This vignette discusses the
default usage of reshaping functions melt (wide to long)
and dcast (long to wide) for data.tables as well
as the new extended functionalities of melting and
casting on multiple columns available from
v1.9.6.
We will load the data sets directly within sections.
The melt and dcast functions for
data.tables are for reshaping wide-to-long and
long-to-wide, respectively; the implementations are specifically
designed with large in-memory data (e.g. 10Gb) in mind.
In this vignette, we will
First briefly look at the default melting and
dcasting of data.tables to convert them from
wide to long format and vice versa
Look at scenarios where the current functionalities become cumbersome and inefficient
Finally look at the new improvements to both melt
and dcast methods for data.tables to handle
multiple columns simultaneously.
The extended functionalities are in line with
data.table’s philosophy of performing operations
efficiently and in a straightforward manner.
melting data.tables (wide to long)Suppose we have a data.table (artificial data) as shown
below:
s1 <- "family_id age_mother dob_child1 dob_child2 dob_child3
1 30 1998-11-26 2000-01-29 NA
2 27 1996-06-22 NA NA
3 26 2002-07-11 2004-04-05 2007-09-02
4 32 2004-10-10 2009-08-27 2012-07-21
5 29 2000-12-05 2005-02-28 NA"
DT <- fread(s1)
DT
# family_id age_mother dob_child1 dob_child2 dob_child3
# <int> <int> <IDat> <IDat> <IDat>
# 1: 1 30 1998-11-26 2000-01-29 <NA>
# 2: 2 27 1996-06-22 <NA> <NA>
# 3: 3 26 2002-07-11 2004-04-05 2007-09-02
# 4: 4 32 2004-10-10 2009-08-27 2012-07-21
# 5: 5 29 2000-12-05 2005-02-28 <NA>
## dob stands for date of birth.
str(DT)
# Classes 'data.table' and 'data.frame': 5 obs. of 5 variables:
# $ family_id : int 1 2 3 4 5
# $ age_mother: int 30 27 26 32 29
# $ dob_child1: IDate, format: "1998-11-26" "1996-06-22" "2002-07-11" ...
# $ dob_child2: IDate, format: "2000-01-29" NA "2004-04-05" ...
# $ dob_child3: IDate, format: NA NA "2007-09-02" ...
# - attr(*, ".internal.selfref")=<externalptr>DT to long form where each
dob is a separate observation.We could accomplish this using melt() by specifying
id.vars and measure.vars arguments as
follows:
DT.m1 = melt(DT, id.vars = c("family_id", "age_mother"),
measure.vars = c("dob_child1", "dob_child2", "dob_child3"))
DT.m1
# family_id age_mother variable value
# <int> <int> <fctr> <IDat>
# 1: 1 30 dob_child1 1998-11-26
# 2: 2 27 dob_child1 1996-06-22
# 3: 3 26 dob_child1 2002-07-11
# 4: 4 32 dob_child1 2004-10-10
# 5: 5 29 dob_child1 2000-12-05
# 6: 1 30 dob_child2 2000-01-29
# 7: 2 27 dob_child2 <NA>
# 8: 3 26 dob_child2 2004-04-05
# 9: 4 32 dob_child2 2009-08-27
# 10: 5 29 dob_child2 2005-02-28
# 11: 1 30 dob_child3 <NA>
# 12: 2 27 dob_child3 <NA>
# 13: 3 26 dob_child3 2007-09-02
# 14: 4 32 dob_child3 2012-07-21
# 15: 5 29 dob_child3 <NA>
str(DT.m1)
# Classes 'data.table' and 'data.frame': 15 obs. of 4 variables:
# $ family_id : int 1 2 3 4 5 1 2 3 4 5 ...
# $ age_mother: int 30 27 26 32 29 30 27 26 32 29 ...
# $ variable : Factor w/ 3 levels "dob_child1","dob_child2",..: 1 1 1 1 1 2 2 2 2 2 ...
# $ value : IDate, format: "1998-11-26" "1996-06-22" "2002-07-11" ...
# - attr(*, ".internal.selfref")=<externalptr>measure.vars specify the set of columns we would
like to collapse (or combine) together.
We can also specify column indices instead of names.
By default, variable column is of type
factor. Set variable.factor argument to
FALSE if you’d like to return a
character vector instead.
