Data.table is a package that extends the functionality of data frames from base R, particularly improving on their performance and syntax. See the package's Docs area at Getting started with data.table for details.
Syntax[edit | edit source]
DT[i, j, by]
- DT[where, select|update|do, by]
################# Shortcuts, special functions and special symbols inside DT[...]
- in several arguments, replaces list()
- in i, replaces list()
- in j, a function used to add or modify columns
- in i, the total number of rows
- in j, the number of rows in a group
- in j, the vector of row numbers in the table (filtered by i)
- in j, the current subset of the data
- selected by the .SDcols argument
- in j, the current index of the subset of the data
- in j, the list of by values for the current subset of data
- V1, V2, ...
- default names for unnamed columns created in j
################# Joins inside DT[...]
- DT1[DT2, on, j]
- join two tables
- special prefix on DT2's columns after the join
- special option available only with a join
- DT1[!DT2, on, j]
- anti-join two tables
- DT1[DT2, on, roll, j]
- join two tables, rolling on the last column in on=
################# Reshaping, stacking and splitting
- melt(DT, id.vars, measure.vars)
- transform to long format
- for multiple columns, use measure.vars = patterns(...)
- dcast(DT, formula)
- transform to wide format
- rbind(DT1, DT2, ...)
- stack enumerated data.tables
- rbindlist(DT_list, idcol)
- stack a list of data.tables
- split(DT, by)
- split a data.table into a list
################# Some other functions specialized for data.tables
- overlap joins
- another way of joining two tables
- another way of adding or modifying columns
- fintersect, fsetdiff, funion, fsetequal, unique, duplicated, anyDuplicated
- set-theory operations with rows as elements
- the number of distinct rows
- rowidv(DT, cols)
- row ID (1 to .N) within each group determined by cols
- rleidv(DT, cols)
- group ID (1 to .GRP) within each group determined by runs of cols
- shift(DT, n, type=c("lag", "lead"))
- apply a shift operator to every column
- setorder, setcolorder, setnames, setkey, setindex, setattr
- modify attributes and order by reference
Remarks[edit | edit source]
Installation and support[edit | edit source]
To install the data.table package:
# install from CRAN install.packages("data.table") # or install development version install.packages("data.table", type = "source", repos = "http://Rdatatable.github.io/data.table") # and to revert from devel to CRAN, the current version must first be removed remove.packages("data.table") install.packages("data.table")
The package's official site has wiki pages providing help getting started, and lists of presentations and articles from around the web. Before asking a question -- here on StackOverflow or anywhere else -- please read the support page.
Loading the package[edit | edit source]
Many of the functions in the examples above exist in the data.table namespace. To use them, you will need to add a line like
library(data.table) first or to use their full path, like
data.table::fread instead of simply
fread. For help on individual functions, the syntax is
?fread. Again, if the package is not loaded, use the full name like
Creating a data.table[edit | edit source]
A data.table is an enhanced version of the data.frame class from base R. As such, its
class() attribute is the vector
"data.table" "data.frame" and functions that work on a data.frame will also work with a data.table. There are many ways to create, load or coerce to a data.table.
Build[edit | edit source]
Don't forget to install and activate the
There is a constructor of the same name:
DT <- data.table( x = letters[1:5], y = 1:5, z = (1:5) > 3 ) # x y z # 1: a 1 FALSE # 2: b 2 FALSE # 3: c 3 FALSE # 4: d 4 TRUE # 5: e 5 TRUE
data.table will not coerce strings to factors:
sapply(DT, class) # x y z # "character" "integer" "logical"
Read in[edit | edit source]
We can read from a text file:
dt <- fread("my_file.csv")
fread will read strings as strings, not as factors.
Modify a data.frame[edit | edit source]
For efficiency, data.table offers a way of altering a data.frame or list to make a data.table in-place (without making a copy or changing its memory location):
# example data.frame DF <- data.frame(x = letters[1:5], y = 1:5, z = (1:5) > 3) # modification setDT(DF)
Note that we do not
<- assign the result, since the object
DF has been modified in-place. The class attributes of the data.frame will be retained:
sapply(DF, class) # x y z # "factor" "integer" "logical"
Coerce object to data.table[edit | edit source]
If you have a
data.table, you should use the
setDT function to convert to a
data.table because it does the conversion by reference instead of making a copy (which
as.data.table does). This is important if you are working with large datasets.
