R Language data.table Special symbols in data.table


Example

.SD

.SD refers to the subset of the data.table for each group, excluding all columns used in by.

.SD along with lapply can be used to apply any function to multiple columns by group in a data.table

We will continue using the same built-in dataset, mtcars:

mtcars = data.table(mtcars) # Let's not include rownames to keep things simpler

Mean of all columns in the dataset by number of cylinders, cyl:

mtcars[ , lapply(.SD, mean), by = cyl]

#   cyl      mpg     disp        hp     drat       wt     qsec        vs        am     gear     carb
#1:   6 19.74286 183.3143 122.28571 3.585714 3.117143 17.97714 0.5714286 0.4285714 3.857143 3.428571
#2:   4 26.66364 105.1364  82.63636 4.070909 2.285727 19.13727 0.9090909 0.7272727 4.090909 1.545455
#3:   8 15.10000 353.1000 209.21429 3.229286 3.999214 16.77214 0.0000000 0.1428571 3.285714 3.500000

Apart from cyl, there are other categorical columns in the dataset such as vs, am, gear and carb. It doesn't really make sense to take the mean of these columns. So let's exclude these columns. This is where .SDcols comes into the picture.

.SDcols

.SDcols specifies the columns of the data.table that are included in .SD.

Mean of all columns (continuous columns) in the dataset by number of gears gear, and number of cylinders, cyl, arranged by gear and cyl:

# All the continuous variables in the dataset
cols_chosen <- c("mpg", "disp", "hp", "drat", "wt", "qsec")

mtcars[order(gear, cyl), lapply(.SD, mean), by = .(gear, cyl), .SDcols = cols_chosen]

#   gear cyl    mpg     disp       hp     drat       wt    qsec
#1:    3   4 21.500 120.1000  97.0000 3.700000 2.465000 20.0100
#2:    3   6 19.750 241.5000 107.5000 2.920000 3.337500 19.8300
#3:    3   8 15.050 357.6167 194.1667 3.120833 4.104083 17.1425
#4:    4   4 26.925 102.6250  76.0000 4.110000 2.378125 19.6125
#5:    4   6 19.750 163.8000 116.5000 3.910000 3.093750 17.6700
#6:    5   4 28.200 107.7000 102.0000 4.100000 1.826500 16.8000
#7:    5   6 19.700 145.0000 175.0000 3.620000 2.770000 15.5000
#8:    5   8 15.400 326.0000 299.5000 3.880000 3.370000 14.5500

Maybe we don't want to calculate the mean by groups. To calculate the mean for all the cars in the dataset, we don't specify the by variable.

mtcars[ , lapply(.SD, mean), .SDcols = cols_chosen] 

#        mpg     disp       hp     drat      wt     qsec
#1: 20.09062 230.7219 146.6875 3.596563 3.21725 17.84875

Note:

  • It is not necessary to define cols_chosen beforehand. .SDcols can directly take column names
  • .SDcols can also directly take a vector of columnnumbers. In the above example this would be mtcars[ , lapply(.SD, mean), .SDcols = c(1,3:7)]

.N

.N is shorthand for the number of rows in a group.

iris[, .(count=.N), by=Species]

#      Species count
#1:     setosa    50
#2: versicolor    50
#3:  virginica    50