34 data.table basics
34.1 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.tablethan adata.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.
34.1.1 Load the data.table package
34.2 Create a data.table
34.2.1 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 adata.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 aNotice 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 aSame 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
34.2.2 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 adat2 <- 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
34.2.3 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"You can similarly convert a data.table to a data.frame, in-place:
34.3 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>
34.4 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 15rbind(dt1, dt1) a
<int>
1: 1
2: 2
3: 3
4: 4
5: 5
6: 1
7: 2
8: 3
9: 4
10: 534.5 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: 8254 36.14195 91.56035
2: 8800 46.17960 91.27711
3: 8752 41.53630 90.85834
4: 8414 47.35282 84.02487
5: 8421 48.39273 88.93596
6: 8551 43.76153 87.14759
7: 8003 41.96480 81.22580
8: 8059 43.20596 83.24410
9: 8020 27.53588 91.31077
10: 8381 37.26614 85.18255Use the syntax:
setnames(dt, old, new)
to change the column names of a data.table in-place.
Changes all column names:
Patient_ID Age Weight
<int> <num> <num>
1: 8254 36.14195 91.56035
2: 8800 46.17960 91.27711
3: 8752 41.53630 90.85834
4: 8414 47.35282 84.02487
5: 8421 48.39273 88.93596
6: 8551 43.76153 87.14759
7: 8003 41.96480 81.22580
8: 8059 43.20596 83.24410
9: 8020 27.53588 91.31077
10: 8381 37.26614 85.18255Change subset of names:
Patient_ID Age_at_Admission Weight_at_Admission
<int> <num> <num>
1: 8254 36.14195 91.56035
2: 8800 46.17960 91.27711
3: 8752 41.53630 90.85834
4: 8414 47.35282 84.02487
5: 8421 48.39273 88.93596
6: 8551 43.76153 87.14759
7: 8003 41.96480 81.22580
8: 8059 43.20596 83.24410
9: 8020 27.53588 91.31077
10: 8381 37.26614 85.18255old 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: 8254 36.14195 91.56035
2: 8800 46.17960 91.27711
3: 8752 41.53630 90.85834
4: 8414 47.35282 84.02487
5: 8421 48.39273 88.93596
6: 8551 43.76153 87.14759
7: 8003 41.96480 81.22580
8: 8059 43.20596 83.24410
9: 8020 27.53588 91.31077
10: 8381 37.26614 85.1825534.6 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 adt[c(1, 3, 5), ] A B C
<int> <num> <char>
1: 1 1.2 a
2: 3 9.7 b
3: 5 8.1 adt[c(1, 3, 5)] A B C
<int> <num> <char>
1: 1 1.2 a
2: 3 9.7 b
3: 5 8.1 aUsing 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 adt[rowid, ] A B C
<int> <num> <char>
1: 1 1.2 a
2: 3 9.7 b
3: 5 8.1 adt[rowid] A B C
<int> <num> <char>
1: 1 1.2 a
2: 3 9.7 b
3: 5 8.1 arowbn <- 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 adt[rowbn, ] A B C
<int> <num> <char>
1: 1 1.2 a
2: 3 9.7 b
3: 5 8.1 adt[rowbn] A B C
<int> <num> <char>
1: 1 1.2 a
2: 3 9.7 b
3: 5 8.1 a34.6.1 Conditional filtering
As a reminder, there are a few ways to conditionally filter cases in a data.frame:
A B C
5 5 8.1 a A B C
5 5 8.1 a A B C
5 5 8.1 adata.table allows you to refer to column names directly and unquoted, which makes writing filter conditions easier/more compact:
The data.table package also includes an S3 method for subset() that works the same way as with a data.frame:
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 0.6179966 1.0724774 -0.68785242 -0.9654427 1.1936329
2 -0.4282275 -0.8042036 0.03552512 -0.1868211 0.6651967
3 -1.6635976 -1.3537230 0.26785997 -0.5746305 -1.0489719
4 -0.8005334 -1.8136314 0.74195929 0.9661759 0.6596389
5 -0.8125215 0.1643694 -0.54882367 0.2485534 0.8148406
6 -1.2815142 -0.8154292 -0.65234682 -0.6914557 -0.7820180adf[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 -0.4282275 -0.8042036 0.03552512 -0.1868211 0.6651967
3 -1.6635976 -1.3537230 0.26785997 NA -1.0489719
4 -0.8005334 -1.8136314 0.74195929 0.9661759 0.6596389
