Selecting the right functions is of course very important for writing efficient code. The degree of optimization present in different functions and packages will impact how objects are stored, their size, and the speed of operations running on them. Please consider the following.

```
library(data.table)
a <- c(1:1000000)
b <- rnorm(1000000)
mat <- as.matrix(cbind(a, b))
df <- data.frame(a, b)
dt <- data.table::as.data.table(mat)
cat(paste0("Matrix size: ",object.size(mat), "\ndf size: ", object.size(df), " (",round(object.size(df)/object.size(mat),2) ,")\ndt size: ", object.size(dt), " (",round(object.size(dt)/object.size(mat),2),")" ))
Matrix size: 16000568
df size: 12000848 (0.75)
dt size: 4001152 (0.25)
```

So here already you see that `data.table`

stores the same data using 4 times less space than your old `matrix`

does, and 3 times less than `data.frame`

. Now about operations speed:

```
> microbenchmark(df[df$a*df$b>500,], mat[mat[,1]*mat[,2]>500,], dt[a*b>500])
Unit: milliseconds
expr min lq mean median uq max neval
df[df$a * df$b > 500, ] 23.766201 24.136201 26.49715 24.34380 30.243300 32.7245 100
mat[mat[, 1] * mat[, 2] > 500, ] 13.010000 13.146301 17.18246 13.41555 20.105450 117.9497 100
dt[a * b > 500] 8.502102 8.644001 10.90873 8.72690 8.879352 112.7840 100
```

`data.table`

does the filtering 1.7 times faster than `base`

on `data.frame`

, and 2.5 times faster than using a `matrix`

.

And that’s not all, for almost any CSV import, using `data.table::fread`

will change your life. Give it a try instead of `read.csv`

or `read_csv`

.

IMHO `data.table`

doesn’t get half the love it deserves, the best all-round package for performance and a very concise syntax. The following vignettes should put you on your way quickly, and that is worth the effort, trust me.

For further performance improvements `Rfast`

contains many `Rcpp`

implementations of popular functions and problems, such as `rowSort()`

for example.

EDIT: `fread`

‘s speed is due to optimizations done at C-code level involving the use of pointers for memory mapping, and coerce-as-you-go techniques, which frankly are beyond my knowledge to explain. This post contains some explanations by the author Matt Dowle, as well as an interesting, if short, piece of discussion between him and the author of `dplyr`

, Hadley Wickham.

CLICK HERE to find out more related problems solutions.