35 data.table basics

Figure 35.1: data.table significantly enhances the base R data.frame

35.1 `data.table` extends the functionality of the `data.frame`

Some of the ways in which a data.table differs from a data.frame:

Many operations can be performed within a data.table’s “frame” (dt[i, j, by]): filter cases, select columns & operate on columns, group-by operations
Access column names directly without quoting
Many operations can be performed “in-place” (i.e. with no assignment)
Working on large datasets (e.g. millions of rows) can be orders of magnitude faster with a data.table than a data.frame.

data.table operations remain as close as possible to data.frame operations, trying to extend rather than replace data.frame functionality.

data.table includes thorough and helpful error messages that often point to a solution. This includes common mistakes new users may make when trying commands that would work on a data.frame but not on a data.table.

35.1.1 Load the `data.table` package

library(data.table)

35.2 Create a `data.table`

35.2.1 By assignment: `data.table()`

Let’s create a data.frame and a data.table to explore side by side.

df <- data.frame(A = 1:5,
                 B = c(1.2, 4.3, 9.7, 5.6, 8.1),
                 C = c("a", "b", "b", "a", "a"))
class(df)

[1] "data.frame"

df

  A   B C
1 1 1.2 a
2 2 4.3 b
3 3 9.7 b
4 4 5.6 a
5 5 8.1 a

data.table() syntax is similar to data.frame() (differs in some arguments)

dt <- data.table(A = 1:5,
                 B = c(1.2, 4.3, 9.7, 5.6, 8.1),
                 C = c("a", "b", "b", "a", "a"))
class(dt)

[1] "data.table" "data.frame"

dt

       A     B      C
   <int> <num> <char>
1:     1   1.2      a
2:     2   4.3      b
3:     3   9.7      b
4:     4   5.6      a
5:     5   8.1      a

Notice how a data.table object also inherits from data.frame. This means that if a method does not exist for data.table, the method for data.frame will be used (See classes and generic functions).

As part of improving efficiency, data.tables do away with row names. Instead of using row names, you should use a dedicated column or column with a row identifier/s (e.g. “ID”). this is advisable when working with data.frames as well.

A rather convenient option is to have data.tables print each column’s class below the column name. You can pass the argument class = TRUE to print() or set the global option datatable.print.class using options()

options(datatable.print.class = TRUE)
dt

       A     B      C
   <int> <num> <char>
1:     1   1.2      a
2:     2   4.3      b
3:     3   9.7      b
4:     4   5.6      a
5:     5   8.1      a

Same as with a data.frame, to automatically convert strings to factors, you can use the stringsAsFactors argument:

dt2 <- data.table(A = 1:5,
                  B = c(1.2, 4.3, 9.7, 5.6, 8.1),
                  C = c("a", "b", "b", "a", "a"),
                  stringsAsFactors = TRUE)
dt2

       A     B      C
   <int> <num> <fctr>
1:     1   1.2      a
2:     2   4.3      b
3:     3   9.7      b
4:     4   5.6      a
5:     5   8.1      a

35.2.2 By coercion: `as.data.table()`

dat <- data.frame(A = 1:5,
                  B = c(1.2, 4.3, 9.7, 5.6, 8.1),
                  C = c("a", "b", "b", "a", "a"),
                  stringsAsFactors = TRUE)
dat

  A   B C
1 1 1.2 a
2 2 4.3 b
3 3 9.7 b
4 4 5.6 a
5 5 8.1 a

dat2 <- as.data.table(dat)
dat2

       A     B      C
   <int> <num> <fctr>
1:     1   1.2      a
2:     2   4.3      b
3:     3   9.7      b
4:     4   5.6      a
5:     5   8.1      a

35.2.3 By coercion in-place: `setDT()`

setDT() converts a list or data.frame into a data.table in-place. This means the object passed to setDT() is changed and you do not need to assign the output to a new object.

dat <- data.frame(A = 1:5,
                  B = c(1.2, 4.3, 9.7, 5.6, 8.1),
                  C = c("a", "b", "b", "a", "a"))
class(dat)

[1] "data.frame"

setDT(dat)
class(dat)

[1] "data.table" "data.frame"

You can similarly convert a data.table to a data.frame, in-place:

setDF(dat)
class(dat)

[1] "data.frame"

35.3 Display `data.table` structure with `str()`

str() works the same (and you should keep using it!)

