Add rows to grouped data with dplyr?

Question

My data is in a data.frame format like this sample data:

data <- 
structure(list(Article = structure(c(1L, 1L, 3L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L         


        

Answer 1

I thought I would provide a dplyr-esque solution.

• use expand.grid() to generate the pair-wise combinations you are looking for.
• use left_join() to join in the demand data (filling the rest with NAs).

Solution:

full_data <- expand.grid(Article=data$Article,Week=data$Week)
out <- left_join(tbl_dt(full_data),data)
out[is.na(out)] <- 0    # fill with zeroes for summarise below.

Then as before:

WeekSums <- out %>%
            group_by(Article, Week) %>%
            summarise(
                     WeekDemand = sum(Demand)
                     )

Fxnal programming?

If you use this transformation often then perhaps a convenience function:

xpand <- function(df, col1, col2,na_to_zero=TRUE){

    require(dplyr)

    # to substitute in the names "as is" need substitute then eval.
    xpand_call <- substitute(     
        expanded <- df %>%
                    select(col1,col2) %>%
                    expand.grid()
    )

    eval(xpand_call)                       

    out <- left_join(tbl_dt(expanded), df)         # join in any other variables from df.

    if(na_to_zero) out[is.na(out)] <- 0    # convert NAs to zeroes?

    return(out)
}

This way you can do:

expanded_df <- xpand(df,Article,Week)