If you have another R object (such as a matrix), you must use
as.data.table to coerce it to a
mat <- matrix(0, ncol = 10, nrow = 10) DT <- as.data.table(mat) # or DT <- data.table(mat)
Special symbols in data.table[edit | edit source]
.SD[edit | edit source]
.SD refers to the subset of the
data.table for each group, excluding all columns used in
.SD along with
lapply can be used to apply any function to multiple columns by group in a
We will continue using the same built-in dataset,
mtcars = data.table(mtcars) # Let's not include rownames to keep things simpler
Mean of all columns in the dataset by number of cylinders,
mtcars[ , lapply(.SD, mean), by = cyl] # cyl mpg disp hp drat wt qsec vs am gear carb #1: 6 19.74286 183.3143 122.28571 3.585714 3.117143 17.97714 0.5714286 0.4285714 3.857143 3.428571 #2: 4 26.66364 105.1364 82.63636 4.070909 2.285727 19.13727 0.9090909 0.7272727 4.090909 1.545455 #3: 8 15.10000 353.1000 209.21429 3.229286 3.999214 16.77214 0.0000000 0.1428571 3.285714 3.500000
cyl, there are other categorical columns in the dataset such as
carb. It doesn't really make sense to take the
mean of these columns. So let's exclude these columns. This is where
.SDcols comes into the picture.
.SDcols[edit | edit source]
.SDcols specifies the columns of the
data.table that are included in
Mean of all columns (continuous columns) in the dataset by number of gears
gear, and number of cylinders,
cyl, arranged by
# All the continuous variables in the dataset cols_chosen <- c("mpg", "disp", "hp", "drat", "wt", "qsec") mtcars[order(gear, cyl), lapply(.SD, mean), by = .(gear, cyl), .SDcols = cols_chosen] # gear cyl mpg disp hp drat wt qsec #1: 3 4 21.500 120.1000 97.0000 3.700000 2.465000 20.0100 #2: 3 6 19.750 241.5000 107.5000 2.920000 3.337500 19.8300 #3: 3 8 15.050 357.6167 194.1667 3.120833 4.104083 17.1425 #4: 4 4 26.925 102.6250 76.0000 4.110000 2.378125 19.6125 #5: 4 6 19.750 163.8000 116.5000 3.910000 3.093750 17.6700 #6: 5 4 28.200 107.7000 102.0000 4.100000 1.826500 16.8000 #7: 5 6 19.700 145.0000 175.0000 3.620000 2.770000 15.5000 #8: 5 8 15.400 326.0000 299.5000 3.880000 3.370000 14.5500
Maybe we don't want to calculate the
mean by groups. To calculate the mean for all the cars in the dataset, we don't specify the
mtcars[ , lapply(.SD, mean), .SDcols = cols_chosen] # mpg disp hp drat wt qsec #1: 20.09062 230.7219 146.6875 3.596563 3.21725 17.84875
- It is not necessary to define
.SDcolscan directly take column names
.SDcolscan also directly take a vector of columnnumbers. In the above example this would be
mtcars[ , lapply(.SD, mean), .SDcols = c(1,3:7)]
.N[edit | edit source]
.N is shorthand for the number of rows in a group.
iris[, .(count=.N), by=Species] # Species count #1: setosa 50 #2: versicolor 50 #3: virginica 50
Adding and modifying columns[edit | edit source]
DT[where, select|update|do, by] syntax is used to work with columns of a data.table.
- The "where" part is the
- The "select|update|do" part is the
These two arguments are usually passed by position instead of by name.
Our example data below is
mtcars = data.table(mtcars, keep.rownames = TRUE)
Editing entire columns[edit | edit source]
:= operator inside
j to assign new columns:
mtcars[, mpg_sq := mpg^2]
Remove columns by setting to
mtcars[, mpg_sq := NULL]
Add multiple columns by using the
:= operator's multivariate format:
mtcars[, `:=`(mpg_sq = mpg^2, wt_sqrt = sqrt(wt))] # or mtcars[, c("mpg_sq", "wt_sqrt") := .(mpg^2, sqrt(wt))]
If the columns are dependent and must be defined in sequence, one way is:
mtcars[, c("mpg_sq", "mpg2_hp") := .(temp1 <- mpg^2, temp1/hp)]
.() syntax is used when the right-hand side of
LHS := RHS is a list of columns.
