Creating blazing fast pivot tables from R with data.table – now with subtotals using grouping sets

March 2, 2019
By

(This article was first published on Jozef's Rblog, and kindly contributed to R-bloggers)

Introduction

Data manipulation and aggregation is one of the classic tasks anyone working with data will come across. We of course can perform data transformation and aggregation with base R, but when speed and memory efficiency come into play, data.table is my package of choice.

In this post we will look at of the fresh and very useful functionality that came to data.table only last year – grouping sets, enabling us, for example, to create pivot table-like reports with sub-totals and grand total quickly and easily.

Basic by-group summaries with data.table

To showcase the functionality, we will use a very slightly modified dataset provided by Hadley Wickham’s nycflights13 package, mainly the flights data frame. Lets prepare a small dataset suitable for the showcase:

library(data.table)
dataurl <- "https://jozefhajnala.gitlab.io/r/post/data/"
flights <- readRDS(url(paste0(dataurl, "r006/flights.rds")))
flights <- as.data.table(flights)[month < 3]

Now, for those unfamiliar with data table, to create a summary of distances flown per month and originating airport with data.table, we could simply use:

flights[, sum(distance), by = c("month", "origin")]
##    month origin       V1
## 1:     1    EWR  9524521
## 2:     1    LGA  6359510
## 3:     1    JFK 11304774
## 4:     2    EWR  8725657
## 5:     2    LGA  5917983
## 6:     2    JFK 10331869

To also name the new column nicely, say distance instead of the default V1:

flights[, .(distance = sum(distance)), by = c("month", "origin")]
##    month origin distance
## 1:     1    EWR  9524521
## 2:     1    LGA  6359510
## 3:     1    JFK 11304774
## 4:     2    EWR  8725657
## 5:     2    LGA  5917983
## 6:     2    JFK 10331869

For more on basic data.table operations, look at the Introduction to data.table vignette.

As you have probably noticed, the above gave us the sums of distances by months and origins. When creating reports, especially readers coming from Excel may expect 2 extra perks

  • Looking at sub-totals and grand total
  • Seeing the data in wide format

Since the wide format is just a reshape and data table has the dcast() function for that for quite a while now, we will only briefly show it in practice. The focus of this post will be on the new functionality that was only released in data.table v1.11 in May last year – creating the grand- and sub-totals.

Quick pivot tables with subtotals and a grand total

To create a “classic” pivot table as known from Excel, we need to aggregate the data and also compute the subtotals for all combinations of the selected dimensions and a grand total. In comes cube(), the function that will do just that:

# Get subtotals for origin, month and month&origin with `cube()`:
cubed <- data.table::cube(
  flights,
  .(distance = sum(distance)),
  by = c("month", "origin")
)
cubed
##     month origin distance
##  1:     1    EWR  9524521
##  2:     1    LGA  6359510
##  3:     1    JFK 11304774
##  4:     2    EWR  8725657
##  5:     2    LGA  5917983
##  6:     2    JFK 10331869
##  7:     1    27188805
##  8:     2    24975509
##  9:    NA    EWR 18250178
## 10:    NA    LGA 12277493
## 11:    NA    JFK 21636643
## 12:    NA    52164314

As we can see, compared to the simple group by summary we did earlier, we have extra rows in the output

  1. Rows 7,8 with months 1,2 and origin , – these are the subtotals per month across all origins
  2. Rows 9,10,11 with months NA, NA, NA and origins EWR, LGA, JFK – these are the subtotals per origin across all months
  3. Row 12 with NA month and origin – this is the Grand total across all origins and months

All that is left to get a familiar pivot table shape is to reshape the data to wide format with the aforementioned dcast() function:

# - Origins in columns, months in rows
data.table::dcast(cubed, month ~ origin,  value.var = "distance")
##    month      EWR      JFK      LGA       NA
## 1:     1  9524521 11304774  6359510 27188805
## 2:     2  8725657 10331869  5917983 24975509
## 3:    NA 18250178 21636643 12277493 52164314
# - Origins in rows, months in columns
data.table::dcast(cubed, origin ~ month,  value.var = "distance")
##    origin        1        2       NA
## 1:    EWR  9524521  8725657 18250178
## 2:    JFK 11304774 10331869 21636643
## 3:    LGA  6359510  5917983 12277493
## 4:    27188805 24975509 52164314
Pivot table with data.table

