require(data.table) knitr::opts_chunk$set( comment = "#", error = FALSE, tidy = FALSE, cache = FALSE, collapse = TRUE ) .old.th = setDTthreads(1)
This vignette introduces the data.table
syntax, its general form, how to subset rows, select and compute on columns, and perform aggregations by group. Familiarity with the data.frame
data structure from base R is useful, but not essential to follow this vignette.
data.table
Data manipulation operations such as subset, group, update, join, etc. are all inherently related. Keeping these related operations together allows for:
concise and consistent syntax irrespective of the set of operations you would like to perform to achieve your end goal.
performing analysis fluidly without the cognitive burden of having to map each operation to a particular function from a potentially huge set of functions available before performing the analysis.
automatically optimising operations internally and very effectively by knowing precisely the data required for each operation, leading to very fast and memory-efficient code.
Briefly, if you are interested in reducing programming and compute time tremendously, then this package is for you. The philosophy that data.table
adheres to makes this possible. Our goal is to illustrate it through this series of vignettes.
In this vignette, we will use NYC-flights14 data obtained from the flights package (available on GitHub only). It contains On-Time flights data from the Bureau of Transportation Statistics for all the flights that departed from New York City airports in 2014 (inspired by nycflights13). The data is available only for Jan-Oct'14.
We can use data.table
's fast-and-friendly file reader fread
to load flights
directly as follows:
options(width = 100L)
input <- if (file.exists("flights14.csv")) { "flights14.csv" } else { "https://raw.githubusercontent.com/Rdatatable/data.table/master/vignettes/flights14.csv" } flights <- fread(input) flights dim(flights)
Aside: fread
accepts http
and https
URLs directly, as well as operating system commands such as sed
and awk
output. See ?fread
for examples.
In this vignette, we will
Start with the basics - what is a data.table
, its general form, how to subset rows, how to select and compute on columns;
Then we will look at performing data aggregations by group
data.table
? {#what-is-datatable-1a}data.table
is an R package that provides an enhanced version of a data.frame
, the standard data structure for storing data in base
R. In the Data section above, we saw how to create a data.table
using fread()
, but alternatively we can also create one using the data.table()
function. Here is an example:
DT = data.table( ID = c("b","b","b","a","a","c"), a = 1:6, b = 7:12, c = 13:18 ) DT class(DT$ID)
You can also convert existing objects to a data.table
using setDT()
(for data.frame
and list
structures) or as.data.table()
(for other structures). For more details pertaining to the difference (goes beyond the scope of this vignette), please see ?setDT
and ?as.data.table
.
Row numbers are printed with a :
in order to visually separate the row number from the first column.
When the number of rows to print exceeds the global option datatable.print.nrows
(default = r getOption("datatable.print.nrows")
), it automatically prints only the top 5 and bottom 5 rows (as can be seen in the Data section). For a large data.frame
, you may have found yourself waiting around while larger tables print-and-page, sometimes seemingly endlessly. This restriction helps with that, and you can query the default number like so:
{.r}
getOption("datatable.print.nrows")
data.table
doesn't set or use row names, ever. We will see why in the "Keys and fast binary search based subset" vignette.
data.table
enhanced? {#enhanced-1b}In contrast to a data.frame
, you can do a lot more than just subsetting rows and selecting columns within the frame of a data.table
, i.e., within [ ... ]
(NB: we might also refer to writing things inside DT[...]
as "querying DT
", as an analogy or in relevance to SQL). To understand it we will have to first look at the general form of the data.table
syntax, as shown below:
DT[i, j, by] ## R: i j by ## SQL: where | order by select | update group by
Users with an SQL background might perhaps immediately relate to this syntax.
Take DT
, subset/reorder rows using i
, then calculate j
, grouped by by
.
Let's begin by looking at i
and j
first - subsetting rows and operating on columns.
i
{#subset-i-1c}ans <- flights[origin == "JFK" & month == 6L] head(ans)
Within the frame of a data.table
, columns can be referred to as if they are variables, much like in SQL or Stata. Therefore, we simply refer to origin
and month
as if they are variables. We do not need to add the prefix flights$
each time. Nevertheless, using flights$origin
and flights$month
would work just fine.
