How to convert list of list into a tibble (dataframe)

Using tidyverse, you could use purrr to help you

library(dplyr)
library(purrr)

tibble(
  pair = map(lol, "pair"),
  genes_vec = map_chr(lol, "genes")
) %>% 
  mutate(
    pair1 = map_chr(pair, 1),
    pair2 = map_chr(pair, 2) 
  ) %>%
  select(pair1, pair2, genes_vec)
#> # A tibble: 3 x 3
#>        pair1     pair2 genes_vec
#>        <chr>     <chr>     <chr>
#> 1 BoneMarrow Pulmonary     PRR11
#> 2 BoneMarrow Umbilical    GNB2L1
#> 3  Pulmonary Umbilical    ATP1B1

with the second example, just replace map_chr(lol, "genes") with map(lol2, "genes") as you want to keep a nested dataframe with a list column.

tibble(
  pair = map(lol2, "pair"),
  genes_vec = map(lol2, "genes")
) %>% 
  mutate(
    pair1 = map_chr(pair, 1),
    pair2 = map_chr(pair, 2) 
  ) %>%
  select(pair1, pair2, genes_vec)
#> # A tibble: 3 x 3
#>        pair1     pair2 genes_vec
#>        <chr>     <chr>    <list>
#> 1 BoneMarrow Pulmonary <chr [2]>
#> 2 BoneMarrow Umbilical <chr [1]>
#> 3  Pulmonary Umbilical <chr [1]>

And a more generic approach would be to work with nested tibbles and unnest them as needed

library(dplyr)
library(purrr)
library(tidyr)

tab1 <-lol %>%
  transpose() %>%
  as_tibble() %>%
  mutate(pair = map(pair, ~as_tibble(t(.x)))) %>%
  mutate(pair = map(pair, ~set_names(.x, c("pair1", "pair2"))))
tab1
#> # A tibble: 3 x 2
#>               pair     genes
#>             <list>    <list>
#> 1 <tibble [1 x 2]> <chr [1]>
#> 2 <tibble [1 x 2]> <chr [1]>
#> 3 <tibble [1 x 2]> <chr [1]>

For lol2 nothing changes unless the list lol2 instead of lol1

tab2 <- lol2 %>%
  transpose() %>%
  as_tibble() %>%
  mutate(pair = map(pair, ~as_tibble(t(.x)))) %>%
  mutate(pair = map(pair, ~set_names(.x, c("pair1", "pair2"))))
tab2
#> # A tibble: 3 x 2
#>               pair     genes
#>             <list>    <list>
#> 1 <tibble [1 x 2]> <chr [2]>
#> 2 <tibble [1 x 2]> <chr [1]>
#> 3 <tibble [1 x 2]> <chr [1]>

You can then unnest what the column you want

tab1 %>%
  unnest()
#> # A tibble: 3 x 3
#>    genes      pair1     pair2
#>    <chr>      <chr>     <chr>
#> 1  PRR11 BoneMarrow Pulmonary
#> 2 GNB2L1 BoneMarrow Umbilical
#> 3 ATP1B1  Pulmonary Umbilical

tab2 %>% 
  unnest(pair)
#> # A tibble: 3 x 3
#>       genes      pair1     pair2
#>      <list>      <chr>     <chr>
#> 1 <chr [2]> BoneMarrow Pulmonary
#> 2 <chr [1]> BoneMarrow Umbilical
#> 3 <chr [1]>  Pulmonary Umbilical

EDIT: updated to work with vector lol2.

Maybe like this:

as.data.frame(do.call(rbind,lapply(lol2, function(x) {c(unlist(x[1]),gene=paste(unlist(x[2]),collapse=","))})),stringsAsFactors = F)




       pair1     pair2         genes
1 BoneMarrow Pulmonary GNB2L1, PRR11
2 BoneMarrow Umbilical        GNB2L1
3  Pulmonary Umbilical        ATP1B1

For the first question, pretty much the same as other answers, slightly shorter/more compact:

library(tidyverse)
lol <- list(structure(list(pair = c("BoneMarrow", "Pulmonary"), genes = "PRR11"),
                      .Names = c("pair", "genes")),
            structure(list(pair = c("BoneMarrow", "Umbilical"), genes = "GNB2L1"),
                      .Names = c("pair", "genes")),
            structure(list(pair = c("Pulmonary", "Umbilical"), genes = "ATP1B1"), .Names = c("pair","genes")))


map_dfr(lol, ~as_tibble(.) %>% 
          mutate(row=paste0("pair", row_number()))%>% 
          spread(row, pair) %>% 
          select(pair1, pair2, genes))
#> # A tibble: 3 x 3
#>   pair1      pair2     genes 
#>   <chr>      <chr>     <chr> 
#> 1 BoneMarrow Pulmonary PRR11 
#> 2 BoneMarrow Umbilical GNB2L1
#> 3 Pulmonary  Umbilical ATP1B1

^{Created on 2020-12-04 by the reprex package (v0.3.0)}

This should work:

data.frame(do.call(rbind,lol2))
data.frame(do.call(rbind,lol2))
                   pair         genes
1 BoneMarrow, Pulmonary GNB2L1, PRR11
2 BoneMarrow, Umbilical        GNB2L1
3  Pulmonary, Umbilical        ATP1B1

The way you treat the genes as a vector is the same way you can treat the pairs as a vector: instead of pair 1 and 2 you just use both of them.

> lol1 <- data.frame(t(sapply(lol,c)))
> as.data.frame(t(apply(lol1, 1, unlist)))
       pair1     pair2  genes
1 BoneMarrow Pulmonary  PRR11
2 BoneMarrow Umbilical GNB2L1
3  Pulmonary Umbilical ATP1B1