Pivot table with data.table

Using more dimensions

We can use the same approach to create summaries with more than two dimensions, for example, apart from months and origins, we can also look at carriers, simply by adding "carrier" into the by argument:

# With 3 dimensions:
cubed2 <- cube(
  flights, 
  .(distance = sum(distance)),
  by = c("month", "origin", "carrier")
)
cubed2
##      month origin carrier distance
##   1:     1    EWR      UA  5084378
##   2:     1    LGA      UA   729667
##   3:     1    JFK      AA  2013434
##   4:     1    JFK      B6  3672655
##   5:     1    LGA      DL  1678965
##  ---                              
## 153:    NA         F9   174960
## 154:    NA         HA   293997
## 155:    NA         YV    21526
## 156:    NA         OO      733
## 157:    NA        52164314

And dcast() to wide format which suits our needs best:

# For example, with month and carrier in rows, origins in columns:
dcast(cubed2, month + carrier ~ origin,  value.var = "distance")
##     month carrier      EWR      JFK      LGA       NA
##  1:     1      9E    46125   666109    37071   749305
##  2:     1      AA   415707  2013434  1344045  3773186
##  3:     1      AS   148924       NA       NA   148924
##  4:     1      B6   484431  3672655   542748  4699834
##  5:     1      DL   245277  2578999  1678965  4503241
##  6:     1      EV  2067900    24624    86309  2178833
##  7:     1      F9       NA       NA    95580    95580
##  8:     1      FL       NA       NA   226658   226658
##  9:     1      HA       NA   154473       NA   154473
## 10:     1      MQ   152428   223510   908715  1284653
## 11:     1      OO       NA       NA      733      733
## 12:     1      UA  5084378   963144   729667  6777189
## 13:     1      US   339595   219387   299838   858820
## 14:     1      VX       NA   788439       NA   788439
## 15:     1      WN   539756       NA   398647   938403
## 16:     1      YV       NA       NA    10534    10534
## 17:     1      9524521 11304774  6359510 27188805
## 18:     2      9E    42581   605085    34990   682656
## 19:     2      AA   373884  1817048  1207701  3398633
## 20:     2      AS   134512       NA       NA   134512
## 21:     2      B6   456151  3390047   490224  4336422
## 22:     2      DL   219998  2384048  1621728  4225774
## 23:     2      EV  1872395    24168   112863  2009426
## 24:     2      F9       NA       NA    79380    79380
## 25:     2      FL       NA       NA   204536   204536
## 26:     2      HA       NA   139524       NA   139524
## 27:     2      MQ   140924   201880   812152  1154956
## 28:     2      UA  4686122   871824   681737  6239683
## 29:     2      US   301832   222720   293736   818288
## 30:     2      VX       NA   675525       NA   675525
## 31:     2      WN   497258       NA   367944   865202
## 32:     2      YV       NA       NA    10992    10992
## 33:     2      8725657 10331869  5917983 24975509
## 34:    NA      9E    88706  1271194    72061  1431961
## 35:    NA      AA   789591  3830482  2551746  7171819
## 36:    NA      AS   283436       NA       NA   283436
## 37:    NA      B6   940582  7062702  1032972  9036256
## 38:    NA      DL   465275  4963047  3300693  8729015
## 39:    NA      EV  3940295    48792   199172  4188259
## 40:    NA      F9       NA       NA   174960   174960
## 41:    NA      FL       NA       NA   431194   431194
## 42:    NA      HA       NA   293997       NA   293997
## 43:    NA      MQ   293352   425390  1720867  2439609
## 44:    NA      OO       NA       NA      733      733
## 45:    NA      UA  9770500  1834968  1411404 13016872
## 46:    NA      US   641427   442107   593574  1677108
## 47:    NA      VX       NA  1463964       NA  1463964
## 48:    NA      WN  1037014       NA   766591  1803605
## 49:    NA      YV       NA       NA    21526    21526
## 50:    NA     18250178 21636643 12277493 52164314
##     month carrier      EWR      JFK      LGA       NA

Custom grouping sets

So far we have focused on the “default” pivot table shapes with all sub-totals and a grand total, however the cube() function could be considered just a useful special case shortcut for a more generic concept – grouping sets. You can read more on grouping sets with MS SQL Server or with PostgreSQL.

