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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.list[[1]], 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
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