# (Re)introducing `skimr` v2

### A year in the life of an open source R project

#### 10/09/2019

Theme song: PSA by Jay-Z

We announced the testing version of `skimr` v2 on June 19, 2018. After more than a year of (admittedly intermittent) work, we’re thrilled to be able to say that the package is ready to go to CRAN. So, what happened over the last year? And why are we so excited for V2?

## Setting the stage

Before we can talk about the last year of `skimr` development, we need to lay out the timeline that got us to this point. For those deeply enmeshed in `skimr` lore, all dozens of you, bear with.

`skimr` was originally an rOpenSci unconf17 project, a big collaboration between eight different participants that resulted in a conceptual outline of the package and a basic working version. Participating in the unconf was a truly magical experience, with everyone bringing a tremendous amount of energy and ideas to the project, and implementation happening over a flurry of “fancy git commits”.

About six months later, we released our first version on CRAN. The time between these two milestones was mostly spent on fleshing out all of the different ideas that were generated during the unconf (like handling grouped data frames) and fixing all the bugs we discovered along the way.

Getting the package on CRAN opened the gates for bug reports and feature requests on GitHub. About the same time we pushed our first version to CRAN, Elin got `skimr`’s rOpenSci’s package peer review started (thank you Jennifer and Jim!), opening another incredibly useful channel for collecting feedback on the package. All of these new ideas and suggestions gave us the opportunity to really push `skimr` to the next level, but doing that would require rethinking the package, from the ground up.

A month after finishing the peer review (and six months after the process began), we announced v2. Over the first phase of `skimr`’s life, we accumulated 700 commits, two release, 400 GitHub stars, 95 percent code coverage and a lifetime’s worth of unicode rendering bugs!

Just kidding! We love our little histograms, even when they don’t love us back! For those of you that might have never seen `skimr`, using the package typically boils down to a single function call:

    library(skimr)
    library(dplyr)

    ##
    ## Attaching package: 'dplyr'

    ## The following objects are masked from 'package:stats':
    ##
    ##     filter, lag

    ## The following objects are masked from 'package:base':
    ##
    ##     intersect, setdiff, setequal, union

    options(width = 90)

    skim(iris)

    ## ── Data Summary ────────────────────────
    ##                            Values
    ## Name                       iris
    ## Number of rows             150
    ## Number of columns          5
    ## _______________________
    ## Column type frequency:
    ##   factor                   1
    ##   numeric                  4
    ## ________________________
    ## Group variables            None
    ##
    ## ── Variable type: factor ─────────────────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate ordered n_unique top_counts
    ## 1 Species               0             1 FALSE          3 set: 50, ver: 50, vir: 50
    ##
    ## ── Variable type: numeric ────────────────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate  mean    sd    p0   p25   p50   p75  p100 hist
    ## 1 Sepal.Length          0             1  5.84 0.828   4.3   5.1  5.8    6.4   7.9 ▆▇▇▅▂
    ## 2 Sepal.Width           0             1  3.06 0.436   2     2.8  3      3.3   4.4 ▁▆▇▂▁
    ## 3 Petal.Length          0             1  3.76 1.77    1     1.6  4.35   5.1   6.9 ▇▁▆▇▂
    ## 4 Petal.Width           0             1  1.20 0.762   0.1   0.3  1.3    1.8   2.5 ▇▁▇▅▃

## Getting it right

Under normal circumstances (i.e. not during a hackathon), most software engineering projects begin with a design phase and series of increasingly detailed design docs. `skimr` is only a few hundred lines of code, which means “increasingly detailed design docs” translates to one doc. But we did actually write it! It’s here. And it still goes a good job of laying out some of the big ideas we were interested in taking on for v2.