By default, the molten columns are automatically named
variable and value.
melt preserves column attributes in result.
variable and value columns to
child and dob respectivelyDT.m1 = melt(DT, measure.vars = c("dob_child1", "dob_child2", "dob_child3"),
variable.name = "child", value.name = "dob")
DT.m1
# family_id age_mother child dob
# <int> <int> <fctr> <IDat>
# 1: 1 30 dob_child1 1998-11-26
# 2: 2 27 dob_child1 1996-06-22
# 3: 3 26 dob_child1 2002-07-11
# 4: 4 32 dob_child1 2004-10-10
# 5: 5 29 dob_child1 2000-12-05
# 6: 1 30 dob_child2 2000-01-29
# 7: 2 27 dob_child2 <NA>
# 8: 3 26 dob_child2 2004-04-05
# 9: 4 32 dob_child2 2009-08-27
# 10: 5 29 dob_child2 2005-02-28
# 11: 1 30 dob_child3 <NA>
# 12: 2 27 dob_child3 <NA>
# 13: 3 26 dob_child3 2007-09-02
# 14: 4 32 dob_child3 2012-07-21
# 15: 5 29 dob_child3 <NA>By default, when one of id.vars or
measure.vars is missing, the rest of the columns are
automatically assigned to the missing argument.
When neither id.vars nor measure.vars
are specified, as mentioned under ?melt, all
non-numeric, integer,
logical columns will be assigned to
id.vars.
In addition, a warning message is issued highlighting the columns
that are automatically considered to be id.vars.
dcasting data.tables (long to
wide)In the previous section, we saw how to get from wide form to long form. Let’s see the reverse operation in this section.
DT
from DT.m1?That is, we’d like to collect all child observations
corresponding to each family_id, age_mother together under
the same row. We can accomplish it using dcast as
follows:
dcast(DT.m1, family_id + age_mother ~ child, value.var = "dob")
# Key: <family_id, age_mother>
# family_id age_mother dob_child1 dob_child2 dob_child3
# <int> <int> <IDat> <IDat> <IDat>
# 1: 1 30 1998-11-26 2000-01-29 <NA>
# 2: 2 27 1996-06-22 <NA> <NA>
# 3: 3 26 2002-07-11 2004-04-05 2007-09-02
# 4: 4 32 2004-10-10 2009-08-27 2012-07-21
# 5: 5 29 2000-12-05 2005-02-28 <NA>dcast uses formula interface. The variables
on the LHS of formula represents the id vars and
RHS the measure vars.
value.var denotes the column to be filled in with
while casting to wide format.
dcast also tries to preserve attributes in result
wherever possible.
DT.m1, how can we get the number of
children in each family?You can also pass a function to aggregate by in dcast
with the argument fun.aggregate. This is particularly
essential when the formula provided does not identify single observation
for each cell.
dcast(DT.m1, family_id ~ ., fun.agg = function(x) sum(!is.na(x)), value.var = "dob")
# Key: <family_id>
# family_id .
# <int> <int>
# 1: 1 2
# 2: 2 1
# 3: 3 3
# 4: 4 3
# 5: 5 2Check ?dcast for other useful arguments and additional
examples.
melt/dcast approachesSo far we’ve seen features of melt and
dcast that are implemented efficiently for
data.tables, using internal data.table
machinery (fast radix ordering, binary search
etc.).
