dplyr on data.table, am I really using data.table?

Question

If I use dplyr syntax on top of a datatable, do I get all the speed benefits of datatable while still using the syntax of dplyr? In other w

Answer 1

You can use dtplyr now, which is part of the tidyverse. It allows you to use dplyr style statements as usual, but utilizes lazy evaluation and translates your statements to data.table code under the hood. The overhead in translation is minimal, but you derive all, if not, most of the benefits of data.table. More details at the official git repo here and the tidyverse page.

Answer 2

There is no straightforward/simple answer because the philosophies of both these packages differ in certain aspects. So some compromises are unavoidable. Here are some of the concerns you may need to address/consider.

Operations involving i (== filter() and slice() in dplyr)

Assume DT with say 10 columns. Consider these data.table expressions:

DT[a > 1, .N]                    ## --- (1)
DT[a > 1, mean(b), by=.(c, d)]   ## --- (2)

(1) gives the number of rows in DT where column a > 1. (2) returns mean(b) grouped by c,d for the same expression in i as (1).

Commonly used dplyr expressions would be:

DT %>% filter(a > 1) %>% summarise(n())                        ## --- (3) 
DT %>% filter(a > 1) %>% group_by(c, d) %>% summarise(mean(b)) ## --- (4)

Clearly, data.table codes are shorter. In addition they are also more memory efficient¹. Why? Because in both (3) and (4), filter() returns rows for all 10 columns first, when in (3) we just need the number of rows, and in (4) we just need columns b, c, d for the successive operations. To overcome this, we have to select() columns apriori:

DT %>% select(a) %>% filter(a > 1) %>% summarise(n()) ## --- (5)
DT %>% select(a,b,c,d) %>% filter(a > 1) %>% group_by(c,d) %>% summarise(mean(b)) ## --- (6)

It is essential to highlight a major philosophical difference between the two packages:

• In data.table, we like to keep these related operations together, and that allows to look at the j-expression (from the same function call) and realise there's no need for any columns in (1). The expression in i gets computed, and .N is just sum of that logical vector which gives the number of rows; the entire subset is never realised. In (2), just column b,c,d are materialised in the subset, other columns are ignored.

• But in dplyr, the philosophy is to have a function do precisely one thing well. There is (at least currently) no way to tell if the operation after filter() needs all those columns we filtered. You'll need to think ahead if you want to perform such tasks efficiently. I personally find it counter-intutitive in this case.

Note that in (5) and (6), we still subset column a which we don't require. But I'm not sure how to avoid that. If filter() function had an argument to select the columns to return, we could avoid this issue, but then the function will not do just one task (which is also a dplyr design choice).

Sub-assign by reference

dplyr will never update by reference. This is another huge (philosophical) difference between the two packages.

For example, in data.table you can do:

DT[a %in% some_vals, a := NA]

which updates column a by reference on just those rows that satisfy the condition. At the moment dplyr deep copies the entire data.table internally to add a new column. @BrodieG already mentioned this in his answer.

But the deep copy can be replaced by a shallow copy when FR #617 is implemented. Also relevant: dplyr: FR#614. Note that still, the column you modify will always be copied (therefore tad slower / less memory efficient). There will be no way to update columns by reference.

Other functionalities

• In data.table, you can aggregate while joining, and this is more straightfoward to understand and is memory efficient since the intermediate join result is never materialised. Check this post for an example. You can't (at the moment?) do that using dplyr's data.table/data.frame syntax.

• data.table's rolling joins feature is not supported in dplyr's syntax as well.

• We recently implemented overlap joins in data.table to join over interval ranges (here's an example), which is a separate function foverlaps() at the moment, and therefore could be used with the pipe operators (magrittr / pipeR? - never tried it myself).

  But ultimately, our goal is to integrate it into [.data.table so that we can harvest the other features like grouping, aggregating while joining etc.. which will have the same limitations outlined above.

• Since 1.9.4, data.table implements automatic indexing using secondary keys for fast binary search based subsets on regular R syntax. Ex: DT[x == 1] and DT[x %in% some_vals] will automatically create an index on the first run, which will then be used on successive subsets from the same column to fast subset using binary search. This feature will continue to evolve. Check this gist for a short overview of this feature.

