Enhanced functions to create, modify, and/or delete variables.
The functions combine the advantages of
within (base),
mutate (dplyr),
transmute (dplyr), and
:= (data.table).
See examples below for the usage and convenience.
Arguments
- data
A
data.table(preferred).- expr
R expression(s) enclosed in
{...}to compute variables.Passing to
data.table:DT[ , `:=`(expr), ]Execute each line of expression in
{...}one by one, such that newly created variables are available immediately. This is an advantage ofmutateand has been implemented here fordata.table.- when
[Optional] Compute for which rows or rows meeting what condition(s)?
Passing to
data.table:DT[when, , ]- by
[Optional] Compute by what group(s)?
Passing to
data.table:DT[ , , by]- drop
Drop existing variables and return only new variables? Defaults to
FALSE, which returns all variables.
Value
add() returns a new
data.table,
with the raw data unchanged.
added() returns nothing and has already changed the raw data.
Examples
## ====== Usage 1: add() ====== ##
d = as.data.table(within.1)
d$XYZ = 1:8
d
#> ID A1 A2 A3 A4 XYZ
#> <char> <num> <num> <num> <num> <int>
#> 1: S1 3 4 8 9 1
#> 2: S2 6 6 9 8 2
#> 3: S3 4 4 8 8 3
#> 4: S4 3 2 7 7 4
#> 5: S5 5 4 5 12 5
#> 6: S6 7 5 6 13 6
#> 7: S7 5 3 7 12 7
#> 8: S8 2 3 6 11 8
# add() does not change the raw data:
add(d, {B = 1; C = 2})
#> ID A1 A2 A3 A4 XYZ B C
#> <char> <num> <num> <num> <num> <int> <num> <num>
#> 1: S1 3 4 8 9 1 1 2
#> 2: S2 6 6 9 8 2 1 2
#> 3: S3 4 4 8 8 3 1 2
#> 4: S4 3 2 7 7 4 1 2
#> 5: S5 5 4 5 12 5 1 2
#> 6: S6 7 5 6 13 6 1 2
#> 7: S7 5 3 7 12 7 1 2
#> 8: S8 2 3 6 11 8 1 2
d
#> ID A1 A2 A3 A4 XYZ
#> <char> <num> <num> <num> <num> <int>
#> 1: S1 3 4 8 9 1
#> 2: S2 6 6 9 8 2
#> 3: S3 4 4 8 8 3
#> 4: S4 3 2 7 7 4
#> 5: S5 5 4 5 12 5
#> 6: S6 7 5 6 13 6
#> 7: S7 5 3 7 12 7
#> 8: S8 2 3 6 11 8
# new data should be assigned to an object:
d = d %>% add({
ID = str_extract(ID, "\\d") # modify a variable
XYZ = NULL # delete a variable
A = .mean("A", 1:4) # create a new variable
B = A * 4 # new variable is immediately available
C = 1 # never need ,/; at the end of any line
})
d
#> ID A1 A2 A3 A4 A B C
#> <char> <num> <num> <num> <num> <num> <num> <num>
#> 1: 1 3 4 8 9 6.00 24 1
#> 2: 2 6 6 9 8 7.25 29 1
#> 3: 3 4 4 8 8 6.00 24 1
#> 4: 4 3 2 7 7 4.75 19 1
#> 5: 5 5 4 5 12 6.50 26 1
#> 6: 6 7 5 6 13 7.75 31 1
#> 7: 7 5 3 7 12 6.75 27 1
#> 8: 8 2 3 6 11 5.50 22 1
## ====== Usage 2: added() ====== ##
d = as.data.table(within.1)
d$XYZ = 1:8
d
#> ID A1 A2 A3 A4 XYZ
#> <char> <num> <num> <num> <num> <int>
#> 1: S1 3 4 8 9 1
#> 2: S2 6 6 9 8 2
#> 3: S3 4 4 8 8 3
#> 4: S4 3 2 7 7 4
#> 5: S5 5 4 5 12 5
#> 6: S6 7 5 6 13 6
#> 7: S7 5 3 7 12 7
#> 8: S8 2 3 6 11 8
# added() has already changed the raw data:
added(d, {B = 1; C = 2})
d
#> ID A1 A2 A3 A4 XYZ B C