str(df)

'data.frame':   5 obs. of  3 variables:
 $ A: int  1 2 3 4 5
 $ B: num  1.2 4.3 9.7 5.6 8.1
 $ C: chr  "a" "b" "b" "a" ...

str(dt)

Classes 'data.table' and 'data.frame':  5 obs. of  3 variables:
 $ A: int  1 2 3 4 5
 $ B: num  1.2 4.3 9.7 5.6 8.1
 $ C: chr  "a" "b" "b" "a" ...
 - attr(*, ".internal.selfref")=<externalptr>

35.4 Combine `data.tables`

cbind() and rbind() work on data.tables the same as on data.frames:

dt1 <- data.table(a = 1:5)
dt2 <- data.table(b = 11:15)
cbind(dt1, dt2)

       a     b
   <int> <int>
1:     1    11
2:     2    12
3:     3    13
4:     4    14
5:     5    15

rbind(dt1, dt1)

35.5 Set column names in-place

dta <- data.table(
  ID = sample(8000:9000, size = 10),
  A = rnorm(10, mean = 47, sd = 8),
  W = rnorm(10, mean = 87, sd = 7)
)
dta

       ID        A        W
    <int>    <num>    <num>
 1:  8683 65.38270 88.34494
 2:  8831 51.19003 96.36451
 3:  8333 63.82806 86.90661
 4:  8044 52.50196 87.35874
 5:  8760 38.77308 86.75976
 6:  8973 41.92050 79.46510
 7:  8873 54.94697 89.09996
 8:  8089 60.04796 70.61579
 9:  8259 48.08217 86.69053
10:  8181 59.72485 90.59614

Use the syntax:

setnames(dt, old, new)

to change the column names of a data.table in-place.

Changes all column names:

setnames(dta, names(dta), c("Patient_ID", "Age", "Weight"))
dta

    Patient_ID      Age   Weight
         <int>    <num>    <num>
 1:       8683 65.38270 88.34494
 2:       8831 51.19003 96.36451
 3:       8333 63.82806 86.90661
 4:       8044 52.50196 87.35874
 5:       8760 38.77308 86.75976
 6:       8973 41.92050 79.46510
 7:       8873 54.94697 89.09996
 8:       8089 60.04796 70.61579
 9:       8259 48.08217 86.69053
10:       8181 59.72485 90.59614

Change subset of names:

old_names <- c("Age", "Weight")
setnames(dta, old_names, paste0(old_names, "_at_Admission"))
dta

    Patient_ID Age_at_Admission Weight_at_Admission
         <int>            <num>               <num>
 1:       8683         65.38270            88.34494
 2:       8831         51.19003            96.36451
 3:       8333         63.82806            86.90661
 4:       8044         52.50196            87.35874
 5:       8760         38.77308            86.75976
 6:       8973         41.92050            79.46510
 7:       8873         54.94697            89.09996
 8:       8089         60.04796            70.61579
 9:       8259         48.08217            86.69053
10:       8181         59.72485            90.59614

old argument can also be integer index of column(s).

For example, change the name of the first column:

setnames(dta, 1, "Hospital_ID")
dta

    Hospital_ID Age_at_Admission Weight_at_Admission
          <int>            <num>               <num>
 1:        8683         65.38270            88.34494
 2:        8831         51.19003            96.36451
 3:        8333         63.82806            86.90661
 4:        8044         52.50196            87.35874
 5:        8760         38.77308            86.75976
 6:        8973         41.92050            79.46510
 7:        8873         54.94697            89.09996
 8:        8089         60.04796            70.61579
 9:        8259         48.08217            86.69053
10:        8181         59.72485            90.59614

35.6 Filter rows

There are many similarities and some notable differences in how indexing works in a data.table vs. a data.frame.