For dynamically-determined column names, use parentheses:
vn = "mpg_sq" mtcars[, (vn) := mpg^2]
Columns can also be modified with
set, though this is rarely necessary:
set(mtcars, j = "hp_over_wt", v = mtcars$hp/mtcars$wt)
Editing subsets of columns[edit | edit source]
i argument to subset to rows "where" edits should be made:
mtcars[1:3, newvar := "Hello"] # or set(mtcars, j = "newvar", i = 1:3, v = "Hello")
As in a data.frame, we can subset using row numbers or logical tests. It is also possible to use a "join" in
i, but that more complicated task is covered in another example.
Editing column attributes[edit | edit source]
Functions that edit attributes, such as
names<-, actually replace an object with a modified copy. Even if only used on one column in a data.table, the entire object is copied and replaced.
To modify an object without copies, use
setnames to change the column names of a data.table or data.frame and
setattr to change an attribute for any object.
# Print a message to the console whenever the data.table is copied tracemem(mtcars) mtcars[, cyl2 := factor(cyl)] # Neither of these statements copy the data.table setnames(mtcars, old = "cyl2", new = "cyl_fac") setattr(mtcars$cyl_fac, "levels", c("four", "six", "eight")) # Each of these statements copies the data.table names(mtcars)[names(mtcars) == "cyl_fac"] <- "cf" levels(mtcars$cf) <- c("IV", "VI", "VIII")
Be aware that these changes are made by reference, so they are global. Changing them within one environment affects the object in all environments.
# This function also changes the levels in the global environment edit_levels <- function(x) setattr(x, "levels", c("low", "med", "high")) edit_levels(mtcars$cyl_factor)
Writing code compatible with both data.frame and data.table[edit | edit source]
Differences in subsetting syntax[edit | edit source]
data.table is one of several two-dimensional data structures available in R, besides
matrix and (2D)
array. All of these classes use a very similar but not identical syntax for subsetting, the
A[rows, cols] schema.
Consider the following data stored in a
data.frame and a
ma <- matrix(rnorm(12), nrow=4, dimnames=list(letters[1:4], c('X', 'Y', 'Z'))) df <- as.data.frame(ma) dt <- as.data.table(ma) ma[2:3] #---> returns the 2nd and 3rd items, as if 'ma' were a vector (because it is!) df[2:3] #---> returns the 2nd and 3rd columns dt[2:3] #---> returns the 2nd and 3rd rows!
If you want to be sure of what will be returned, it is better to be explicit.
To get specific rows, just add a comma after the range:
ma[2:3, ] # \ df[2:3, ] # }---> returns the 2nd and 3rd rows dt[2:3, ] # /
But, if you want to subset columns, some cases are interpreted differently. All three can be subset the same way with integer or character indices not stored in a variable.
ma[, 2:3] # \ df[, 2:3] # \ dt[, 2:3] # }---> returns the 2nd and 3rd columns ma[, c("Y", "Z")] # / df[, c("Y", "Z")] # / dt[, c("Y", "Z")] # /
However, they differ for unquoted variable names
mycols <- 2:3 ma[, mycols] # \ df[, mycols] # }---> returns the 2nd and 3rd columns dt[, mycols, with = FALSE] # / dt[, mycols] # ---> Raises an error
In the last case,
mycols is evaluated as the name of a column. Because
dt cannot find a column named
mycols, an error is raised.
Note: For versions of the
data.table package priorto 1.9.8, this behavior was slightly different. Anything in the column index would have been evaluated using
dt as an environment. So both
dt[, 2:3] and
dt[, mycols] would return the vector
2:3. No error would be raised for the second case, because the variable
mycols does exist in the parent environment.
