Reputation: 1335
I have a list with this structure:
$ List (length 13) ; 13 Types
$ --- Lists (Length 4) ; Each have 4 subsets of the same original data
$ ------- Dataframes 1, 2, 3, and 4 ; for each of 13 types
I want
$ List (length 52) ; 52 Versions (Type_Subset)
$ --- Dataframes 1, 2, 3, ... 52 ; As separate elements in list
How would I do this using the below mtcars
example?
df <- list(Blue = list(mtcars[1:3,], mtcars[4:6,], mtcars[7:9,]),
Red = list(mtcars[10:12,], mtcars[13:15,], mtcars[16:18,]),
Green = list(mtcars[18:20,], mtcars[21:23,], mtcars[24:26,]))
# Need function on df ...
# new_df <- SingleNestLevel(df)
# Which yields:
list(Blue1 = mtcars[1:3,],
Blue2 = mtcars[4:6,],
Blue3 = mtcars[7:9,],
Red1 = mtcars[10:12,],
Red2 = mtcars[13:15,],
Red3 = mtcars[16:18,],
Green1 = mtcars[18:20,],
Green2 = mtcars[21:23,],
Green3 = mtcars[24:26,])
Note: I have looked at analogous questions like this one, but I want to convert to one nested level, not flatten my structure entirely.
Upvotes: 1
Views: 216
Reputation: 1101
Using the same library data.table
you can try
library(data.table)
df <- copy(mtcars[1:27,]) # copying reserved dataset mtcars.
setDT(df, keep.rownames = TRUE)[,v1 :=rep(unlist(lapply(c("Blue","Red", "Green"),paste, 1:3, sep = "")),
each = 3)] #including temporary variable v1
df <- split(df, df$v1) #spliting into a list
df <- lapply(df, function(x) x[,v1 := NULL]) #removing temporary variable nv1
df #Returns
$Blue1
rn mpg cyl disp hp drat wt qsec vs am gear carb
1: Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
2: Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
3: Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
$Blue2
rn mpg cyl disp hp drat wt qsec vs am gear carb
1: Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
2: Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
3: Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
$Blue3
rn mpg cyl disp hp drat wt qsec vs am gear carb
1: Duster 360 14.3 8 360.0 245 3.21 3.57 15.84 0 0 3 4
2: Merc 240D 24.4 4 146.7 62 3.69 3.19 20.00 1 0 4 2
3: Merc 230 22.8 4 140.8 95 3.92 3.15 22.90 1 0 4 2
$Green1
rn mpg cyl disp hp drat wt qsec vs am gear carb
1: Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
2: Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
3: Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
$Green2
rn mpg cyl disp hp drat wt qsec vs am gear carb
1: Dodge Challenger 15.5 8 318 150 2.76 3.520 16.87 0 0 3 2
2: AMC Javelin 15.2 8 304 150 3.15 3.435 17.30 0 0 3 2
3: Camaro Z28 13.3 8 350 245 3.73 3.840 15.41 0 0 3 4
$Green3
rn mpg cyl disp hp drat wt qsec vs am gear carb
1: Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
2: Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
3: Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
$Red1
rn mpg cyl disp hp drat wt qsec vs am gear carb
1: Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4
2: Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4
3: Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3
$Red2
rn mpg cyl disp hp drat wt qsec vs am gear carb
1: Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.60 0 0 3 3
2: Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18.00 0 0 3 3
3: Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.25 17.98 0 0 3 4
$Red3
rn mpg cyl disp hp drat wt qsec vs am gear carb
1: Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
2: Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
3: Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Upvotes: 0
Reputation: 5405
I think this generalizes your issue to any nested list:
library(purrr)
new_df <- flatten(df) %>%
setNames(paste0(rep(names(df), times = map_int(df, ~length(.x))),
unlist(map(df, ~1:length(.x)))))
Upvotes: 1