However, there are situations we might run into where the desired
operation is not expressed in a straightforward manner. For example,
consider the data.table shown below:
s2 <- "family_id age_mother dob_child1 dob_child2 dob_child3 gender_child1 gender_child2 gender_child3
1 30 1998-11-26 2000-01-29 NA 1 2 NA
2 27 1996-06-22 NA NA 2 NA NA
3 26 2002-07-11 2004-04-05 2007-09-02 2 2 1
4 32 2004-10-10 2009-08-27 2012-07-21 1 1 1
5 29 2000-12-05 2005-02-28 NA 2 1 NA"
DT <- fread(s2)
DT
# family_id age_mother dob_child1 dob_child2 dob_child3 gender_child1 gender_child2 gender_child3
# <int> <int> <IDat> <IDat> <IDat> <int> <int> <int>
# 1: 1 30 1998-11-26 2000-01-29 <NA> 1 2 NA
# 2: 2 27 1996-06-22 <NA> <NA> 2 NA NA
# 3: 3 26 2002-07-11 2004-04-05 2007-09-02 2 2 1
# 4: 4 32 2004-10-10 2009-08-27 2012-07-21 1 1 1
# 5: 5 29 2000-12-05 2005-02-28 <NA> 2 1 NA
## 1 = female, 2 = maleAnd you’d like to combine (melt) all the
dob columns together, and gender columns
together. Using the current functionality, we can do something like
this:
DT.m1 = melt(DT, id = c("family_id", "age_mother"))
DT.m1[, c("variable", "child") := tstrsplit(variable, "_", fixed = TRUE)]
DT.c1 = dcast(DT.m1, family_id + age_mother + child ~ variable, value.var = "value")
DT.c1
# Key: <family_id, age_mother, child>
# family_id age_mother child dob gender
# <int> <int> <char> <IDat> <IDat>
# 1: 1 30 child1 1998-11-26 1970-01-02
# 2: 1 30 child2 2000-01-29 1970-01-03
# 3: 1 30 child3 <NA> <NA>
# 4: 2 27 child1 1996-06-22 1970-01-03
# 5: 2 27 child2 <NA> <NA>
# 6: 2 27 child3 <NA> <NA>
# 7: 3 26 child1 2002-07-11 1970-01-03
# 8: 3 26 child2 2004-04-05 1970-01-03
# 9: 3 26 child3 2007-09-02 1970-01-02
# 10: 4 32 child1 2004-10-10 1970-01-02
# 11: 4 32 child2 2009-08-27 1970-01-02
# 12: 4 32 child3 2012-07-21 1970-01-02
# 13: 5 29 child1 2000-12-05 1970-01-03
# 14: 5 29 child2 2005-02-28 1970-01-02
# 15: 5 29 child3 <NA> <NA>
str(DT.c1) ## gender column is character type now!
# Classes 'data.table' and 'data.frame': 15 obs. of 5 variables:
# $ family_id : int 1 1 1 2 2 2 3 3 3 4 ...
# $ age_mother: int 30 30 30 27 27 27 26 26 26 32 ...
# $ child : chr "child1" "child2" "child3" "child1" ...
# $ dob : IDate, format: "1998-11-26" "2000-01-29" NA ...
# $ gender : IDate, format: "1970-01-02" "1970-01-03" NA ...
# - attr(*, ".internal.selfref")=<externalptr>
# - attr(*, "sorted")= chr [1:3] "family_id" "age_mother" "child"What we wanted to do was to combine all the dob and
gender type columns together respectively. Instead, we are
combining everything together, and then splitting them again. I
think it’s easy to see that it’s quite roundabout (and inefficient).
As an analogy, imagine you’ve a closet with four shelves of clothes and you’d like to put together the clothes from shelves 1 and 2 together (in 1), and 3 and 4 together (in 3). What we are doing is more or less to combine all the clothes together, and then split them back on to shelves 1 and 3!
The columns to melt may be of different types, as in
this case (character and integer types). By
melting them all together, the columns will be coerced in
result, as explained by the warning message above and shown from output
of str(DT.c1), where gender has been converted
to character type.
We are generating an additional column by splitting the
variable column into two columns, whose purpose is quite
cryptic. We do it because we need it for casting in the next
step.
Finally, we cast the data set. But the issue is it’s a much more computationally involved operation than melt. Specifically, it requires computing the order of the variables in formula, and that’s costly.
In fact, stats::reshape is capable of performing this
operation in a very straightforward manner. It is an extremely useful
and often underrated function. You should definitely give it a try!
meltSince we’d like for data.tables to perform this
operation straightforward and efficient using the same interface, we
went ahead and implemented an additional functionality, where
we can melt to multiple columns
simultaneously.
melt multiple columns simultaneouslyThe idea is quite simple. We pass a list of columns to
measure.vars, where each element of the list contains the
columns that should be combined together.
