Casting and Melting with reshape in R - r

Question

As an example let's say that I have the following dataframe:
datas=data.frame(Variables=c("Power","Happiness","Power","Happiness"),
Country=c("France", "France", "UK", "UK"), y2000=c(1213,1872,1726,2234), y2001=c(1234,2345,6433,9082))
Resulting in the following output:
Variables Country 2000 2001
1 Power France 1213 1234
2 Happiness France 1872 2345
3 Power UK 1726 6433
4 Happiness UK 2234 9082
I would like to reshape this dataframe as follows:
Year Country Power Happiness
1 2000 France 1213 1872
2 2001 France 1234 2345
3 2000 UK 1726 2234
4 2001 UK 6433 9082
I started out with:
q2=cast(datas, Country~Variables, value="2000")
But then got the following error:
Aggregation requires fun.aggregate: length used as default
Error in `[.data.frame`(sort_df(data, variables), , c(variables, "value"), :
undefined columns selected
Any suggestions?
Also: Would it matter for the solution that my dataframe is really big (417120 by 62)?

Answer 1

Perhaps you're interested in a tidyverse alternative
library(tidyverse)
df %>%
gather(Year, val, -Variables, -Country) %>%
spread(Variables, val)
# Country Year Happiness Power
#1 France 2000 1872 1213
#2 France 2001 2345 1234
#3 UK 2000 2234 1726
#4 UK 2001 9082 6433
Or using reshape2::melt and reshape2::dcast
reshape2::dcast(
reshape2::melt(df, id.vars = c("Country", "Variables"), variable.name = "Year"),
Country + Year ~ Variables)
# Country Year Happiness Power
#1 France 2000 1872 1213
#2 France 2001 2345 1234
#3 UK 2000 2234 1726
#4 UK 2001 9082 6433
Or (identically) using data.table::melt and data.table::dcast
data.table::dcast(
data.table::melt(df, id.vars = c("Country", "Variables"), variable.name = "Year"),
Country + Year ~ Variables)
# Country Year Happiness Power
#1 France 2000 1872 1213
#2 France 2001 2345 1234
#3 UK 2000 2234 1726
#4 UK 2001 9082 6433
In terms of performance/runtime, I imagine the data.table or tidyr solutions to be the most efficient. You can check by running a microbenchmark on some larger sample data.
Sample data
df <-read.table(text =
" Variables Country 2000 2001
1 Power France 1213 1234
2 Happiness France 1872 2345
3 Power UK 1726 6433
4 Happiness UK 2234 9082", header = T)
colnames(df)[3:4] <- c("2000", "2001")
Benchmark analysis
Following results from a microbenchmark analysis of the four methods, based on a (slightly) larger 78x22 sample dataset.
set.seed(2017)
df <- data.frame(
Variables = rep(c("Power", "Happiness", "something_else"), 26),
Country = rep(LETTERS[1:26], each = 3),
matrix(sample(10000, 20 * 26 * 3), nrow = 26 * 3))
colnames(df)[3:ncol(df)] <- 2000:2019
library(microbenchmark)
library(tidyr)
res <- microbenchmark(
reshape2 = {
reshape2::dcast(
reshape2::melt(df, id.vars = c("Country", "Variables"), variable.name = "Year"),
Country + Year ~ Variables)
},
tidyr = {
df %>%
gather(Year, val, -Variables, -Country) %>%
spread(Variables, val)
},
datatable = {
data.table::dcast(
data.table::melt(df, id.vars = c("Country", "Variables"), variable.name = "Year"),
Country + Year ~ Variables)
},
reshape = {
reshape::cast(reshape::melt(df), Country + variable ~ Variables)
}
)
res
#Unit: milliseconds
# expr min lq mean median uq max neval
# reshape2 3.088740 3.449686 4.313044 3.919372 5.112560 7.856902 100
# tidyr 4.482361 4.982017 6.215872 5.771133 6.931964 28.293377 100
# datatable 3.179035 3.511542 4.861192 4.040188 5.123103 46.010810 100
# reshape 27.371094 30.226222 32.425667 32.504644 34.118499 41.286803 100
library(ggplot2)
autoplot(res)

Answer 2

As above, I would strongly recommend using tidyr instead of reshape, or at least using reshape2 instead of reshape, as it fixes many of the performance issues with reshape.
In reshape itself, you have to melt datas first
> cast(melt(datas), Country + variable ~ Variables)
Using Variables, Country as id variables
Country variable Happiness Power
1 France y2000 1872 1213
2 France y2001 2345 1234
3 UK y2000 2234 1726
4 UK y2001 9082 6433
And then renaming and converting the columns as necessary.
In reshape2 the code is identical but you would use dcast instead of cast. tidyr, as in #Maurits Evers's solution above is a better solution and most development has shifted from reshape2 to the tidyverse