R - converting a list into a data frame

Question

I have a list that consists of lists that all contain four elements. It looks like this:

> summary(allMorans)
                      Length Class  Mode
trait1                 4      -none- list
trait2                 4      -none- list
trait3                 4      -none- list
traitB                 4      -none- list
traitX                 4      -none- list

The four elements are the result of a certain statistical test and contain the elements "observed", "expected", "sd", "pvalue".

If I do this:

MoransResults <- as.data.frame(allMorans)

I get a single very long row that looks like this:

 trait1.observed    trait1.expected    trait1.sd      trait1.pvalue       trait2.observed    trait2.expected   trait2.sd      trait2.pvalue    ...etc  
 0.1                0.2                0.01           0.09                0.3                0.2               0.01           0.07             ...etc

However, I need to turn this list of lists into a dataframe that looks like this:

TRAIT    observed   expected   sd     pvalue
trait1   0.1        0.2        0.01   0.09
trait2   0.3        0.2        0.01   0.07
trait3   0.1        0.1        0.01   0.55
traitB   0.2        0.2        0.01   0.49
traitX   0.3        0.2        0.01   0.07

How do I achieve this?

UPDATE:

As requested in the comments, here is some example data:

> dput(allMorans)
structure(list(trait1 = structure(list(observed = -0.00820649454281412, 
expected = -0.0001000100010001, sd = 6.44860382275647e-05, 
p.value = 0), .Names = c("observed", "expected", "sd", "p.value"
)), trait2 = structure(list(observed = -0.16378930443073, expected = -0.0001000100010001, 
sd = 6.44860728086603e-05, p.value = 0), .Names = c("observed", 
"expected", "sd", "p.value")), trait3 = structure(list(observed = -0.348047732487769, 
expected = -0.0001000100010001, sd = 6.44872069930741e-05, 
p.value = 0), .Names = c("observed", "expected", "sd", "p.value"
))), .Names = c("trait1", "trait2", "trait3"))

Answer 1

Another alternative:

t(simplify2array(allMorans))

#           observed     expected             sd   p.value
#trait1 -0.008206495  -0.00010001   6.448604e-05      0      
#trait2 -0.1637893    -0.00010001   6.448607e-05      0      
#trait3 -0.3480477    -0.00010001   6.448721e-05      0  

Answer 2

In base R (no additional libraries necessary)

do.call(rbind, lapply(allMorans, as.data.frame))
#           observed    expected           sd p.value
#trait1 -0.008206495 -0.00010001 6.448604e-05       0
#trait2 -0.163789304 -0.00010001 6.448607e-05       0
#trait3 -0.348047732 -0.00010001 6.448721e-05       0

Answer 3

A solution using the tidyverse package. as.tibble convert each element in the list to be a tibble. map_dfr can then combine all these tibbles to form the final output.

library(tidyverse)

dt <- allMorans %>% map_dfr(as.tibble, .id = "TRAIT")
dt
# # A tibble: 3 x 5
#    TRAIT     observed    expected           sd p.value
#    <chr>        <dbl>       <dbl>        <dbl>   <dbl>
# 1 trait1 -0.008206495 -0.00010001 6.448604e-05       0
# 2 trait2 -0.163789304 -0.00010001 6.448607e-05       0
# 3 trait3 -0.348047732 -0.00010001 6.448721e-05       0