The row indices that satisfy the condition origin == "JFK" & month == 6L
are computed, and since there is nothing else left to do, all columns from flights
at rows corresponding to those row indices are simply returned as a data.table
.
A comma after the condition in i
is not required. But flights[origin == "JFK" & month == 6L, ]
would work just fine. In a data.frame
, however, the comma is necessary.
flights
. {#subset-rows-integer}ans <- flights[1:2] ans
i
. We therefore return a data.table
with all columns from flights
at rows for those row indices.flights
first by column origin
in ascending order, and then by dest
in descending order:We can use the R function order()
to accomplish this.
ans <- flights[order(origin, -dest)] head(ans)
order()
is internally optimisedWe can use "-" on character
columns within the frame of a data.table
to sort in decreasing order.
In addition, order(...)
within the frame of a data.table
uses data.table
's internal fast radix order forder()
. This sort provided such a compelling improvement over R's base::order
that the R project adopted the data.table
algorithm as its default sort in 2016 for R 3.3.0 (for reference, check ?sort
and the R Release NEWS).
We will discuss data.table
's fast order in more detail in the data.table
internals vignette.
j
{#select-j-1d}arr_delay
column, but return it as a vector.ans <- flights[, arr_delay] head(ans)
Since columns can be referred to as if they are variables within the frame of a data.table
, we directly refer to the variable we want to subset. Since we want all the rows, we simply skip i
.
It returns all the rows for the column arr_delay
.
arr_delay
column, but return as a data.table
instead.ans <- flights[, list(arr_delay)] head(ans)
We wrap the variables (column names) within list()
, which ensures that a data.table
is returned. In the case of a single column name, not wrapping with list()
returns a vector instead, as seen in the previous example.
data.table
also allows wrapping columns with .()
instead of list()
. It is an alias to list()
; they both mean the same. Feel free to use whichever you prefer; we have noticed most users seem to prefer .()
for conciseness, so we will continue to use .()
hereafter.
A data.table
(and a data.frame
too) is internally a list
as well, with the stipulation that each element has the same length and the list
has a class
attribute. Allowing j
to return a list
enables converting and returning data.table
very efficiently.
As long as j-expression
returns a list
, each element of the list will be converted to a column in the resulting data.table
. This makes j
quite powerful, as we will see shortly. It is also very important to understand this for when you'd like to make more complicated queries!!
arr_delay
and dep_delay
columns.ans <- flights[, .(arr_delay, dep_delay)] head(ans) ## alternatively # ans <- flights[, list(arr_delay, dep_delay)]
.()
, or list()
. That's it.arr_delay
and dep_delay
columns and rename them to delay_arr
and delay_dep
.Since .()
is just an alias for list()
, we can name columns as we would while creating a list
.
ans <- flights[, .(delay_arr = arr_delay, delay_dep = dep_delay)] head(ans)
j
ans <- flights[, sum( (arr_delay + dep_delay) < 0 )] ans
data.table
's j
can handle more than just selecting columns - it can handle expressions, i.e., computing on columns. This shouldn't be surprising, as columns can be referred to as if they are variables. Then we should be able to compute by calling functions on those variables. And that's what precisely happens here.i
and do in j
ans <- flights[origin == "JFK" & month == 6L, .(m_arr = mean(arr_delay), m_dep = mean(dep_delay))] ans
We first subset in i
to find matching row indices where origin
airport equals "JFK"
, and month
equals 6L
. We do not subset the entire data.table
corresponding to those rows yet.
Now, we look at j
and find that it uses only two columns. And what we have to do is to compute their mean()
. Therefore, we subset just those columns corresponding to the matching rows, and compute their mean()
.
Because the three main components of the query (i
, j
and by
) are together inside [...]
