1. Ch.4 Data Manipulation

2. 4.1 Vector subscripts # x[subscript] for a vector x x <- c(6,7,2,4)
[1] 6
x[length(x)]
[1] 4

3. 4.1 Vector subscripts # A vector of positive natural numbers x
[1]
x[1:3]
[1]
x[c(4,1,4)]
[1]
x[3] <- 4
x[1:3] <- 4
x[1:3] <- c(1,2)

4. 4.1 Vector subscripts # A logical vector x <- c(10,4,6,7,8)
y <- x>9 y
[1] TRUE FALSE FALSE FALSE FALSE
x[y]
[1] 10
x[x>9]
[1] 10)
x[x>9] <- 9

5. 4.1 Vector subscripts # A vector of negative natural numbers
x <- c(1,2,3,6)
x[-(1:2)] # gives (x[3], x[4])
[1] 3 6
x <- c(1,2,3,4,5)
x[7]
[1] NA
x[1:6]
[1] NA

6. 4.1 Vector subscripts # Some useful functions
length(x); sum(x); prod(x); max(x); min(x);
x <- 10:71
y <- 45:21
sum(x,y); prod(x,y); max(x,y); min(x,y)
# chop off last part of a vector
x <- 10:100
length(x) = 20
cumsum(rep(2,10))
[1]

7. 4.1 Vector subscripts # Some useful functions
x <- c(2,6,4,5,5,8,8,1,3,0)
length(x)
[1] 10
sort(x)
[1]
sort(x, decr = TRUE)
[1]

8. 4.1 Vector subscripts # Some useful functions
x <- rnorm(10) # create 10 random numbers
y <- 1: # create the numbers 1,2,3,..,10
z <- order(x) # create a permutation vector
sort(x) # sort x
[1] …
y[z] # change the order of elements of y
[1]
x <- rnorm(10)
round(rev(sort(x)),2)
[1] …

9. 4.1 Vector subscripts # Some useful functions
x <- c(2,6,4,5,5,8,8,1,3,0)
unique(x)
[1]
duplicated(x)
[1] FALSE FALSE FALSE FALSE TRUE FALSE TRUE ...
x <- c(1,3,5,8,15)
diff(x)
[1]
diff(x, lag=2)
[1]

10. 4.2 Matrix subscripts # x[subscript] x <- matrix(1:36, ncol=6)
# the element in row 2 and column 6 of x
x[2,6]
[1] 32
# the third row of x
x[3, ]
[1]

11. 4.2 Matrix subscripts # the element in row 3 and column 1 and
x[3,c(1,5)]
[1] 3 27
# show x, except for the first column
x[,-1]
[,1] [,2] [,3] [,4] [,5]
[1,]
[2,]
[3,]
[4,]
[5,]
[6,]

12. 4.2 Matrix subscripts x[-3,-4] [,1] [,2] [,3] [,4] [,5]
[1,]
[2,]
[3,]
[4,]
[5,]
x <- x[-3,4]
x[4,5] <- 5
x <- matrix(rnorm(100),ncol=10)
x[ ,1] <- 1:10

13. 4.2 Matrix subscripts x <- matrix(1:36,ncol=6) y <- x>19 y
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] FALSE FALSE FALSE FALSE TRUE TRUE
[2,] FALSE FALSE FALSE TRUE TRUE TRUE
[3,] FALSE FALSE FALSE TRUE TRUE TRUE
[4,] FALSE FALSE FALSE TRUE TRUE TRUE
[5,] FALSE FALSE FALSE TRUE TRUE TRUE
[6,] FALSE FALSE FALSE TRUE TRUE TRUE
x[y]
[1] …
x <- matrix(rnorm(100),ncol=10)
x[x>0] <- 0

14. 4.2 Matrix subscripts x <- matrix(1:36,ncol=6) x
[,1] [,2] [,3] [,4] [,5] [,6]
[1,]
[2,]
[3,]
[4,]
[5,]
[6,]
r <- cbind( c(1,2,5), c(3,4,4)) r
[,1] [,2]
[1,]
[2,]
[3,]

15. 4.2 Matrix subscripts x <- matrix(1:36,ncol=6) x[3];x[9];x[36]
[1] 3
[1] 9
[1] 36
x[21:30]
[1]

16. 4.3 manipulating Data frames
4.3.1 Extracting data from data frames
test <- read.csv(file.choose(),header=F)
test
V1 V2 V3
1 1 a 1.2
2 2 b 1.5
3 3 c 1.3
4 c 1.2
V1 <- test$V1
V2 <- test[["V2"]] # column factor
V2 <- test["V2"] # data.frame
V3 <- test[["V3"]] # numeric vector
V4 <- test[c("V1", "V3")] # data.frame

17. 4.3 manipulating Data frames
4.3.4 Merging data frames
x <- data.frame(k1=c(NA,NA,3,4,5), k2=c(1,NA,NA,4,5), data=1:5)
y <- data.frame(k1=c(NA,2,NA,4,5), k2=c(NA,NA,3,4,5), data=1:5) x
k1 k2 data
1 NA
2 NA NA 2
3 3 NA 3 y
k1 k2 data
1 NA NA 1
2 2 NA 2
3 NA

