Reshaping data

1. Each variable forms a column
2. Each observation forms a row
3. Each table/file stores data about one kind of observation (e.g. people/hospitals).

http://vita.had.co.nz/papers/tidy-data.pdf

Leek, Taub, and Pineda 2011 PLoS One

library(reshape2)
head(mtcars)

                   mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

mtcars$carname <- rownames(mtcars)
carMelt <- melt(mtcars,id=c("carname","gear","cyl"),measure.vars=c("mpg","hp"))
head(carMelt,n=3)

        carname gear cyl variable value
1     Mazda RX4    4   6      mpg  21.0
2 Mazda RX4 Wag    4   6      mpg  21.0
3    Datsun 710    4   4      mpg  22.8

tail(carMelt,n=3)

         carname gear cyl variable value
62  Ferrari Dino    5   6       hp   175
63 Maserati Bora    5   8       hp   335
64    Volvo 142E    4   4       hp   109

http://www.statmethods.net/management/reshape.html

cylData <- dcast(carMelt, cyl ~ variable)
cylData

  cyl mpg hp
1   4  11 11
2   6   7  7
3   8  14 14

cylData <- dcast(carMelt, cyl ~ variable,mean)
cylData

  cyl   mpg     hp
1   4 26.66  82.64
2   6 19.74 122.29
3   8 15.10 209.21

http://www.statmethods.net/management/reshape.html

head(InsectSprays)

  count spray
1    10     A
2     7     A
3    20     A
4    14     A
5    14     A
6    12     A

tapply(InsectSprays$count,InsectSprays$spray,sum)

  A   B   C   D   E   F 
174 184  25  59  42 200 

http://www.r-bloggers.com/a-quick-primer-on-split-apply-combine-problems/

spIns =  split(InsectSprays$count,InsectSprays$spray)
spIns

$A
 [1] 10  7 20 14 14 12 10 23 17 20 14 13

$B
 [1] 11 17 21 11 16 14 17 17 19 21  7 13

$C
 [1] 0 1 7 2 3 1 2 1 3 0 1 4

$D
 [1]  3  5 12  6  4  3  5  5  5  5  2  4

$E
 [1] 3 5 3 5 3 6 1 1 3 2 6 4

$F
 [1] 11  9 15 22 15 16 13 10 26 26 24 13

sprCount = lapply(spIns,sum)
sprCount

$A
[1] 174

$B
[1] 184

$C
[1] 25

$D
[1] 59

$E
[1] 42

$F
[1] 200

unlist(sprCount)

  A   B   C   D   E   F 
174 184  25  59  42 200 

sapply(spIns,sum)

  A   B   C   D   E   F 
174 184  25  59  42 200 

ddply(InsectSprays,.(spray),summarize,sum=sum(count))

  spray sum
1     A 174
2     B 184
3     C  25
4     D  59
5     E  42
6     F 200

spraySums <- ddply(InsectSprays,.(spray),summarize,sum=ave(count,FUN=sum))
dim(spraySums)

[1] 72  2

head(spraySums)

  spray sum
1     A 174
2     A 174
3     A 174
4     A 174
5     A 174
6     A 174

• A tutorial from the developer of plyr - http://plyr.had.co.nz/09-user/
• A nice reshape tutorial http://www.slideshare.net/jeffreybreen/reshaping-data-in-r
• A good plyr primer - http://www.r-bloggers.com/a-quick-primer-on-split-apply-combine-problems/
• See also the functions
  • acast - for casting as multi-dimensional arrays
  • arrange - for faster reordering without using order() commands
  • mutate - adding new variables