Unlist column to create unique row in dataframe

Question

I am faced with the following R transformation issue. I have the following dataframe:

 test_df <-  structure(list(word = c("list of XYZ schools", 
"list of basketball", "list of usa"), results = c("58", "151", "29"), key_list = structure(list(`coRq,coG,coQ,co7E,coV98` = c("coRq", "coG", "coQ", "co7E", "coV98"), `coV98,coUD,coHF,cobK,con7` = c("coV98","coUD", "coHF", "cobK", "con7"), `coV98,coX7,couC,coD3,copW` = c("coV98", "coX7", "couC", "coD3", "copW")), .Names = c("coRq,coG,coQ,co7E,coV98", "coV98,coUD,coHF,cobK,con7", "coV98,coX7,couC,coD3,copW"))), .Names = c("word", "results", "key_list"), row.names = c(116L, 150L, 277L), class = "data.frame")

In short there are three columns, unique on "word" and then a corresponding "key_list" that has a list of keys comma separated. I am interested in creating a new data frame where each key is unique and the word information is duplicated as well as the result information. So a dataframe that looks as follows:

key          word                    results                    
coV98       "list of XYZ schools"    58
coRq        "list of XYZ schools"    58
coV98       "list of basketball"     151
coV98       "list of usa"            29

And so on for all the keys, so I would like to expand the keys unlist them and then reshape into a dataframe with repeating words and other columns.

I have tried a bunch of the following: Created a unique list of keys and then attempted to grep for each of those keys in the column and loop through to create a new smaller dataframe and then rbind those together, the resulting dataframe however does not contain the key column:

keys <- as.data.frame(table(unname(unlist(test_df$key_list))))
ttt <- lapply(keys, function(xx){
      idx <- grep(xx, test_df$key_list)
      df <- all_data_sub[idx,]})
      final_df <- do.call(rbind, ttt)

I have also played around with unlisting and reshaping, but I am not getting the right combination. Any advice would be great! thanks

Answer 1

May be we can use listCol_l from splitstackshape

library(splitstackshape)
listCol_l(test_df, 'key_list')[]

Answer 2

In case a base R solution is helpful for someone:

do.call(rbind, lapply(seq_along(test_df$key_list), function(i) {
    merge(test_df$key_list[[i]], test_df[i,-3], by=NULL)
  }))