Thanks everyone in advance for the help even if this seems very basic!

I have a function that uploads a series of surveys from the survey software Qualtrics. The package qualtRics uses an API to get survey data directly to R.

I created a function that uploads multiple surveys using the map function. So each survey is then stored as its own list. What I want to do is bind rows on all lists since they have some common variables, but I do not know how to manipulate each list to keep common variable names. I am assuming it is another map function using pluck but can't figure exactly what to do.

It is difficult to really help you without a small example of what you want to do, what you tried and what you want to get as result.
Can you help us help you by providing a reprex ?

Here one dummy example from what I understood from your question.

library(dplyr)
#>
#> Attachement du package : 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
dummylist <- list(
tab1 =tibble(col1 = 1:10,
col2 = sample(LETTERS, 10)),
tab2 = tibble(col1 = 1:10,
col2 = sample(LETTERS, 10))
)
dummylist %>%
bind_rows(.id = "tab_num")
#> # A tibble: 20 x 3
#> tab_num col1 col2
#> <chr> <int> <chr>
#> 1 tab1 1 J
#> 2 tab1 2 P
#> 3 tab1 3 D
#> 4 tab1 4 I
#> 5 tab1 5 B
#> 6 tab1 6 X
#> 7 tab1 7 G
#> 8 tab1 8 Y
#> 9 tab1 9 Z
#> 10 tab1 10 L
#> 11 tab2 1 A
#> 12 tab2 2 H
#> 13 tab2 3 S
#> 14 tab2 4 D
#> 15 tab2 5 U
#> 16 tab2 6 O
#> 17 tab2 7 K
#> 18 tab2 8 R
#> 19 tab2 9 X
#> 20 tab2 10 F

you could also use directly purrr::map_df to directly get a tibble output

# dummy example with identity function
dummylist %>%
purrr::map_df(identity, .id = "tab_num")
#> # A tibble: 20 x 3
#> tab_num col1 col2
#> <chr> <int> <chr>
#> 1 tab1 1 J
#> 2 tab1 2 P
#> 3 tab1 3 D
#> 4 tab1 4 I
#> 5 tab1 5 B
#> 6 tab1 6 X
#> 7 tab1 7 G
#> 8 tab1 8 Y
#> 9 tab1 9 Z
#> 10 tab1 10 L
#> 11 tab2 1 A
#> 12 tab2 2 H
#> 13 tab2 3 S
#> 14 tab2 4 D
#> 15 tab2 5 U
#> 16 tab2 6 O
#> 17 tab2 7 K
#> 18 tab2 8 R
#> 19 tab2 9 X
#> 20 tab2 10 F

Created on 2018-02-02 by the reprex package (v0.1.1.9000).

Thanks @cderv for the response! I tried purrr:map_df but I don't think it binds rows in a "smart" way like dplyr::bind_rows does. When i used purrr:reduce and used function dplyr::bind_rows this seemed to do what I wanted.