Filtering rows with an integer or logical index is largely the same in a data.frame and a data.table, but in a data.table you can omit the comma to select all columns:

df[c(1, 3, 5), ]

  A   B C
1 1 1.2 a
3 3 9.7 b
5 5 8.1 a

dt[c(1, 3, 5), ]

       A     B      C
   <int> <num> <char>
1:     1   1.2      a
2:     3   9.7      b
3:     5   8.1      a

dt[c(1, 3, 5)]

       A     B      C
   <int> <num> <char>
1:     1   1.2      a
2:     3   9.7      b
3:     5   8.1      a

Using a variable that holds a row index, whether integer or logical:

rowid <- c(1, 3, 5)
df[rowid, ]

  A   B C
1 1 1.2 a
3 3 9.7 b
5 5 8.1 a

dt[rowid, ]

       A     B      C
   <int> <num> <char>
1:     1   1.2      a
2:     3   9.7      b
3:     5   8.1      a

dt[rowid]

       A     B      C
   <int> <num> <char>
1:     1   1.2      a
2:     3   9.7      b
3:     5   8.1      a

rowbn <- c(T, F, T, F, T)
df[rowbn, ]

  A   B C
1 1 1.2 a
3 3 9.7 b
5 5 8.1 a

dt[rowbn, ]

       A     B      C
   <int> <num> <char>
1:     1   1.2      a
2:     3   9.7      b
3:     5   8.1      a

dt[rowbn]

       A     B      C
   <int> <num> <char>
1:     1   1.2      a
2:     3   9.7      b
3:     5   8.1      a

35.6.1 Conditional filtering

As a reminder, there are a few ways to conditionally filter cases in a data.frame:

df[df$A > mean(df$A) & df$B > mean(df$B), ]

  A   B C
5 5 8.1 a

subset(df, A > mean(A) & B > mean(B))

  A   B C
5 5 8.1 a

with(df, df[A > mean(A) & B > mean(B), ])

  A   B C
5 5 8.1 a

data.table allows you to refer to column names directly and unquoted, which makes writing filter conditions easier/more compact:

dt[A > mean(A) & B > mean(B)]

       A     B      C
   <int> <num> <char>
1:     5   8.1      a

The data.table package also includes an S3 method for subset() that works the same way as with a data.frame:

subset(dt, A > mean(A) & B > mean(B))

       A     B      C
   <int> <num> <char>
1:     5   8.1      a

As another example, exclude cases based on missingness in a specific column:

adf <- as.data.frame(sapply(1:5, function(i) rnorm(10)))
adf |> head()

            V1         V2         V3         V4        V5
1  0.902411777  1.2930875  1.0407948  1.0559136 0.5616985
2  0.580267346 -0.7832870 -0.6968492  1.2373361 1.6807925
3 -0.003170238 -1.0047477  0.7405467 -2.5859252 0.3343055
4 -0.922350665 -0.3273166 -1.1876403  0.4917402 1.7102410
5  0.739687336 -0.1770125 -0.9235757 -1.5588145 1.4051705
6  0.228370331 -1.2771214 -0.9437845  1.4142239 1.5548526

adf[1, 3] <- adf[3, 4] <- adf[5, 3] <- adf[7, 3] <- NA
adt <- as.data.table(adf)

adf[!is.na(adf$V3), ]

             V1         V2         V3          V4         V5
2   0.580267346 -0.7832870 -0.6968492  1.23733614  1.6807925
3  -0.003170238 -1.0047477  0.7405467          NA  0.3343055
4  -0.922350665 -0.3273166 -1.1876403  0.49174021  1.7102410
6   0.228370331 -1.2771214 -0.9437845  1.41422387  1.5548526
8   0.844595224  0.3321310  0.5219202  0.85338170 -2.2573230
9   0.019894582  0.6560011  0.9025611 -0.68491892  1.6959256
10  2.077229177  1.8165615  0.9781020  0.03906486 -2.2223410

adt[!is.na(V3)]

             V1         V2         V3          V4         V5
          <num>      <num>      <num>       <num>      <num>
1:  0.580267346 -0.7832870 -0.6968492  1.23733614  1.6807925
2: -0.003170238 -1.0047477  0.7405467          NA  0.3343055
3: -0.922350665 -0.3273166 -1.1876403  0.49174021  1.7102410
4:  0.228370331 -1.2771214 -0.9437845  1.41422387  1.5548526
5:  0.844595224  0.3321310  0.5219202  0.85338170 -2.2573230
6:  0.019894582  0.6560011  0.9025611 -0.68491892  1.6959256
7:  2.077229177  1.8165615  0.9781020  0.03906486 -2.2223410

35.7 Select columns

35.7.1 By position(s)

Selecting a single column in data.table does not drop to a vector, similar to using drop = FALSE in a data.frame:

df[, 1]

[1] 1 2 3 4 5

df[, 1, drop = FALSE]

dt[, 1]

