R – determining row indices of data.table group members

data.tabler

The data.table package in R provides the option:

which: ‘TRUE’ returns the integer row numbers of ‘x’ that ‘i’
matches to.

However, I see no way of obtaining, within j, the integer row numbers of 'x' within the groups established using by.

For example, given…

DT = data.table(x=rep(c("a","b","c"),each=3), y=c(1,3,6))

…I would like to know the indices into DT for each value of y.

The value to me is that I am using a data.table in parallel with Another Data Structure (ADS) to which I intend to perform groupwise computations based on the efficiently computed groupings of the data.table.

For example, assuming ADS is a vector with a value for each row in DT:

ADS<-sample(100,nrow(DT))

I can, as a workaround, compute the groupwise mean of ADS determined by DT$y the group if I first add a new sequence column to the data.table.

DT[,seqNum:=seq_len(nrow(DT))]
DT[,mean(ADS[seqNum]),by=y]

Which gives the result I want at the cost of adding a new column.

I realize that in this example I can get the same answer using tapply:

tapply(ADS,DT$y,mean)

However, I will not then get the performance benefit of data.tables efficient grouping (especially when the 'by' columns are indexed).

Perhaps there is some syntax I am overlooking???

Perhaps this is an easy feature to add to data.table and I should request it (wink, wink)???

Proposed syntax: optionally set '.which' to the group indices, allowing to write:

DT[,mean(ADS[.which]),by=y,which=TRUE]

Best Answer

Available since data.table 1.8.3 you can use .I in the j of a data.table to get the row indices by groups...

DT[ , list( yidx = list(.I) ) , by = y ]
#   y  yidx
#1: 1 1,4,7
#2: 3 2,5,8
#3: 6 3,6,9

What exactly does `setkey(DT, a, b)` do?

It does two things:

reorders the rows of the data.table DT by the column(s) provided (a, b) by reference, always in increasing order.
marks those columns as key columns by setting an attribute called sorted to DT.

The reordering is both fast (due to data.table's internal radix sorting) and memory efficient (only one extra column of type double is allocated).

When is `setkey()` required?

For grouping operations, setkey() was never an absolute requirement. That is, we can perform a cold-by or adhoc-by.

## "cold" by
require(data.table)
DT <- data.table(x=rep(1:5, each=2), y=1:10)
DT[, mean(y), by=x] # no key is set, order of groups preserved in result

However, prior to v1.9.6, joins of the form x[i] required key to be set on x. With the new on= argument from v1.9.6+, this is not true anymore, and setting keys is therefore not an absolute requirement here as well.

## joins using < v1.9.6 
setkey(X, a) # absolutely required
setkey(Y, a) # not absolutely required as long as 'a' is the first column
X[Y]

## joins using v1.9.6+
X[Y, on="a"]
# or if the column names are x_a and y_a respectively
X[Y, on=c("x_a" = "y_a")]

Note that on= argument can be explicitly specified even for keyed joins as well.

The only operation that requires key to be absolutely set is the foverlaps() function. But we are working on some more features which when done would remove this requirement.

So what's the reason for implementing on= argument?

There are quite a few reasons.
1. It allows to clearly distinguish the operation as an operation involving two data.tables. Just doing X[Y] does not distinguish this as well, although it could be clear by naming the variables appropriately.
2. It also allows to understand the columns on which the join/subset is being performed immediately by looking at that line of code (and not having to traceback to the corresponding setkey() line).
3. In operations where columns are added or updated by reference, on= operations are much more performant as it doesn't need the entire data.table to be reordered just to add/update column(s). For example,
```
## compare 
setkey(X, a, b) # why physically reorder X to just add/update a column?
X[Y, col := i.val]

## to
X[Y, col := i.val, on=c("a", "b")]
```
  In the second case, we did not have to reorder. It's not computing the order that's time consuming, but physically reordering the data.table in RAM, and by avoiding it, we retain the original order, and it is also performant.
4. Even otherwise, unless you're performing joins repetitively, there should be no noticeable performance difference between a keyed and ad-hoc joins.

This leads to the question, what advantage does keying a data.table have anymore?

Is there an advantage to keying a data.table?

