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?
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)
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 ▇▁▇▅▃
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.
skimr
skimr
skimr
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:
skim()
produces always starts off with some
metadata columnsbase_skimmers
and don’t need a namespaceA 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.
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
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:
sfl()
for managing skimr
function
lists; more belowYes! 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.
skim()
function in a package or create it in a
.Rprofile
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)
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: 12Variable 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!
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!
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