Iterate over dplyr code using purrr::map2

Question

I am relatively new to R, so my apologies if this question is too basic.

I have transactions that show quantity sold and revenue earned from different products. Because there are three products, there are 2^3 = 8 combinations for selling these products in a "basket." Each basket could be sold in any of the three given years (2016, 2017, 2018) and in any of the zones (East and West). [I have 3 years worth of transactions for the two zones: East and West.]

My objective is to analyze how much revenue is earned, how many quantities are sold, and how many transactions occurred for each combination of these products in a given year for a given zone.

I was able to do the above operation (using purrr::map) by splitting the data based on zones. I have created a list of two data frames that hold data grouped by "year" for each combination described above. This works well. However, the code is a little clunky in my opinion. There are a lot of repetitive statements. I want to be able to create a list of 2X3 (i.e. 2 zones and 3 years)

Here's my code using zone-wise splitting.

First Try

UZone <- unique(Input_File$Zone)
FYear <- unique(Input_File$Fiscal.Year)

  #Split based on zone
  a<-purrr::map(UZone, ~ dplyr::filter(Input_File, Zone == .)) %>%

  #Create combinations of products
  purrr::map(~mutate_each(.,funs(Exists = . > 0), L.Rev:I.Qty )) %>% 

  #group by Fiscal Year
  purrr::map(~group_by_(.,.dots = c("Fiscal.Year", grep("Exists", names(.), value = TRUE)))) %>% 

  #Summarize, delete unwanted columns and rename the "number of transactions" column
  purrr::map(~summarise_each(., funs(sum(., na.rm = TRUE), count = n()), L.Rev:I.Qty)) %>%
    purrr::map(~select(., Fiscal.Year:L.Rev_count)) %>%
    purrr::map(~plyr::rename(.,c("L.Rev_count" = "No.Trans")))

  #Now do Zone and Year-wise splitting : Try 1
  EastList<-a[[1]]
  EastList <- EastList %>% split(.$Fiscal.Year) 

  WestList<-a[[2]]
  WestList <- WestList %>% split(.$Fiscal.Year) 
  write.xlsx(EastList , file = "East.xlsx",row.names = FALSE)
  write.xlsx(WestList , file = "West.xlsx",row.names = FALSE)      

As you can see, the above code is very clunky. With limited knowledge of R, I researched https://blog.rstudio.org/2016/01/06/purrr-0-2-0/ and read purrr::map2() manual but I couldn't find too many examples. After reading the solution at How to add list of vector to list of data.frame objects as new slot by parallel?, I am assuming that I could use X = zone and Y= Fiscal Year to do what I have done above.

Here's what I tried: Second Try

  #Now try Zone and Year-wise splitting : Try 2
  purrr::map2(UZone,FYear, ~ dplyr::filter(Input_File, Zone == ., Fiscal.Year == .))

But this code doesn't work. I get an error message that : Error: .x (2) and .y (3) are different lengths

Question 1: Can I use map2 to do what I am trying to do? If not, is there any other better way?

Question 2: Just in case, we are able to use map2, how can I generate two Excel files using one command? As you can see above, I have two function calls above. I'd want to have only one.

Question 3: Instead of two statements below, is there any way to do sum and count in one statement? I am looking for more cleaner ways to do sum and count.

purrr::map(~summarise_each(., funs(sum(., na.rm = TRUE), count = n()), L.Rev:I.Qty)) %>%
    purrr::map(~select(., Fiscal.Year:L.Rev_count)) %>%

Can someone please help me?

---

Here's my data:

dput(Input_File)

structure(list(Zone = c("East", "East", "East", "East", "East", 
"East", "East", "West", "West", "West", "West", "West", "West", 
"West"), Fiscal.Year = c(2016, 2016, 2016, 2016, 2016, 2016, 
2017, 2016, 2016, 2016, 2017, 2017, 2018, 2018), Transaction.ID = c(132, 
133, 134, 135, 136, 137, 171, 171, 172, 173, 175, 176, 177, 178
), L.Rev = c(3, 0, 0, 1, 0, 0, 2, 1, 1, 2, 2, 1, 2, 1), L.Qty = c(3, 
0, 0, 1, 0, 0, 1, 1, 1, 2, 2, 1, 2, 1), A.Rev = c(0, 0, 0, 1, 
1, 1, 0, 0, 0, 0, 0, 1, 0, 0), A.Qty = c(0, 0, 0, 2, 2, 3, 0, 
0, 0, 0, 0, 3, 0, 0), I.Rev = c(4, 4, 4, 0, 1, 0, 3, 0, 0, 0, 
1, 0, 1, 1), I.Qty = c(2, 2, 2, 0, 1, 0, 3, 0, 0, 0, 1, 0, 1, 
1)), .Names = c("Zone", "Fiscal.Year", "Transaction.ID", "L.Rev", 
"L.Qty", "A.Rev", "A.Qty", "I.Rev", "I.Qty"), row.names = c(NA, 
14L), class = "data.frame")

---

Output Format: Here's the code to generate the output. I would love to see EastList.2016 and EastList.2017 as two sheets in one Excel file, and WestList.2016, WestList.2017 and WestList.2018 as 3 sheets in one Excel file.

  #generate the output:
  EastList.2016 <- EastList[[1]]
  EastList.2017 <- EastList[[2]]
  WestList.2016 <- WestList[[1]]
  WestList.2017 <- WestList[[2]]
  WestList.2018 <- WestList[[3]]

Answer 1

Two lists broken down by year with sums and counts for each?

In dplyr: (df <- your dataframe)

df %>% 
group_by(Zone, Fiscal.Year) %>%
summarise_at(vars(L.Rev:I.Qty), funs(sum = sum, cnt = n()))

Source: local data frame [5 x 14]
Groups: Zone [?]

   Zone Fiscal.Year L.Rev_sum L.Qty_sum A.Rev_sum A.Qty_sum I.Rev_sum I.Qty_sum L.Rev_cnt L.Qty_cnt A.Rev_cnt A.Qty_cnt I.Rev_cnt I.Qty_cnt
  <chr>       <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <int>     <int>     <int>     <int>     <int>     <int>
1  East        2016         4         4         3         7        13         7         6         6         6         6         6         6
2  East        2017         2         1         0         0         3         3         1         1         1         1         1         1
3  West        2016         4         4         0         0         0         0         3         3         3         3         3         3
4  West        2017         3         3         1         3         1         1         2         2         2         2         2         2
5  West        2018         3         3         0         0         2         2         2         2         2         2         2         2