Analogous function for nest and unnest in tidyr.
unnest will automatically remove other list-columns except for the
target list-columns (which would be unnested later). Also, squeeze is
designed to merge multiple columns into list column.
nest(.data, ..., mcols = NULL, .name = "ndt")
unnest(.data, ...)
squeeze(.data, ..., .name = "ndt")
chop(.data, ...)
unchop(.data, ...)data.table, nested or unnested
The variables for nest group(for nest),
columns to be nested(for squeeze and chop),
or column(s) to be unnested(for unnest).
Could recieve anything that select_dt could receive.
Name-variable pairs in the list, form like
Character. The nested column name. Defaults to "ndt".
list(petal="^Pe",sepal="^Se"), see example.
data.table, nested or unnested
In the nest, the data would be nested to a column named `ndt`,
which is short for nested data.table.
The squeeze would not remove the originial columns.
The unchop is the reverse operation of chop.
These functions are experiencing the experimental stage, especially
the unnest. If they don't work on some circumtances, try tidyr
package.
https://www.r-bloggers.com/much-faster-unnesting-with-data-table/
https://stackoverflow.com/questions/25430986/create-nested-data-tables-by-collapsing-rows-into-new-data-tables
mtcars = as.data.table(mtcars)
iris = as.data.table(iris)
# examples for nest
# nest by which columns?
mtcars %>% nest(cyl)
#> cyl ndt
#> <num> <list>
#> 1: 6 <data.table[7x10]>
#> 2: 4 <data.table[11x10]>
#> 3: 8 <data.table[14x10]>
mtcars %>% nest("cyl")
#> cyl ndt
#> <num> <list>
#> 1: 6 <data.table[7x10]>
#> 2: 4 <data.table[11x10]>
#> 3: 8 <data.table[14x10]>
mtcars %>% nest(cyl,vs)
#> cyl vs ndt
#> <num> <num> <list>
#> 1: 6 0 <data.table[3x9]>
#> 2: 4 1 <data.table[10x9]>
#> 3: 6 1 <data.table[4x9]>
#> 4: 8 0 <data.table[14x9]>
#> 5: 4 0 <data.table[1x9]>
mtcars %>% nest(vs:am)
#> vs am ndt
#> <num> <num> <list>
#> 1: 0 1 <data.table[6x9]>
#> 2: 1 1 <data.table[7x9]>
#> 3: 1 0 <data.table[7x9]>
#> 4: 0 0 <data.table[12x9]>
mtcars %>% nest("cyl|vs")
#> cyl vs ndt
#> <num> <num> <list>
#> 1: 6 0 <data.table[3x9]>
#> 2: 4 1 <data.table[10x9]>
#> 3: 6 1 <data.table[4x9]>
#> 4: 8 0 <data.table[14x9]>
#> 5: 4 0 <data.table[1x9]>
mtcars %>% nest(c("cyl","vs"))
#> cyl vs ndt
#> <num> <num> <list>
#> 1: 6 0 <data.table[3x9]>
#> 2: 4 1 <data.table[10x9]>
#> 3: 6 1 <data.table[4x9]>
#> 4: 8 0 <data.table[14x9]>
#> 5: 4 0 <data.table[1x9]>
# nest two columns directly
iris %>% nest(mcols = list(petal="^Pe",sepal="^Se"))
#> Key: <Species>
#> Species petal sepal
#> <fctr> <list> <list>
#> 1: setosa <data.table[50x4]> <data.table[50x4]>
#> 2: versicolor <data.table[50x4]> <data.table[50x4]>
#> 3: virginica <data.table[50x4]> <data.table[50x4]>
# nest more flexibly
iris %>% nest(mcols = list(ndt1 = 1:3,
ndt2 = "Pe",
ndt3 = Sepal.Length:Sepal.Width))