colA = paste0("dob_child", 1:3)
colB = paste0("gender_child", 1:3)
DT.m2 = melt(DT, measure = list(colA, colB), value.name = c("dob", "gender"))
DT.m2
# family_id age_mother variable dob gender
# <int> <int> <fctr> <IDat> <int>
# 1: 1 30 1 1998-11-26 1
# 2: 2 27 1 1996-06-22 2
# 3: 3 26 1 2002-07-11 2
# 4: 4 32 1 2004-10-10 1
# 5: 5 29 1 2000-12-05 2
# 6: 1 30 2 2000-01-29 2
# 7: 2 27 2 <NA> NA
# 8: 3 26 2 2004-04-05 2
# 9: 4 32 2 2009-08-27 1
# 10: 5 29 2 2005-02-28 1
# 11: 1 30 3 <NA> NA
# 12: 2 27 3 <NA> NA
# 13: 3 26 3 2007-09-02 1
# 14: 4 32 3 2012-07-21 1
# 15: 5 29 3 <NA> NA
str(DT.m2) ## col type is preserved
# Classes 'data.table' and 'data.frame': 15 obs. of 5 variables:
# $ family_id : int 1 2 3 4 5 1 2 3 4 5 ...
# $ age_mother: int 30 27 26 32 29 30 27 26 32 29 ...
# $ variable : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 2 2 2 2 2 ...
# $ dob : IDate, format: "1998-11-26" "1996-06-22" "2002-07-11" ...
# $ gender : int 1 2 2 1 2 2 NA 2 1 1 ...
# - attr(*, ".internal.selfref")=<externalptr>We can remove the variable column if
necessary.
The functionality is implemented entirely in C, and is therefore both fast and memory efficient in addition to being straightforward.
patterns()Usually in these problems, the columns we’d like to melt can be
distinguished by a common pattern. We can use the function
patterns(), implemented for convenience, to provide regular
expressions for the columns to be combined together. The above operation
can be rewritten as:
DT.m2 = melt(DT, measure = patterns("^dob", "^gender"), value.name = c("dob", "gender"))
DT.m2
# family_id age_mother variable dob gender
# <int> <int> <fctr> <IDat> <int>
# 1: 1 30 1 1998-11-26 1
# 2: 2 27 1 1996-06-22 2
# 3: 3 26 1 2002-07-11 2
# 4: 4 32 1 2004-10-10 1
# 5: 5 29 1 2000-12-05 2
# 6: 1 30 2 2000-01-29 2
# 7: 2 27 2 <NA> NA
# 8: 3 26 2 2004-04-05 2
# 9: 4 32 2 2009-08-27 1
# 10: 5 29 2 2005-02-28 1
# 11: 1 30 3 <NA> NA
# 12: 2 27 3 <NA> NA
# 13: 3 26 3 2007-09-02 1
# 14: 4 32 3 2012-07-21 1
# 15: 5 29 3 <NA> NAmeasure() to specify measure.vars
via separator or patternIf, as in the data above, the input columns to melt have regular
names, then we can use measure, which allows specifying the
columns to melt via a separator or a regex. For example consider the
iris data,
(two.iris = data.table(datasets::iris)[c(1,150)])
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# <num> <num> <num> <num> <fctr>
# 1: 5.1 3.5 1.4 0.2 setosa
# 2: 5.9 3.0 5.1 1.8 virginicaThe iris data has four numeric columns with a regular structure:
first the flower part, then a period, then the measurement dimension. To
specify that we want to melt those four columns, we can use
measure with sep="." which means to use
strsplit on all column names; the columns which result in
the maximum number of groups after splitting will be used as
measure.vars:
melt(two.iris, measure.vars = measure(part, dim, sep="."))
# Species part dim value
# <fctr> <char> <char> <num>
# 1: setosa Sepal Length 5.1
# 2: virginica Sepal Length 5.9
# 3: setosa Sepal Width 3.5
# 4: virginica Sepal Width 3.0
# 5: setosa Petal Length 1.4
# 6: virginica Petal Length 5.1
# 7: setosa Petal Width 0.2
# 8: virginica Petal Width 1.8The first two arguments to measure in the code above
(part and dim) are used to name the output
columns; the number of arguments must equal the max number of groups
after splitting with sep.
If we want two value columns, one for each part, we can use the
special value.name keyword, which means to output a value
column for each unique name found in that group:
melt(two.iris, measure.vars = measure(value.name, dim, sep="."))
# Species dim Sepal Petal
# <fctr> <char> <num> <num>
# 1: setosa Length 5.1 1.4
# 2: virginica Length 5.9 5.1
# 3: setosa Width 3.5 0.2
# 4: virginica Width 3.0 1.8Using the code above we get one value column per flower part. If we instead want a value column for each measurement dimension, we can do
melt(two.iris, measure.vars = measure(part, value.name, sep="."))
