# Manage data columns with dict.table

## Motivation

The dplyr functions `select` and `mutate` are widely used to manage data `data.frame` (or `tibble`) columns. They cover a wide range of use cases and are applied in quick data exploration as well as in data analysis pipelines. On the other hand, when implementing critical code or building R packages, developers may revert to base R to minimize errors and code dependencies. At least, both `mutate` and `select` may require additional checking, for example, to catch column name clashes.

The container package in parts was developed to close this gap. With version 1.0.0, it provides `dict.table`, which can be considered a data.table with an extended set of functions to add, extract, remove and replace data columns with minimal required additional checking, hopefully resulting in lean and robust code.

This vignette compares basic `dplyr` and `dict.table` data column operations and at the end shows that both frameworks can be easily combined.

## Column operations

To keep matters simple, we use a tiny data set.

    library(container)
    library(dplyr)

    data <- dict.table(x = c(0.2, 0.5), y = letters[1:2])
    data
    # <dict.table> with 2 rows and 2 columns
    #        x      y
    #    <num> <char>
    # 1:   0.2      a
    # 2:   0.5      b

### Add

Let’s add columns using `mutate`.

    data %>%
        mutate(ID = 1:2, z = 1)
    # <dict.table> with 2 rows and 4 columns
    #        x      y    ID     z
    #    <num> <char> <int> <num>
    # 1:   0.2      a     1     1
    # 2:   0.5      b     2     1

For someone not familar with the tidyverse, this code block might read somewhat odd as the column is added and not mutated. To add a column via `dict.table` use `add`.

    data %>%
        add(ID = 1:2, z = 1)
    # <dict.table> with 2 rows and 4 columns
    #        x      y    ID     z
    #    <num> <char> <int> <num>
    # 1:   0.2      a     1     1
    # 2:   0.5      b     2     1

The intend to add a column thus is stated more clearly. Next, instead of ID, let’s add another numeric column `y`, which happens to “name-clash” with the already existing column.

    data %>%
        mutate(y = 1)
    # <dict.table> with 2 rows and 2 columns
    #        x     y
    #    <num> <num>
    # 1:   0.2     1
    # 2:   0.5     1

Of course, the initial y-column has been overwritten. While this was easy to see here, it may not if the data has a lot of columns or if column names are created dynamically during runtime. To catch this, usually some overhead is required.

    if ("y" %in% colnames(data)) {
        stop("column y already exists")
    } else {
        data %>%
            mutate(y = 1)
    }
    # Error: column y already exists

Let’s see the `dict.table`-operation in comparison.

    data %>%
        add(y = 1)
    # Error: name 'y' exists already

The name clash is caught by default and therefore requires no additional checking.

### Modify

If the intend was indeed to overwrite the value, the `dict.table` function `replace_at` can be used.

    data %>%
        replace_at(y = 1)
    # <dict.table> with 2 rows and 2 columns
    #        x      y
    #    <num> <char>
    # 1:   0.2      1
    # 2:   0.5      1

    # or programmatically
    data %>%
        replace_at("y", 1)
    # <dict.table> with 2 rows and 2 columns
    #        x      y
    #    <num> <char>
    # 1:   0.2      1
    # 2:   0.5      1

As we saw above, if a column does not exist, `mutate` silently creates it for you. If this is not what you want, which means, you want to make sure something is overwritten, again, a workaround is needed.

    if ("ID" %in% colnames(data)) {
        data %>%
            mutate(ID = 1:2)
    } else {
        stop("column ID not in data.frame")
    }
    # Error: column ID not in data.frame

Once again, the workaround is already “built-in” in the `dict.table`-framework,

    data %>%
        replace_at(ID = 1:2)
    # Error: column(s) not found: 'ID'

that is, `replace_at` expects the column to exist.

If we were to paraphrase the intend of the `mutate` function, it probably would be something like “Replace a column or, if it does not exist, add it.”. As you may already have guessed, this can also be expressed within the `dict.table`-framework.

    data %>%
        replace_at(ID = 1:2, .add = TRUE)
    # <dict.table> with 2 rows and 3 columns
    #        x      y    ID
    #    <num> <char> <int>
    # 1:   0.2      a     1
    # 2:   0.5      b     2

### Remove

A common tidyverse approach to remove a column is based on the `select` function. One corresponding `dict.table`-function is `delete`.

    data %>%
      select(-"y")
    # <dict.table> with 2 rows and 1 column
    #        x
    #    <num>
    # 1:   0.2
    # 2:   0.5

    data %>%
        delete_at("y")
    # <dict.table> with 2 rows and 1 column
    #        x
    #    <num>
    # 1:   0.2
    # 2:   0.5

Let’s see what happens if the column does not exist in the first place.