#> Key: <Species>
#> Species ndt1 ndt2 ndt3
#> <fctr> <list> <list> <list>
#> 1: setosa <data.table[50x4]> <data.table[50x4]> <data.table[50x4]>
#> 2: versicolor <data.table[50x4]> <data.table[50x4]> <data.table[50x4]>
#> 3: virginica <data.table[50x4]> <data.table[50x4]> <data.table[50x4]>
# examples for unnest
# unnest which column?
mtcars %>% nest("cyl|vs") %>%
unnest(ndt)
#> cyl vs mpg disp hp drat wt qsec am gear carb
#> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 6 0 21.0 160.0 110 3.90 2.620 16.46 1 4 4
#> 2: 6 0 21.0 160.0 110 3.90 2.875 17.02 1 4 4
#> 3: 6 0 19.7 145.0 175 3.62 2.770 15.50 1 5 6
#> 4: 4 1 22.8 108.0 93 3.85 2.320 18.61 1 4 1
#> 5: 4 1 24.4 146.7 62 3.69 3.190 20.00 0 4 2
#> 6: 4 1 22.8 140.8 95 3.92 3.150 22.90 0 4 2
#> 7: 4 1 32.4 78.7 66 4.08 2.200 19.47 1 4 1
#> 8: 4 1 30.4 75.7 52 4.93 1.615 18.52 1 4 2
#> 9: 4 1 33.9 71.1 65 4.22 1.835 19.90 1 4 1
#> 10: 4 1 21.5 120.1 97 3.70 2.465 20.01 0 3 1
#> 11: 4 1 27.3 79.0 66 4.08 1.935 18.90 1 4 1
#> 12: 4 1 30.4 95.1 113 3.77 1.513 16.90 1 5 2
#> 13: 4 1 21.4 121.0 109 4.11 2.780 18.60 1 4 2
#> 14: 6 1 21.4 258.0 110 3.08 3.215 19.44 0 3 1
#> 15: 6 1 18.1 225.0 105 2.76 3.460 20.22 0 3 1
#> 16: 6 1 19.2 167.6 123 3.92 3.440 18.30 0 4 4
#> 17: 6 1 17.8 167.6 123 3.92 3.440 18.90 0 4 4
#> 18: 8 0 18.7 360.0 175 3.15 3.440 17.02 0 3 2
#> 19: 8 0 14.3 360.0 245 3.21 3.570 15.84 0 3 4
#> 20: 8 0 16.4 275.8 180 3.07 4.070 17.40 0 3 3
#> 21: 8 0 17.3 275.8 180 3.07 3.730 17.60 0 3 3
#> 22: 8 0 15.2 275.8 180 3.07 3.780 18.00 0 3 3
#> 23: 8 0 10.4 472.0 205 2.93 5.250 17.98 0 3 4
#> 24: 8 0 10.4 460.0 215 3.00 5.424 17.82 0 3 4
#> 25: 8 0 14.7 440.0 230 3.23 5.345 17.42 0 3 4
#> 26: 8 0 15.5 318.0 150 2.76 3.520 16.87 0 3 2
#> 27: 8 0 15.2 304.0 150 3.15 3.435 17.30 0 3 2
#> 28: 8 0 13.3 350.0 245 3.73 3.840 15.41 0 3 4
#> 29: 8 0 19.2 400.0 175 3.08 3.845 17.05 0 3 2
#> 30: 8 0 15.8 351.0 264 4.22 3.170 14.50 1 5 4
#> 31: 8 0 15.0 301.0 335 3.54 3.570 14.60 1 5 8
#> 32: 4 0 26.0 120.3 91 4.43 2.140 16.70 1 5 2
#> cyl vs mpg disp hp drat wt qsec am gear carb
mtcars %>% nest("cyl|vs") %>%
unnest("ndt")
#> cyl vs mpg disp hp drat wt qsec am gear carb
#> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 6 0 21.0 160.0 110 3.90 2.620 16.46 1 4 4
#> 2: 6 0 21.0 160.0 110 3.90 2.875 17.02 1 4 4
#> 3: 6 0 19.7 145.0 175 3.62 2.770 15.50 1 5 6
#> 4: 4 1 22.8 108.0 93 3.85 2.320 18.61 1 4 1