, data.table
can see all three and optimise the query altogether before evaluation, rather than optimizing each separately. We are able to therefore avoid the entire subset (i.e., subsetting the columns besides arr_delay
and dep_delay
), for both speed and memory efficiency.
ans <- flights[origin == "JFK" & month == 6L, length(dest)] ans
The function length()
requires an input argument. We just need to compute the number of rows in the subset. We could have used any other column as the input argument to length()
. This approach is reminiscent of SELECT COUNT(dest) FROM flights WHERE origin = 'JFK' AND month = 6
in SQL.
This type of operation occurs quite frequently, especially while grouping (as we will see in the next section), to the point where data.table
provides a special symbol .N
for it.
i
When querying a data.table
for elements that do not exist, the behavior differs based on the method used.
setkeyv(flights, "origin")
dt["d"]
This performs a right join on the key column x
, resulting in a row with d
and NA
for columns not found. When using setkeyv
, the table is sorted by the specified keys and an internal index is created, enabling binary search for efficient subsetting.
r
flights["XYZ"]
# Returns:
# origin year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier flight tailnum ...
# 1: XYZ NA NA NA NA NA NA NA NA NA NA NA NA ...
dt[x == "d"]
This performs a standard subset operation that does not find any matching rows and thus returns an empty data.table
.
r
flights[origin == "XYZ"]
# Returns:
# Empty data.table (0 rows and 19 cols): year,month,day,dep_time,sched_dep_time,dep_delay,arr_time,sched_arr_time,arr_delay,...
nomatch=NULL
For exact matches without NA
for non-existing elements, use nomatch=NULL
:
r
flights["XYZ", nomatch=NULL]
# Returns:
# Empty data.table (0 rows and 19 cols): year,month,day,dep_time,sched_dep_time,dep_delay,arr_time,sched_arr_time,arr_delay,...
Understanding these behaviors can help prevent confusion when dealing with non-existing elements in your data.
.N
: {#special-N}.N
is a special built-in variable that holds the number of observations in the current group. It is particularly useful when combined with by
as we'll see in the next section. In the absence of group by operations, it simply returns the number of rows in the subset.
Now that we now, we can now accomplish the same task by using .N
as follows:
ans <- flights[origin == "JFK" & month == 6L, .N] ans
Once again, we subset in i
to get the row indices where origin
airport equals "JFK", and month
equals 6.
We see that j
uses only .N
and no other columns. Therefore, the entire subset is not materialised. We simply return the number of rows in the subset (which is just the length of row indices).
Note that we did not wrap .N
with list()
or .()
. Therefore, a vector is returned.
We could have accomplished the same operation by doing nrow(flights[origin == "JFK" & month == 6L])
. However, it would have to subset the entire data.table
first corresponding to the row indices in i
and then return the rows using nrow()
, which is unnecessary and inefficient. We will cover this and other optimisation aspects in detail under the data.table
design vignette.
j
(like in a data.frame
)? {#refer_j}If you're writing out the column names explicitly, there's no difference compared to a data.frame
(since v1.9.8).
arr_delay
and dep_delay
columns the data.frame
way.ans <- flights[, c("arr_delay", "dep_delay")] head(ans)
If you've stored the desired columns in a character vector, there are two options: Using the ..
prefix, or using the with
argument.
..
prefixselect_cols = c("arr_delay", "dep_delay") flights[ , ..select_cols]
For those familiar with the Unix terminal, the ..
prefix should be reminiscent of the "up-one-level" command, which is analogous to what's happening here -- the ..
signals to data.table
to look for the select_cols
variable "up-one-level", i.e., within the global environment in this case.
with = FALSE
flights[ , select_cols, with = FALSE]
The argument is named with
after the R function with()
because of similar functionality. Suppose you have a data.frame
DF
and you'd like to subset all rows where x > 1
. In base
R you can do the following:
DF = data.frame(x = c(1,1,1,2,2,3,3,3), y = 1:8) ## (1) normal way DF[DF$x > 1, ] # data.frame needs that ',' as well ## (2) using with DF[with(DF, x > 1), ]
Using with()
in (2) allows using DF
's column x
as if it were a variable.
