Better way to produce data frame using table() - r

Question

Recently, I have found that I am using the following pattern over and over again. The process is:
cross-tabulate numeric variable by factor using table
create data frame from created table
add original numeric values to data frame (from row names (!))
remove row names
reorder columns of aggregated data frame
In R, it looks like this:
# Sample data
df <- data.frame(x = round(runif(100), 1),
y = factor(ifelse(runif(100) > .5, 1, 0),
labels = c('failure', 'success'))
)
# Get frequencies
dfSummary <- as.data.frame.matrix(table(df$x, df$y))
# Add column of original values from rownames
dfSummary$x <- as.numeric(rownames(dfSummary))
# Remove rownames
rownames(dfSummary) <- NULL
# Reorder columns
dfSummary <- dfSummary[, c(3, 1, 2)]
Is there anything more elegant in R, preferably using base functions? I know I can use sql to do this in single command - I think that it has to be possible to achieve similar behavior in R.
sqldf solution:
library(sqldf)
dfSummary <- sqldf("select
x,
sum(y = 'failure') as failure,
sum(y = 'success') as success
from df group by x")

Answer 1

An alternative with base R could be:
aggregate(. ~ x, transform(df, success = y == "sucess",
failure = y == "failure", y = NULL), sum)
# x success failure
#1 0.0 2 4
#2 0.1 6 8
#3 0.2 1 7
#4 0.3 5 4
#5 0.4 6 6
#6 0.5 3 3
#7 0.6 4 6
#8 0.7 6 6
#9 0.8 4 5
#10 0.9 6 7
#11 1.0 1 0

Answer 2

Your code modified as a function would be efficient compared to the other solutions in base R (so far). If you wanted the code in one-line, a "reshape/table" combo from base R could be used.
reshape(as.data.frame(table(df)), idvar='x', timevar='y',
direction='wide')
# x Freq.failure Freq.success
#1 0 3 2
#2 0.1 3 9
#3 0.2 5 5
#4 0.3 8 7
#5 0.4 5 3
#6 0.5 9 4
#7 0.6 3 6
#8 0.7 7 6
#9 0.8 3 1
#10 0.9 4 3
#11 1 0 4
In case you want to try data.table
library(data.table)
dcast.data.table(setDT(df), x~y)
# x failure success
# 1: 0.0 3 2
# 2: 0.1 3 9
# 3: 0.2 5 5
# 4: 0.3 8 7
# 5: 0.4 5 3
# 6: 0.5 9 4
# 7: 0.6 3 6
# 8: 0.7 7 6
# 9: 0.8 3 1
#10: 0.9 4 3
#11: 1.0 0 4
Update
I didn't notice the as.data.frame(table( converts to "factor" columns (thanks to #Hadley's comment). A workaround is:
res <- transform(reshape(as.data.frame(table(df), stringsAsFactors=FALSE),
idvar='x', timevar='y', direction='wide'), x= as.numeric(x))
data
set.seed(24)
df <- data.frame(x = round(runif(100), 1),
y = factor(ifelse(runif(100) > .5, 1, 0),
labels = c('failure', 'success'))
)
Benchmarks
set.seed(24)
df <- data.frame(x = round(runif(1e6), 1),
y = factor(ifelse(runif(1e6) > .5, 1, 0),
labels = c('failure', 'success'))
)
tomas <- function(){
dfSummary <- as.data.frame.matrix(table(df$x, df$y))
dfSummary$x <- as.numeric(rownames(dfSummary))
dfSummary <- dfSummary[, c(3, 1, 2)]}
doc <- function(){aggregate(. ~ x, transform(df,
success = y == "success", failure = y == "failure",
y = NULL), sum)}
akrun <- function(){reshape(as.data.frame(table(df)),
idvar='x', timevar='y', direction='wide')}
library(microbenchmark)
microbenchmark(tomas(), doc(), akrun(), unit='relative', times=20L)
Unit: relative
#expr min lq mean median uq max neval cld
#tomas() 1.000000 1.0000000 1.000000 1.000000 1.0000000 1.000000 20 a
#doc() 13.451037 11.5050997 13.082074 13.043584 12.8048306 19.715535 20 b
#akrun() 1.019977 0.9522809 1.012332 1.007569 0.9993835 1.533191 20 a
Updated with dcast.data.table
df1 <- copy(df)
akrun2 <- function() {dcast.data.table(setDT(df1), x~y)}
microbenchmark(tomas(), akrun2(), unit='relative', times=20L)
# Unit: relative
# expr min lq mean median uq max neval cld
# tomas() 6.493231 6.345752 6.410853 6.51594 6.502044 5.591753 20 b
# akrun2() 1.000000 1.000000 1.000000 1.00000 1.000000 1.000000 20 a

Answer 3

This should be relatively efficient. You cannot really suppress rownames in a dataframe, since they are a requirement of a valid dataframe
X <- table(df$x,df$y)
cbind( data.frame(x=rownames(X)), unclass(X) )
x failure success
0 0 5 3
0.1 0.1 6 1
0.2 0.2 7 8
0.3 0.3 7 3
0.4 0.4 6 6
0.5 0.5 6 4
0.6 0.6 2 5
0.7 0.7 2 7
0.8 0.8 3 7
0.9 0.9 4 6
1 1 2 0