Reorganize list into dataframe using dplyr
Solution 1
Here's another approach with a little more dplyr/tidyr functions and piping, however I haven't tested its performance against the original approach in the question and whether it is more elegant depends on personal preference.
library(dplyr); library(tidyr)
lapply(l, `[[`, 2) %>%
data.frame %>%
add_rownames("key") %>%
gather(x, value, -key) %>%
select(-x)
# key value
#1 b -1.1476570
#2 c -0.2894616
#3 d -0.2992151
#4 b 0.2522234
#5 c -0.8919211
#6 d 0.4356833
#7 b -0.2242679
#8 c 0.3773956
#9 d 0.1333364
Solution 2
Also from the Hadleyverse, but not using "dplyr" would be to consider using melt
from "reshape2":
library(reshape2)
melt(l)
# value Var1 Var2 L2 L1
# 1 -0.6264538 <NA> <NA> member1 1
# 2 0.1836433 b sample member2 1
# 3 -0.8356286 c sample member2 1
# 4 1.5952808 d sample member2 1
# 5 0.3295078 <NA> <NA> member1 2
# 6 -0.8204684 b sample member2 2
# 7 0.4874291 c sample member2 2
# 8 0.7383247 d sample member2 2
# 9 0.5757814 <NA> <NA> member1 3
# 10 -0.3053884 b sample member2 3
# 11 1.5117812 c sample member2 3
# 12 0.3898432 d sample member2 3
From there, you can consider using "dplyr" to do some cleanup. For instance, to get the two-column result you describe, you can do something like:
library(reshape2)
library(dplyr)
melt(l) %>%
filter(L2 != "member1") %>%
select(value, Var1)
(Sample data created using set.seed(1)
).
Solution 3
Another pure tidyverse solution:
ll <- l %>% map_df(enframe) %>%
mutate(key1=map(value, rownames),
key2=map(value, names),
key=map2(key1, key2, ~c(.x, .y))) %>%
select(-key1, -key2) %>%
unnest()
ll
# A tibble: 12 × 3
name value key
<chr> <dbl> <chr>
1 member1 2.12962812 a
2 member2 -0.87049458 b
3 member2 0.96190007 c
4 member2 0.56403433 d
5 member1 -0.41447472 a
6 member2 0.27270458 b
7 member2 -0.01384829 c
8 member2 -0.71561501 d
9 member1 -0.81835698 a
10 member2 -2.12746977 b
11 member2 0.66185843 c
12 member2 0.07878841 d
UPDATE I had thought you want to combine information in member1 and member2, if only member2 is needed, it'be simpler:
ll <- l %>% map_df(enframe) %>%
filter(name=="member2") %>%
mutate(key=map(value, rownames)) %>%
unnest()
ll
# A tibble: 9 × 3
name value key
<chr> <dbl> <chr>
1 member2 -0.87049458 b
2 member2 0.96190007 c
3 member2 0.56403433 d
4 member2 0.27270458 b
5 member2 -0.01384829 c
6 member2 -0.71561501 d
7 member2 -2.12746977 b
8 member2 0.66185843 c
9 member2 0.07878841 d
Comments
-
Fabio almost 2 years
I would convert a structured list in a tidy dataFrame using the speed of the dplyr package. I would know if the solution I am posting right now is "state-of-art" or there's something faster.
Here is an example of my starting list:
l = list() l[[1]] = list(member1=c(a=rnorm(1)),member2=matrix(rnorm(3),nrow=3,ncol=1,dimnames=list(c(letters[2:4]),c("sample")))) l[[2]] = list(member1=c(a=rnorm(1)),member2=matrix(rnorm(3),nrow=3,ncol=1,dimnames=list(c(letters[2:4]),c("sample")))) l[[3]] = list(member1=c(a=rnorm(1)),member2=matrix(rnorm(3),nrow=3,ncol=1,dimnames=list(c(letters[2:4]),c("sample"))))
With this result (to show you the toy structure):
l [[1]] [[1]]$member1 a 0.3340196 [[1]]$member2 sample b 1.0098830 c 0.6413375 d 0.9080675 [[2]] [[2]]$member1 a 0.0590878 [[2]]$member2 sample b 0.5585736 c -0.5936157 d -0.3985687 [[3]] [[3]]$member1 a 0.06242458 [[3]]$member2 sample b -0.2873391 c 0.5326067 d -1.1635551
Now I'll use a convenience function to rearrange the data an
lapply
to navigate through the list:organizeSamples = function(x){ member = x$member2 output = data.frame(key=rownames(member),value=member[,1]) return(output) } l_new = lapply(l, organizeSamples)
Now dplyr does the magic:
samples = dplyr::bind_rows(l_new) samples : key value 1 b 1.0098830 2 c 0.6413375 3 d 0.9080675 4 b 0.5585736 5 c -0.5936157 6 d -0.3985687 7 b -0.2873391 8 c 0.5326067 9 d -1.1635551
There's a way to do it faster, more elegant & compact using dplyr?
-
Fabio about 9 yearsnever used the
[[
syntax. But I will definetly give it a try and use microbenchmark package to test on a larger list... By the way, I like the piping system!