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

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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()
  • 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$short.rivers[1]
    • some function work on lists: boxplot(rivers.list); some don't: hist(rivers)
  • matrix: lets create the table from the homework as a matrix
    • create from vectors: start with 1:9, then add real numbers: matrix(x, ncol=3)
  • data.frames: the most important data structure in R. we will be using them constantly
    • lets explore the faithful data.frame first
      • head(faithful); colnames(faithful); nrow(faithful); ncol(faithful)
      • work with the columns faithful$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
    • first, install the package and load it with install.packages() and library()
    • 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(); read.delim() can be useful as well! options can be helpful!
    • library foreign can be very helpful: read.dta(); read.sav(); etc