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

Since dplyr is under active development, I thought I would post an update that also incorporates tidyr:

library(dplyr)
library(tidyr)

data %>%
  expand(Article, Week) %>%
  left_join(data) %>%
  group_by(Article, Week) %>%
  summarise(WeekDemand = sum(Demand, na.rm=TRUE))

Which produces:

   Article     Week WeekDemand
1    10004 2013-W01       1215
2    10004 2013-W02        900
3    10004 2013-W03        774
4    10004 2013-W04       1170
5    10006 2013-W01          0
6    10006 2013-W02          0
7    10006 2013-W03          0
8    10006 2013-W04          5
9    10007 2013-W01          2
10   10007 2013-W02          0
11   10007 2013-W03          0
12   10007 2013-W04          0

---

Using tidyr >= 0.3.1 this can now be written as:

data %>% 
  complete(Article, Week) %>%  
  group_by(Article, Week) %>% 
  summarise(Demand = sum(Demand, na.rm = TRUE))

Answer 2

Without dplyr it can be done like this:

as.data.frame(xtabs(Demand ~ Week + Article, data))

giving:

       Week Article Freq
1  2013-W01   10004 1215
2  2013-W02   10004  900
3  2013-W03   10004  774
4  2013-W04   10004 1170
5  2013-W01   10006    0
6  2013-W02   10006    0
7  2013-W03   10006    0
8  2013-W04   10006    5
9  2013-W01   10007    2
10 2013-W02   10007    0
11 2013-W03   10007    0
12 2013-W04   10007    0

and this can be rewritten as a magrittr or dplyr pipeline like this:

data %>% xtabs(formula = Demand ~ Week + Article) %>% as.data.frame()

The as.data.frame() at the end could be omitted if a wide form solution were desired.

Answer 3

For this situation you can also use dcast and melt.

   library(dplyr)
   library(reshape2)
   data %>%
      dcast(Article ~ Week, value.var = "Demand", fun.aggregate = sum) %>%
      melt(id = "Article") %>%
      arrange(Article, variable)

Answer 4

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)