I'm having trouble working with a very large data set. I have an Item ID, Purchase Date, and Purchase Quantity.
str(Output0)
'data.frame': 183847 obs. of 3 variables:
$ D: Factor w/ 460 levels "2015-09-21","2015-09-24",..: 3 3 3 3 3 3 3 3 3 3 ...
$ P: int 1 2 3 4 5 6 7 8 9 10 ...
$ Q: num 7 1 2 1 1 1 1 1 1 1 ...
As a note, P=Item ID, D=Date, and Q=Purchase Quantity
I would like to sum the purchase quantity for each individual item by a 3 day period (So there may still be duplicate item IDs). For example:
P Date Purchase Q
1234 1/1/16 1
1235 1/1/16 1
1235 1/2/16 1
1235 1/3/16 1
1444 1/1/16 1
1444 1/2/16 1
1444 1/3/16 1
Would look like:
ItemID DateEndPoint Purchase Q
1234 1/1/16 1
1235 1/3/16 3
1444 1/3/16 3
I've tried using:
Output2 <- aggregate(Output0$Q, by=list(PS=P,
Date = cut(as.Date(Output0$D, format="%d/%m/%Y"),breaks="3 day")), FUN=sum)
but it is coming up with this error:
Error in seq.int(0, to0 - from, by) : 'to' cannot be NA, NaN or infinite
In addition: Warning messages: 1: In min.default(c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, : no non-missing arguments to min; returning Inf 2: In max.default(c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, : no non-missing arguments to max; returning -Inf
I'd also like to do the same for other time periods as needed (1 day, 1 week) so something reproducible would be wonderful.
In response to P Lapointe: I tried the below and it looks great, except that the last column is totalling all items across all dates instead of for each period
Output1 <- POData%>%mutate(Date=as.Date(POData$`PO Date`,"%m-%d-%Y"),Date_Group=cut(Date,breaks="3 days"))%>% group_by(POData$`ItemID`,Date_Group)%>%summarise(DateEndPoint=max(Date),Purchase_Q=sum(POData$`POQty`,na.rm=TRUE))
It displays as:
> View(Output1)
> str(Output1)
Classes ‘grouped_df’, ‘tbl_df’, ‘tbl’ and 'data.frame': 116749 obs. of 4 variables:
$ POData$`Item ID`: int 11 11 11 11 11 11 11 11 11 11 ...
$ Date_Group : Factor w/ 216 levels "2015-09-21","2015-09-24",..: 4 6 11 13 14 15 18 19 24 25 ...
$ DateEndPoint : Date, format: "2015-10-02" "2015-10-08" ...
$ Purchase_Q : num 2691020 2691020 2691020 2691020 2691020 ...
- attr(*, "vars")= chr "POData$`Item ID`"
- attr(*, "drop")= logi TRUE
Thank you in advance!
Here's how to do that with dplyr. Note that I extended your example by one day to show that it can handle additional 3-day groups. Basically, you want to create a new Date_group column to group on. Then, summarise.
df <- read.table(text="P Date Purchase_Q
1234 1/1/16 1
1235 1/1/16 1
1235 1/2/16 1
1235 1/3/16 1
1444 1/1/16 1
1444 1/2/16 1
1444 1/3/16 1
1444 1/5/16 1",header=TRUE,stringsAsFactors=FALSE)
library(dplyr)
df%>%
mutate(Date=as.Date(Date,"%m/%d/%y"),Date_group=cut(Date,breaks="3 days")) %>%
group_by(P,Date_group) %>%
summarise(DateEndPoint=max(Date),Purchase_Q=sum(Purchase_Q,na.rm=TRUE))
P Date_group DateEndPoint Purchase_Q
<int> <fctr> <date> <int>
1 1234 2016-01-01 2016-01-01 1
2 1235 2016-01-01 2016-01-03 3
3 1444 2016-01-01 2016-01-03 3
4 1444 2016-01-04 2016-01-05 1