R: Avoid using a loop or line
I am following two dataframes df_sales and df_supply .
I want to merge sale for delivery in such a way that my df_sales table has DATE_SUPPLY and QNT_SUPPLY from df_supply below conditions
* Condition : DATE_SUPPLY must be the last DATE_SUPPLY of the corresponding "ITEM" for the corresponding "STORE", i.e.DATE_SALE <- max(df_supply[df_supply$DATE_SUPPLY <= df_sales$DATE_SALE & df_supply$STORE == df_sales$STORE & df_supply$ITEM == df_sales$ITEM,]$DATE_SUPPLY)*
This can be possible with the string apply function, or simply by writing a loop. But I have a huge array of data, so I don't want to loop.
df_sales <- data.frame("STORE"=c(1001,1001,1001,1001,1001,1002,1002,1002,1002,1002),"ITEM"=c(13048, 13057, 13082, 13048, 13057, 13145, 13166, 13229, 13057, 13048),"DATE_SALE"=as.Date(c("1/1/2014","1/1/2014","1/2/2014","1/2/2014","1/2/2014","1/2/2014","1/3/2014","1/3/2014","1/3/2014","1/4/2014"),"%m/%d/%Y"),"QNT_SALE"=c(1,1,1,1,1,1,1,1,1,1))
df_sales
STORE ITEM DATE_SALE QNT_SALE
1 1001 13048 2014-01-01 1
2 1001 13057 2014-01-01 1
3 1001 13082 2014-01-02 1
4 1001 13048 2014-01-02 1
5 1001 13057 2014-01-02 1
6 1002 13145 2014-01-02 1
7 1002 13166 2014-01-03 1
8 1002 13229 2014-01-03 1
9 1002 13057 2014-01-03 1
10 1002 13048 2014-01-04 1
df_supply <- data.frame("STORE"=c(1001,1002,1001,1001,1002,1002,1002,1002,1002,1002),"ITEM"=c(13048,13229,13057,13082,13145,13166,13229,13057,13048,13048),"DATE_SUPPLY"=as.Date(c("1/31/2013","1/31/2013","1/31/2013","1/1/2014","1/2/2014","1/2/2014","1/2/2014","1/2/2014","1/3/2014","2/1/2014"),"%m/%d/%Y"),"QNT_SUPPLY"=c(2,1,2,1,1,1,2,3,1,2))
df_supply
STORE ITEM DATE_SUPPLY CUM_QNT_SUPPLY
1 1001 13048 2013-01-31 2
2 1002 13229 2013-01-31 1
3 1001 13057 2013-01-31 2
4 1001 13082 2014-01-01 1
5 1002 13145 2014-01-02 1
6 1002 13166 2014-01-02 1
7 1002 13229 2014-01-02 2
8 1002 13057 2014-01-02 3
9 1002 13048 2014-01-03 1
10 1002 13048 2014-02-01 2
Output Required:
Sales Vs Supply
STORE ITEM DATE_SALE QNT_SALE DATE_SUPPLY QNT_SUPPLY
1 1001 13048 2014-01-01 1 2013-01-31 2
2 1001 13057 2014-01-01 1 2013-01-31 2
3 1001 13082 2014-01-02 1 2014-01-01 1
4 1001 13048 2014-01-02 1 2013-01-31 2
5 1001 13057 2014-01-02 1 2013-01-31 2
6 1002 13145 2014-01-03 1 2014-01-02 1
7 1002 13166 2014-01-03 1 2014-01-02 1
8 1002 13229 2014-01-03 1 2014-01-02 2
9 1002 13057 2014-01-03 1 2014-01-02 3
10 1002 13048 2014-01-04 1 2014-01-03 1
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Here's what you can try using merge
and the appropriate order ( order
):
# order the data.frames
df_sales <- df_sales[order(-df_sales$STORE, -df_sales$ITEM, df_sales$DATE_SALE, decreasing=T), ]
df_supply <- df_supply[order(-df_supply$STORE, -df_supply$ITEM, df_supply$DATE_SUPPLY, decreasing=T), ]
# merge the data.frames
res <- merge(df_sales, df_supply, by=c("STORE","ITEM"), all=T)
# keep only records with DATE_SUPPLY anterior to DATE_SALE
res <- res[with(res, DATE_SUPPLY <= DATE_SALE), ]
# remove duplicates (based on STORE, ITEM and DATE_SALE)
res <- res[!duplicated(res[, 1:3]), ]
res
# STORE ITEM DATE_SALE QNT_SALE DATE_SUPPLY QNT_SUPPLY
# 1 1001 13048 2014-01-02 1 2013-01-31 2
# 2 1001 13048 2014-01-01 1 2013-01-31 2
# 3 1001 13057 2014-01-02 1 2013-01-31 2
# 4 1001 13057 2014-01-01 1 2013-01-31 2
# 5 1001 13082 2014-01-02 1 2014-01-01 1
# 7 1002 13048 2014-01-04 1 2014-01-03 1
# 8 1002 13057 2014-01-03 1 2014-01-02 3
# 9 1002 13145 2014-01-02 1 2014-01-02 1
# 10 1002 13166 2014-01-03 1 2014-01-02 1
# 11 1002 13229 2014-01-03 1 2014-01-02 2
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Using sliding joints from data.table
:
require(data.table)
setkey(setDT(df_supply), STORE, ITEM, DATE_SUPPLY)
idx = df_supply[df_sales, roll=Inf, which=TRUE]
cbind(df_sales, df_supply[idx, 3:4, with=FALSE])
# STORE ITEM DATE_SALE QNT_SALE DATE_SUPPLY QNT_SUPPLY
# 1 1001 13048 2014-01-01 1 2013-01-31 2
# 2 1001 13057 2014-01-01 1 2013-01-31 2
# 3 1001 13082 2014-01-02 1 2014-01-01 1
# 4 1001 13048 2014-01-02 1 2013-01-31 2
# 5 1001 13057 2014-01-02 1 2013-01-31 2
# 6 1002 13145 2014-01-02 1 2014-01-02 1
# 7 1002 13166 2014-01-03 1 2014-01-02 1
# 8 1002 13229 2014-01-03 1 2014-01-02 2
# 9 1002 13057 2014-01-03 1 2014-01-02 3
# 10 1002 13048 2014-01-04 1 2014-01-03 1
cbind
returns a completely new object. If you want to add new columns by reference instead df_sales
, use :=
. There are many examples of using this here on SO and also explained in the new HTML Widgets .
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