Keying a data.table physically reorders it based on those column(s) in RAM. Computing the order is not usually the time consuming part, rather the reordering itself. However, once we've the data sorted in RAM, the rows belonging to the same group are all contiguous in RAM, and is therefore very cache efficient. It's the sortedness that speeds up operations on keyed data.tables.

It is therefore essential to figure out if the time spent on reordering the entire data.table is worth the time to do a cache-efficient join/aggregation. Usually, unless there are repetitive grouping / join operations being performed on the same keyed data.table, there should not be a noticeable difference.

In most cases therefore, there shouldn't be a need to set keys anymore. We recommend using on= wherever possible, unless setting key has a dramatic improvement in performance that you'd like to exploit.

Question: What do you think would be the performance like in comparison to a keyed join, if you use setorder() to reorder the data.table and use on=? If you've followed thus far, you should be able to figure it out :-).

R – data.table vs dplyr: can one do something well the other can’t or does poorly

We need to cover at least these aspects to provide a comprehensive answer/comparison (in no particular order of importance): Speed, Memory usage, Syntax and Features.

My intent is to cover each one of these as clearly as possible from data.table perspective.

Note: unless explicitly mentioned otherwise, by referring to dplyr, we refer to dplyr's data.frame interface whose internals are in C++ using Rcpp.

The data.table syntax is consistent in its form - DT[i, j, by]. To keep i, j and by together is by design. By keeping related operations together, it allows to easily optimise operations for speed and more importantly memory usage, and also provide some powerful features, all while maintaining the consistency in syntax.

1. Speed

Quite a few benchmarks (though mostly on grouping operations) have been added to the question already showing data.table gets faster than dplyr as the number of groups and/or rows to group by increase, including benchmarks by Matt on grouping from 10 million to 2 billion rows (100GB in RAM) on 100 - 10 million groups and varying grouping columns, which also compares pandas. See also updated benchmarks, which include Spark and pydatatable as well.

On benchmarks, it would be great to cover these remaining aspects as well:

Grouping operations involving a subset of rows - i.e., DT[x > val, sum(y), by = z] type operations.
Benchmark other operations such as update and joins.
Also benchmark memory footprint for each operation in addition to runtime.

2. Memory usage

Operations involving filter() or slice() in dplyr can be memory inefficient (on both data.frames and data.tables). See this post.

Note that Hadley's comment talks about speed (that dplyr is plentiful fast for him), whereas the major concern here is memory.
data.table interface at the moment allows one to modify/update columns by reference (note that we don't need to re-assign the result back to a variable).
```
 # sub-assign by reference, updates 'y' in-place
 DT[x >= 1L, y := NA]
```
But dplyr will never update by reference. The dplyr equivalent would be (note that the result needs to be re-assigned):
```
 # copies the entire 'y' column
 ans <- DF %>% mutate(y = replace(y, which(x >= 1L), NA))
```
A concern for this is referential transparency. Updating a data.table object by reference, especially within a function may not be always desirable. But this is an incredibly useful feature: see this and this posts for interesting cases. And we want to keep it.

Therefore we are working towards exporting shallow() function in data.table that will provide the user with both possibilities. For example, if it is desirable to not modify the input data.table within a function, one can then do:
```
 foo <- function(DT) {
     DT = shallow(DT)          ## shallow copy DT
     DT[, newcol := 1L]        ## does not affect the original DT 
     DT[x > 2L, newcol := 2L]  ## no need to copy (internally), as this column exists only in shallow copied DT
     DT[x > 2L, x := 3L]       ## have to copy (like base R / dplyr does always); otherwise original DT will 
                               ## also get modified.
 }
```
By not using shallow(), the old functionality is retained:
```
 bar <- function(DT) {
     DT[, newcol := 1L]        ## old behaviour, original DT gets updated by reference
     DT[x > 2L, x := 3L]       ## old behaviour, update column x in original DT.
 }
```
By creating a shallow copy using shallow(), we understand that you don't want to modify the original object. We take care of everything internally to ensure that while also ensuring to copy columns you modify only when it is absolutely necessary. When implemented, this should settle the referential transparency issue altogether while providing the user with both possibilties.