# Species part Length Width
# <fctr> <char> <num> <num>
# 1: setosa Sepal 5.1 3.5
# 2: virginica Sepal 5.9 3.0
# 3: setosa Petal 1.4 0.2
# 4: virginica Petal 5.1 1.8Going back to the example of the data with families and children, we
can see a more complex usage of measure, involving a
function which is used to convert the child string values
to integers:
DT.m3 = melt(DT, measure = measure(value.name, child=as.integer, sep="_child"))
DT.m3
# family_id age_mother child dob gender
# <int> <int> <int> <IDat> <int>
# 1: 1 30 1 1998-11-26 1
# 2: 2 27 1 1996-06-22 2
# 3: 3 26 1 2002-07-11 2
# 4: 4 32 1 2004-10-10 1
# 5: 5 29 1 2000-12-05 2
# 6: 1 30 2 2000-01-29 2
# 7: 2 27 2 <NA> NA
# 8: 3 26 2 2004-04-05 2
# 9: 4 32 2 2009-08-27 1
# 10: 5 29 2 2005-02-28 1
# 11: 1 30 3 <NA> NA
# 12: 2 27 3 <NA> NA
# 13: 3 26 3 2007-09-02 1
# 14: 4 32 3 2012-07-21 1
# 15: 5 29 3 <NA> NAIn the code above we used sep="_child" which results in
melting only the columns which contain that string (six column names
split into two groups each). The child=as.integer argument
means the second group will result in an output column named
child with values defined by plugging the character strings
from that group into the function as.integer.
Finally we consider an example (borrowed from tidyr package) where we need to define the groups using a regular expression rather than a separator.
(who <- data.table(id=1, new_sp_m5564=2, newrel_f65=3))
# id new_sp_m5564 newrel_f65
# <num> <num> <num>
# 1: 1 2 3
melt(who, measure.vars = measure(
diagnosis, gender, ages, pattern="new_?(.*)_(.)(.*)"))
# id diagnosis gender ages value
# <num> <char> <char> <char> <num>
# 1: 1 sp m 5564 2
# 2: 1 rel f 65 3When using the pattern argument, it must be a
Perl-compatible regular expression containing the same number of capture
groups (parenthesized sub-expressions) as the number other arguments
(group names). The code below shows how to use a more complex regex with
five groups, two numeric output columns, and an anonymous type
conversion function,
melt(who, measure.vars = measure(
diagnosis, gender, ages,
ymin=as.numeric,
ymax=function(y) ifelse(nzchar(y), as.numeric(y), Inf),
pattern="new_?(.*)_(.)(([0-9]{2})([0-9]{0,2}))"
))
# id diagnosis gender ages ymin ymax value
# <num> <char> <char> <char> <num> <num> <num>
# 1: 1 sp m 5564 55 64 2
# 2: 1 rel f 65 65 Inf 3dcastOkay great! We can now melt into multiple columns simultaneously. Now
given the data set DT.m2 as shown above, how can we get
back to the same format as the original data we started with?
If we use the current functionality of dcast, then we’d
have to cast twice and bind the results together. But that’s once again
verbose, not straightforward and is also inefficient.
value.vars simultaneouslyWe can now provide multiple value.var
columns to dcast for data.tables
directly so that the operations are taken care of internally and
efficiently.
## new 'cast' functionality - multiple value.vars
DT.c2 = dcast(DT.m2, family_id + age_mother ~ variable, value.var = c("dob", "gender"))
DT.c2
# Key: <family_id, age_mother>
# family_id age_mother dob_1 dob_2 dob_3 gender_1 gender_2 gender_3
# <int> <int> <IDat> <IDat> <IDat> <int> <int> <int>
# 1: 1 30 1998-11-26 2000-01-29 <NA> 1 2 NA
# 2: 2 27 1996-06-22 <NA> <NA> 2 NA NA
# 3: 3 26 2002-07-11 2004-04-05 2007-09-02 2 2 1
# 4: 4 32 2004-10-10 2009-08-27 2012-07-21 1 1 1
# 5: 5 29 2000-12-05 2005-02-28 <NA> 2 1 NAAttributes are preserved in result wherever possible.
Everything is taken care of internally, and efficiently. In addition to being fast, it is also very memory efficient.
fun.aggregate:You can also provide multiple functions to
fun.aggregate to dcast for
data.tables. Check the examples in ?dcast which
illustrates this functionality.