CODEWITHSUNDEEP
r
Programmer Man
Programmer Man

Reputation: 1305

How to insert all missing weeks between new and old date in a datatable to calculate weekly stock R

So I have a datatable df with column ID DATE and STOCK

In this table, the same ID has multiple values with their date and stock:

ID        DATE        STOCK
a1     2017-05-04       1
a1     2017-06-04       4
a1     2017-06-05       1
a1     2018-05-04       1
a1     2018-06-04       3
a1     2018-06-05       1
a2     2016-11-26       2
a2      ...             ..

Using lubridate I can get which week a date is as follows:

dfWeeks=df[,"WEEK" := floor_date(df$`Date`, "week")]

ID        DATE        STOCK        WEEK
a1     2017-05-04       1       2017-04-30
a1     2017-06-04       4       2017-06-04
a1     2017-06-05       1       2017-06-04
a1     2018-05-04       1       2018-04-29
a1     2018-06-04       3       2018-06-03
a1     2018-06-05       1       2018-06-03
a2     2016-11-26       2       2016-11-20
a2      ...             ..

So from column DATE I know my old date is 2017-05-04 and newest date 2018-06-05, which has about 56.71429 weeks:

dates <- c( "2017-05-04","2018-06-05")
dif <- diff(as.numeric(strptime(dates, format = "%Y-%m-%d")))/(60 * 60 * 24 * 7)

And my table has only 4 unique weeks, so the idea is to sum stock for each week and insert the missing (57-4=53 weeks) ones with 0 value in stock.

Then I can do the mean of all the weeks like 

meanStock<- dfWeeks[, .(mean=sum(Stock, na.rm = T)/dif <- diff(as.numeric(strptime(c(min(Date), max(Date)), format = "%Y-%m-%d")))/(60 * 60 * 24 * 7) ), by = .(ID)]

But I don't know if it will work, Hope I made it clear and any advice or approach is welcomed.

UPDATE:

This is how I get the max and min date

max = aggregate(df$`Date`,by=list(df$ID),max)
colnames(max) = c("ID", "MAX")
min = aggregate(df$`Date`,by=list(df$ID),min)
colnames(min) = c("ID", "MIN")
test <- merge(max, min, by="ID", all=T)

Upvotes: 3

Views: 116

Answers (1)

arg0naut91
arg0naut91

Reputation: 14764

Something like:

library(data.table)

setDT(df)[, DATE := as.Date(DATE)][, `:=` (st = min(DATE), end = max(DATE) + 7), by = ID][
  , .(ID = ID, DATE = DATE, STOCK = STOCK, Expanded = seq(st, end, by = "week")), by = 1:nrow(df)][
    , `:=` (WEEK = floor_date(Expanded, "week"), WEEK2 = floor_date(DATE, "week"))][
      WEEK != WEEK2, STOCK := 0][
        , .(SUM_STOCK = sum(STOCK)), by = .(WEEK, ID)]

Output (rows for the weeks of 2017-04-02 until 2017-06-11 and ID a1):

          WEEK ID SUM_STOCK
 1: 2017-04-02 a1         0
 2: 2017-04-09 a1         0
 3: 2017-04-16 a1         0
 4: 2017-04-23 a1         0
 5: 2017-04-30 a1         1
 6: 2017-05-07 a1         0
 7: 2017-05-14 a1         0
 8: 2017-05-21 a1         0
 9: 2017-05-28 a1         0
10: 2017-06-04 a1         5
11: 2017-06-11 a1         0

Upvotes: 1

Related Questions