# Efficient reshaping using data.tables

### 2026-01-24

Translations of this document are available in: en | fr | ru

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`.

## Data

We will load the data sets directly within sections.

## Introduction

The `melt` and `dcast` functions for `data.table`s 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

1. First briefly look at the default `melt`ing and `dcast`ing of `data.table`s to convert them from wide to long format and vice versa

2. Look at scenarios where the current functionalities become cumbersome and inefficient

3. Finally look at the new improvements to both `melt` and `dcast` methods for `data.table`s 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.

## 1. Default functionality

### a) `melt`ing `data.table`s (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>

#### - Convert `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 positions 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.

#### - Name the `variable` and `value` columns to `child` and `dob` respectively

    DT.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`.

### b) `dcast`ing `data.table`s (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.

#### - How can we get back to the original data table `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.

#### - Starting from `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.aggregate = 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     2

Check `?dcast` for other useful arguments and additional examples.

## 2. Limitations in previous `melt/dcast` approaches

So far we’ve seen features of `melt` and `dcast` that are implemented efficiently for `data.table`s, 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 name_child1 name_child2 name_child3 gender_child1 gender_child2 gender_child3
    1         30         Ben        Anna          NA             1             2            NA
    2         27         Tom          NA          NA             2            NA            NA
    3         26         Lia         Sam         Amy             2             2             1
    4         32         Max         Zoe         Joe             1             1             1
    5         29         Dan         Eva          NA             2             1            NA"
    DT <- fread(s2)
    DT
    #    family_id age_mother name_child1 name_child2 name_child3 gender_child1 gender_child2
    #        <int>      <int>      <char>      <char>      <char>         <int>         <int>
    # 1:         1         30         Ben        Anna        <NA>             1             2
    # 2:         2         27         Tom        <NA>        <NA>             2            NA
    # 3:         3         26         Lia         Sam         Amy             2             2
    # 4:         4         32         Max         Zoe         Joe             1             1
    # 5:         5         29         Dan         Eva        <NA>             2             1
    #    gender_child3
    #            <int>
    # 1:            NA
    # 2:            NA
    # 3:             1
    # 4:             1
    # 5:            NA
    ## 1 = female, 2 = male

And you’d like to combine (`melt`) all the `name` columns together, and `gender` columns together. Using the old functionality, we could do something like this:

    DT.m1 = melt(DT, id.vars = c("family_id", "age_mother"))
    # Warning in melt.data.table(DT, id.vars = c("family_id", "age_mother")): 'measure.vars'
    # [name_child1, name_child2, name_child3, gender_child1, ...] are not all of the same type. By order
    # of hierarchy, the molten data value column will be of type 'character'. All measure variables not
    # of type 'character' will be coerced too. Check DETAILS in ?melt.data.table for more on coercion.
    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 gender   name
    #         <int>      <int> <char> <char> <char>
    #  1:         1         30 child1      1    Ben
    #  2:         1         30 child2      2   Anna
    #  3:         1         30 child3   <NA>   <NA>
    #  4:         2         27 child1      2    Tom
    #  5:         2         27 child2   <NA>   <NA>
    #  6:         2         27 child3   <NA>   <NA>
    #  7:         3         26 child1      2    Lia
    #  8:         3         26 child2      2    Sam
    #  9:         3         26 child3      1    Amy
    # 10:         4         32 child1      1    Max
    # 11:         4         32 child2      1    Zoe
    # 12:         4         32 child3      1    Joe
    # 13:         5         29 child1      2    Dan
    # 14:         5         29 child2      1    Eva
    # 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" ...
    #  $ gender    : chr  "1" "2" NA "2" ...
    #  $ name      : chr  "Ben" "Anna" NA "Tom" ...
    #  - attr(*, ".internal.selfref")=<externalptr>
    #  - attr(*, "sorted")= chr [1:3] "family_id" "age_mother" "child"

#### Issues

1. What we wanted to do was to combine all the `name` 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!

2. The columns to `melt` may be of different types. By `melt`ing them all together, the columns will be coerced in result.

3. 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.

4. 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!

