2021-12-05

Add/Merge/Melt only specific columns and give out one unique row

I am trying to transform a dataset that has multiple product sales on a date. At the end I want to keep only unique columns with the sum of the product sales per day.

My MRE:

df <- data.frame(created = as.Date(c("2020-01-01", "2020-01-01", "2020-01-02", "2020-01-02", "2020-01-03", "2020-01-03"), "%Y-%m-%d", tz = "GMT"),
                soldUnits = c(1, 1, 1, 1, 1, 1),
Weekday = c("Mo","Mo","Tu","Tu","Th","Th"),
Sunshinehours = c(7.8,7.8,6.0,6.0,8.0,8.0))

Which looks like this:

       Date   soldUnits Weekday Sunshinehours
     2020-01-01   1       Mo      7.8
     2020-01-01   1       Mo      7.8
     2020-01-02   1       Tu      6.0
     2020-01-02   1       Tu      6.0
     2020-01-03   1       We      8.0
     2020-01-03   1       We      8.0

And should look like this after transforming:

       Date   soldUnits Weekday Sunshinehours
     2020-01-01   2      Mo      7.8
     2020-01-02   2      Tu      6.0
     2020-01-03   2      We      8.0
     

I tried aggregate() and group_by but without success because my data was dropped.

Is there anyone who has an idea, how i can transform and clean up my dataset according to the specifications i mentioned?



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