SO24891419.R

Col1 <- c("a", "b","b",NA)
Col2 <- c(NA, "a", "c", NA)
Col3 <- c(NA,NA,"b", "a")
dat <- data.frame(Col1, Col2, Col3)

fun1 <- function() {
  cbind(dat, 
        apply(table(cbind(rn = 1:nrow(dat), 
                          stack(lapply(dat, as.character)))), 
              c(1, 2), sum))
}

fun2 <- function() {
  levs <- unique(unlist(dat))
  count <- t(apply(dat, 1, function(x) table(factor(x, levels = levs))))
  cbind(dat, count)
}

fun3 <- function() {
  cbind(dat,aggregate(value~Var2, melt(t(dat)), FUN=table)[,-1])
}

fun4 <- function() {
  unq_values <- unique(unlist(dat))
  unq_values <- unq_values[!is.na(unq_values)]
  freq_vec <- function(u) apply(dat, 1, function(x) sum(grepl(u, x)))
  cbind(dat, sapply(unq_values, freq_vec))
}

library(microbenchmark)
library(reshape2)
microbenchmark(fun1(), fun2(), fun3(), fun4())
# Unit: milliseconds
#    expr      min       lq   median       uq       max neval
#  fun1() 1.882373 1.981502 2.031227 2.074144  4.193716   100
#  fun2() 2.201289 2.271821 2.316432 2.346138  5.147774   100
#  fun3() 6.565937 6.821392 6.928942 7.078843 11.700034   100
#  fun4() 2.043613 2.120811 2.151803 2.206342  5.283656   100

dat <- do.call(rbind, replicate(5000, dat, FALSE))
dim(dat)
# [1] 20000     3
system.time(fun1())
#    user  system elapsed 
#   0.657   0.004   0.662 
system.time(fun2())
#    user  system elapsed 
#   7.730   0.029   7.787 
system.time(fun3())
#    user  system elapsed 
#  16.795   0.063  16.887 
system.time(fun4())
#    user  system elapsed 
#   2.128   0.011   2.141