Example using tidyr::uncount()

uncount.R

# data source ------------------------------------------------------------------
# Are federal laws on gun ownership too strict? Not strict enough? Or just about right?
# http://www.surveyusa.com/client/PollReport.aspx?g=e2893631-b41f-40e1-a049-86e0849670ce

# load packages ----------------------------------------------------------------
library(tidyverse)

# frequency table --------------------------------------------------------------

freq_table <- tribble(
  ~response,           ~n,
  "too strict",        99,
  "not strict enough", 507,
  "just about right",  151,
  "not sure",          42
)

freq_table
#> # A tibble: 4 x 2
#>   response              n
#>   <chr>             <dbl>
#> 1 too strict           99
#> 2 not strict enough   507
#> 3 just about right    151
#> 4 not sure             42

# frequency table to data frame of individual observations
df <- uncount(freq_table, weights = n)
df
#> # A tibble: 799 x 1
#>    response  
#>    <chr>     
#>  1 too strict
#>  2 too strict
#>  3 too strict
#>  4 too strict
#>  5 too strict
#>  6 too strict
#>  7 too strict
#>  8 too strict
#>  9 too strict
#> 10 too strict
#> # … with 789 more rows

# back to frequency table
df %>%
  count(response)
#> # A tibble: 4 x 2
#>   response              n
#>   <chr>             <int>
#> 1 just about right    151
#> 2 not strict enough   507
#> 3 not sure             42
#> 4 too strict           99

# contingency table ------------------------------------------------------------

cont_table <- tribble(
  ~response,           ~male, ~female,
  "too strict",        65,    34,
  "not strict enough", 216,   291,
  "just about right",  100,   51,
  "not sure",          12,    30
)

cont_table
#> # A tibble: 4 x 3
#>   response           male female
#>   <chr>             <dbl>  <dbl>
#> 1 too strict           65     34
#> 2 not strict enough   216    291
#> 3 just about right    100     51
#> 4 not sure             12     30

# contingency table to data frame of individual observations
df <- cont_table %>%
  pivot_longer(cols = c(male, female), names_to = "gender") %>%
  uncount(weights = value)
df
#> # A tibble: 799 x 2
#>    response   gender
#>    <chr>      <chr> 
#>  1 too strict male  
#>  2 too strict male  
#>  3 too strict male  
#>  4 too strict male  
#>  5 too strict male  
#>  6 too strict male  
#>  7 too strict male  
#>  8 too strict male  
#>  9 too strict male  
#> 10 too strict male  
#> # … with 789 more rows

# back to contingency table
df %>%
  count(response, gender) %>%
  pivot_wider(names_from = gender, values_from = n)
#> # A tibble: 4 x 3
#>   response          female  male
#>   <chr>              <int> <int>
#> 1 just about right      51   100
#> 2 not strict enough    291   216
#> 3 not sure              30    12
#> 4 too strict            34    65