1. R tutorial

2. R interface Unix, linux, windows batching file: File -> open script
run commands: Ctrl-R
Save session: sink([filename])….sink()
Quit session: q()

3. General Syntax result <- function(object(s), options…)
Object-oriented programming
Note that ‘result’ is an object

4. First things first: help([function]) help.search(“linear model”)
help.start()

5. Choosing your default setwd(“[pathname for directory]”)
need “\\” instead of “\” when giving paths
.Rdata
.Rhistory

6. Start with data
read.table
read.csv
scan
dget

7. Extracting variables from data
Use $: data$AGE
note it is case-sensitive!
attach([data]) and detach([data])

8. Descriptive statistics
summary
mean, median
var
quantile
range, max, min

9. Missing values sometimes cause ‘error’ message na.rm=T
na.option=na.omit

10. Objects data.frame, as.data.frame, is.data.frame
names([data])
row.names([data])
matrix, as.matrix, is.matrix
dimnames([data])
factor, as.factor, is.factor
levels([factor])
arrays
lists
functions
vectors
scalars

11. Creating and manipulating
combine: c
cbind: combine as columns
rbind: combine as rows
list: make a list
rep(x,n): repeat x n times
seq(a,b,i): create a sequence between a and b in increments of i
seq(a,b, length=k): create a sequence between a and b with length k with equally spaced increments

12. ifelse ifelse(condition, true, false) cut(x, breaks)
agelt50 <- ifelse(data$AGE<50,1,0)
note for equality must use “==“
cut(x, breaks)
agegrp <- cut(data$AGE, breaks=c(0,50,60,130))
agegrp <- cut(data$AGE, breaks=c(0,50,60,130),
labels=c(0,1,2))
labels=F)

13. Looking at objects
dim
length
sort

14. Subsetting Use [ ] Vectors Matrices & Dataframes
data$AGE[data$REGION==1]
data$AGE[data$LOS<10]
Matrices & Dataframes
data[data$AGE<50, ]
data[ , 2:5]
data[data$AGE<50, 2:5]

15. Some math abs(x) sqrt(x) x^k log(x) (natural log, by default)
choose(n,k)

16. Matrix Manipulation Matrix multiplication: A%*%B transpose: t(X)
diag(X)

17. Table
table(x,y)
tabulate(x)

18. Statistical Tests and CI’s
t.test
fisher.test and binom.exact
wilcox.test

19. Plots
hist
boxplot
plot
pch
xlab, ylab
xlim, ylim
xaxt, yaxt
axis

20. Plot Layout par(mfrow=c(2,1)) par(mfrow=c(1,1)) par(mfcol=c(2,2))
help(par)

21. Probability Distributions
Normal:
rnorm(N,m,s): generate random normal data
dnorm(x,m,s): density at x for normal with mean m, std dev s
qnorm(p,m,s): quantile associated with cumulative probability of p for normal with mean m, std dev s
pnorm(q,m,s): cumulative probability at quantile q for normal with mean m, std dev s
Binomial
rbinom
etc.

22. Libraries Additional packages that can be loaded Example: epitools
library
library(help=[libname])

23. Keeping things tidy
ls() and objects()
rm()
rm(list=ls())

24. Future Topics linear regression sourcing R code creating functions
organizing R files