Moving sum over date range - sql

Question

I have this table that has wide range of dates and a corresponding value for each one of those dates, an example shown below.
Date Value
6/01/2013 8
6/02/2013 4
6/03/2013 1
6/04/2013 7
6/05/2013 1
6/06/2013 1
6/07/2013 3
6/08/2013 8
6/09/2013 4
6/10/2013 2
6/11/2013 10
6/12/2013 4
6/13/2013 7
6/14/2013 3
6/15/2013 2
6/16/2013 1
6/17/2013 7
6/18/2013 5
6/19/2013 1
6/20/2013 4
What I am trying to do is create a query that will create a new column that will display the sum of the Value’s column for a specified date range. For example down below, the sum column contains the sum of its corresponding date going back one full week. So the Sum of the date 6/9/2013 would be the sum of the values from 6/03/2013 to 6/09/2013.
Date Sum
6/01/2013 8
6/02/2013 12
6/03/2013 13
6/04/2013 20
6/05/2013 21
6/06/2013 22
6/07/2013 25
6/08/2013 25
6/09/2013 25
6/10/2013 26
6/11/2013 29
6/12/2013 32
6/13/2013 38
6/14/2013 38
6/15/2013 32
6/16/2013 29
6/17/2013 34
6/18/2013 29
6/19/2013 26
6/20/2013 23
I’ve tried to using the LIMIT clause but I could not get it to work, any help would be greatly appreciated.

Answer 1

zoo has a function rollapply which can do what you need:
z <- zoo(x$Value, order.by=x$Date)
rollapply(z, width = 7, FUN = sum, partial = TRUE, align = "right")
## 2013-06-01 8
## 2013-06-02 12
## 2013-06-03 13
## 2013-06-04 20
## 2013-06-05 21
## 2013-06-06 22
## 2013-06-07 25
## 2013-06-08 25
## 2013-06-09 25
## 2013-06-10 26
## 2013-06-11 29
## 2013-06-12 32
## 2013-06-13 38
## 2013-06-14 38
## 2013-06-15 32
## 2013-06-16 29
## 2013-06-17 34
## 2013-06-18 29
## 2013-06-19 26
## 2013-06-20 23

Answer 2

Using data.table
require(data.table)
#Build some sample data
data <- data.table(Date=1:20,Value=rpois(20,10))
#Build reference table
Ref <- data[,list(Compare_Value=list(I(Value)),Compare_Date=list(I(Date)))]
#Use lapply to get last seven days of value by id
data[,Roll.Val := lapply(Date, function(x) {
d <- as.numeric(Ref$Compare_Date[[1]] - x)
sum((d <= 0 & d >= -7)*Ref$Compare_Value[[1]])})]
head(data,10)
Date Value Roll.Val
1: 1 14 14
2: 2 7 21
3: 3 9 30
4: 4 5 35
5: 5 10 45
6: 6 10 55
7: 7 15 70
8: 8 14 84
9: 9 8 78
10: 10 12 83
Here is another solution if anyone is interested:
library("devtools")
install_github("boRingTrees","mgahan")
require(boRingTrees)
rollingByCalcs(data,dates="Date",target="Value",stat=sum,lower=0,upper=7)

Answer 3

Here is one way of doing it
> input <- read.table(text = "Date Value
+ 6/01/2013 8
+ 6/02/2013 4
+ 6/03/2013 1
+ 6/04/2013 7
+ 6/05/2013 1
+ 6/06/2013 1
+ 6/07/2013 3
+ 6/08/2013 8
+ 6/09/2013 4
+ 6/10/2013 2
+ 6/11/2013 10
+ 6/12/2013 4
+ 6/13/2013 7
+ 6/14/2013 3
+ 6/15/2013 2
+ 6/16/2013 1
+ 6/17/2013 7
+ 6/18/2013 5
+ 6/19/2013 1
+ 6/20/2013 4 ", as.is = TRUE, header = TRUE)
> input$Date <- as.Date(input$Date, format = "%m/%d/%Y") # convert Date
>
> # create a sequence that goes a week back from the current data
> x <- data.frame(Date = seq(min(input$Date) - 6, max(input$Date), by = '1 day'))
>
> # merge
> merged <- merge(input, x, all = TRUE)
>
> # replace NAs with zero
> merged$Value[is.na(merged$Value)] <- 0L
>
> # use 'filter' for the running sum and delete first 6
> input$Sum <- filter(merged$Value, rep(1, 7), sides = 1)[-(1:6)]
> input
Date Value Sum
1 2013-06-01 8 8
2 2013-06-02 4 12
3 2013-06-03 1 13
4 2013-06-04 7 20
5 2013-06-05 1 21
6 2013-06-06 1 22
7 2013-06-07 3 25
8 2013-06-08 8 25
9 2013-06-09 4 25
10 2013-06-10 2 26
11 2013-06-11 10 29
12 2013-06-12 4 32
13 2013-06-13 7 38
14 2013-06-14 3 38
15 2013-06-15 2 32
16 2013-06-16 1 29
17 2013-06-17 7 34
18 2013-06-18 5 29
19 2013-06-19 1 26
20 2013-06-20 4 23
>