# 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()

- factors: for categorical data
- 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

- lets explore the faithful data.frame first
- 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