Here’s my solution using purrr & dplyr:
library(purrr)
library(dplyr)
lst1 <- list(mtcars=mtcars, iris=iris, chick=chickwts, cars=cars, airqual=airquality)
lst1 %>%
map_dfr(select, value=1, .id="df") %>% # select first column of every dataframe and name it "value"
group_by(value) %>%
summarise(freq=n(), # frequency over all dataframes
n_df=n_distinct(df), # number of dataframes this value ocurrs
dfs = paste(unique(df), collapse=",")) %>%
filter(n_df > 1) %>%
filter(n_df == 5) # if value has to be in all 5 dataframes
solved Frequency table with common values of 5 tables