1. Introduction to R user-friendly and absolutely free
Ho Kim
SCHOOL OF PUBLIC HEALTH, SNU

2. Useful sites R is a free software with powerful tools
The Comprehensive R Archives Network -> Download R for Windows -> base -> Download R for Windows
Textbook : Simple R by John Verzani

3. Features of R
R is free. R is open-source and runs on UNIX, Windows and Macintosh.
R has an excellent built-in help system.
R has excellent graphing capabilities.
Students can easily migrate to the commercially supported S-Plus program if commercial software is desired.
R's language has a powerful, easy to learn syntax with many built-in statistical functions.
The language is easy to extend with user-written functions.
R is a computer programming language. For programmers it will feel more familiar than others and for new computer users, the next leap to programming will not be so large.

4. Starting the R

5. Data manipulation Data input Data management Data types Importing data
Exporting data
Viewing data
Value labels
Missing data
Variables
Operators
Sorting data
Merging data
Subsetting data
Source: (Quick r)

6. Data types Vectors Matrices Factors [Data Input]
a <- c(1,2,5.3,6,-2,4) #numeric vector
b <- c("one","two","three") #character vector
c <- c(TRUE,TRUE,TRUE,FALSE,TRUE,FALSE) #logical vector
a[c(2,4)] #2nd and 4th elements of vector
Factors
gender <- c(rep("male",20), rep("female", 30))
gender <- factor(gender)
# R now treats gender as a nominal variable
summary(gender)
Matrices
# generates 5 x 4 numeric matrix
y<-matrix(1:20, nrow=5,ncol=4)
y[,4] # 4th column of matrix
y[3,] # 3rd row of matrix
y[2:4,1:3] # rows 2,3,4 of columns 1,2,3

7. Data types Dataframes Lists # example of a list with 4 components -
[Data Input]
Data types
Dataframes
d <- c(1,2,3,4)
e <- c("red", "white", "red", NA)
f <- c(TRUE,TRUE,TRUE,FALSE)
mydata <- data.frame(d,e,f)
names(mydata) <- c("ID","Color","Passed") # variable names
Lists
# example of a list with 4 components -
# a string, a numeric vector, a matrix, and a scaler
w <- list(name="Fred", mynumbers=a, mymatrix=y, age=5.3)

8. Importing data From CSV file From Excel From txt file [Data Input]
malaria <-read.table("C:\\R_data\\malaria.csv", header=TRUE, sep=",")
From Excel
library(RODBC)
channel <- odbcConnectExcel("C:\\R_data\\malaria.xls")
malaria <- sqlFetch(channel, "mal")
*odbcConnectExcel is only usable with 32-bit Windows
From txt file
malaria <- read.table("C:\\ R_data\\malaria.txt", header=TRUE, sep="\t")

9. Exporting data To an CSV file To a tab delimited text file
[Data Input]
Exporting data
To an CSV file
write.table(malaria, "C:\\ R_data\\mal01.csv", row.names=F)
To a tab delimited text file
write.table(malaria, "C:\\ R_data\\mal02.txt", sep="\t", row.names=F)

10. Viewing data ls() # list objects in the working environment
names(malaria) # list the variables in malaria
str(malaria) # list the structure of malaria
levels(malaria $v1) # list levels of factor v1 in malaria
malaria$v1<-factor(malaria$mal)
dim(malaria) # dimensions of an malaria
class(malaria) # class of an malaria (numeric, matrix, dataframe, etc)
malaria # print malaria
head(malaria, n=10) # print first 10 rows of malaria
tail(malaria, n=5) # print last 5 rows of malaria
summary(malaria)

11. Value labels # variable v1 is coded 1, 2 or 3
# we want to attach value labels 1=red, 2=blue, 3=green
v1<-c(1,1,1,2,2,3)
v2 <- factor(v1, levels = c(1,2,3), labels = c("red", "blue", "green"))

12. Missing data Testing for missing values Recoding values to missing
y <- c(1,2,3,NA)
is.na(y) # returns a vector (F F F T)
Recoding values to missing
malaria[malaria$age==99,“age"] <- NA
Excluding missing values from analyses
x <- c(1,2,NA,3)
mean(x) # returns NA
mean(x, na.rm=TRUE) # returns 2

13. Help
> help(mean)
> ?mean

14. Data manipulation Data input Data management Data types Importing data
Exporting data
Viewing data
Value labels
Missing data
Variables
Operators
Sorting data
Merging data
Subsetting data

15. Variables Recoding variables [Data management]
# create 2 age categories
malaria$agecat <- ifelse(malaria$age >7, c(“student"), c(“baby"))
attach(malaria)
malaria$agecat2[age > 7] <- "student"
malaria$agecat2[age <= 7] <- "baby"
detach(malaria)

16. Operators Comparison operators Logical operators == equals
[Data management]
Operators
Comparison operators
== equals
!= not equals
<= less than or equals
>= greater than or equals
= assignment (arrow ‘<-’ 와 같다)
Logical operators
& and
| or
! not

17. Sorting Data Avoid “Attach” command when sorting the data
[Data management]
Sorting Data
# sort by mal
newdata <- malaria[order(malaria$mal),]
# sort by mal and age
newdata2 <- malaria[order(malaria$mal, malaria$age),]
#sort by mal (ascending) and age (descending)
newdata3 <- malaria[order(malaria$mal, -malaria$age),]
Avoid “Attach” command when sorting the data

18. Merging Data Raw dataset Adding rows Adding columns [Data management]
malaria2<-read.table("C:\\R_data\\malaria.csv", header=TRUE, sep=",")
Adding rows
extra<-read.table ("C:\\R_data\\extra15.csv",header=T, sep=",")
malaria3<-rbind(malaria2,extra)
Adding columns
region<-read.table ("C:\\R_data\\region.csv", header=T, sep=",")
malaria4<-merge(malaria3, region, by="subject")

19. Subsetting Data mal.1 <- subset(malaria,mal==1) summary(mal.1)
[Data management]
Subsetting Data
mal.1 <- subset(malaria,mal==1)
summary(mal.1)
mal.baby <- subset(malaria, mal == 1 & age < 8)