The groupingsets() function allows us to create sub-totals on arbitrary groups of dimensions. Custom subtotals are defined by the sets argument, a list of character vectors, each of them defining one subtotal. Now let us have a look at a few practical examples:

Replicate a simple group by, without any subtotals or grand total

For reference, to replicate a simple group by with grouping sets, we could use:

groupingsets(
  flights,
  j = .(distance = sum(distance)),
  by = c("month", "origin", "carrier"),
  sets = list(c("month", "origin", "carrier")),
)

Which would give the same results as

flights[, .(distance = sum(distance)), by = c("month", "origin", "carrier")]

Custom subtotals

To give only the subtotals for each of the dimensions:

groupingsets(
  flights,
  j = .(distance = sum(distance)),
  by = c("month", "origin", "carrier"),
  sets = list(
    c("month"),
    c("origin"),
    c("carrier")
  )
)
##     month origin carrier distance
##  1:     1        27188805
##  2:     2        24975509
##  3:    NA    EWR     18250178
##  4:    NA    LGA     12277493
##  5:    NA    JFK     21636643
##  6:    NA         UA 13016872
##  7:    NA         AA  7171819
##  8:    NA         B6  9036256
##  9:    NA         DL  8729015
## 10:    NA         EV  4188259
## 11:    NA         MQ  2439609
## 12:    NA         US  1677108
## 13:    NA         WN  1803605
## 14:    NA         VX  1463964
## 15:    NA         FL   431194
## 16:    NA         AS   283436
## 17:    NA         9E  1431961
## 18:    NA         F9   174960
## 19:    NA         HA   293997
## 20:    NA         YV    21526
## 21:    NA         OO      733
##     month origin carrier distance

To give only the subtotals per combinations of 2 dimensions:

groupingsets(
  flights,
  j = .(distance = sum(distance)),
  by = c("month", "origin", "carrier"),
  sets = list(
    c("month", "origin"),
    c("month", "carrier"),
    c("origin", "carrier")
  )
)
##     month origin carrier distance
##  1:     1    EWR      9524521
##  2:     1    LGA      6359510
##  3:     1    JFK     11304774
##  4:     2    EWR      8725657
##  5:     2    LGA      5917983
##  6:     2    JFK     10331869
##  7:     1         UA  6777189
##  8:     1         AA  3773186
##  9:     1         B6  4699834
## 10:     1         DL  4503241
## 11:     1         EV  2178833
## 12:     1         MQ  1284653
## 13:     1         US   858820
## 14:     1         WN   938403
## 15:     1         VX   788439
## 16:     1         FL   226658
## 17:     1         AS   148924
## 18:     1         9E   749305
## 19:     1         F9    95580
## 20:     1         HA   154473
## 21:     1         YV    10534
## 22:     1         OO      733
## 23:     2         US   818288
## 24:     2         UA  6239683
## 25:     2         B6  4336422
## 26:     2         AA  3398633
## 27:     2         EV  2009426
## 28:     2         FL   204536
## 29:     2         MQ  1154956
## 30:     2         DL  4225774
## 31:     2         WN   865202
## 32:     2         9E   682656
## 33:     2         VX   675525
## 34:     2         AS   134512
## 35:     2         F9    79380
## 36:     2         HA   139524
## 37:     2         YV    10992
## 38:    NA    EWR      UA  9770500
## 39:    NA    LGA      UA  1411404
## 40:    NA    JFK      AA  3830482
## 41:    NA    JFK      B6  7062702
## 42:    NA    LGA      DL  3300693
## 43:    NA    EWR      B6   940582
## 44:    NA    LGA      EV   199172
## 45:    NA    LGA      AA  2551746
## 46:    NA    JFK      UA  1834968
## 47:    NA    LGA      B6  1032972
## 48:    NA    LGA      MQ  1720867
## 49:    NA    EWR      AA   789591
## 50:    NA    JFK      DL  4963047
## 51:    NA    EWR      MQ   293352
## 52:    NA    EWR      DL   465275
## 53:    NA    EWR      US   641427
## 54:    NA    EWR      EV  3940295
## 55:    NA    JFK      US   442107
## 56:    NA    LGA      WN   766591
## 57:    NA    JFK      VX  1463964
## 58:    NA    LGA      FL   431194
## 59:    NA    EWR      AS   283436
## 60:    NA    LGA      US   593574
## 61:    NA    JFK      MQ   425390
## 62:    NA    JFK      9E  1271194
## 63:    NA    LGA      F9   174960
## 64:    NA    EWR      WN  1037014
## 65:    NA    JFK      HA   293997
## 66:    NA    JFK      EV    48792
## 67:    NA    EWR      9E    88706
## 68:    NA    LGA      9E    72061
## 69:    NA    LGA      YV    21526
## 70:    NA    LGA      OO      733
##     month origin carrier distance