  From the way filter() is implemented for data.tables, it doesn't take advantage of this feature.

• A dplyr feature is that it also provides interface to databases using the same syntax, which data.table doesn't at the moment.

So, you will have to weigh in these (and probably other points) and decide based on whether these trade-offs are acceptable to you.

HTH

---

(1) Note that being memory efficient directly impacts speed (especially as data gets larger), as the bottleneck in most cases is moving the data from main memory onto cache (and making use of data in cache as much as possible - reduce cache misses - so as to reduce accessing main memory). Not going into details here.

Answer 3

To answer your questions:

• Yes, you are using data.table
• But not as efficiently as you would with pure data.table syntax

In many cases this will be an acceptable compromise for those wanting the dplyr syntax, though it will possibly be slower than dplyr with plain data frames.

One big factor appears to be that dplyr will copy the data.table by default when grouping. Consider (using microbenchmark):

Unit: microseconds
                                                               expr       min         lq    median
                                diamondsDT[, mean(price), by = cut]  3395.753  4039.5700  4543.594
                                          diamondsDT[cut != "Fair"] 12315.943 15460.1055 16383.738
 diamondsDT %>% group_by(cut) %>% summarize(AvgPrice = mean(price))  9210.670 11486.7530 12994.073
                               diamondsDT %>% filter(cut != "Fair") 13003.878 15897.5310 17032.609

The filtering is of comparable speed, but the grouping isn't. I believe the culprit is this line in dplyr:::grouped_dt:

if (copy) {
    data <- data.table::copy(data)
}

where copy defaults to TRUE (and can't easily be changed to FALSE that I can see). This likely doesn't account for 100% of the difference, but general overhead alone on something the size of diamonds most likely isn't the full difference.

The issue is that in order to have a consistent grammar, dplyr does the grouping in two steps. It first sets keys on a copy of the original data table that match the groups, and only later does it group. data.table just allocates memory for the largest result group, which in this case is just one row, so that makes a big difference in how much memory needs to be allocated.

FYI, if anyone cares, I found this by using treeprof (install_github("brodieg/treeprof")), an experimental (and still very much alpha) tree viewer for Rprof output:

Note the above is currently only works on macs AFAIK. Also, unfortunately, Rprof records calls of the type packagename::funname as anonymous so it could actually be any and all of the datatable:: calls inside grouped_dt that are responsible, but from quick testing it looked like datatable::copy is the big one.

That said, you can quickly see how there isn't that much overhead around the [.data.table call, but there is also a completely separate branch for the grouping.

---

EDIT: to confirm the copying:

> tracemem(diamondsDT)
[1] "<0x000000002747e348>"    
> diamondsDT %>% group_by(cut) %>% summarize(AvgPrice = mean(price))
tracemem[0x000000002747e348 -> 0x000000002a624bc0]: <Anonymous> grouped_dt group_by_.data.table group_by_ group_by <Anonymous> freduce _fseq eval eval withVisible %>% 
Source: local data table [5 x 2]

        cut AvgPrice
1      Fair 4358.758
2      Good 3928.864
3 Very Good 3981.760
4   Premium 4584.258
5     Ideal 3457.542
> diamondsDT[, mean(price), by = cut]
         cut       V1
1:     Ideal 3457.542
2:   Premium 4584.258
3:      Good 3928.864
4: Very Good 3981.760
5:      Fair 4358.758
> untracemem(diamondsDT)

Answer 4

Just try it.

library(rbenchmark)
library(dplyr)
library(data.table)

benchmark(
dplyr = diamondsDT %>%
    filter(cut != "Fair") %>%
    group_by(cut) %>%
    summarize(AvgPrice = mean(price),
                 MedianPrice = as.numeric(median(price)),
                 Count = n()) %>%
    arrange(desc(Count)),
data.table = diamondsDT[cut != "Fair", 
                        list(AvgPrice = mean(price),
                             MedianPrice = as.numeric(median(price)),
                             Count = .N), by = cut][order(-Count)])[1:4]

On this problem it seems data.table is 2.4x faster than dplyr using data.table:

        test replications elapsed relative
2 data.table          100    2.39    1.000
1      dplyr          100    5.77    2.414

Revised based on Polymerase's comment.