Here is toy data set for this example:
data <- data.frame(x=rep(c("red","blue","green"),each=4), y=rep(letters[1:4],3), value.1 = 1:12, value.2 = 13:24)
x y value.1 value.2
1 red a 1 13
2 red b 2 14
3 red c 3 15
4 red d 4 16
5 blue a 5 17
6 blue b 6 18
7 blue c 7 19
8 blue d 8 20
9 green a 9 21
10 green b 10 22
11 green c 11 23
12 green d 12 24
How can I cast or spread variable y, to produce the following wide data.frame:
x a.value.1 b.value.1 c.value.1 d.value.1 a.value.2 b.value.2 c.value.2 d.value.2
1 blue 5 6 7 8 17 18 19 20
2 green 9 10 11 12 21 22 23 24
3 red 1 2 3 4 13 14 15 16
melt
first then dcast
:
library(reshape2)
data1 <- melt(data, id.vars = c("x", "y"))
dcast(data1, x ~ variable + y)
# x value.1_a value.1_b value.1_c value.1_d value.2_a value.2_b value.2_c value.2_d
#1 blue 5 6 7 8 17 18 19 20
#2 green 9 10 11 12 21 22 23 24
#3 red 1 2 3 4 13 14 15 16
We could do this using dplyr/tidyr
. We reshape the 'data' from 'wide' to 'long' format with gather
specifying the columns (starts_with('value')
) to be combined to a key/value column pair ('Var/Val'), unite
the 'Var' and 'y' column to create a single 'Var1' column, and reconvert back to 'wide' format with spread
.
library(dplyr)
library(tidyr)
data %>%
gather(Var, val, starts_with("value")) %>%
unite(Var1,Var, y) %>%
spread(Var1, val)
# x value.1_a value.1_b value.1_c value.1_d value.2_a value.2_b value.2_c
#1 blue 5 6 7 8 17 18 19
#2 green 9 10 11 12 21 22 23
#3 red 1 2 3 4 13 14 15
# value.2_d
#1 20
#2 24
#3 16
(After 6 months)
Reshaping multiple value columns to wide is now possible with dcast
from data.table_1.9.5
without using the melt
. We can install the devel version from here
library(data.table)
dcast(setDT(data), x~y, value.var=c('value.1', 'value.2'))
# x a_value.1 b_value.1 c_value.1 d_value.1 a_value.2 b_value.2 c_value.2
#1: blue 5 6 7 8 17 18 19
#2: green 9 10 11 12 21 22 23
#3: red 1 2 3 4 13 14 15
# d_value.2
#1: 20
#2: 24
#3: 16
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