fast frequency and percentage table with dplyr

I have been using a small tab function for some time, which shows the frequency, percent, and cumulative percent for a vector. The output looks like this

```          Freq    Percent        cum
ARSON      462 0.01988893 0.01988893
BURGLARY 22767 0.98011107 1.00000000
23229 1.00000000         NA
```

The excellent dplyr package motivated me to update the function. Now I am wondering how I can make the updated version even faster. Here is the old function

```tab = function(x,useNA =FALSE) {
k=length(unique(x[!is.na(x)]))+1
if (useNA) k=k+1
tab=array(NA,c(k,3))
colnames(tab)=c("freq.","prob.","cum.")
useNA=ifelse(useNA,"always","no")
rownames(tab)=names(c(table(x,useNA=useNA),""))

tab[-nrow(tab),1]=table(x,useNA=useNA)
tab[-nrow(tab),2]=prop.table(table(x,useNA=useNA))
tab[,3] = cumsum(tab[,2])
if(k>2)  tab[nrow(tab),-3]=colSums(tab[-nrow(tab),-3])
if(k==2) tab[nrow(tab),-3]=tab[-nrow(tab),-3]

tab
}
```

and the new based on dplyr

```tab2 = function(x, useNA =FALSE) {
if(!useNA) if(any(is.na(x))) x = na.omit(x)
n = length(x)
out = data.frame(x,1) %.%
group_by(x) %.%
dplyr::summarise(
Freq    = length(X1),
Percent = Freq/n
) %.%
dplyr::arrange(x)
ids = as.character(out\$x)
ids[is.na(ids)] = '<NA>'
out = select(out, Freq, Percent)
out\$cum = cumsum(out\$Percent)
class(out)="data.frame"
out = rbind(out,c(n,1,NA))
rownames(out) = c(ids,'')
out
}
```

Finally, some performance benchmarks:

```x1 = c(rep('ARSON',462),rep('BURGLARY',22767))
x2 = c(rep('ARSON',462),rep('BURGLARY',22767),rep(NA,100))
x3 = c(c(1:10),c(1:10),1,4)
x4 = c(rep(c(1:100),500),rep(c(1:50),20),1,4)

library('rbenchmark')

benchmark(tab(x1), tab2(x1), replications=100)[,c('test','elapsed','relative')]
#       test elapsed relative
# 1  tab(x1)   1.412    2.307
# 2 tab2(x1)   0.612    1.000

benchmark(tab(x2),tab2(x2), replications=100)[,c('test','elapsed','relative')]
#       test elapsed relative
# 1  tab(x2)   1.351    1.475
# 2 tab2(x2)   0.916    1.000

benchmark(tab(x2,useNA=TRUE), tab2(x2,useNA=TRUE), replications=100)[,c('test','elapsed','relative')]
#                     test elapsed relative
# 1  tab(x2, useNA = TRUE)   1.883    2.282
# 2 tab2(x2, useNA = TRUE)   0.825    1.000

benchmark(tab(x3), tab2(x3), replications=1000)[,c('test','elapsed','relative')]
#       test elapsed relative
# 1  tab(x3)   0.997    1.000
# 2 tab2(x3)   2.194    2.201

benchmark(tab(x4), tab2(x4), table(x4), replications=100)[,c('test','elapsed','relative')]
#        test elapsed relative
# 1   tab(x4)  19.481   18.714
# 2  tab2(x4)   1.041    1.000
# 3 table(x4)   6.515    6.258
```

tab2 is faster except for the very short vector. The performance gain becomes evident in the larger vector (see x4 with 51002 obs). It's also faster than table even thought the function is doing much more.

Now to my question: How can I further improve performance? Creating tables with frequencies and percent is a pretty standard application and a fast implementation is very nice when you work with large datasets.

EDIT: Here is an additional test case with a 2e6 vector (including the data.table solution proposed below)

```x5 = sample(c(1:100),2e6, replace=TRUE)
benchmark(tab(x5), tab2(x5), table(x5), tabdt(x5), replications=100)[,c('test','elapsed','relative')]
#        test elapsed relative
# 1   tab(x5) 350.878   19.444
# 2  tab2(x5)  52.917    2.932
# 4 tabdt(x5)  18.046    1.000
# 3 table(x5)  98.429    5.454
```

As I'm a big fan of library(data.table) I wrote similar function:

```tabdt <- function(x){
n <- length(which(!is.na(x)))
dt <- data.table(x)
out <- dt[, list(Freq = .N, Percent = .N / n), by = x]
out[!is.na(x), CumSum := cumsum(Percent)]
out
}

> benchmark(tabdt(x1), tab2(x1), replications=1000)[,c('test','elapsed','relative')]
test elapsed relative
2  tab2(x1)    5.60    1.879
1 tabdt(x1)    2.98    1.000
> benchmark(tabdt(x2), tab2(x2), replications=1000)[,c('test','elapsed','relative')]
test elapsed relative
2  tab2(x2)    6.34    1.686
1 tabdt(x2)    3.76    1.000
> benchmark(tabdt(x3), tab2(x3), replications=1000)[,c('test','elapsed','relative')]
test elapsed relative
2  tab2(x3)    1.65    1.000
1 tabdt(x3)    2.34    1.418
> benchmark(tabdt(x4), tab2(x4), replications=1000)[,c('test','elapsed','relative')]
test elapsed relative
2  tab2(x4)   14.35    1.000
1 tabdt(x4)   22.04    1.536
```

And so data.table approach was faster for x1 and x2 while dplyr was faster for x3 and x4. Actually I don't see any room for improvement using these approaches.

p.s. Would you add data.table keyword to this question? I believe people would love to see dplyr vs. data.table performance comparison (see data.table vs dplyr: can one do something well the other can't or does poorly? for example).