how to reshape the matrix and fill the missing value as 0 - r

Question

I have a question about matrix structure manipulation in R, here I need to first transpose the matrix and combine the month and status columns, filling the missing values with 0. Here I have an example, currently my data is like belows. It seems very tricky. I would appreciate if anyone could help on this. Thank you.
Hi, my data looks like the follows:
structure(list(Customer = c("1096261", "1096261", "1169502",
"1169502"), Phase = c("2", "3", "1", "2"), Status = c("Ontime",
"Ontime", "Ontime", "Ontime"), Amount = c(21216.32, 42432.65,
200320.05, 84509.24)), .Names = c("Customer", "Phase", "Status",
"Amount"), row.names = c(NA, -4L), class = c("grouped_df", "tbl_df",
"tbl", "data.frame"), vars = c("Customer", "Phase"), drop = TRUE, indices
= list(
0L, 1L, 2L, 3L), group_sizes = c(1L, 1L, 1L, 1L), biggest_group_size = 1L,
labels = structure(list(
Customer = c("1096261", "1096261", "1169502", "1169502"),
Phase = c("2", "3", "1", "2")), row.names = c(NA, -4L), class =
"data.frame", vars = c("Customer",
"Phase"), drop = TRUE, .Names = c("Customer", "Phase")))
I need to have the reshaped matrix with the following columns:
Customer Phase1earlyTotal Phase2earlyTotal....Phase4earlyTotal...Phase1_ Ontimetotal...Phase4_Ontimetotal...Phase1LateTotal_Phase4LateTotal. For example Phase1earlytotal includes the sum of the amount with the Phase=1 and Status=Early.
Currently I use the following scripts, which does not work, coz I dont know
how to combine Phase and Stuatus Column.
mydata2<-data.table(mydata2,V3,V4)
mydata2$V4<-NULL
datacus <- data.frame(mydata2[-1,],stringsAsFactors = F);
datacus <- datacus %>% mutate(Phase= as.numeric(Phase),Amount=
as.numeric(Amount)) %>%
complete(Phase = 1:4,fill= list(Amount = 0)) %>%
dcast(datacus~V3, value.var = 'Amount',fill = 0) %>% select(Phase, V3)
%>%t()

Answer 1

I believe you are looking for somethink like this?
sample data
df <- structure(list(Customer = c("1096261", "1096261", "1169502",
"1169502"), Phase = c("2", "3", "1", "2"), Status = c("Ontime",
"Ontime", "Ontime", "Ontime"), Amount = c(21216.32, 42432.65,
200320.05, 84509.24)), .Names = c("Customer", "Phase", "Status",
"Amount"), row.names = c(NA, -4L), class = c("grouped_df", "tbl_df",
"tbl", "data.frame"), vars = c("Customer", "Phase"), drop = TRUE, indices
= list(
0L, 1L, 2L, 3L), group_sizes = c(1L, 1L, 1L, 1L), biggest_group_size = 1L,
labels = structure(list(
Customer = c("1096261", "1096261", "1169502", "1169502"),
Phase = c("2", "3", "1", "2")), row.names = c(NA, -4L), class =
"data.frame", vars = c("Customer",
"Phase"), drop = TRUE, .Names = c("Customer", "Phase")))
# Customer Phase Status Amount
# 1: 1096261 2 Ontime 21216.32
# 2: 1096261 3 Ontime 42432.65
# 3: 1169502 1 Ontime 200320.05
# 4: 1169502 2 Ontime 84509.24
code
library( data.table )
dcast( setDT( df ), Customer ~ Phase + Status, fun = sum, value.var = "Amount" )[]
output
# Customer 1_Ontime 2_Ontime 3_Ontime
# 1: 1096261 0 21216.32 42432.65
# 2: 1169502 200320 84509.24 0.00