Grand total

To give only the grand total:

groupingsets(
  flights,
  j = .(distance = sum(distance)),
  by = c("month", "origin", "carrier"),
  sets = list(
    character(0)
  )
)
##    month origin carrier distance
## 1:    NA        52164314

Cube and rollup as special cases of grouping sets

Implementation of cube

We mentioned above that cube() can be considered just a shortcut to a useful special case of groupingsets(). And indeed, looking at the implementation of the data.table method data.table:::cube.data.table, most of what it does is to define the sets to represent the given vector and all of its possible subsets, and passes that to groupingsets():

function (x, j, by, .SDcols, id = FALSE, ...) {
  if (!is.data.table(x)) 
    stop("Argument 'x' must be a data.table object")
  if (!is.character(by)) 
    stop("Argument 'by' must be a character vector of column names used in grouping.")
  if (!is.logical(id)) 
    stop("Argument 'id' must be a logical scalar.")
  n = length(by)
  keepBool = sapply(2L^(seq_len(n) - 1L), function(k) rep(c(FALSE, 
    TRUE), times = k, each = ((2L^n)/(2L * k))))
  sets = lapply((2L^n):1L, function(j) by[keepBool[j, ]])
  jj = substitute(j)
  groupingsets.data.table(x, by = by, sets = sets, .SDcols = .SDcols, 
    id = id, jj = jj)
}

This means for example that

cube(flights, sum(distance),  by = c("month", "origin", "carrier"))
##      month origin carrier       V1
##   1:     1    EWR      UA  5084378
##   2:     1    LGA      UA   729667
##   3:     1    JFK      AA  2013434
##   4:     1    JFK      B6  3672655
##   5:     1    LGA      DL  1678965
##  ---                              
## 153:    NA         F9   174960
## 154:    NA         HA   293997
## 155:    NA         YV    21526
## 156:    NA         OO      733
## 157:    NA        52164314

Is equivalent to

groupingsets(
  flights,
  j = .(distance = sum(distance)),
  by = c("month", "origin", "carrier"),
  sets = list(
    c("month", "origin", "carrier"),
    c("month", "origin"),
    c("month", "carrier"),
    c("month"),
    c("origin", "carrier"),
    c("origin"),
    c("carrier"),
    character(0)
  )
)
##      month origin carrier distance
##   1:     1    EWR      UA  5084378
##   2:     1    LGA      UA   729667
##   3:     1    JFK      AA  2013434
##   4:     1    JFK      B6  3672655
##   5:     1    LGA      DL  1678965
##  ---                              
## 153:    NA         F9   174960
## 154:    NA         HA   293997
## 155:    NA         YV    21526
## 156:    NA         OO      733
## 157:    NA        52164314

Implementation of rollup

The same can be said about rollup(), another shortcut than can be useful. Instead of all possible subsets, it will create a list representing the vector passed to by and its subsets “from right to left”, including the empty vector to get a grand total. Looking at the implementation of the data.table method data.table::rollup.data.table:

function (x, j, by, .SDcols, id = FALSE, ...) {
  if (!is.data.table(x)) 
    stop("Argument 'x' must be a data.table object")
  if (!is.character(by)) 
    stop("Argument 'by' must be a character vector of column names used in grouping.")
  if (!is.logical(id)) 
    stop("Argument 'id' must be a logical scalar.")
  sets = lapply(length(by):0L, function(i) by[0L:i])
  jj = substitute(j)
  groupingsets.data.table(x, by = by, sets = sets, .SDcols = .SDcols, 
    id = id, jj = jj)
}

For example, the following:

rollup(flights, sum(distance),  by = c("month", "origin", "carrier"))

Is equivalent to

groupingsets(
  flights,
  j = .(distance = sum(distance)),
  by = c("month", "origin", "carrier"),
  sets = list(
    c("month", "origin", "carrier"),
    c("month", "origin"),
    c("month"),
    character(0)
  )
)

To leave a comment for the author, please follow the link and comment on their blog: Jozef's Rblog.

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