This vignette discusses data.table's reference semantics which allows to add/update/delete columns of a data.table by reference, and also combine them with i and by. It is aimed at those who are already familiar with data.table syntax, its general form, how to subset rows in i, select and compute on columns, and perform aggregations by group. If you're not familiar with these concepts, please read the “Introduction to data.table” vignette first.
We will use the same flights data as in the “Introduction to data.table” vignette.
flights <- fread("flights14.csv")
flights
# year month day dep_delay arr_delay carrier origin dest air_time distance hour
# 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9
# 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11
# 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19
# 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7
# 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13
# ---
# 253312: 2014 10 31 1 -30 UA LGA IAH 201 1416 14
# 253313: 2014 10 31 -5 -14 UA EWR IAH 189 1400 8
# 253314: 2014 10 31 -8 16 MQ LGA RDU 83 431 11
# 253315: 2014 10 31 -4 15 MQ LGA DTW 75 502 11
# 253316: 2014 10 31 -5 1 MQ LGA SDF 110 659 8
dim(flights)
# [1] 253316 11
In this vignette, we will
first discuss reference semantics briefly and look at the two different forms in which the := operator can be used
then see how we can add/update/delete columns by reference in j using the := operator and how to combine with i and by.
and finally we will look at using := for its side-effect and how we can avoid the side effects using copy().
All the operations we have seen so far in the previous vignette resulted in a new data set. We will see how to add new column(s), update or delete existing column(s) on the original data.
Before we look at reference semantics, consider the data.frame shown below:
DF = data.frame(ID = c("b","b","b","a","a","c"), a = 1:6, b = 7:12, c = 13:18)
DF
# ID a b c
# 1 b 1 7 13
# 2 b 2 8 14
# 3 b 3 9 15
# 4 a 4 10 16
# 5 a 5 11 17
# 6 c 6 12 18
When we did:
DF$c <- 18:13 # (1) -- replace entire column
# or
DF$c[DF$ID == "b"] <- 15:13 # (2) -- subassign in column 'c'
both (1) and (2) resulted in deep copy of the entire data.frame in versions of R versions < 3.1. It copied more than once. To improve performance by avoiding these redundant copies, data.table utilised the available but unused := operator in R.
Great performance improvements were made in R v3.1 as a result of which only a shallow copy is made for (1) and not deep copy. However, for (2) still, the entire column is deep copied even in R v3.1+. This means the more columns one subassigns to in the same query, the more deep copies R does.
A shallow copy is just a copy of the vector of column pointers (corresponding to the columns in a data.frame or data.table). The actual data is not physically copied in memory.
A deep copy on the other hand copies the entire data to another location in memory.
#
With data.table's := operator, absolutely no copies are made in both (1) and (2), irrespective of R version you are using. This is because := operator updates data.table columns in-place (by reference).
:= operatorIt can be used in j in two ways:
(a) The LHS := RHS form
```r
DT[, c("colA", "colB", ...) := list(valA, valB, ...)]
# when you have only one column to assign to you
# can drop the quotes and list(), for convenience
DT[, colA := valA]
```
(b) The functional form
```r
DT[, `:=`(colA = valA, # valA is assigned to colA
colB = valB, # valB is assigned to colB
...
)]
```
Note that the code above explains how := can be used. They are not working examples. We will start using them on flights data.table from the next section.
#
In (a), LHS takes a character vector of column names and RHS a list of values. RHS just needs to be a list, irrespective of how its generated (e.g., using lapply(), list(), mget(), mapply() etc.). This form is usually easy to program with and is particularly useful when you don't know the columns to assign values to in advance.
On the other hand, (b) is handy if you would like to jot some comments down for later.
The result is returned invisibly.
Since := is available in j, we can combine it with i and by operations just like the aggregation operations we saw in the previous vignette.
#
In the two forms of := shown above, note that we don't assign the result back to a variable. Because we don't need to. The input data.table is modified by reference. Let's go through examples to understand what we mean by this.
For the rest of the vignette, we will work with flights data.table.
flights data.table?flights[, `:=`(speed = distance / (air_time/60), # speed in mph (mi/h)
delay = arr_delay + dep_delay)] # delay in minutes
head(flights)
# year month day dep_delay arr_delay carrier origin dest air_time distance hour speed delay
# 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9 413.6490 27
# 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11 409.0909 10
# 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19 423.0769 11
# 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7 395.5414 -34
# 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13 424.2857 3
# 6: 2014 1 1 4 0 AA EWR LAX 339 2454 18 434.3363 4
## alternatively, using the 'LHS := RHS' form
# flights[, c("speed", "delay") := list(distance/(air_time/60), arr_delay + dep_delay)]
We did not have to assign the result back to flights.
The flights data.table now contains the two newly added columns. This is what we mean by added by reference.
We used the functional form so that we could add comments on the side to explain what the computation does. You can also see the LHS := RHS form (commented).