#> 5: 4 1 24.4 146.7 62 3.69 3.190 20.00 0 4 2
#> 6: 4 1 22.8 140.8 95 3.92 3.150 22.90 0 4 2
#> 7: 4 1 32.4 78.7 66 4.08 2.200 19.47 1 4 1
#> 8: 4 1 30.4 75.7 52 4.93 1.615 18.52 1 4 2
#> 9: 4 1 33.9 71.1 65 4.22 1.835 19.90 1 4 1
#> 10: 4 1 21.5 120.1 97 3.70 2.465 20.01 0 3 1
#> 11: 4 1 27.3 79.0 66 4.08 1.935 18.90 1 4 1
#> 12: 4 1 30.4 95.1 113 3.77 1.513 16.90 1 5 2
#> 13: 4 1 21.4 121.0 109 4.11 2.780 18.60 1 4 2
#> 14: 6 1 21.4 258.0 110 3.08 3.215 19.44 0 3 1
#> 15: 6 1 18.1 225.0 105 2.76 3.460 20.22 0 3 1
#> 16: 6 1 19.2 167.6 123 3.92 3.440 18.30 0 4 4
#> 17: 6 1 17.8 167.6 123 3.92 3.440 18.90 0 4 4
#> 18: 8 0 18.7 360.0 175 3.15 3.440 17.02 0 3 2
#> 19: 8 0 14.3 360.0 245 3.21 3.570 15.84 0 3 4
#> 20: 8 0 16.4 275.8 180 3.07 4.070 17.40 0 3 3
#> 21: 8 0 17.3 275.8 180 3.07 3.730 17.60 0 3 3
#> 22: 8 0 15.2 275.8 180 3.07 3.780 18.00 0 3 3
#> 23: 8 0 10.4 472.0 205 2.93 5.250 17.98 0 3 4
#> 24: 8 0 10.4 460.0 215 3.00 5.424 17.82 0 3 4
#> 25: 8 0 14.7 440.0 230 3.23 5.345 17.42 0 3 4
#> 26: 8 0 15.5 318.0 150 2.76 3.520 16.87 0 3 2
#> 27: 8 0 15.2 304.0 150 3.15 3.435 17.30 0 3 2
#> 28: 8 0 13.3 350.0 245 3.73 3.840 15.41 0 3 4
#> 29: 8 0 19.2 400.0 175 3.08 3.845 17.05 0 3 2
#> 30: 8 0 15.8 351.0 264 4.22 3.170 14.50 1 5 4
#> 31: 8 0 15.0 301.0 335 3.54 3.570 14.60 1 5 8
#> 32: 4 0 26.0 120.3 91 4.43 2.140 16.70 1 5 2
#> cyl vs mpg disp hp drat wt qsec am gear carb
df <- data.table(
a = list(c("a", "b"), "c"),
b = list(c(TRUE,TRUE),FALSE),
c = list(3,c(1,2)),
d = c(11, 22)
)
df
#> a b c d
#> <list> <list> <list> <num>
#> 1: a,b TRUE,TRUE 3 11
#> 2: c FALSE 1,2 22
df %>% unnest(a)
#> d a
#> <num> <char>
#> 1: 11 a
#> 2: 11 b
#> 3: 22 c
df %>% unnest(2)
#> d b
#> <num> <lgcl>
#> 1: 11 TRUE
#> 2: 11 TRUE
#> 3: 22 FALSE
df %>% unnest("c")
#> d c
#> <num> <num>
#> 1: 11 3
#> 2: 22 1
#> 3: 22 2
df %>% unnest(cols = names(df)[3])
#> d c
#> <num> <num>
#> 1: 11 3
#> 2: 22 1
#> 3: 22 2
# You can unnest multiple columns simultaneously
df %>% unnest(1:3)
#> Key: <d>
#> d a b c
#> <num> <char> <lgcl> <num>
#> 1: 11 a TRUE 3
#> 2: 11 a TRUE 3
#> 3: 11 b TRUE 3
#> 4: 11 b TRUE 3
#> 5: 22 c FALSE 1
#> 6: 22 c FALSE 2
df %>% unnest(a,b,c)
#> Key: <d>
#> d a b c
#> <num> <char> <lgcl> <num>
#> 1: 11 a TRUE 3
#> 2: 11 a TRUE 3
#> 3: 11 b TRUE 3
#> 4: 11 b TRUE 3
#> 5: 22 c FALSE 1
#> 6: 22 c FALSE 2
df %>% unnest("a|b|c")
