise they work like lists
  • index recursively: rivers.list$short.rivers[1]
  • some function work on lists: boxplot(rivers.list); some don't: hist(rivers)
• data.frames: *the* most important data structure in R. we will be using them constantly
  • lets explore the faithful data.frame first
    • head(faithful); colnames(faithful); nrow(faithful); ncol(faithful)
    • work with the columns faithful$eruptions and faithful$waiting (mean, boxplot, hist)
    • but the real power is doing bivariate analysis: plot()
    • dataframes can have more than one columns: mtcars
  • indexing by numbers: faithful[1,]; faithful[,2]; faithful[1,2], faithful[1:2, 2:3], etc
• how do we plot things in that space? we use the formula "~" symbol
  • plot(var1 ~ var2, data=dataframe); boxplot works too
• making/modifying new dataframes: lets work on a copy of mtcars (call it mako.cars)
  • several ways: data.frame() is the basic one:
  • modification/building up: cbind(); rbind(); as.data.frame()
  • modifying values: d[1,2] <- NA
  • removing lines, columns d[1,] <- NULL
  • recoding/transforming data: lets log a column
  • changing types (lets turn a number into a factor) (e.g., gear)
  • creating subsets of new data.frames using logical vectors
• useful functions with data.frames:
  • is.na()
  • complete.cases()
• apply functions: super, useful!
  • sapply, lapply: lets work on the rivers dataset
  • apply: more complicated, but can be very useful with matrixes
• graphing with ggplot2: this is what I use so it's what we'll use moving forward
  • lets just play around with examples from mtcars
  • philosophy: a graphics grammar. you start out by using ggplot
  • ggplot(data=mtcars) + aes(x=hp, y=mpg, color=gear, size=carb) + geom_point()
• read data from a CSV file: read.csv()