# Efficient reshaping using data.tables

#### 2019-12-08

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
    # 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: chr  "1998-11-26" "1996-06-22" "2002-07-11" "2004-10-10" ...
    #  $ dob_child2: chr  "2000-01-29" NA "2004-04-05" "2009-08-27" ...
    #  $ dob_child3: chr  NA NA "2007-09-02" "2012-07-21" ...
    #  - 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
    #  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     : chr  "1998-11-26" "1996-06-22" "2002-07-11" "2004-10-10" ...
    #  - 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.

#### - 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
    #  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.m`?

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")
    #    family_id age_mother dob_child1 dob_child2 dob_child3
    # 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.m`, 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")
    #    family_id .
    # 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 current `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 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
    # 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 = male

And 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"))
    # Warning in melt.data.table(DT, id = c("family_id", "age_mother")): 'measure.vars' [dob_child1,
    # dob_child2, dob_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
    #     family_id age_mother  child        dob gender
    #  1:         1         30 child1 1998-11-26      1
    #  2:         1         30 child2 2000-01-29      2
    #  3:         1         30 child3       <NA>   <NA>
    #  4:         2         27 child1 1996-06-22      2
    #  5:         2         27 child2       <NA>   <NA>
    #  6:         2         27 child3       <NA>   <NA>
    #  7:         3         26 child1 2002-07-11      2
    #  8:         3         26 child2 2004-04-05      2
    #  9:         3         26 child3 2007-09-02      1
    # 10:         4         32 child1 2004-10-10      1
    # 11:         4         32 child2 2009-08-27      1
    # 12:         4         32 child3 2012-07-21      1
    # 13:         5         29 child1 2000-12-05      2
    # 14:         5         29 child2 2005-02-28      1
    # 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       : chr  "1998-11-26" "2000-01-29" NA "1996-06-22" ...
    #  $ gender    : chr  "1" "2" NA "2" ...
    #  - attr(*, ".internal.selfref")=<externalptr>
    #  - attr(*, "sorted")= chr  "family_id" "age_mother" "child"

#### Issues

1. 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!

2. The columns to `melt` may be of different types, as in this case (`character` and `integer` types). By `melt`ing 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.

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 = paste("dob_child", 1:3, sep = "")
    colB = paste("gender_child", 1:3, sep = "")
    DT.m2 = melt(DT, measure = list(colA, colB), value.name = c("dob", "gender"))
    DT.m2
    #     family_id age_mother variable        dob gender
    #  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       : chr  "1998-11-26" "1996-06-22" "2002-07-11" "2004-10-10" ...
    #  $ gender    : int  1 2 2 1 2 2 NA 2 1 1 ...
    #  - attr(*, ".internal.selfref")=<externalptr>

#### - 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 = patterns("^dob", "^gender"), value.name = c("dob", "gender"))
    DT.m2
    #     family_id age_mother variable        dob gender
    #  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

That’s it!

####

- 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.

### 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("dob", "gender"))
    DT.c2
    #    family_id age_mother      dob_1      dob_2      dob_3 gender_1 gender_2 gender_3
    # 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

####

- 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.

#