- Eliminating frictions that resulted from differences in the way we stored data vs how it was displayed to users
- Getting away from using a global environment to configure `skimr`
- Making it easier for others to extend `skimr`
- Create more useful ways to use `skimr`

## Better internal data structures

In v1, `skimr` stored all of its data in a “long format”, data frame. Although hidden from the user by its print methods, this format would appear any time you’d try do something with the results of a `skim()` call. It looked something like this:

    skim(mtcars) %>% dplyr::filter(stat=="hist")

    # A tibble: 11 x 6
       variable type    stat  level value formatted
       <chr>    <chr>   <chr> <chr> <dbl> <chr>
     1 mpg      numeric hist  .all     NA ▃▇▇▇▃▂▂▂
     2 cyl      numeric hist  .all     NA ▆▁▁▃▁▁▁▇
     3 disp     numeric hist  .all     NA ▇▆▁▂▅▃▁▂
     4 hp       numeric hist  .all     NA ▃▇▃▅▂▃▁▁
     5 drat     numeric hist  .all     NA ▃▇▁▅▇▂▁▁
     6 wt       numeric hist  .all     NA ▃▃▃▇▆▁▁▂
     7 qsec     numeric hist  .all     NA ▃▂▇▆▃▃▁▁
     8 vs       numeric hist  .all     NA ▇▁▁▁▁▁▁▆
     9 am       numeric hist  .all     NA ▇▁▁▁▁▁▁▆
    10 gear     numeric hist  .all     NA ▇▁▁▆▁▁▁▂
    11 carb     numeric hist  .all     NA ▆▇▂▇▁▁▁▁

Big ups to anyone who looked at the rendered output and saw that this was how you actually filtered the results. Hopefully there are even better applications of your near-telepathic abilities.

Now, working with `skimr` is a bit more sane.

    skimmed <- iris %>%
      skim() %>%
      dplyr::filter(numeric.sd > 1)

    skimmed

    ## ── Data Summary ────────────────────────
    ##                            Values
    ## Name                       Piped data
    ## Number of rows             150
    ## Number of columns          5
    ## _______________________
    ## Column type frequency:
    ##   numeric                  1
    ## ________________________
    ## Group variables            None
    ##
    ## ── Variable type: numeric ────────────────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate  mean    sd    p0   p25   p50   p75  p100 hist
    ## 1 Petal.Length          0             1  3.76  1.77     1   1.6  4.35   5.1   6.9 ▇▁▆▇▂

And

    dplyr::glimpse(skimmed)

    ## Observations: 1
    ## Variables: 15
    ## $ skim_type         <chr> "numeric"
    ## $ skim_variable     <chr> "Petal.Length"
    ## $ n_missing         <int> 0
    ## $ complete_rate     <dbl> 1
    ## $ factor.ordered    <lgl> NA
    ## $ factor.n_unique   <int> NA
    ## $ factor.top_counts <chr> NA
    ## $ numeric.mean      <dbl> 3.758
    ## $ numeric.sd        <dbl> 1.765298
    ## $ numeric.p0        <dbl> 1
    ## $ numeric.p25       <dbl> 1.6
    ## $ numeric.p50       <dbl> 4.35
    ## $ numeric.p75       <dbl> 5.1
    ## $ numeric.p100      <dbl> 6.9
    ## $ numeric.hist      <chr> "▇▁▆▇▂"

It’s still not perfect, as you need to rely on a pseudo-namespace to refer to the column that you want. But this is unfortunately a necessary trade-off. As the Rstats Bible, errr Hadley Wickham’s Advanced R, states, all elements of an atomic vector must have the same type. This normally isn’t something that you have to think too much about, that is until you try to combine the means of all your `Date` columns with the means of your `numeric` columns and everything comes out utterly garbled. So instead of that basket of laughs, we prefix columns names by their data type.

