Hi, I'm not totally sure about your expected output here, but one thing you could do is to use purrr::reduce() annd dplyr::bind_rows() to combine all the data frames in your list. Not sure if it makes sense to combine all these columns based on the data, but this is how you would do it. Also note that your reprex above returned an error because you didn't attach all the packages that you used in your code.
library(purrr)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(rvest)
#> Loading required package: xml2
#>
#> Attaching package: 'rvest'
#> The following object is masked from 'package:purrr':
#>
#> pluck
library(stringr)
url <- file.path(str_c("https://amx.am/en/9/trading/10/instruments", '?page=', 1:3))
tbl <- lapply(url, read_html) %>%
lapply(html_table)
tbl %>%
reduce(bind_rows) %>%
head()
#> X1 X2
#> 1 Ticker 0N60294
#> 2 Issuer Ministry of Finance of the Republic of Armenia
#> 3 ISIN AMGN60294201
#> 4 Admittance date 29.04.2015
#> 5 Class Coupon bond
#> 6 Listing category Gbond
Created on 2020-01-25 by the reprex package (v0.3.0)