Hence, the argument name with
in data.table
. Setting with = FALSE
disables the ability to refer to columns as if they are variables, thereby restoring the "data.frame
mode".
We can also deselect columns using -
or !
. For example:
```r
ans <- flights[, !c("arr_delay", "dep_delay")]
ans <- flights[, -c("arr_delay", "dep_delay")] ```
From v1.9.5+
, we can also select by specifying start and end column names, e.g., year:day
to select the first three columns.
```r
ans <- flights[, year:day]
ans <- flights[, day:year]
ans <- flights[, -(year:day)] ans <- flights[, !(year:day)] ```
This is particularly handy while working interactively.
with = TRUE
is the default in data.table
because we can do much more by allowing j
to handle expressions - especially when combined with by
, as we'll see in a moment.
We've already seen i
and j
from data.table
's general form in the previous section. In this section, we'll see how they can be combined together with by
to perform operations by group. Let's look at some examples.
by
ans <- flights[, .(.N), by = .(origin)] ans ## or equivalently using a character vector in 'by' # ans <- flights[, .(.N), by = "origin"]
We know .N
is a special variable that holds the number of rows in the current group. Grouping by origin
obtains the number of rows, .N
, for each group.
By doing head(flights)
you can see that the origin airports occur in the order "JFK", "LGA", and "EWR". The original order of grouping variables is preserved in the result. This is important to keep in mind!
Since we did not provide a name for the column returned in j
, it was named N
automatically by recognising the special symbol .N
.
by
also accepts a character vector of column names. This is particularly useful for coding programmatically, e.g., designing a function with the grouping columns (in the form of a character
vector) as a function argument.
When there's only one column or expression to refer to in j
and by
, we can drop the .()
notation. This is purely for convenience. We could instead do:
r
ans <- flights[, .N, by = origin]
ans
We'll use this convenient form wherever applicable hereafter.
"AA"
? {#origin-.N}The unique carrier code "AA"
corresponds to American Airlines Inc.
ans <- flights[carrier == "AA", .N, by = origin] ans
We first obtain the row indices for the expression carrier == "AA"
from i
.
Using those row indices, we obtain the number of rows while grouped by origin
. Once again no columns are actually materialised here, because the j-expression
does not require any columns to be actually subsetted and is therefore fast and memory efficient.
origin, dest
pair for carrier code "AA"
? {#origin-dest-.N}ans <- flights[carrier == "AA", .N, by = .(origin, dest)] head(ans) ## or equivalently using a character vector in 'by' # ans <- flights[carrier == "AA", .N, by = c("origin", "dest")]
by
accepts multiple columns. We just provide all the columns by which to group by. Note the use of .()
again in by
-- again, this is just shorthand for list()
, and list()
can be used here as well. Again, we'll stick with .()
in this vignette.orig,dest
pair for each month for carrier code "AA"
? {#origin-dest-month}ans <- flights[carrier == "AA", .(mean(arr_delay), mean(dep_delay)), by = .(origin, dest, month)] ans
Since we did not provide column names for the expressions in j
, they were automatically generated as V1
and V2
.
Once again, note that the input order of grouping columns is preserved in the result.
Now what if we would like to order the result by those grouping columns origin
, dest
and month
?
by
: keyby
data.table
retaining the original order of groups is intentional and by design. There are cases when preserving the original order is essential. But at times we would like to automatically sort by the variables in our grouping.
ans <- flights[carrier == "AA", .(mean(arr_delay), mean(dep_delay)), keyby = .(origin, dest, month)] ans
by
to keyby
. This automatically orders the result by the grouping variables in increasing order. In fact, due to the internal implementation of by
first requiring a sort before recovering the original table's order, keyby
is typically faster than by
because it doesn't require this second step.Keys: Actually keyby
does a little more than just ordering. It also sets a key after ordering by setting an attribute
called sorted
.
We'll learn more about keys
in the Keys and fast binary search based subset vignette; for now, all you have to know is that you can use keyby
to automatically order the result by the columns specified in by
.