6 -1.2815142 -0.8154292 -0.65234682 -0.6914557 -0.7820180
8 0.4322433 -0.2981248 -0.03369739 -1.5637058 -1.1015393
9 -0.5805915 -0.1185931 0.16524979 -0.2436720 0.7952899
10 0.1470787 -0.7587722 0.59074531 0.3789524 1.2539244adt[!is.na(V3)] V1 V2 V3 V4 V5
<num> <num> <num> <num> <num>
1: -0.4282275 -0.8042036 0.03552512 -0.1868211 0.6651967
2: -1.6635976 -1.3537230 0.26785997 NA -1.0489719
3: -0.8005334 -1.8136314 0.74195929 0.9661759 0.6596389
4: -1.2815142 -0.8154292 -0.65234682 -0.6914557 -0.7820180
5: 0.4322433 -0.2981248 -0.03369739 -1.5637058 -1.1015393
6: -0.5805915 -0.1185931 0.16524979 -0.2436720 0.7952899
7: 0.1470787 -0.7587722 0.59074531 0.3789524 1.253924434.7 Select columns
34.7.1 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 5df[, 1, drop = FALSE] A
1 1
2 2
3 3
4 4
5 5dt[, 1] A
<int>
1: 1
2: 2
3: 3
4: 4
5: 5Double 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 8.1dt[[2]][1] 1.2 4.3 9.7 5.6 8.1A vector of column positions returns a smaller data.table, similar to how it returns a smaller data.frame :
34.7.2 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 8.1df$B[1] 1.2 4.3 9.7 5.6 8.1dt[, B][1] 1.2 4.3 9.7 5.6 8.1Because 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:
A B
1 1 1.2
2 2 4.3
3 3 9.7
4 4 5.6
5 5 8.1df[, colbn] B C
1 1.2 a
2 4.3 b
3 9.7 b
4 5.6 a
5 8.1 adf[, colnm] A C
1 1 a
2 2 b
3 3 b
4 4 a
5 5 aTo 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 8.1dt[, ..colbn] B C
<num> <char>
1: 1.2 a
2: 4.3 b
3: 9.7 b
4: 5.6 a
5: 8.1 adt[, ..colnm] A C
<int> <char>
1: 1 a
2: 2 b
3: 3 b
4: 4 a
5: 5 aThink 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).
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 5Selecting 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)] A
<int>
1: 1
2: 2
3: 3
4: 4
5: 5dt[, .(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
34.7.3 .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 8.1One of the main uses of .SD is shown below in combination with lapply().
34.8 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 13.1Note how dt was modified even though we did not run dt <- dt[, AplusB := A + B]
34.9 Add multiple columns in-place
You can add multiple new columns in-place and at the same time using the := operator with:
- a character vector of new column names on the left hand side
- a list of values on the right hand side
Here, we add two new columns, AtimesB and AoverB:
It is often convenient to use lapply() on the right hand side, since it always returns a list:
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 2.0918641You 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: 0.617284034.10 Convert column type
34.10.1 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
34.10.2 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 0.6172840Delete 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
34.10.3 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)
-
dtthe data.table to operate on -
ioptionally define which rows to operate on.i = NULLto operate on all rows -
jcolumn names or index to be assignedvalue -
valuevalues to be assigned tojby 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)
}34.11 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_max A_min A_sd
<num> <num> <num>
1: 25 1 9.66954Example: Get the standard deviation of all numeric columns:
A B AtimesB AoverC
<num> <num> <num> <num>
1: 9.66954 37.35521 15.74462 0.2060219If 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 0.833333334.12 Set order
You can set the row order of a data.table in-place based on one or multiple columns’ values using setorder()
PatientID Height Weight Group
<int> <num> <num> <fctr>
1: 4527 164.7702 72.51216 A
2: 2830 172.0150 85.21636 B
3: 2411 169.8776 87.68722 A
4: 6937 169.4237 73.00400 B
5: 7062 186.9313 82.31618 B
6: 9570 161.6996 75.82902 B
7: 4363 164.7490 74.86255 A
8: 3636 204.5409 68.81390 A