#> <char> <num> <num> <num> <num> <int> <num> <num>
#> 1: S1 3 4 8 9 1 1 2
#> 2: S2 6 6 9 8 2 1 2
#> 3: S3 4 4 8 8 3 1 2
#> 4: S4 3 2 7 7 4 1 2
#> 5: S5 5 4 5 12 5 1 2
#> 6: S6 7 5 6 13 6 1 2
#> 7: S7 5 3 7 12 7 1 2
#> 8: S8 2 3 6 11 8 1 2
# raw data has already become the new data:
added(d, {
ID = str_extract(ID, "\\d")
XYZ = NULL
A = .mean("A", 1:4)
B = A * 4
C = 1
})
d
#> ID A1 A2 A3 A4 B C A
#> <char> <num> <num> <num> <num> <num> <num> <num>
#> 1: 1 3 4 8 9 24 1 6.00
#> 2: 2 6 6 9 8 29 1 7.25
#> 3: 3 4 4 8 8 24 1 6.00
#> 4: 4 3 2 7 7 19 1 4.75
#> 5: 5 5 4 5 12 26 1 6.50
#> 6: 6 7 5 6 13 31 1 7.75
#> 7: 7 5 3 7 12 27 1 6.75
#> 8: 8 2 3 6 11 22 1 5.50
## ====== Using `when` and `by` ====== ##
d = as.data.table(between.2)
d
#> A B SCORE
#> <num> <num> <num>
#> 1: 1 1 3
#> 2: 1 1 6
#> 3: 1 1 4
#> 4: 1 1 3
#> 5: 1 2 4
#> 6: 1 2 6
#> 7: 1 2 4
#> 8: 1 2 2
#> 9: 1 3 5
#> 10: 1 3 7
#> 11: 1 3 5
#> 12: 1 3 2
#> 13: 2 1 4
#> 14: 2 1 5
#> 15: 2 1 3
#> 16: 2 1 3
#> 17: 2 2 8
#> 18: 2 2 9
#> 19: 2 2 8
#> 20: 2 2 7
#> 21: 2 3 12
#> 22: 2 3 13
#> 23: 2 3 12
#> 24: 2 3 11
#> A B SCORE
added(d, {SCORE2 = SCORE - mean(SCORE)},
A == 1 & B %in% 1:2, # `when`: for what conditions
by=B) # `by`: by what groups
d
#> Index: <B__A>
#> A B SCORE SCORE2
#> <num> <num> <num> <num>
#> 1: 1 1 3 -1
#> 2: 1 1 6 2
#> 3: 1 1 4 0
#> 4: 1 1 3 -1
#> 5: 1 2 4 0
#> 6: 1 2 6 2
#> 7: 1 2 4 0
#> 8: 1 2 2 -2
#> 9: 1 3 5 NA
#> 10: 1 3 7 NA
#> 11: 1 3 5 NA
#> 12: 1 3 2 NA
#> 13: 2 1 4 NA
#> 14: 2 1 5 NA
#> 15: 2 1 3 NA
#> 16: 2 1 3 NA
#> 17: 2 2 8 NA
#> 18: 2 2 9 NA
#> 19: 2 2 8 NA
#> 20: 2 2 7 NA
#> 21: 2 3 12 NA
#> 22: 2 3 13 NA
#> 23: 2 3 12 NA
#> 24: 2 3 11 NA
#> A B SCORE SCORE2
na.omit(d)
#> A B SCORE SCORE2
#> <num> <num> <num> <num>
#> 1: 1 1 3 -1
#> 2: 1 1 6 2
#> 3: 1 1 4 0
#> 4: 1 1 3 -1
#> 5: 1 2 4 0
#> 6: 1 2 6 2
#> 7: 1 2 4 0
#> 8: 1 2 2 -2
## ====== Return Only New Variables ====== ##
newvars = add(within.1, {
ID = str_extract(ID, "\\d")
A = .mean("A", 1:4)
}, drop=TRUE)
newvars
#> ID A
#> <char> <num>
#> 1: 1 6.00
#> 2: 2 7.25
#> 3: 3 6.00
#> 4: 4 4.75
#> 5: 5 6.50
#> 6: 6 7.75
#> 7: 7 6.75
#> 8: 8 5.50
## ====== Better Than `base::within()` ====== ##
d = as.data.table(within.1)
# wrong order: C B A
within(d, {
A = 4
B = A + 1
C = 6
})
#> ID A1 A2 A3 A4 C B A
#> <char> <num> <num> <num> <num> <num> <num> <num>
#> 1: S1 3 4 8 9 6 5 4
#> 2: S2 6 6 9 8 6 5 4
#> 3: S3 4 4 8 8 6 5 4
#> 4: S4 3 2 7 7 6 5 4
#> 5: S5 5 4 5 12 6 5 4
#> 6: S6 7 5 6 13 6 5 4
#> 7: S7 5 3 7 12 6 5 4
#> 8: S8 2 3 6 11 6 5 4
# correct order: A B C
add(d, {
A = 4
B = A + 1
C = 6
})
#> ID A1 A2 A3 A4 A B C
#> <char> <num> <num> <num> <num> <num> <num> <num>
#> 1: S1 3 4 8 9 4 5 6
#> 2: S2 6 6 9 8 4 5 6
#> 3: S3 4 4 8 8 4 5 6
#> 4: S4 3 2 7 7 4 5 6
#> 5: S5 5 4 5 12 4 5 6
#> 6: S6 7 5 6 13 4 5 6
#> 7: S7 5 3 7 12 4 5 6
#> 8: S8 2 3 6 11 4 5 6