Im working with a transaction data set, new data is update every in a Daily-Monthly file. Actually i'm load Historical and Daily files (10 files), then processed and finally created a Full data (Last year sales current year sales), this procces takes 20 min every day.

Now i want to change the process, load Full data (1 file) and then add new data. This works much faster, like 2 min. The problem is the next one:

• FULL data contains 2021 Sales January 2022 Sales.
• New data contains January 2022 Sales February Sales.

when I merge the data with the following code, i lost data, because in FULL data exists duplicate data (this is correct).

DATA <- bind_rows(FULL, Daily)%>%distinct()

Here is an example of what I need:

FULL DATA:

DAILY DATA:

Final Output (Just add bold data)

CodePudding user response：

You can use merge() for that.

merge(full_data, daily_data, all = T)

   Sales.ID Sales.Order    SKU Price Quantity Description     Date
1       ABC        ABC1 123452   100        1      Tshirt 21-12-31
2      ABCD        ABCD  12345   200        2       Shoes 21-12-31
3     ABCDE      ABCDE1  ERROR  -100        1  Devolution 22-01-01
4     ABCDE      ABCDE1  ERROR  -100        1  Devolution 22-01-01
5     ABCDE      ABCDE1  ERROR  -100        1  Devolution 22-01-01
6       FGH        FGH1  12112    10        1       Socks 22-01-02
7       FGH        FGH1  12412    10        1       Socks 22-01-02
8     FGHRT      FGHRT1  12112   100        1        Shoe 22-01-03
9     FGHRT      FGHRT1  12342    10        2    Backpack 22-01-03
10   FGHRTY     FGHRTY1 126342  1000        1         Bag 22-01-04
11   FGHRUT     FGHRUT3 121534   107        2       Tshit 22-01-04