There’s a couple of other nuances here:

- The data frame `skim()` produces always starts off with some metadata columns
- Functions that always produce the same, regardless of input type, can be treated as `base_skimmers` and don’t need a namespace

### Manipulating internal data

A better representation of internal data comes with better tools for reshaping the data and getting it for other contexts. A common request in v1 was tooling to handle the `skimr` subtables separately. We now do this with `partition()`. It replaces the v1 function `skim_to_list()`.

    partition(skimmed)

    ## $numeric
    ##
    ## ── Variable type: numeric ────────────────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate  mean    sd    p0   p25   p50   p75  p100 hist
    ## 1 Petal.Length          0             1  3.76  1.77     1   1.6  4.35   5.1   6.9 ▇▁▆▇▂

You can undo a call to `partition()` with `bind()`, which joins the subtables into the original `skim_df` object and properly accounts for metadata. You can skip a step with the function `yank()`, which calls partition and pulls out a particular subtable

    yank(skimmed, "numeric")

    ##
    ## ── Variable type: numeric ────────────────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate  mean    sd    p0   p25   p50   p75  p100 hist
    ## 1 Petal.Length          0             1  3.76  1.77     1   1.6  4.35   5.1   6.9 ▇▁▆▇▂

Last, with support something close to the older format with the `to_long()` function. This can be added for something close to backwards compatibility. Being realistic on open source sustainability means that we are not able to support 100% backward compatibility in v2 even with new functions. Meanwhile you can keep using v1 if you are happy with it. However, because `skimr`’s dependencies are under ongoing development, sooner or later skimr v1 will no longer work with updates to them.

### Working with dplyr

Using `skimr` in a `dplyr` pipeline was part of the original package design, and we’ve needed to devote some extra love to making sure that everything is as seamless as possible. Part of this is due to the object produce by `skim()`, which we call `skim_df`. It’s a little weird in that it needs both metadata and columns in the underlying data frame.

In practice, this means that you can coerce it into a different type through normal `dplyr` operations. Here’s one:

    select(skimmed, numeric.mean)

    ## # A tibble: 1 x 1
    ##   numeric.mean
    ##          <dbl>
    ## 1         3.76

To get around this, we’ve added some helper functions and methods. The more `skimr`-like replacement for `select()` is `focus()`, which preserves metadata columns.

    focus(skimmed, numeric.mean)

    ## ── Data Summary ────────────────────────
    ##                            Values
    ## Name                       Piped data
    ## Number of rows             150
    ## Number of columns          5
    ## _______________________
    ## Column type frequency:
    ##   numeric                  1
    ## ________________________
    ## Group variables            None
    ##
    ## ── Variable type: numeric ────────────────────────────────────────────────────────────────
    ##   skim_variable  mean
    ## 1 Petal.Length   3.76

## Configuring and extending skimr

Most of `skimr`’s magic, to steal a term, comes from the fact that you can do most everything with one function. But believe it or not, there’s actually a bit more to the package.

One big one is customization. We like the `skimr` defaults, but that doesn’t guarantee you will. So what if you want to do something different, we have a function factory for that!

    my_skim <- skim_with(numeric = sfl(iqr = IQR, p25 = NULL, p75 = NULL))
    my_skim(faithful)

    ## ── Data Summary ────────────────────────
    ##                            Values
    ## Name                       faithful
    ## Number of rows             272
    ## Number of columns          2
    ## _______________________
    ## Column type frequency:
    ##   numeric                  2
    ## ________________________
    ## Group variables            None
    ##
    ## ── Variable type: numeric ────────────────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate  mean    sd    p0   p50  p100 hist    iqr
    ## 1 eruptions             0             1  3.49  1.14   1.6     4   5.1 ▇▂▂▇▇  2.29
    ## 2 waiting               0             1 70.9  13.6   43      76  96   ▃▃▂▇▂ 24

Those of you familiar with customizing `skim()` in v1 will notice a couple differences:

- we now has an object called `sfl()` for managing `skimr` function lists; more below
- instead of setting global options, we now have a function factory

Yes! A function factory. `skim_with()` gives us a new function each time we call it, and the returned function is configured by the arguments in `skim_with()`. This works the same way as `ecdf()` in the `stats` package or `colorRamp` in `grDevices`. Creating new functions has a few advantages over the previous approach.

