json - speed and memory comparison between rowwise with do and transmute -

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i wondering, memory , speed comparison between rowwise , transmute function in dplyr

i have list in data frame column, want know 1 better. currently, due limited knowlege, using rowwise collect information list,

i have 3 column.

first column unique id each row. second column json response third column list extracted json response following code 

vectorize_fromjson <- vectorize(fromjson) z <- vectorize_fromjson(x)

example of json response

x extracted data frame

x = c('{"company_name": "a", "employees":[ {"firstname":"john", "lastname":"doe"}, {"firstname":"anna", "lastname":"smith"}, {"firstname":"peter", "lastname":"jones"} ]}',  '{"company_name": "a", "employees":[ {"firstname":"john", "lastname":"doe"}, {"firstname":"anna", "lastname":"smith"}, {"firstname":"peter", "lastname":"jones"} ]}')

currently, extract company_name json response follows:

data_df$json_list <- z  data_df %>% rowwise %>% do(company_name = .$json_list$company_name)

i not able use transmute or mutate company name list.

main question, how efficient rowwise compare transmute ?


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