Choose or rename variables from a data.table.
select() keeps only the variables you mention;
rename() keeps all variables.
select(.data, ...)
select_vars(.data, ..., rm.dup = TRUE)
select_dt(.data, ..., cols = NULL, negate = FALSE)
select_mix(.data, ..., rm.dup = TRUE)
rename(.data, ...)A data.table
One or more unquoted expressions separated by commas.
Very flexible, same as tidyfst::select_dt and tidyfst::select_mix.
details find select_dt.
Should duplicated columns be removed? Defaults to TRUE.
(Optional)A numeric or character vector.
Applicable when regular expression and "cols" is used.
If TRUE, return the non-matched pattern. Default uses FALSE.
A data.table
No copy is made. Once you select or rename a data.table,
they would be changed forever. select_vars could select across
different data types, names and index. See examples.
select_dt and select_mix is the safe mode of
select and select_vars, they keey the original copy but
are not memory-efficient when dealing with large data sets.
a = as.data.table(iris)
a %>% select(1:3)
#> Sepal.Length Sepal.Width Petal.Length
#> <num> <num> <num>
#> 1: 5.1 3.5 1.4
#> 2: 4.9 3.0 1.4
#> 3: 4.7 3.2 1.3
#> 4: 4.6 3.1 1.5
#> 5: 5.0 3.6 1.4
#> ---
#> 146: 6.7 3.0 5.2
#> 147: 6.3 2.5 5.0
#> 148: 6.5 3.0 5.2
#> 149: 6.2 3.4 5.4
#> 150: 5.9 3.0 5.1
a
#> Sepal.Length Sepal.Width Petal.Length
#> <num> <num> <num>
#> 1: 5.1 3.5 1.4
#> 2: 4.9 3.0 1.4
#> 3: 4.7 3.2 1.3
#> 4: 4.6 3.1 1.5
#> 5: 5.0 3.6 1.4
#> ---
#> 146: 6.7 3.0 5.2
#> 147: 6.3 2.5 5.0
#> 148: 6.5 3.0 5.2
#> 149: 6.2 3.4 5.4
#> 150: 5.9 3.0 5.1
a = as.data.table(iris)
a %>% select_vars(is.factor,"Se")
#> Sepal.Length Sepal.Width Species
#> <num> <num> <fctr>
#> 1: 5.1 3.5 setosa
#> 2: 4.9 3.0 setosa
#> 3: 4.7 3.2 setosa
#> 4: 4.6 3.1 setosa
#> 5: 5.0 3.6 setosa
#> ---
#> 146: 6.7 3.0 virginica
#> 147: 6.3 2.5 virginica
#> 148: 6.5 3.0 virginica
#> 149: 6.2 3.4 virginica
#> 150: 5.9 3.0 virginica
a
#> Sepal.Length Sepal.Width Species
#> <num> <num> <fctr>
#> 1: 5.1 3.5 setosa
#> 2: 4.9 3.0 setosa
#> 3: 4.7 3.2 setosa
#> 4: 4.6 3.1 setosa
#> 5: 5.0 3.6 setosa
#> ---
#> 146: 6.7 3.0 virginica
#> 147: 6.3 2.5 virginica
#> 148: 6.5 3.0 virginica
#> 149: 6.2 3.4 virginica
#> 150: 5.9 3.0 virginica
a = as.data.table(iris)
a %>% select("Se") %>%
rename(sl = Sepal.Length,
sw = Sepal.Width)
#> sl sw
#> <num> <num>
#> 1: 5.1 3.5
#> 2: 4.9 3.0
#> 3: 4.7 3.2
#> 4: 4.6 3.1
#> 5: 5.0 3.6
#> ---
#> 146: 6.7 3.0
#> 147: 6.3 2.5
#> 148: 6.5 3.0
#> 149: 6.2 3.4
#> 150: 5.9 3.0
a
#> sl sw
#> <num> <num>
#> 1: 5.1 3.5
#> 2: 4.9 3.0
#> 3: 4.7 3.2
#> 4: 4.6 3.1
#> 5: 5.0 3.6
#> ---
#> 146: 6.7 3.0
#> 147: 6.3 2.5
#> 148: 6.5 3.0
#> 149: 6.2 3.4
#> 150: 5.9 3.0
DT = data.table(a=1:2,b=3:4,c=5:6)
DT
#> a b c
#> <int> <int> <int>
#> 1: 1 3 5
#> 2: 2 4 6
DT %>% rename(B=b)
#> a B c
#> <int> <int> <int>
#> 1: 1 3 5
#> 2: 2 4 6