Let's take a look at all the hours available in the flights data.table:
# get all 'hours' in flights
flights[, sort(unique(hour))]
# [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
We see that there are totally 25 unique values in the data. Both 0 and 24 hours seem to be present. Let's go ahead and replace 24 with 0.
hour == 24 with the value 0# subassign by reference
flights[hour == 24L, hour := 0L]
We can use i along with := in j the very same way as we have already seen in the “Introduction to data.table” vignette.
Column hour is replaced with 0 only on those row indices where the condition hour == 24L specified in i evaluates to TRUE.
:= returns the result invisibly. Sometimes it might be necessary to see the result after the assignment. We can accomplish that by adding an empty [] at the end of the query as shown below:
flights[hour == 24L, hour := 0L][]
# year month day dep_delay arr_delay carrier origin dest air_time distance hour speed
# 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9 413.6490
# 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11 409.0909
# 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19 423.0769
# 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7 395.5414
# 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13 424.2857
# ---
# 253312: 2014 10 31 1 -30 UA LGA IAH 201 1416 14 422.6866
# 253313: 2014 10 31 -5 -14 UA EWR IAH 189 1400 8 444.4444
# 253314: 2014 10 31 -8 16 MQ LGA RDU 83 431 11 311.5663
# 253315: 2014 10 31 -4 15 MQ LGA DTW 75 502 11 401.6000
# 253316: 2014 10 31 -5 1 MQ LGA SDF 110 659 8 359.4545
# delay
# 1: 27
# 2: 10
# 3: 11
# 4: -34
# 5: 3
# ---
# 253312: -29
# 253313: -19
# 253314: 8
# 253315: 11
# 253316: -4
#
Let's look at all the hours to verify.
# check again for '24'
flights[, sort(unique(hour))]
# [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
What is the difference between flights[hour == 24L, hour := 0L] and flights[hour == 24L][, hour := 0L]? Hint: The latter needs an assignment (<-) if you would want to use the result later.
If you can't figure it out, have a look at the Note section of ?":=".
delay columnflights[, c("delay") := NULL]
head(flights)
# year month day dep_delay arr_delay carrier origin dest air_time distance hour speed
# 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9 413.6490
# 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11 409.0909
# 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19 423.0769
# 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7 395.5414
# 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13 424.2857
# 6: 2014 1 1 4 0 AA EWR LAX 339 2454 18 434.3363
## or using the functional form
# flights[, `:=`(delay = NULL)]
Assigning NULL to a column deletes that column. And it happens instantly.
We can also pass column numbers instead of names in the LHS, although it is good programming practice to use column names.
When there is just one column to delete, we can drop the c() and double quotes and just use the column name unquoted, for convenience. That is:
flights[, delay := NULL]
is equivalent to the code above.
:= along with grouping using by {#ref-j-by}We have already seen the use of i along with := in Section 2b. Let's now see how we can use := along with by.
orig,dest pair the maximum speed?flights[, max_speed := max(speed), by = .(origin, dest)]
head(flights)
# year month day dep_delay arr_delay carrier origin dest air_time distance hour speed max_speed
# 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9 413.6490 526.5957
# 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11 409.0909 526.5957
# 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19 423.0769 526.5957
# 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7 395.5414 517.5000
# 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13 424.2857 526.5957
# 6: 2014 1 1 4 0 AA EWR LAX 339 2454 18 434.3363 518.4507
We add a new column max_speed using the := operator by reference.
We provide the columns to group by the same way as shown in the Introduction to data.table vignette. For each group, max(speed) is computed, which returns a single value. That value is recycled to fit the length of the group. Once again, no copies are being made at all. flights data.table is modified in-place.
We could have also provided by with a character vector as we saw in the Introduction to data.table vignette, e.g., by = c("origin", "dest").
#
:=max() of dep_delay and arr_delay for each month, using .SD?in_cols = c("dep_delay", "arr_delay")
out_cols = c("max_dep_delay", "max_arr_delay")
flights[, c(out_cols) := lapply(.SD, max), by = month, .SDcols = in_cols]
head(flights)
# year month day dep_delay arr_delay carrier origin dest air_time distance hour speed max_speed
# 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9 413.6490 526.5957
# 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11 409.0909 526.5957
# 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19 423.0769 526.5957
# 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7 395.5414 517.5000
# 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13 424.2857 526.5957
# 6: 2014 1 1 4 0 AA EWR LAX 339 2454 18 434.3363 518.4507
# max_dep_delay max_arr_delay
# 1: 973 996
# 2: 973 996
# 3: 973 996
# 4: 973 996
# 5: 973 996
# 6: 973 996
We use the LHS := RHS form. We store the input column names and the new columns to add in separate variables and provide them to .SDcols and for LHS (for better readability).
Note that since we allow assignment by reference without quoting column names when there is only one column as explained in Section 2c, we can not do out_cols := lapply(.SD, max). That would result in adding one new column named out_col. Instead we should do either c(out_cols) or simply (out_cols). Wrapping the variable name with ( is enough to differentiate between the two cases.