Also, once shallow() is exported dplyr's data.table interface should avoid almost all copies. So those who prefer dplyr's syntax can use it with data.tables.

But it will still lack many features that data.table provides, including (sub)-assignment by reference.
Aggregate while joining:

Suppose you have two data.tables as follows:
```
 DT1 = data.table(x=c(1,1,1,1,2,2,2,2), y=c("a", "a", "b", "b"), z=1:8, key=c("x", "y"))
 #    x y z
 # 1: 1 a 1
 # 2: 1 a 2
 # 3: 1 b 3
 # 4: 1 b 4
 # 5: 2 a 5
 # 6: 2 a 6
 # 7: 2 b 7
 # 8: 2 b 8
 DT2 = data.table(x=1:2, y=c("a", "b"), mul=4:3, key=c("x", "y"))
 #    x y mul
 # 1: 1 a   4
 # 2: 2 b   3
```
And you would like to get sum(z) * mul for each row in DT2 while joining by columns x,y. We can either:
- 1. aggregate DT1 to get sum(z), 2) perform a join and 3) multiply (or)
    
    data.table way
    
    DT1[, .(z = sum(z)), keyby = .(x,y)][DT2][, z := z*mul][]
    
    dplyr equivalent
    
    DF1 %>% group_by(x, y) %>% summarise(z = sum(z)) %>% right_join(DF2) %>% mutate(z = z * mul)
- 1. do it all in one go (using by = .EACHI feature):
    
    DT1[DT2, list(z=sum(z) * mul), by = .EACHI]
What is the advantage?
- We don't have to allocate memory for the intermediate result.
- We don't have to group/hash twice (one for aggregation and other for joining).
- And more importantly, the operation what we wanted to perform is clear by looking at j in (2).
Check this post for a detailed explanation of by = .EACHI. No intermediate results are materialised, and the join+aggregate is performed all in one go.

Have a look at this, this and this posts for real usage scenarios.

In dplyr you would have to join and aggregate or aggregate first and then join, neither of which are as efficient, in terms of memory (which in turn translates to speed).
Update and joins:

Consider the data.table code shown below:
```
 DT1[DT2, col := i.mul]
```
adds/updates DT1's column col with mul from DT2 on those rows where DT2's key column matches DT1. I don't think there is an exact equivalent of this operation in dplyr, i.e., without avoiding a *_join operation, which would have to copy the entire DT1 just to add a new column to it, which is unnecessary.

Check this post for a real usage scenario.

To summarise, it is important to realise that every bit of optimisation matters. As Grace Hopper would say, Mind your nanoseconds!

3. Syntax

Let's now look at syntax. Hadley commented here:

Data tables are extremely fast but I think their concision makes it harder to learn and code that uses it is harder to read after you have written it ...

I find this remark pointless because it is very subjective. What we can perhaps try is to contrast consistency in syntax. We will compare data.table and dplyr syntax side-by-side.

We will work with the dummy data shown below:

DT = data.table(x=1:10, y=11:20, z=rep(1:2, each=5))
DF = as.data.frame(DT)

Basic aggregation/update operations.
```
 # case (a)
 DT[, sum(y), by = z]                       ## data.table syntax
 DF %>% group_by(z) %>% summarise(sum(y)) ## dplyr syntax
 DT[, y := cumsum(y), by = z]
 ans <- DF %>% group_by(z) %>% mutate(y = cumsum(y))

 # case (b)
 DT[x > 2, sum(y), by = z]
 DF %>% filter(x>2) %>% group_by(z) %>% summarise(sum(y))
 DT[x > 2, y := cumsum(y), by = z]
 ans <- DF %>% group_by(z) %>% mutate(y = replace(y, which(x > 2), cumsum(y)))

 # case (c)
 DT[, if(any(x > 5L)) y[1L]-y[2L] else y[2L], by = z]
 DF %>% group_by(z) %>% summarise(if (any(x > 5L)) y[1L] - y[2L] else y[2L])
 DT[, if(any(x > 5L)) y[1L] - y[2L], by = z]
 DF %>% group_by(z) %>% filter(any(x > 5L)) %>% summarise(y[1L] - y[2L])
```
- data.table syntax is compact and dplyr's quite verbose. Things are more or less equivalent in case (a).
- In case (b), we had to use filter() in dplyr while summarising. But while updating, we had to move the logic inside mutate(). In data.table however, we express both operations with the same logic - operate on rows where x > 2, but in first case, get sum(y), whereas in the second case update those rows for y with its cumulative sum.
  