Strategies for maintaining compatibility with data.frame and data.table[edit | edit source]
There are many reasons to write code that is guaranteed to work with
data.table. Maybe you are forced to use
data.frame, or you may need to share some code that you don't know how will be used. So, there are some main strategies for achieving this, in order of convenience:
- Use syntax that behaves the same for both classes.
- Use a common function that does the same thing as the shortest syntax.
data.tableto behave as
data.frame(ex.: call the specific method
- Treat them as
list, which they ultimately are.
- Convert the table to a
data.framebefore doing anything (bad idea if it is a huge table).
- Convert the table to
data.table, if dependencies are not a concern.
Subset rows. Its simple, just use the
[, ] selector, with the comma:
A[1:10, ] A[A$var > 17, ] # A[var > 17, ] just works for data.table
Subset columns. If you want a single column, use the
$ or the
[[ ]] selector:
A$var colname <- 'var' A[[colname]] A[]
If you want a uniform way to grab more than one column, it's necessary to appeal a bit:
B <- `[.data.frame`(A, 2:4) # We can give it a better name select <- `[.data.frame` B <- select(A, 2:4) C <- select(A, c('foo', 'bar'))
Subset 'indexed' rows. While
data.table has its unique
key feature. The best thing is to avoid
row.names entirely and take advantage of the existing optimizations in the case of
data.table when possible.
B <- A[A$var != 0, ] # or... B <- with(A, A[var != 0, ]) # data.table will silently index A by var before subsetting stuff <- c('a', 'c', 'f') C <- A[match(stuff, A$name), ] # really worse than: setkey(A); A[stuff, ]
Get a 1-column table, get a row as a vector. These are easy with what we have seen until now:
B <- select(A, 2) #---> a table with just the second column C <- unlist(A[1, ]) #---> the first row as a vector (coerced if necessary)
Setting keys in data.table[edit | edit source]
Yes, you need to SETKEY pre 1.9.6
In the past (pre 1.9.6), your
data.table was sped up by setting columns as keys to the table, particularly for large tables. [See intro vignette page 5 of September 2015 version, where speed of search was 544 times better.] You may find older code making use of this setting keys with 'setkey' or setting a 'key=' column when setting up the table.
library(data.table) DT <- data.table( x = letters[1:5], y = 5:1, z = (1:5) > 3 ) #> DT # x y z #1: a 5 FALSE #2: b 4 FALSE #3: c 3 FALSE #4: d 2 TRUE #5: e 1 TRUE
Set your key with the
setkey command. You can have a key with multiple columns.
Check your table's key in tables()
tables() > tables() NAME NROW NCOL MB COLS KEY [1,] DT 5 3 1 x,y,z y Total: 1MB
Note this will re-sort your data.
#> DT # x y z #1: e 1 TRUE #2: d 2 TRUE #3: c 3 FALSE #4: b 4 FALSE #5: a 5 FALSE
Now it is unnecessary
Prior to v1.9.6 you had to have set a key for certain operations especially joining tables. The developers of data.table have sped up and introduced a
"on=" feature that can replace the dependency on keys. See SO answer here for a detailed discussion.
In Jan 2017, the developers have written a vignette around secondary indices which explains the "on" syntax and allows for other columns to be identified for fast indexing.
Creating secondary indices?
In a manner similar to key, you can
setindex(DT, key.col) or
setindexv(DT, "key.col.string"), where DT is your data.table. Remove all indices with
See your secondary indices with
Why secondary indices?
This does not sort the table (unlike key), but does allow for quick indexing using the "on" syntax. Note there can be only one key, but you can use multiple secondary indices, which saves having to rekey and resort the table. This will speed up your subsetting when changing the columns you want to subset on.
Recall, in example above y was the key for table DT:
DT # x y z # 1: e 1 TRUE # 2: d 2 TRUE # 3: c 3 FALSE # 4: b 4 FALSE # 5: a 5 FALSE # Let us set x as index setindex(DT, x) # Use indices to see what has been set indices(DT) #  "x" # fast subset using index and not keyed column DT["c", on ="x"] #x y z #1: c 3 FALSE # old way would have been rekeying DT from y to x, doing subset and # perhaps keying back to y (now we save two sorts) # This is a toy example above but would have been more valuable with big data sets