Reputation: 5729
I have a list of dataframes
my_list <- list(structure(c("23000 Vs 23500", "23500 Vs 24000", "1.03546847852537",
"0.735744771309744", "15", "29"), .Dim = 2:3, .Dimnames = list(
NULL, c("Group", "EffectSize", "RequiredReplicates"))), structure(c("23500 Vs 24000",
"24000 Vs 25000", "25000 Vs 25500", "0.735744771309744", "1.48620682621918",
"0.418877850096638", "29", "7", "89"), .Dim = c(3L, 3L), .Dimnames = list(
NULL, c("Group", "EffectSize", "RequiredReplicates"))), structure(c("26000 Vs 26500",
"26500 Vs 27000", "27000 Vs 27500", "0.0739021800199834", "0.14116830704947",
"0.135704984161555", "2874", "788", "852"), .Dim = c(3L, 3L), .Dimnames = list(
NULL, c("Group", "EffectSize", "RequiredReplicates"))))
names(my_list) <- paste0("tt", 1:3)
What I wanted is add a new column grp
with the dataframe name and rbind them all to make one dataframe.
lapply(
my_list,
function(x) {
x$grp <- deparse(substitute(x))
rbind(x)
}
)
Result I want:
Group EffectSize RequiredReplicates grp
"23000 Vs 23500" "1.03546847852537" "15" tt1
"23500 Vs 24000" "0.735744771309744" "29" tt1
"23500 Vs 24000" "0.735744771309744" "29" tt2
"24000 Vs 25000" "1.48620682621918" "7" tt2
"25000 Vs 25500" "0.418877850096638" "89" tt2
"26000 Vs 26500" "0.0739021800199834" "2874" tt3
"26500 Vs 27000" "0.14116830704947" "788" tt3
"27000 Vs 27500" "0.135704984161555" "852 tt3
Thanks for your help!
Upvotes: 0
Views: 41
Reputation: 270448
1) data.table Convert each component to data.table and then use rbindlist
with idcol
argument.
library(data.table)
my_list_nms <- setNames(my_list, paste0("tt", seq_along(my_list)))
rbindlist(lapply(my_list_nms, as.data.table), idcol = "id")
giving this data.table:
id Group EffectSize RequiredReplicates
1: tt1 23000 Vs 23500 1.03546847852537 15
2: tt1 23500 Vs 24000 0.735744771309744 29
3: tt2 23500 Vs 24000 0.735744771309744 29
4: tt2 24000 Vs 25000 1.48620682621918 7
5: tt2 25000 Vs 25500 0.418877850096638 89
6: tt3 26000 Vs 26500 0.0739021800199834 2874
7: tt3 26500 Vs 27000 0.14116830704947 788
8: tt3 27000 Vs 27500 0.135704984161555 852
2) purrr Using purrr and tibble it can also be done. my_list_nms
is from above.
library(purrr)
library(tibble)
map_dfr(my_list_nms, as_data_frame, .id = "id")
giving this tibble:
# A tibble: 8 x 4
id Group EffectSize RequiredReplicates
<chr> <chr> <chr> <chr>
1 tt1 23000 Vs 23500 1.03546847852537 15
2 tt1 23500 Vs 24000 0.735744771309744 29
3 tt2 23500 Vs 24000 0.735744771309744 29
4 tt2 24000 Vs 25000 1.48620682621918 7
5 tt2 25000 Vs 25500 0.418877850096638 89
6 tt3 26000 Vs 26500 0.0739021800199834 2874
7 tt3 26500 Vs 27000 0.14116830704947 788
8 tt3 27000 Vs 27500 0.135704984161555 852
Upvotes: 1