October 29, 2019 From rOpenSci (https://deploy-preview-121--ropensci.netlify.app/blog/2019/10/29/skimrv2/). Except where otherwise noted, content on this site is licensed under the CC-BY license.
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?
Wait, what is a “skimr”?
skimr is an R package for summarizing your data. It extends tidyverse packages, and dplyr in particular, so that you can get a broad set of summary statistics with a single function call. You can install a pre-release version from the package’s GitHub repo.
devtools::install_github("ropensci/skimr")
skimr is also on CRAN, and v2 should be appearing there soon. For those of you that might have never seen skimr, here’s a typical call.
library(skimr)
library(dplyr)
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 ▇▁▇▅▃
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 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 releases, 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!
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_skimmersand 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)
| 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!
If you want to learn more about skimr, check out our GitHub repo. GitHub is also the best place to file issues. We’d love to hear from you!