Double bracket indexing of a single column works the same on a data.frame and a data.table, returning a vector:

df[[2]]

[1] 1.2 4.3 9.7 5.6 8.1

dt[[2]]

[1] 1.2 4.3 9.7 5.6 8.1

A vector of column positions returns a smaller data.table, similar to how it returns a smaller data.frame :

df[, c(1, 2)]

dt[, c(1, 2)]

       A     B
   <int> <num>
1:     1   1.2
2:     2   4.3
3:     3   9.7
4:     4   5.6
5:     5   8.1

35.7.2 By name(s)

In data.table, you access column names directly without quoting or using the $ notation:

df[, "B"]

[1] 1.2 4.3 9.7 5.6 8.1

df$B

[1] 1.2 4.3 9.7 5.6 8.1

dt[, B]

[1] 1.2 4.3 9.7 5.6 8.1

Because of this, data.table requires a slightly different syntax to use a variable as a column index which can contain integer positions, logical index, or column names. While on a data.frame you can do pass an index vector directly:

colid <- c(1, 2)
colbn <- c(FALSE, TRUE, TRUE)
colnm <- c("A", "C")
df[, colid]

df[, colbn]

    B C
1 1.2 a
2 4.3 b
3 9.7 b
4 5.6 a
5 8.1 a

df[, colnm]

To do the same in a data.table, you must prefix the index vector with two dots:

dt[, ..colid]

       A     B
   <int> <num>
1:     1   1.2
2:     2   4.3
3:     3   9.7
4:     4   5.6
5:     5   8.1

dt[, ..colbn]

       B      C
   <num> <char>
1:   1.2      a
2:   4.3      b
3:   9.7      b
4:   5.6      a
5:   8.1      a

dt[, ..colnm]

       A      C
   <int> <char>
1:     1      a
2:     2      b
3:     3      b
4:     4      a
5:     5      a

Think of working inside the data.table frame (i.e. within the “[…]”) like an environment: you have direct access to the variables, i.e. columns within it. If you want to refer to variables outside the data.table, you must prefix their names with .. (similar to how you access the directory above your current working directory in the system shell).

Important

Always read error messages carefully, no matter what function or package you are using. In the case of data.table, the error messages are very informative and often point to the solution.

See what happens if you try to use the data.frame syntax by accident:

dt[, colid]

Error in `[.data.table`(dt, , colid): j (the 2nd argument inside [...]) is a single symbol but column name 'colid' is not found. If you intended to select columns using a variable in calling scope, please try DT[, ..colid]. The .. prefix conveys one-level-up similar to a file system path.

Selecting a single column by name returns a vector:

dt[, A]

[1] 1 2 3 4 5

Selecting one or more columns by name enclosed in list() or .() (which, in this case, is short for list()), always returns a data.table:

dt[, .(A)]

dt[, .(A, B)]

       A     B
   <int> <num>
1:     1   1.2
2:     2   4.3
3:     3   9.7
4:     4   5.6
5:     5   8.1

35.7.3 `.SD` & `.SDcols`

.SDcols is a special symbol that can be used to select columns of a data.table as an alternative to j. It can accept a vector of integer positions or column names. .SD is another special symbol that can be used in j and refers to either the entire data.table, or the subset defined by .SDcols, if present. The following can be used to select columns:

dt[, .SD, .SDcols = colid]

       A     B
   <int> <num>
1:     1   1.2
2:     2   4.3
3:     3   9.7
4:     4   5.6
5:     5   8.1

One of the main uses of .SD is shown below in combination with lapply().

35.8 Add new column in-place

Use := assignment to add a new column in the existing data.table.

dt[, AplusB := A + B]
dt

       A     B      C AplusB
   <int> <num> <char>  <num>
1:     1   1.2      a    2.2
2:     2   4.3      b    6.3
3:     3   9.7      b   12.7
4:     4   5.6      a    9.6
5:     5   8.1      a   13.1

Note how dt was modified even though we did not run dt <- dt[, AplusB := A + B]

35.9 Add multiple columns in-place

You can define multiple new column names using a character vector of new column names on the left of := and a list on the right.

dt[, c("AtimesB", "AoverB") := list(A*B, A/B)]

We can use lapply() since it always returns a list:

vnames <- c("A", "B")
dt[, paste0("log", vnames) := lapply(.SD, log), .SDcols = vnames]
dt