  This is what we mean when we say the DT[i, j, by] form is consistent.
- Similarly in case (c), when we have if-else condition, we are able to express the logic "as-is" in both data.table and dplyr. However, if we would like to return just those rows where the if condition satisfies and skip otherwise, we cannot use summarise() directly (AFAICT). We have to filter() first and then summarise because summarise() always expects a single value.
  
  While it returns the same result, using filter() here makes the actual operation less obvious.
  
  It might very well be possible to use filter() in the first case as well (does not seem obvious to me), but my point is that we should not have to.
Aggregation / update on multiple columns
```
 # case (a)
 DT[, lapply(.SD, sum), by = z]                     ## data.table syntax
 DF %>% group_by(z) %>% summarise_each(funs(sum)) ## dplyr syntax
 DT[, (cols) := lapply(.SD, sum), by = z]
 ans <- DF %>% group_by(z) %>% mutate_each(funs(sum))

 # case (b)
 DT[, c(lapply(.SD, sum), lapply(.SD, mean)), by = z]
 DF %>% group_by(z) %>% summarise_each(funs(sum, mean))

 # case (c)
 DT[, c(.N, lapply(.SD, sum)), by = z]     
 DF %>% group_by(z) %>% summarise_each(funs(n(), mean))
```
- In case (a), the codes are more or less equivalent. data.table uses familiar base function lapply(), whereas dplyr introduces *_each() along with a bunch of functions to funs().
- data.table's := requires column names to be provided, whereas dplyr generates it automatically.
- In case (b), dplyr's syntax is relatively straightforward. Improving aggregations/updates on multiple functions is on data.table's list.
- In case (c) though, dplyr would return n() as many times as many columns, instead of just once. In data.table, all we need to do is to return a list in j. Each element of the list will become a column in the result. So, we can use, once again, the familiar base function c() to concatenate .N to a list which returns a list.
Note: Once again, in data.table, all we need to do is return a list in j. Each element of the list will become a column in result. You can use c(), as.list(), lapply(), list() etc... base functions to accomplish this, without having to learn any new functions.

You will need to learn just the special variables - .N and .SD at least. The equivalent in dplyr are n() and .

Joins

dplyr provides separate functions for each type of join where as data.table allows joins using the same syntax DT[i, j, by] (and with reason). It also provides an equivalent merge.data.table() function as an alternative.

 setkey(DT1, x, y)

 # 1. normal join
 DT1[DT2]            ## data.table syntax
 left_join(DT2, DT1) ## dplyr syntax

 # 2. select columns while join    
 DT1[DT2, .(z, i.mul)]
 left_join(select(DT2, x, y, mul), select(DT1, x, y, z))

 # 3. aggregate while join
 DT1[DT2, .(sum(z) * i.mul), by = .EACHI]
 DF1 %>% group_by(x, y) %>% summarise(z = sum(z)) %>% 
     inner_join(DF2) %>% mutate(z = z*mul) %>% select(-mul)

 # 4. update while join
 DT1[DT2, z := cumsum(z) * i.mul, by = .EACHI]
 ??

 # 5. rolling join
 DT1[DT2, roll = -Inf]
 ??

 # 6. other arguments to control output
 DT1[DT2, mult = "first"]
 ??

Some might find a separate function for each joins much nicer (left, right, inner, anti, semi etc), whereas as others might like data.table's DT[i, j, by], or merge() which is similar to base R.
However dplyr joins do just that. Nothing more. Nothing less.
data.tables can select columns while joining (2), and in dplyr you will need to select() first on both data.frames before to join as shown above. Otherwise you would materialiase the join with unnecessary columns only to remove them later and that is inefficient.
data.tables can aggregate while joining using by = .EACHI feature (3) and also update while joining (4). Why materialize the entire join result to add/update just a few columns?
data.table is capable of rolling joins (5) - roll forward, LOCF, roll backward, NOCB, nearest.
data.table also has mult = argument which selects first, last or all matches (6).
data.table has allow.cartesian = TRUE argument to protect from accidental invalid joins.