## 3. Enhanced (new) functionality

### a) Enhanced `melt`

Since we’d like for `data.table`s 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 simultaneously

The 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("name_child", 1:3)
    colB = paste0("gender_child", 1:3)
    DT.m2 = melt(DT, measure.vars = list(colA, colB), value.name = c("name", "gender"))
    DT.m2
    #     family_id age_mother variable   name gender
    #         <int>      <int>   <fctr> <char>  <int>
    #  1:         1         30        1    Ben      1
    #  2:         2         27        1    Tom      2
    #  3:         3         26        1    Lia      2
    #  4:         4         32        1    Max      1
    #  5:         5         29        1    Dan      2
    #  6:         1         30        2   Anna      2
    #  7:         2         27        2   <NA>     NA
    #  8:         3         26        2    Sam      2
    #  9:         4         32        2    Zoe      1
    # 10:         5         29        2    Eva      1
    # 11:         1         30        3   <NA>     NA
    # 12:         2         27        3   <NA>     NA
    # 13:         3         26        3    Amy      1
    # 14:         4         32        3    Joe      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 ...
    #  $ name      : chr  "Ben" "Tom" "Lia" "Max" ...
    #  $ 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.

#### - Using `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.vars = patterns("^name", "^gender"), value.name = c("name", "gender"))
    DT.m2
    #     family_id age_mother variable   name gender
    #         <int>      <int>   <fctr> <char>  <int>
    #  1:         1         30        1    Ben      1
    #  2:         2         27        1    Tom      2
    #  3:         3         26        1    Lia      2
    #  4:         4         32        1    Max      1
    #  5:         5         29        1    Dan      2
    #  6:         1         30        2   Anna      2
    #  7:         2         27        2   <NA>     NA
    #  8:         3         26        2    Sam      2
    #  9:         4         32        2    Zoe      1
    # 10:         5         29        2    Eva      1
    # 11:         1         30        3   <NA>     NA
    # 12:         2         27        3   <NA>     NA
    # 13:         3         26        3    Amy      1
    # 14:         4         32        3    Joe      1
    # 15:         5         29        3   <NA>     NA

#### - Using `measure()` to specify `measure.vars` via separator or pattern

If, 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 virginica

The 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   1.8

The 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   1.8

Using 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   1.8

Going 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.vars = measure(value.name, child=as.integer, sep="_child"))
    DT.m3
    #     family_id age_mother child   name gender
    #         <int>      <int> <int> <char>  <int>
    #  1:         1         30     1    Ben      1
    #  2:         2         27     1    Tom      2
    #  3:         3         26     1    Lia      2
    #  4:         4         32     1    Max      1
    #  5:         5         29     1    Dan      2
    #  6:         1         30     2   Anna      2
    #  7:         2         27     2   <NA>     NA
    #  8:         3         26     2    Sam      2
    #  9:         4         32     2    Zoe      1
    # 10:         5         29     2    Eva      1
    # 11:         1         30     3   <NA>     NA
    # 12:         2         27     3   <NA>     NA
    # 13:         3         26     3    Amy      1
    # 14:         4         32     3    Joe      1
    # 15:         5         29     3   <NA>     NA

In 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     3

When 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 of 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     3

### b) Enhanced `dcast`

Okay 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.

#### - Casting multiple `value.var`s simultaneously

We can now provide multiple `value.var` columns to `dcast` for `data.table`s 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("name", "gender"))
    DT.c2
    # Key: <family_id, age_mother>
    #    family_id age_mother name_1 name_2 name_3 gender_1 gender_2 gender_3
    #        <int>      <int> <char> <char> <char>    <int>    <int>    <int>
    # 1:         1         30    Ben   Anna   <NA>        1        2       NA
    # 2:         2         27    Tom   <NA>   <NA>        2       NA       NA
    # 3:         3         26    Lia    Sam    Amy        2        2        1
    # 4:         4         32    Max    Zoe    Joe        1        1        1
    # 5:         5         29    Dan    Eva   <NA>        2        1       NA

- Attributes 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.

#

#### Multiple functions to `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.

#