Let me show an example. Consider we have 3 tables (focus on columns N):
Table 1 Table 2 Table 3
------------- ------------- -------------
Here's a more functional way that will work with any list of tables. First we extract all the 'N' columns and then get the intersection of all these values. Then we just filter each of the tables.
library('tidyverse')
tables <- list(Table1, Table2, Table3)
common <- tables %>%
map('N') %>%
reduce(intersect)
tables %>%
map(filter, N %in% common)
# [[1]]
# N Values
# 1 5 1
# 2 10 2
# 3 15 3
#
# [[2]]
# N Values
# 1 5 -1
# 2 10 -3
# 3 15 -4
#
# [[3]]
# N Values
# 1 5 1
# 2 10 5
# 3 15 3