r - Reshape multiple values at once -

i have long data set make wide , i'm curious if there way in 1 step using reshape2 or tidyr packages in r.

the data frame df looks this:

id  type    transactions    amount 20  income       20          100 20  expense      25          95 30  income       50          300 30  expense      45          250 

i'd this:

id  income_transactions expense_transactions    income_amount   expense_amount 20       20                           25                 100             95 30       50                           45                 300             250 

i know can part of way there reshape2 via example:

dcast(df, id ~  type, value.var="transactions") 

but there way reshape entire df in 1 shot addressing both "transactions" , "amount" variables @ once? , ideally new more appropriate column names?

in "reshape2", can use recast (though in experience, isn't known function).

library(reshape2) recast(mydf, id ~ variable + type, id.var = c("id", "type")) #   id transactions_expense transactions_income amount_expense amount_income # 1 20                   25                  20             95           100 # 2 30                   45                  50            250           300 

you can use base r's reshape:

reshape(mydf, direction = "wide", idvar = "id", timevar = "type") #   id transactions.income amount.income transactions.expense amount.expense # 1 20                  20           100                   25             95 # 3 30                  50           300                   45            250 

or, can melt , dcast, (here "data.table"):

library(data.table) library(reshape2) dcast.data.table(melt(as.data.table(mydf), id.vars = c("id", "type")),                   id ~ variable + type, value.var = "value") #    id transactions_expense transactions_income amount_expense amount_income # 1: 20                   25                  20             95           100 # 2: 30                   45                  50            250           300 

in later versions of dcast.data.table "data.table" (1.9.8) you able directly. if understand correctly, @arun trying implement doing reshaping without first having melt data, happens presently recast, wrapper melt + dcast sequence of operations.

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and, thoroughness, here's tidyr approach:

library(dplyr) library(tidyr) mydf %>%    gather(var, val, transactions:amount) %>%    unite(var2, type, var) %>%    spread(var2, val) #   id expense_amount expense_transactions income_amount income_transactions # 1 20             95                   25           100                  20 # 2 30            250                   45           300                  50