The LHS := RHS form allows us to operate on multiple columns. In the RHS, to compute the max on columns specified in .SDcols, we make use of the base function lapply() along with .SD in the same way as we have seen before in the “Introduction to data.table” vignette. It returns a list of two elements, containing the maximum value corresponding to dep_delay and arr_delay for each group.
#
Before moving on to the next section, let's clean up the newly created columns speed, max_speed, max_dep_delay and max_arr_delay.
# RHS gets automatically recycled to length of LHS
flights[, c("speed", "max_speed", "max_dep_delay", "max_arr_delay") := NULL]
head(flights)
# year month day dep_delay arr_delay carrier origin dest air_time distance hour
# 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9
# 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11
# 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19
# 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7
# 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13
# 6: 2014 1 1 4 0 AA EWR LAX 339 2454 18
:= and copy():= modifies the input object by reference. Apart from the features we have discussed already, sometimes we might want to use the update by reference feature for its side effect. And at other times it may not be desirable to modify the original object, in which case we can use copy() function, as we will see in a moment.
:= for its side effectLet's say we would like to create a function that would return the maximum speed for each month. But at the same time, we would also like to add the column speed to flights. We could write a simple function as follows:
foo <- function(DT) {
DT[, speed := distance / (air_time/60)]
DT[, .(max_speed = max(speed)), by = month]
}
ans = foo(flights)
head(flights)
# year month day dep_delay arr_delay carrier origin dest air_time distance hour speed
# 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9 413.6490
# 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11 409.0909
# 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19 423.0769
# 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7 395.5414
# 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13 424.2857
# 6: 2014 1 1 4 0 AA EWR LAX 339 2454 18 434.3363
head(ans)
# month max_speed
# 1: 1 535.6425
# 2: 2 535.6425
# 3: 3 549.0756
# 4: 4 585.6000
# 5: 5 544.2857
# 6: 6 608.5714
Note that the new column speed has been added to flights data.table. This is because := performs operations by reference. Since DT (the function argument) and flights refer to the same object in memory, modifying DT also reflects on flights.
And ans contains the maximum speed for each month.
copy() functionIn the previous section, we used := for its side effect. But of course this may not be always desirable. Sometimes, we would like to pass a data.table object to a function, and might want to use the := operator, but wouldn't want to update the original object. We can accomplish this using the function copy().
The copy() function deep copies the input object and therefore any subsequent update by reference operations performed on the copied object will not affect the original object.
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There are two particular places where copy() function is essential:
Contrary to the situation we have seen in the previous point, we may not want the input data.table to a function to be modified by reference. As an example, let's consider the task in the previous section, except we don't want to modify flights by reference.
Let's first delete the speed column we generated in the previous section.
flights[, speed := NULL]
Now, we could accomplish the task as follows:
foo <- function(DT) {
DT <- copy(DT) ## deep copy
DT[, speed := distance / (air_time/60)] ## doesn't affect 'flights'
DT[, .(max_speed = max(speed)), by = month]
}
ans <- foo(flights)
head(flights)
# year month day dep_delay arr_delay carrier origin dest air_time distance hour
# 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9
# 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11
# 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19
# 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7
# 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13
# 6: 2014 1 1 4 0 AA EWR LAX 339 2454 18
head(ans)
# month max_speed
# 1: 1 535.6425
# 2: 2 535.6425
# 3: 3 549.0756
# 4: 4 585.6000
# 5: 5 544.2857
# 6: 6 608.5714
Using copy() function did not update flights data.table by reference. It doesn't contain the column speed.
And ans contains the maximum speed corresponding to each month.
However we could improve this functionality further by shallow copying instead of deep copying. In fact, we would very much like to provide this functionality for v1.9.8. We will touch up on this again in the data.table design vignette.
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When we store the column names on to a variable, e.g., DT_n = names(DT), and then add/update/delete column(s) by reference. It would also modify DT_n, unless we do copy(names(DT)).
DT = data.table(x = 1L, y = 2L)
DT_n = names(DT)
DT_n
# [1] "x" "y"
## add a new column by reference
DT[, z := 3L]
## DT_n also gets updated
DT_n
# [1] "x" "y" "z"
## use `copy()`
DT_n = copy(names(DT))
DT[, w := 4L]
## DT_n doesn't get updated
DT_n
# [1] "x" "y" "z"
:= operator {.bs-callout .bs-callout-info}It is used to add/update/delete columns by reference.
We have also seen how to use := along with i and by the same way as we have seen in the Introduction to data.table vignette. We can in the same way use keyby, chain operations together, and pass expressions to by as well all in the same way. The syntax is consistent.
We can use := for its side effect or use copy() to not modify the original object while updating by reference.
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So far we have seen a whole lot in j, and how to combine it with by and little of i. Let's turn our attention back to i in the next vignette “Keys and fast binary search based subset” to perform blazing fast subsets by keying data.tables.