#> Key: <d>
#> d a b c
#> <num> <char> <lgcl> <num>
#> 1: 11 a TRUE 3
#> 2: 11 a TRUE 3
#> 3: 11 b TRUE 3
#> 4: 11 b TRUE 3
#> 5: 22 c FALSE 1
#> 6: 22 c FALSE 2
# examples for squeeze
# nest which columns?
iris %>% squeeze(1:2)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species ndt
#> <num> <num> <num> <num> <fctr> <list>
#> 1: 5.1 3.5 1.4 0.2 setosa 5.1,3.5
#> 2: 4.9 3.0 1.4 0.2 setosa 4.9,3.0
#> 3: 4.7 3.2 1.3 0.2 setosa 4.7,3.2
#> 4: 4.6 3.1 1.5 0.2 setosa 4.6,3.1
#> 5: 5.0 3.6 1.4 0.2 setosa 5.0,3.6
#> ---
#> 146: 6.7 3.0 5.2 2.3 virginica 6.7,3.0
#> 147: 6.3 2.5 5.0 1.9 virginica 6.3,2.5
#> 148: 6.5 3.0 5.2 2.0 virginica 6.5,3.0
#> 149: 6.2 3.4 5.4 2.3 virginica 6.2,3.4
#> 150: 5.9 3.0 5.1 1.8 virginica 5.9,3.0
iris %>% squeeze("Se")
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species ndt
#> <num> <num> <num> <num> <fctr> <list>
#> 1: 5.1 3.5 1.4 0.2 setosa 5.1,3.5
#> 2: 4.9 3.0 1.4 0.2 setosa 4.9,3.0
#> 3: 4.7 3.2 1.3 0.2 setosa 4.7,3.2
#> 4: 4.6 3.1 1.5 0.2 setosa 4.6,3.1
#> 5: 5.0 3.6 1.4 0.2 setosa 5.0,3.6
#> ---
#> 146: 6.7 3.0 5.2 2.3 virginica 6.7,3.0
#> 147: 6.3 2.5 5.0 1.9 virginica 6.3,2.5
#> 148: 6.5 3.0 5.2 2.0 virginica 6.5,3.0
#> 149: 6.2 3.4 5.4 2.3 virginica 6.2,3.4
#> 150: 5.9 3.0 5.1 1.8 virginica 5.9,3.0
iris %>% squeeze(Sepal.Length:Petal.Width)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species ndt
#> <num> <num> <num> <num> <fctr> <list>
#> 1: 5.1 3.5 1.4 0.2 setosa 5.1,3.5
#> 2: 4.9 3.0 1.4 0.2 setosa 4.9,3.0
#> 3: 4.7 3.2 1.3 0.2 setosa 4.7,3.2
#> 4: 4.6 3.1 1.5 0.2 setosa 4.6,3.1
#> 5: 5.0 3.6 1.4 0.2 setosa 5.0,3.6
#> ---
#> 146: 6.7 3.0 5.2 2.3 virginica 6.7,3.0
#> 147: 6.3 2.5 5.0 1.9 virginica 6.3,2.5
#> 148: 6.5 3.0 5.2 2.0 virginica 6.5,3.0
#> 149: 6.2 3.4 5.4 2.3 virginica 6.2,3.4
#> 150: 5.9 3.0 5.1 1.8 virginica 5.9,3.0
# examples for chop
df <- data.table(x = c(1, 1, 1, 2, 2, 3), y = 1:6, z = 6:1)
df %>% chop(y,z)
#> x y z
#> <num> <list> <list>
#> 1: 1 1,2,3 6,5,4
#> 2: 2 4,5 3,2
#> 3: 3 6 1
df %>% chop(y,z) %>% unchop(y,z)
#> x y z
#> <num> <int> <int>
#> 1: 1 1 6
#> 2: 1 2 5
#> 3: 1 3 4
#> 4: 2 4 3
#> 5: 2 5 2
#> 6: 3 6 1