Let's reconsider the task of getting the total number of trips for each origin, dest
pair for carrier "AA".
ans <- flights[carrier == "AA", .N, by = .(origin, dest)]
ans
using the columns origin
in ascending order, and dest
in descending order?We can store the intermediate result in a variable, and then use order(origin, -dest)
on that variable. It seems fairly straightforward.
ans <- ans[order(origin, -dest)] head(ans)
Recall that we can use -
on a character
column in order()
within the frame of a data.table
. This is possible due to data.table
's internal query optimisation.
Also recall that order(...)
with the frame of a data.table
is automatically optimised to use data.table
's internal fast radix order forder()
for speed.
But this requires having to assign the intermediate result and then overwriting that result. We can do one better and avoid this intermediate assignment to a temporary variable altogether by chaining expressions.
ans <- flights[carrier == "AA", .N, by = .(origin, dest)][order(origin, -dest)] head(ans, 10)
We can tack expressions one after another, forming a chain of operations, i.e., DT[ ... ][ ... ][ ... ]
.
Or you can also chain them vertically:
r
DT[ ...
][ ...
][ ...
]
by
by
accept expressions as well or does it just take columns?Yes it does. As an example, if we would like to find out how many flights started late but arrived early (or on time), started and arrived late etc...
ans <- flights[, .N, .(dep_delay>0, arr_delay>0)] ans
The last row corresponds to dep_delay > 0 = TRUE
and arr_delay > 0 = FALSE
. We can see that r flights[!is.na(arr_delay) & !is.na(dep_delay), .N, .(dep_delay>0, arr_delay>0)][, N[4L]]
flights started late but arrived early (or on time).
Note that we did not provide any names to by-expression
. Therefore, names have been automatically assigned in the result. As with j
, you can name these expressions as you would for elements of any list
, like for e.g. DT[, .N, .(dep_delayed = dep_delay>0, arr_delayed = arr_delay>0)]
.
You can provide other columns along with expressions, for example: DT[, .N, by = .(a, b>0)]
.
j
- .SD
mean()
for each column individually?It is of course not practical to have to type mean(myCol)
for every column one by one. What if you had 100 columns to average mean()
?
How can we do this efficiently and concisely? To get there, refresh on this tip - "As long as the j
-expression returns a list
, each element of the list
will be converted to a column in the resulting data.table
". If we can refer to the data subset for each group as a variable while grouping, we can then loop through all the columns of that variable using the already- or soon-to-be-familiar base function lapply()
. No new names to learn specific to data.table
.
.SD
: {#special-SD}data.table
provides a special symbol called .SD
. It stands for Subset of Data. It by itself is a data.table
that holds the data for the current group defined using by
.
Recall that a data.table
is internally a list
as well with all its columns of equal length.
Let's use the data.table
DT
from before to get a glimpse of what .SD
looks like.
DT DT[, print(.SD), by = ID]
.SD
contains all the columns except the grouping columns by default.
It is also generated by preserving the original order - data corresponding to ID = "b"
, then ID = "a"
, and then ID = "c"
.
To compute on (multiple) columns, we can then simply use the base R function lapply()
.
DT[, lapply(.SD, mean), by = ID]
.SD
holds the rows corresponding to columns a
, b
and c
for that group. We compute the mean()
on each of these columns using the already-familiar base function lapply()
.
Each group returns a list of three elements containing the mean value which will become the columns of the resulting data.table
.
Since lapply()
returns a list
, so there is no need to wrap it with an additional .()
(if necessary, refer to this tip).
We are almost there. There is one little thing left to address. In our flights
data.table
, we only wanted to calculate the mean()
of the two columns arr_delay
and dep_delay
. But .SD
would contain all the columns other than the grouping variables by default.
mean()
on?Using the argument .SDcols
. It accepts either column names or column indices. For example, .SDcols = c("arr_delay", "dep_delay")
ensures that .SD
contains only these two columns for each group.
