data.table - Widening a dataframe to get monthly sums of revenue for all unique values of catogorical columns in R -


i have df has data this:

sub = c("x001","x002", "x001","x003","x002","x001","x001","x003","x002","x003","x003","x002")  month = c("201506", "201507", "201506","201507","201507","201508", "201508","201507","201508","201508", "201508", "201508")  tech = c("mobile", "tablet", "pc","mobile","mobile","tablet", "pc","tablet","pc","pc", "mobile", "tablet")  brand = c("apple", "samsung", "dell","apple","samsung","apple", "samsung","dell","samsung","dell", "dell", "dell")  revenue = c(20, 15, 10,25,20,20, 17,9,14,12, 9, 11)  df = data.frame(sub, month, brand, tech, revenue)

i want use sub , month key , 1 row every subscriber per month displays sum of revenues unique values in tech , brand subscriber month. example simplified , less columns have huge data set decided try doing data.table.

i have managed 1 catagorical column, either tech or brand using this:

df1 <- dcast(df, sub + month ~ tech,  fun=sum, value.var = "revenue")

but want 2 or more caqtogorical columns, far i've tried this:

df2 <- dcast(df, sub + month ~ tech+brand,  fun=sum, value.var = "revenue")

and concatenates unique values of both catogorical columns , sums not want that. wan seperate columns each unique value of catogorical columns.

i'm new r , appreciate help.

(i assume df data.table rather data.frame in example.)

one possible solution first melt data while keeping sub, month , revenue keys. way, brand , tech converted single variable value corresponding each existing combination of keys. way able dcast operating against single column- in first example

dcast(melt(df, c(1:2, 5)), sub + month ~ value, sum, value.var = "revenue") #     sub  month pc apple dell mobile samsung tablet # 1: x001 201506 10    20   10     20       0      0 # 2: x001 201508 17    20    0      0      17     20 # 3: x002 201507  0     0    0     20      35     15 # 4: x002 201508 14     0   11      0      14     11 # 5: x003 201507  0    25    9     25       0      9 # 6: x003 201508 12     0   21      9       0      0

as per ops comment, can add prefix adding variable formula. way, column ordered properly

dcast(melt(df, c(1:2, 5)), sub + month ~ variable + value, sum, value.var = "revenue") #     sub  month brand_apple brand_dell brand_samsung tech_pc tech_mobile tech_tablet # 1: x001 201506          20         10             0      10          20           0 # 2: x001 201508          20          0            17      17           0          20 # 3: x002 201507           0          0            35       0          20          15 # 4: x002 201508           0         11            14      14           0          11 # 5: x003 201507          25          9             0       0          25           9 # 6: x003 201508           0         21             0      12           9           0

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