       A     B      C AplusB AtimesB    AoverB      logA      logB
   <int> <num> <char>  <num>   <num>     <num>     <num>     <num>
1:     1   1.2      a    2.2     1.2 0.8333333 0.0000000 0.1823216
2:     2   4.3      b    6.3     8.6 0.4651163 0.6931472 1.4586150
3:     3   9.7      b   12.7    29.1 0.3092784 1.0986123 2.2721259
4:     4   5.6      a    9.6    22.4 0.7142857 1.3862944 1.7227666
5:     5   8.1      a   13.1    40.5 0.6172840 1.6094379 2.0918641

You can also use := in a little more awkward syntax:

dt[, `:=`(AminusB = A - B, AoverC = A / B)]
dt

       A     B      C AplusB AtimesB    AoverB      logA      logB AminusB
   <int> <num> <char>  <num>   <num>     <num>     <num>     <num>   <num>
1:     1   1.2      a    2.2     1.2 0.8333333 0.0000000 0.1823216    -0.2
2:     2   4.3      b    6.3     8.6 0.4651163 0.6931472 1.4586150    -2.3
3:     3   9.7      b   12.7    29.1 0.3092784 1.0986123 2.2721259    -6.7
4:     4   5.6      a    9.6    22.4 0.7142857 1.3862944 1.7227666    -1.6
5:     5   8.1      a   13.1    40.5 0.6172840 1.6094379 2.0918641    -3.1
      AoverC
       <num>
1: 0.8333333
2: 0.4651163
3: 0.3092784
4: 0.7142857
5: 0.6172840

35.10 Convert column type

35.10.1 Assignment by reference with `:=`

Use any base R coercion function (as.*) to convert a column in-place using the := notation

dt[, A := as.numeric(A)]
dt

       A     B      C AplusB AtimesB    AoverB      logA      logB AminusB
   <num> <num> <char>  <num>   <num>     <num>     <num>     <num>   <num>
1:     1   1.2      a    2.2     1.2 0.8333333 0.0000000 0.1823216    -0.2
2:     2   4.3      b    6.3     8.6 0.4651163 0.6931472 1.4586150    -2.3
3:     3   9.7      b   12.7    29.1 0.3092784 1.0986123 2.2721259    -6.7
4:     4   5.6      a    9.6    22.4 0.7142857 1.3862944 1.7227666    -1.6
5:     5   8.1      a   13.1    40.5 0.6172840 1.6094379 2.0918641    -3.1
      AoverC
       <num>
1: 0.8333333
2: 0.4651163
3: 0.3092784
4: 0.7142857
5: 0.6172840

35.10.2 Delete columns in-place with `:=`

To delete a column, use := to set it to NULL:

dt[, AoverB := NULL]
dt

       A     B      C AplusB AtimesB      logA      logB AminusB    AoverC
   <num> <num> <char>  <num>   <num>     <num>     <num>   <num>     <num>
1:     1   1.2      a    2.2     1.2 0.0000000 0.1823216    -0.2 0.8333333
2:     2   4.3      b    6.3     8.6 0.6931472 1.4586150    -2.3 0.4651163
3:     3   9.7      b   12.7    29.1 1.0986123 2.2721259    -6.7 0.3092784
4:     4   5.6      a    9.6    22.4 1.3862944 1.7227666    -1.6 0.7142857
5:     5   8.1      a   13.1    40.5 1.6094379 2.0918641    -3.1 0.6172840

Delete multiple columns

dt[, c("logA", "logB") := NULL]

Or:

dt[, `:=`(AplusB = NULL, AminusB = NULL)]
dt

       A     B      C AtimesB    AoverC
   <num> <num> <char>   <num>     <num>
1:     1   1.2      a     1.2 0.8333333
2:     2   4.3      b     8.6 0.4651163
3:     3   9.7      b    29.1 0.3092784
4:     4   5.6      a    22.4 0.7142857
5:     5   8.1      a    40.5 0.6172840

35.10.3 Fast loop-able assignment with `set()`

data.table’s set() is a loop-able version of the := operator. Use it in a for loop to operate on multiple columns.

Syntax: set(dt, i, j, value)

dt the data.table to operate on
i optionally define which rows to operate on. i = NULL to operate on all rows
j column names or index to be assigned value
value values to be assigned to j by reference

As a simple example, transform the first two columns in-place by squaring:

for (i in 1:2) {
  set(dt, i = NULL, j = i, value = dt[[i]]^2)
}

35.11 Summarize

You can apply one or multiple summary functions on one or multiple columns. Surround the operations in list() or .() to output a new data.table holding the outputs of the operations, i.e. the input data.table remains unchanged.

