How to convert to genre, production companies, cast which are JSON column into tibble or df?

https://www.kaggle.com/rounakbanik/the-movies-dataset this is the dataset I was using.

I tried the following code in R:

toJSON(movies) %>% validate()
json_to_df <- function(df, column){
    column_1 <- df[apply(df[,column],1,nchar)>2,]  #Checks if there is an entry

    list_1 <- lapply(column_1[[column]], fromJSON) #Converts the JSON to a list
    values <- data.frame(unlist(lapply(list_1, function(x) paste(x$name,collapse = ",")))) #Collapsing all the values of the list as a key value pair

    final_df <- cbind(column_1$id, column_1$title, values) #new data frame with the key and values a s columns
    names(final_df)  <- c("id", "title", column)
    return(final_df)
}

#Calling the json_to_df() to generate the dataframes for all the JSON Columns
library("df2json")
genres_df %>% fromJSON(genre_csv)
keywords_df <- parse_json_to_df(keywords, "keywords")
prod_cntry_df <- json_to_df(movies, "production_countries")
prod_cmpny_df <- json_to_df(movies, "production_companies")
spoken_lang_df <- json_to_df(movies, "spoken_languages")

this is the error I got: Error in json2df(movies, "genres") : unused argument ("genres")

I'm trying to run a logistic regression. Thus, would like to look at how these small detail affect the movie's budget or production companies.