18. 4.3 manipulating Data frames
4.3.4 Merging data frames
merge(x, y, by=c("k1","k2")) # NA's match
k1 k2 data.x data.y
3 NA NA x
k1 k2 data
1 NA
2 NA NA 2
3 3 NA 3 y
k1 k2 data
1 NA NA 1
2 2 NA 2
3 NA

19. 4.3 manipulating Data frames
4.3.4 Merging data frames
merge(x, y, by="k1") # NA's match, so 6 rows
k1 k2.x data.x k2.y data.y
3 NA NA
4 NA
5 NA NA NA
6 NA NA x
k1 k2 data
1 NA
2 NA NA 2
3 3 NA 3 y
k1 k2 data
1 NA NA 1
2 2 NA 2
3 NA

20. 4.3 manipulating Data frames
Aggregating data frames
gr <- c("A","A",“B","B")
x <- c(1,2,3,4)
y <- c(4,3,2,1)
myf <- data.frame(gr, x, y)
aggregate(myf, list(myf$gr), mean)
Group.1 gr x y
A NA
B NA
Warning messages:
1: In mean.default(X[[1L]], ...) :
argument is not numeric or logical: returning NA
2: In mean.default(X[[2L]], ...) :
aggregate(myf[,c("x","y")],list(myf$gr),mean)

21. 4.3 manipulating Data frames
4.3.6 Stacking columns of data frames
group1 <- rnorm(3); group2 <- rnorm(3)
df <- data.frame(group1,group2)
stack(df) # data.frame
values ind
group1
group1
group1
group2
group2
group2
cbind(group1,group2) # numeric matrix
group1 group2
[1,]
[2,]
[3,]

22. 4.3 manipulating Data frames
4.3.7 Reshaping data
df.wide <- data.frame(Subject = c(1, 2),
m1 = c(4, 5),
m2 = c(5.6, 7.8),
m3 = c(3.6, 6.7) )
df.wide
Subject m1 m2 m3

23. 4.3 manipulating Data frames
4.3.7 Reshaping data
df.long <- reshape(df.wide,
varying = list(c("m1", "m2", "m3")),
idvar = "Subject",
direction = "long",
v.names = "Measurement" )
df.long
Subject time Measurement

24. 4.4 Attributes x <- rnorm(10) attributes(x) NULL
attr(x, "description") <- "The unit is month" x
[1]
[6]
attr(, "description")
[1] "The unit is month"

25. 4.4 Attributes x <- structure(x, atr1=8,atr2="test") x
[1]
[6]
attr(, "description"):
[1] "The unit is month"
attr(, "atr1"):
[1] 8
attr(, "atr2"):
[1] "test"

26. 4.4 Attributes attributes(x) $description: [1] "The unit is month“
$atr1:
[1] 8
$atr2:
[1] "test“
attributes(x)$description
attr(x,"description")
[1] "The unit is month"

27. 4.5 Character manipulation
4.5.1 nchar, substring and paste
x <- c("a","b","c")
mychar1 <- "This is a test"
mychar2 <- "This is another test"
charvector <- c("a", "b", "c", "test")
nchar(mychar1)
[1] 15
nchar(charvector)
[1]
x <- c("Gose", "Longhow", "David")
substring(x,first=2,last=4)
[1] "ose" "ong" "avi“

28. 4.5 Character manipulation
4.5.1 nchar, substring and paste
paste("number",1:10, sep=".")
[1] "number.1" "number.2" "number.3" "number.4"
[5] "number.5" "number.6" "number.7" "number.8"
[9] "number.9" "number.10“
paste("number",1:10, sep="-")
[1] "number-1" "number-2" "number-3" "number-4"
[5] "number-5" "number-6" "number-7" "number-8"
[9] "number-9" "number-10"

29. 4.6 Creating factors from continuous data
x <- 1:15
breaks <- c(0,5,10,15,20)
cut(x,breaks)
[1] (0,5] (0,5] (0,5] (0,5] (0,5] (5,10] (5,10] (5,10] (5,10]
[10] (5,10] (10,15] (10,15] (10,15] (10,15] (10,15]
Levels: (0,5] (5,10] (10,15] (15,20]
cut(x,breaks=5)
[1] (0.986,3.79] (0.986,3.79] (0.986,3.79] (3.79,6.6] (3.79,6.6]
[6] (3.79,6.6] (6.6,9.4] (6.6,9.4] (6.6,9.4] (9.4,12.2]
[11] (9.4,12.2] (9.4,12.2] (12.2,15] (12.2,15] (12.2,15]
Levels: (0.986,3.79] (3.79,6.6] (6.6,9.4] (9.4,12.2] (12.2,15]

30. 4.6 Creating factors from continuous data
x <- rnorm(15)
cut(x, breaks=3, labels=c("low","medium","high"))
[1] high medium medium medium medium high low high low
low
[11] high low low medium high
Levels: low medium high