A_summary <- dt[, .(A_max = max(A), A_min = min(A), A_sd = sd(A))]
A_summary

   A_max A_min    A_sd
   <num> <num>   <num>
1:    25     1 9.66954

Example: Get the standard deviation of all numeric columns:

numid <- sapply(dt, is.numeric)
dt_mean <- dt[, lapply(.SD, sd), .SDcols = numid]
dt_mean

         A        B  AtimesB    AoverC
     <num>    <num>    <num>     <num>
1: 9.66954 37.35521 15.74462 0.2060219

If your function returns more than one value, the output will have multiple rows:

dt_range <- dt[, lapply(.SD, range), .SDcols = numid]
dt_range

       A     B AtimesB    AoverC
   <num> <num>   <num>     <num>
1:     1  1.44     1.2 0.3092784
2:    25 94.09    40.5 0.8333333

35.12 Set order

You can set the row order of a data.table in-place based on one or multiple columns’ values using setorder()

dt <- data.table(PatientID = sample(1001:9999, size = 10),
                 Height = rnorm(10, mean = 175, sd = 14),
                 Weight = rnorm(10, mean = 78, sd = 10),
                 Group = factor(sample(c("A", "B"), size = 10, replace = TRUE)))
dt

    PatientID   Height   Weight  Group
        <int>    <num>    <num> <fctr>
 1:      6771 173.7232 82.62411      A
 2:      3090 191.7491 75.01137      B
 3:      1234 195.2610 66.14451      B
 4:      2111 152.4823 65.67971      B
 5:      9473 173.5505 68.74644      B
 6:      3894 154.0196 77.19524      A
 7:      6942 183.7460 83.96044      A
 8:      4712 165.1607 64.85735      A
 9:      3430 184.5817 98.64208      B
10:      9606 170.3381 82.52088      B

Let’s set the order by PatientID:

setorder(dt, PatientID)
dt

    PatientID   Height   Weight  Group
        <int>    <num>    <num> <fctr>
 1:      1234 195.2610 66.14451      B
 2:      2111 152.4823 65.67971      B
 3:      3090 191.7491 75.01137      B
 4:      3430 184.5817 98.64208      B
 5:      3894 154.0196 77.19524      A
 6:      4712 165.1607 64.85735      A
 7:      6771 173.7232 82.62411      A
 8:      6942 183.7460 83.96044      A
 9:      9473 173.5505 68.74644      B
10:      9606 170.3381 82.52088      B

Let’s re-order, always in-place, by group and then by height:

setorder(dt, Group, Height)
dt

    PatientID   Height   Weight  Group
        <int>    <num>    <num> <fctr>
 1:      3894 154.0196 77.19524      A
 2:      4712 165.1607 64.85735      A
 3:      6771 173.7232 82.62411      A
 4:      6942 183.7460 83.96044      A
 5:      2111 152.4823 65.67971      B
 6:      9606 170.3381 82.52088      B
 7:      9473 173.5505 68.74644      B
 8:      3430 184.5817 98.64208      B
 9:      3090 191.7491 75.01137      B
10:      1234 195.2610 66.14451      B

35.13 Group-by operations

Up to now, we have learned how to use the data.table frame dat[i, j] to filter cases in i or add/remove/transform columns in-place in j. dat[i, j, by] allows to perform operations separately on groups of cases.

dt <- data.table(A = 1:5,
                 B = c(1.2, 4.3, 9.7, 5.6, 8.1),
                 C = rnorm(5),
                 Group = c("a", "b", "b", "a", "a"))
dt

       A     B          C  Group
   <int> <num>      <num> <char>
1:     1   1.2 -1.7243334      a
2:     2   4.3 -0.8421823      b
3:     3   9.7  0.7892740      b
4:     4   5.6  0.2640193      a
5:     5   8.1 -0.1807966      a

35.13.1 Group-by summary

As we’ve seen, using .() or list() in j, returns a new data.table:

dt[, .(mean_A_by_Group = mean(A)), by = Group]

    Group mean_A_by_Group
   <char>           <num>
1:      a        3.333333
2:      b        2.500000

dt[, list(median_B_by_Group = median(B)), by = Group]