Once again, the syntax is consistent with DT[i, j, by] with additional arguments allowing for controlling the output further.

do()...

dplyr's summarise is specially designed for functions that return a single value. If your function returns multiple/unequal values, you will have to resort to do(). You have to know beforehand about all your functions return value.

 DT[, list(x[1], y[1]), by = z]                 ## data.table syntax
 DF %>% group_by(z) %>% summarise(x[1], y[1]) ## dplyr syntax
 DT[, list(x[1:2], y[1]), by = z]
 DF %>% group_by(z) %>% do(data.frame(.$x[1:2], .$y[1]))

 DT[, quantile(x, 0.25), by = z]
 DF %>% group_by(z) %>% summarise(quantile(x, 0.25))
 DT[, quantile(x, c(0.25, 0.75)), by = z]
 DF %>% group_by(z) %>% do(data.frame(quantile(.$x, c(0.25, 0.75))))

 DT[, as.list(summary(x)), by = z]
 DF %>% group_by(z) %>% do(data.frame(as.list(summary(.$x))))

.SD's equivalent is .
In data.table, you can throw pretty much anything in j - the only thing to remember is for it to return a list so that each element of the list gets converted to a column.
In dplyr, cannot do that. Have to resort to do() depending on how sure you are as to whether your function would always return a single value. And it is quite slow.

Once again, data.table's syntax is consistent with DT[i, j, by]. We can just keep throwing expressions in j without having to worry about these things.

Have a look at this SO question and this one. I wonder if it would be possible to express the answer as straightforward using dplyr's syntax...

To summarise, I have particularly highlighted several instances where dplyr's syntax is either inefficient, limited or fails to make operations straightforward. This is particularly because data.table gets quite a bit of backlash about "harder to read/learn" syntax (like the one pasted/linked above). Most posts that cover dplyr talk about most straightforward operations. And that is great. But it is important to realise its syntax and feature limitations as well, and I am yet to see a post on it.

data.table has its quirks as well (some of which I have pointed out that we are attempting to fix). We are also attempting to improve data.table's joins as I have highlighted here.

But one should also consider the number of features that dplyr lacks in comparison to data.table.

4. Features

I have pointed out most of the features here and also in this post. In addition:

fread - fast file reader has been available for a long time now.
fwrite - a parallelised fast file writer is now available. See this post for a detailed explanation on the implementation and #1664 for keeping track of further developments.
Automatic indexing - another handy feature to optimise base R syntax as is, internally.
Ad-hoc grouping: dplyr automatically sorts the results by grouping variables during summarise(), which may not be always desirable.
Numerous advantages in data.table joins (for speed / memory efficiency and syntax) mentioned above.
Non-equi joins: Allows joins using other operators <=, <, >, >= along with all other advantages of data.table joins.
Overlapping range joins was implemented in data.table recently. Check this post for an overview with benchmarks.
setorder() function in data.table that allows really fast reordering of data.tables by reference.
dplyr provides interface to databases using the same syntax, which data.table does not at the moment.
data.table provides faster equivalents of set operations (written by Jan Gorecki) - fsetdiff, fintersect, funion and fsetequal with additional all argument (as in SQL).
data.table loads cleanly with no masking warnings and has a mechanism described here for [.data.frame compatibility when passed to any R package. dplyr changes base functions filter, lag and [ which can cause problems; e.g. here and here.

Finally:

On databases - there is no reason why data.table cannot provide similar interface, but this is not a priority now. It might get bumped up if users would very much like that feature.. not sure.
On parallelism - Everything is difficult, until someone goes ahead and does it. Of course it will take effort (being thread safe).
- Progress is being made currently (in v1.9.7 devel) towards parallelising known time consuming parts for incremental performance gains using OpenMP.

Best Answer

Related Solutions

R – the purpose of setting a key in data.table

What exactly does setkey(DT, a, b) do?

When is setkey() required?