- you can export a `skim()` function in a package or create it in a `.Rprofile`
- you avoid a bunch of potential side effects from setting options with `skim_with()`

The other big change is how we now handle different data types. Although many will never see it, a key piece of `skimr` customization comes from the `get_skimmers()` generic. It’s used to detect different column types in your data and set the appropriate summary functions for that type. It’s also designed to work with `sfl()`. Here’s an example from the “Supporting additional objects” vignette. Here, we’ll create some skimmers for `sf` data types:

    get_skimmers.sfc_POINT <- function(column) {
      sfl(
        skim_type = "sfc_POINT",
        n_unique = n_unique,
        valid = ~ sum(sf::st_is_valid(.))
      )
    }

While it was required in `skim_with()`, users must provide a `skim_type` value when creating new methods. With that, you can export this method in a new package (be sure to import the generic), and the new default skimmer is added when you load the package.

    get_default_skimmer_names()

    ...
    $sfc_POINT
    [1] "missing"  "complete" "n"        "n_unique" "valid"
    ...

Even if you don’t go the full route of supporting a new data type, creating a couple of `skimr` function lists has other benefits. For example, you can add some to your `.Rprofile` as a way to quickly configure `skimr` interactively.

    sfc_point_sfl <- sfl(
      n_unique = n_unique,
      valid = ~ sum(sf::st_is_valid(.))
    )

    my_skimmer <- skim_with(sfc_POINT = sfc_point_sfl)

## Using skimr in other contexts

In `skimr` v1, we developed some slightly hacky approaches to getting nicer `skim()` output in RMarkdown docs. These have been removed in favor of the actually-supported `knit_print` API. Now, calling `skim()`, within an RMarkdown doc should produce something nice by default.

    skim(chickwts)

Data summary
Name | chickwts
Number of rows | 71
Number of columns | 2
_______________________ |
Column type frequency: |
factor | 1
numeric | 1
________________________ |
Group variables | None

Variable type: factor

skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts
--- | --- | --- | --- | --- | ---
feed | 0 | 1 | FALSE | 6 | soy: 14, cas: 12, lin: 12, sun: 12

Variable type: numeric

skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
weight | 0 | 1 | 261.31 | 78.07 | 108 | 204.5 | 258 | 323.5 | 423 | ▆▆▇▇▃

You get a nice html version of both the summary header and the `skimr` subtables for each type of data.

In this context, you configure the output the same way you handle other `knitr` code chunks.

This means that we’re dropping direct support for `kable.skim_df()` and `pander.skim_df()`. But you can still get pretty similar results to these functions by using the reshaping functions described above to get subtables. You can also still use `Pander` and other nice rendering packages on an ad hoc basis as you would for other data frames or tibbles.

We also have a similarly-nice rendered output in Jupyter and RMarkdown notebooks. In the latter, the summary is separated from the rest of the output when working interactively. We like it that way, but we’d be happy to hear what the rest of you think!

## Wait, that took over a year?

Well, we think that’s a lot! But to be fair, it wasn’t exactly simple to keep up with `skimr`. Real talk, open source development takes up a lot of time, and the `skimr` developers have additional important priorities. Michael’s family added a new baby, and despite swearing up and down otherwise, he got absolutely nothing not-baby-related done during his paternity leave (take note new dads!). Elin ended up taking a much bigger role on at Lehman, really limiting time for any other work.

Even so, these are just the highlights in the normal ebb and flow of this sort of work. Since it’s no one’s real job, it might not always be the first focus. And that’s OK! We’ve been really lucky to have a group of new users that have been very patient with this slow development cycle while still providing really good feedback throughout. Thank you all!

We’re really excited about this next step in the `skimr` journey. We’ve put a huge amount of work into this new version. Hopefully it shows. And hopefully it inspires some of you to send more feedback and help us find even more ways to improve!