    Group median_B_by_Group
   <char>             <num>
1:      a               5.6
2:      b               7.0

35.13.2 Group-by operation and assignment

Making an assignment with := in j, adds a column in-place. If you combine such an assignment with a group-by operation, the same value will be assigned to all cases of the group:

dt[, mean_A_by_Group := mean(A), by = Group]
dt

       A     B          C  Group mean_A_by_Group
   <int> <num>      <num> <char>           <num>
1:     1   1.2 -1.7243334      a        3.333333
2:     2   4.3 -0.8421823      b        2.500000
3:     3   9.7  0.7892740      b        2.500000
4:     4   5.6  0.2640193      a        3.333333
5:     5   8.1 -0.1807966      a        3.333333

35.14 Apply functions to columns

Any function that returns a list can be used in j to return a new data.table - therefore lapply is perfect for getting summary on multiple columns. This is another example where you have to use the .SD notation:

dt1 <- as.data.table(sapply(1:3, \(i) rnorm(10)))
dt1

            V1         V2         V3
         <num>      <num>      <num>
 1: -0.8596756 -0.3872235 -0.4501584
 2:  0.1268567 -0.7755533  0.3603807
 3:  0.7298289 -1.6452208  0.9137217
 4: -0.5078484  0.3122644 -1.2696068
 5: -0.9108845 -1.4404961 -0.5620521
 6:  1.5740202 -2.1390065  1.4832881
 7: -2.3917697 -0.3722932 -0.1926057
 8: -0.6004372 -0.2706900  0.8909017
 9: -0.2267385  0.5405553  1.0833964
10: -0.6378043  1.6665854  1.6748571

setnames(dt1, names(dt1), c("Alpha", "Beta", "Gamma"))
dt1[, lapply(.SD, mean)]

        Alpha       Beta     Gamma
        <num>      <num>     <num>
1: -0.3704452 -0.4511078 0.3932123

You can specify which columns to operate on using the .SDcols argument:

dt2 <- data.table(A = 1:5,
                  B = c(1.2, 4.3, 9.7, 5.6, 8.1),
                  C = rnorm(5),
                  Group = c("a", "b", "b", "a", "a"))
dt2

       A     B          C  Group
   <int> <num>      <num> <char>
1:     1   1.2 -0.5960686      a
2:     2   4.3  1.4363967      b
3:     3   9.7 -0.5264020      b
4:     4   5.6  1.6849226      a
5:     5   8.1  0.5502497      a

dt2[, lapply(.SD, mean), .SDcols = 1:2]

       A     B
   <num> <num>
1:     3  5.78

# same as
dt2[, lapply(.SD, mean), .SDcols = c("A", "B")]

       A     B
   <num> <num>
1:     3  5.78

cols <- c("A", "B")
dt2[, lapply(.SD, mean), .SDcols = cols]

       A     B
   <num> <num>
1:     3  5.78

You can combine .SDcols and by:

dt2[, lapply(.SD, median), .SDcols = c("B", "C"), by = Group]

    Group     B         C
   <char> <num>     <num>
1:      a   5.6 0.5502497
2:      b   7.0 0.4549973

Create multiple new columns from transformation of existing and store with custom prefix:

dt1

         Alpha       Beta      Gamma
         <num>      <num>      <num>
 1: -0.8596756 -0.3872235 -0.4501584
 2:  0.1268567 -0.7755533  0.3603807
 3:  0.7298289 -1.6452208  0.9137217
 4: -0.5078484  0.3122644 -1.2696068
 5: -0.9108845 -1.4404961 -0.5620521
 6:  1.5740202 -2.1390065  1.4832881
 7: -2.3917697 -0.3722932 -0.1926057
 8: -0.6004372 -0.2706900  0.8909017
 9: -0.2267385  0.5405553  1.0833964
10: -0.6378043  1.6665854  1.6748571