    data %>%
        select(-"ID")
    # Error in `select()`:
    # ! Can't select columns that don't exist.
    # ✖ Column `ID` doesn't exist.

    data %>%
        delete_at("ID")
    # Error: column(s) not found: 'ID'

So in this case, both frameworks will complain. Now assume we want the column to be removed if it exist but otherwise silently ignore the command, for example:

    if ("ID" %in% colnames(data)) {
        data %>%
            select(-"ID")
    }

The `dict.table` provides a straight-forward solution via the `discard` function:

    data %>%
        discard_at("ID")
    # <dict.table> with 2 rows and 2 columns
    #        x      y
    #    <num> <char>
    # 1:   0.2      a
    # 2:   0.5      b

## Benchmark

To compare the performance of both frameworks, we benchmark some column operations using the standard ‘cars’ data set. As a hallmark reference we use data.table.

    library(microbenchmark)
    library(ggplot2)
    library(data.table)
    library(tibble)

    data = cars
    head(cars)
    #   speed dist
    # 1     4    2
    # 2     4   10
    # 3     7    4
    # 4     7   22
    # 5     8   16
    # 6     9   10

For the benchmark, we add, replace and finally delete a column.

    bm <- microbenchmark(control = list(order="inorder"), times = 100,

        dict.table =
            as.dict.table(data) %>%
            add(time = .[["dist"]] / .[["speed"]]) %>%
            replace_at(dist = 0) %>%
            delete_at("speed"),

        `data.table[` =
            as.data.table(data)[
            ][, time := dist / speed
            ][, dist := 0
            ][, speed := NULL],

        dplyr =
            as_tibble(data) %>%
            mutate(time = dist / speed) %>%
            mutate(dist = 0) %>%
            select(-speed)
    )
    autoplot(bm) + theme_bw()

While `dict.table` and `data.table` performed nearly the same there is some distance to `dplyr` (about 3x). Let’s examine each operation in more detail.

    data = cars
    bm <- microbenchmark(control = list(order="inorder"), times = 100,

        dit <- as.dict.table(data),
        dit <- add(dit, time = dit[["dist"]] / dit[["speed"]]),
        dit <- replace_at(dit, dist = 0),
        dit <- delete_at(dit, "speed"),

        dat <-  as.data.table(data),
        dat[, time := dist / speed],
        dat[, dist := 0],
        dat[, speed := NULL],

        tbl <- as_tibble(data),
        tbl <- mutate(tbl, time = dist / speed),
        tbl <- mutate(tbl, dist = 0),
        tbl <- select(tbl, -speed)
    )
    autoplot(bm) + theme_bw()

Apparently, the mutate and select operations are the slowest in comparison, which for the most part should be a result of these functions providing non-standard evaluation (NSE) and generally a wide range of ways to specify the desired operation. Unsurprisingly such flexibility comes at a cost.

Since the data.table expressions also involve NSE terms and some overhead, in this benchmark the `dict.table` performs even best. Having said that, of course, the data.table code can be further improved by avoiding the overhead and instead use reference semantics via the `data.table` built-in `set` function.

    data = cars
    bm <- microbenchmark(control = list(order="inorder"), times = 100,

        dict.table =
            as.dict.table(data) %>%
            add(time = dit[["dist"]] / dit[["speed"]]) %>%
            replace_at(dist = 0) %>%
            delete_at("speed"),

        ref_dict.table =
            as.dict.table(data) %>%
            ref_add(time = .[["dist"]] / .[["speed"]]) %>%
            ref_replace_at(dist = 0) %>%
            ref_delete_at("speed"),

        `data.table[` =
            as.data.table(data)[
            ][, time := dist / speed
            ][, dist := 0
            ][, speed := NULL],

        set_data.table =
            as.data.table(data) %>%
            set(j = "ID", value = .[["dist"]] / .[["speed"]]) %>%
            set(j = "dist", value = 0) %>%
            set(j = "speed", value = NULL)
    )

    autoplot(bm) + theme_bw()

This puts things back into perspective. We also provided a `dict.table` version using reference semantic, which is also `built-in` and results in a slight speed improvement over the standard version. As a result, `data.table` remains the way to go when speed is key.

## Combine dplyr and dict.table

Since a `dict.table` is fully compatible with `dplyr` and `data.table`, all of the presented frameworks can be easily combined in any order.

    res = data %>%
        as.dict.table %>%
        .[, time := dist / speed] %>%   # data.table
        replace_at(dist = 0) %>%        # container
        select(-speed)                  # dplyr

## Summary

In critical code, it is usually of high priority to avoid unintended data column operations. For this, usually additional code is required to check for the existence or absence of columns. The `dict.table` framework provides a set of column operations with built-in checking, thereby yielding safer and leaner code out of the box and ultimately freeing the developer from writing some annoying checks over and over again.