9: 1184 174.7388 74.44918 B
10: 8920 185.7749 71.20984 BLet’s set the order by PatientID:
setorder(dt, PatientID)
dt PatientID Height Weight Group
<int> <num> <num> <fctr>
1: 1184 174.7388 74.44918 B
2: 2411 169.8776 87.68722 A
3: 2830 172.0150 85.21636 B
4: 3636 204.5409 68.81390 A
5: 4363 164.7490 74.86255 A
6: 4527 164.7702 72.51216 A
7: 6937 169.4237 73.00400 B
8: 7062 186.9313 82.31618 B
9: 8920 185.7749 71.20984 B
10: 9570 161.6996 75.82902 BLet’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: 4363 164.7490 74.86255 A
2: 4527 164.7702 72.51216 A
3: 2411 169.8776 87.68722 A
4: 3636 204.5409 68.81390 A
5: 9570 161.6996 75.82902 B
6: 6937 169.4237 73.00400 B
7: 2830 172.0150 85.21636 B
8: 1184 174.7388 74.44918 B
9: 8920 185.7749 71.20984 B
10: 7062 186.9313 82.31618 B34.13 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 -0.16052987 a
2: 2 4.3 2.15715743 b
3: 3 9.7 0.08882041 b
4: 4 5.6 -0.56086440 a
5: 5 8.1 -0.37153819 a34.13.1 Group-by summary
As we’ve seen, using .() or list() in j, returns a new data.table:
34.13.2 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 -0.16052987 a 3.333333
2: 2 4.3 2.15715743 b 2.500000
3: 3 9.7 0.08882041 b 2.500000
4: 4 5.6 -0.56086440 a 3.333333
5: 5 8.1 -0.37153819 a 3.33333334.14 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: 0.9454059 -0.7930885 -2.5190603
2: 0.6705971 1.0630810 -1.6928279
3: 0.2245473 -1.3417530 0.9264019
4: -1.0387699 -0.6186632 0.2471459
5: -1.3092584 0.7749935 -0.8194771
6: -0.8237848 0.2016330 1.6369376
7: 0.1904401 -0.7306494 0.6267110
8: 0.6673175 1.4075066 1.7622587
9: 0.1397172 -1.0341271 0.4757392
10: 2.9029260 0.1636206 1.0136611 Alpha Beta Gamma
<num> <num> <num>
1: 0.2569138 -0.09074465 0.165749You 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 -0.04075861 a
2: 2 4.3 1.07937920 b
3: 3 9.7 -0.16047609 b
4: 4 5.6 0.45827880 a
5: 5 8.1 0.31630213 adt2[, lapply(.SD, mean), .SDcols = 1:2] A B
<num> <num>
1: 3 5.78 A B
<num> <num>
1: 3 5.78 A B
<num> <num>
1: 3 5.78You can combine .SDcols and by:
Group B C
<char> <num> <num>
1: a 5.6 0.3163021
2: b 7.0 0.4594516Create multiple new columns from transformation of existing and store with custom prefix:
dt1 Alpha Beta Gamma
<num> <num> <num>
1: 0.9454059 -0.7930885 -2.5190603
2: 0.6705971 1.0630810 -1.6928279
3: 0.2245473 -1.3417530 0.9264019
4: -1.0387699 -0.6186632 0.2471459
5: -1.3092584 0.7749935 -0.8194771
6: -0.8237848 0.2016330 1.6369376
7: 0.1904401 -0.7306494 0.6267110
8: 0.6673175 1.4075066 1.7622587
9: 0.1397172 -1.0341271 0.4757392
10: 2.9029260 0.1636206 1.0136611 Alpha Beta Gamma Alpha_abs Beta_abs Gamma_abs
<num> <num> <num> <num> <num> <num>
1: 0.9454059 -0.7930885 -2.5190603 0.9454059 0.7930885 2.5190603
2: 0.6705971 1.0630810 -1.6928279 0.6705971 1.0630810 1.6928279
3: 0.2245473 -1.3417530 0.9264019 0.2245473 1.3417530 0.9264019
4: -1.0387699 -0.6186632 0.2471459 1.0387699 0.6186632 0.2471459
5: -1.3092584 0.7749935 -0.8194771 1.3092584 0.7749935 0.8194771
6: -0.8237848 0.2016330 1.6369376 0.8237848 0.2016330 1.6369376
7: 0.1904401 -0.7306494 0.6267110 0.1904401 0.7306494 0.6267110
8: 0.6673175 1.4075066 1.7622587 0.6673175 1.4075066 1.7622587
9: 0.1397172 -1.0341271 0.4757392 0.1397172 1.0341271 0.4757392
10: 2.9029260 0.1636206 1.0136611 2.9029260 0.1636206 1.0136611dt2 A B C Group
<int> <num> <num> <char>
1: 1 1.2 -0.04075861 a
2: 2 4.3 1.07937920 b
3: 3 9.7 -0.16047609 b
4: 4 5.6 0.45827880 a
5: 5 8.1 0.31630213 a A B C Group A_groupMean C_groupMean
<int> <num> <num> <char> <num> <num>
1: 1 1.2 -0.04075861 a 3.333333 0.2446074
2: 2 4.3 1.07937920 b 2.500000 0.4594516
3: 3 9.7 -0.16047609 b 2.500000 0.4594516
4: 4 5.6 0.45827880 a 3.333333 0.2446074
5: 5 8.1 0.31630213 a 3.333333 0.244607434.15 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 45To 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:
34.16 Watch out for data.table error messages
For example