dt1[, paste0(names(dt1), "_abs") := lapply(.SD, abs)]
dt1

         Alpha       Beta      Gamma Alpha_abs  Beta_abs Gamma_abs
         <num>      <num>      <num>     <num>     <num>     <num>
 1: -0.8596756 -0.3872235 -0.4501584 0.8596756 0.3872235 0.4501584
 2:  0.1268567 -0.7755533  0.3603807 0.1268567 0.7755533 0.3603807
 3:  0.7298289 -1.6452208  0.9137217 0.7298289 1.6452208 0.9137217
 4: -0.5078484  0.3122644 -1.2696068 0.5078484 0.3122644 1.2696068
 5: -0.9108845 -1.4404961 -0.5620521 0.9108845 1.4404961 0.5620521
 6:  1.5740202 -2.1390065  1.4832881 1.5740202 2.1390065 1.4832881
 7: -2.3917697 -0.3722932 -0.1926057 2.3917697 0.3722932 0.1926057
 8: -0.6004372 -0.2706900  0.8909017 0.6004372 0.2706900 0.8909017
 9: -0.2267385  0.5405553  1.0833964 0.2267385 0.5405553 1.0833964
10: -0.6378043  1.6665854  1.6748571 0.6378043 1.6665854 1.6748571

dt2

       A     B          C  Group
   <int> <num>      <num> <char>
1:     1   1.2 -0.5960686      a
2:     2   4.3  1.4363967      b
3:     3   9.7 -0.5264020      b
4:     4   5.6  1.6849226      a
5:     5   8.1  0.5502497      a

cols <- c("A", "C")
dt2[, paste0(cols, "_groupMean") := lapply(.SD, mean), .SDcols = cols, by = Group]
dt2

       A     B          C  Group A_groupMean C_groupMean
   <int> <num>      <num> <char>       <num>       <num>
1:     1   1.2 -0.5960686      a    3.333333   0.5463679
2:     2   4.3  1.4363967      b    2.500000   0.4549973
3:     3   9.7 -0.5264020      b    2.500000   0.4549973
4:     4   5.6  1.6849226      a    3.333333   0.5463679
5:     5   8.1  0.5502497      a    3.333333   0.5463679

35.15 Row-wise operations

dt <- data.table(a = 1:5, b = 11:15, c = 21:25, 
                 d = 31:35, e = 41:45)
dt

       a     b     c     d     e
   <int> <int> <int> <int> <int>
1:     1    11    21    31    41
2:     2    12    22    32    42
3:     3    13    23    33    43
4:     4    14    24    34    44
5:     5    15    25    35    45

To operate row-wise, we can use by = 1:nrow(dt). For example, to add a column, in-place, with row-wise sums of variables b through d:

dt[, bcd.sum := sum(.SD[, b:d]), by = 1:nrow(dt)]
dt

       a     b     c     d     e bcd.sum
   <int> <int> <int> <int> <int>   <int>
1:     1    11    21    31    41      63
2:     2    12    22    32    42      66
3:     3    13    23    33    43      69
4:     4    14    24    34    44      72
5:     5    15    25    35    45      75

35.16 Watch out for `data.table` error messages

For example

35.1 data.table extends the functionality of the data.frame

35.1.1 Load the data.table package

35.2 Create a data.table

35.2.1 By assignment: data.table()

35.2.2 By coercion: as.data.table()

35.2.3 By coercion in-place: setDT()

35.3 Display data.table structure with str()

35.4 Combine data.tables

35.5 Set column names in-place

35.6 Filter rows

35.6.1 Conditional filtering

35.7 Select columns

35.7.1 By position(s)

35.7.2 By name(s)

35.7.3 .SD & .SDcols

35.8 Add new column in-place

35.9 Add multiple columns in-place

35.10 Convert column type

35.10.1 Assignment by reference with :=

35.10.2 Delete columns in-place with :=

35.10.3 Fast loop-able assignment with set()

35.11 Summarize

35.12 Set order

35.13 Group-by operations

35.13.1 Group-by summary

35.13.2 Group-by operation and assignment

35.14 Apply functions to columns

35.15 Row-wise operations

35.16 Watch out for data.table error messages

35.17 Resources

35.1 `data.table` extends the functionality of the `data.frame`

35.1.1 Load the `data.table` package

35.2 Create a `data.table`

35.2.1 By assignment: `data.table()`

35.2.2 By coercion: `as.data.table()`

35.2.3 By coercion in-place: `setDT()`

35.3 Display `data.table` structure with `str()`

35.4 Combine `data.tables`

35.7.3 `.SD` & `.SDcols`

35.10.1 Assignment by reference with `:=`

35.10.2 Delete columns in-place with `:=`

35.10.3 Fast loop-able assignment with `set()`

35.16 Watch out for `data.table` error messages