Similar to part g), you can also specify the columns to remove instead of columns to keep using -
or !
. Additionally, you can select consecutive columns as colA:colB
and deselect them as !(colA:colB)
or -(colA:colB)
.
Now let us try to use .SD
along with .SDcols
to get the mean()
of arr_delay
and dep_delay
columns grouped by origin
, dest
and month
.
flights[carrier == "AA", ## Only on trips with carrier "AA" lapply(.SD, mean), ## compute the mean by = .(origin, dest, month), ## for every 'origin,dest,month' .SDcols = c("arr_delay", "dep_delay")] ## for just those specified in .SDcols
.SD
for each group:month
?ans <- flights[, head(.SD, 2), by = month] head(ans)
.SD
is a data.table
that holds all the rows for that group. We simply subset the first two rows as we have seen here already.
For each group, head(.SD, 2)
returns the first two rows as a data.table
, which is also a list
, so we do not have to wrap it with .()
.
j
so flexible?So that we have a consistent syntax and keep using already existing (and familiar) base functions instead of learning new functions. To illustrate, let us use the data.table
DT
that we created at the very beginning under the section What is a data.table?.
a
and b
for each group in ID
?DT[, .(val = c(a,b)), by = ID]
c()
which concatenates vectors and the tip from before.a
and b
concatenated, but returned as a list column?DT[, .(val = list(c(a,b))), by = ID]
Here, we first concatenate the values with c(a,b)
for each group, and wrap that with list()
. So for each group, we return a list of all concatenated values.
Note that those commas are for display only. A list column can contain any object in each cell, and in this example, each cell is itself a vector and some cells contain longer vectors than others.
Once you start internalising usage in j
, you will realise how powerful the syntax can be. A very useful way to understand it is by playing around, with the help of print()
.
For example:
## look at the difference between DT[, print(c(a,b)), by = ID] # (1) ## and DT[, print(list(c(a,b))), by = ID] # (2)
In (1), for each group, a vector is returned, with length = 6,4,2 here. However, (2) returns a list of length 1 for each group, with its first element holding vectors of length 6,4,2. Therefore, (1) results in a length of 6+4+2 =
r 6+4+2, whereas (2) returns `1+1+1=`r 1+1+1
.
The general form of data.table
syntax is:
DT[i, j, by]
We have seen so far that,
i
:We can subset rows similar to a data.frame
- except you don't have to use DT$
repetitively since columns within the frame of a data.table
are seen as if they are variables.
We can also sort a data.table
using order()
, which internally uses data.table's fast order for better performance.
We can do much more in i
by keying a data.table
, which allows for blazing fast subsets and joins. We will see this in the "Keys and fast binary search based subsets" and "Joins and rolling joins" vignette.
j
:Select columns the data.table
way: DT[, .(colA, colB)]
.
Select columns the data.frame
way: DT[, c("colA", "colB")]
.
Compute on columns: DT[, .(sum(colA), mean(colB))]
.
Provide names if necessary: DT[, .(sA =sum(colA), mB = mean(colB))]
.
Combine with i
: DT[colA > value, sum(colB)]
.
by
:Using by
, we can group by columns by specifying a list of columns or a character vector of column names or even expressions. The flexibility of j
, combined with by
and i
, makes for a very powerful syntax.
by
can handle multiple columns and also expressions.
We can keyby
grouping columns to automatically sort the grouped result.
We can use .SD
and .SDcols
in j
to operate on multiple columns using already familiar base functions. Here are some examples:
DT[, lapply(.SD, fun), by = ..., .SDcols = ...]
- applies fun
to all columns specified in .SDcols
while grouping by the columns specified in by
.
DT[, head(.SD, 2), by = ...]
- return the first two rows for each group.
DT[col > val, head(.SD, 1), by = ...]
- combine i
along with j
and by
.
As long as j
returns a list
, each element of the list will become a column in the resulting data.table
.
We will see how to add/update/delete columns by reference and how to combine them with i
and by
in the next vignette.
setDTthreads(.old.th)
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