skimr provides a frictionless approach to summary
statistics which conforms to the principle
of least surprise, displaying summary statistics the user can skim
quickly to understand their data. It handles different data types and
returns a skim_df object which can be included in a
pipeline or displayed nicely for the human reader.
Note: skimr version 2 has major changes when
skimr is used programmatically. Upgraders should review this document,
the release notes and vignettes carefully.
The current released version of skimr can be installed
from CRAN. If you wish to install the current build of the next release
you can do so using the following:
# install.packages("devtools")
devtools::install_github("ropensci/skimr")The APIs for this branch should be considered reasonably stable but still subject to change if an issue is discovered.
To install the version with the most recent changes that have not yet been incorporated in the main branch (and may not be):
devtools::install_github("ropensci/skimr", ref = "develop")Do not rely on APIs from the develop branch, as they are likely to change.
skimr:
summary(),
including missing, complete, n, and sd.skim(chickwts)
## ── Data Summary ────────────────────────
## Values
## 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
## 1 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
## 1 weight 0 1 261. 78.1 108 204. 258 324. 423 ▆▆▇▇▃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 ▇▁▇▅▃skim(dplyr::starwars)
## ── Data Summary ────────────────────────
## Values
## Name dplyr::starwars
## Number of rows 87
## Number of columns 14
## _______________________
## Column type frequency:
## character 8
## list 3
## numeric 3
## ________________________
## Group variables None
##
## ── Variable type: character ────────────────────────────────────────────────────────────────────────
## skim_variable n_missing complete_rate min max empty n_unique whitespace
## 1 name 0 1 3 21 0 87 0
## 2 hair_color 5 0.943 4 13 0 12 0
## 3 skin_color 0 1 3 19 0 31 0
## 4 eye_color 0 1 3 13 0 15 0
## 5 sex 4 0.954 4 14 0 4 0
## 6 gender 4 0.954 8 9 0 2 0
## 7 homeworld 10 0.885 4 14 0 48 0
## 8 species 4 0.954 3 14 0 37 0
##
## ── Variable type: list ─────────────────────────────────────────────────────────────────────────────
## skim_variable n_missing complete_rate n_unique min_length max_length
## 1 films 0 1 24 1 7
## 2 vehicles 0 1 11 0 2
## 3 starships 0 1 17 0 5
##
## ── Variable type: numeric ──────────────────────────────────────────────────────────────────────────
## skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
## 1 height 6 0.931 174. 34.8 66 167 180 191 264 ▁▁▇▅▁
## 2 mass 28 0.678 97.3 169. 15 55.6 79 84.5 1358 ▇▁▁▁▁
## 3 birth_year 44 0.494 87.6 155. 8 35 52 72 896 ▇▁▁▁▁skim(iris) %>%
summary()
## ── Data Summary ────────────────────────
## Values
## Name iris
## Number of rows 150
## Number of columns 5
## _______________________
## Column type frequency:
## factor 1
## numeric 4
## ________________________
## Group variables Noneskim(iris, Sepal.Length, Petal.Length)
## ── Data Summary ────────────────────────
## Values
## Name iris
## Number of rows 150
## Number of columns 5
## _______________________
## Column type frequency:
## numeric 2
## ________________________
## Group variables None
##
## ── 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 Petal.Length 0 1 3.76 1.77 1 1.6 4.35 5.1 6.9 ▇▁▆▇▂skim() can handle data that has been grouped using
dplyr::group_by().
iris %>%
dplyr::group_by(Species) %>%
skim()
## ── Data Summary ────────────────────────
## Values
## Name Piped data
## Number of rows 150
## Number of columns 5
## _______________________
## Column type frequency:
## numeric 4
## ________________________
## Group variables Species
##
## ── Variable type: numeric ──────────────────────────────────────────────────────────────────────────
## skim_variable Species n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
## 1 Sepal.Length setosa 0 1 5.01 0.352 4.3 4.8 5 5.2 5.8 ▃▃▇▅▁
## 2 Sepal.Length versicolor 0 1 5.94 0.516 4.9 5.6 5.9 6.3 7 ▂▇▆▃▃
## 3 Sepal.Length virginica 0 1 6.59 0.636 4.9 6.22 6.5 6.9 7.9 ▁▃▇▃▂
## 4 Sepal.Width setosa 0 1 3.43 0.379 2.3 3.2 3.4 3.68 4.4 ▁▃▇▅▂
## 5 Sepal.Width versicolor 0 1 2.77 0.314 2 2.52 2.8 3 3.4 ▁▅▆▇▂
## 6 Sepal.Width virginica 0 1 2.97 0.322 2.2 2.8 3 3.18 3.8 ▂▆▇▅▁
## 7 Petal.Length setosa 0 1 1.46 0.174 1 1.4 1.5 1.58 1.9 ▁▃▇▃▁
## 8 Petal.Length versicolor 0 1 4.26 0.470 3 4 4.35 4.6 5.1 ▂▂▇▇▆
## 9 Petal.Length virginica 0 1 5.55 0.552 4.5 5.1 5.55 5.88 6.9 ▃▇▇▃▂
## 10 Petal.Width setosa 0 1 0.246 0.105 0.1 0.2 0.2 0.3 0.6 ▇▂▂▁▁
## 11 Petal.Width versicolor 0 1 1.33 0.198 1 1.2 1.3 1.5 1.8 ▅▇▃▆▁
## 12 Petal.Width virginica 0 1 2.03 0.275 1.4 1.8 2 2.3 2.5 ▂▇▆▅▇iris %>%
skim() %>%
dplyr::filter(numeric.sd > 1)
## ── 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 ▇▁▆▇▂Simply skimming a data frame will produce the horizontal print layout
shown above. We provide a knit_print method for the types
of objects in this package so that similar results are produced in
documents. To use this, make sure the skimmed object is the
last item in your code chunk.
faithful %>%
skim()| Name | Piped data |
| Number of rows | 272 |
| Number of columns | 2 |
| _______________________ | |
| Column type frequency: | |
| numeric | 2 |
| ________________________ | |
| Group variables | None |
Data summary
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| eruptions | 0 | 1 | 3.49 | 1.14 | 1.6 | 2.16 | 4 | 4.45 | 5.1 | ▇▂▂▇▇ |
| waiting | 0 | 1 | 70.90 | 13.59 | 43.0 | 58.00 | 76 | 82.00 | 96.0 | ▃▃▂▇▂ |
Although skimr provides opinionated defaults, it is highly customizable. Users can specify their own statistics, change the formatting of results, create statistics for new classes and develop skimmers for data structures that are not data frames.
Users can specify their own statistics using a list combined with the
skim_with() function factory. skim_with()
returns a new skim function that can be called on your
data. You can use this factory to produce summaries for any type of
column within your data.
Assignment within a call to skim_with() relies on a
helper function, sfl or skimr function list.
By default, functions in the sfl call are appended to the
default skimmers, and names are automatically generated as well.
my_skim <- skim_with(numeric = sfl(mad))
my_skim(iris, Sepal.Length)But you can also helpers from the tidyverse to create
new anonymous functions that set particular function arguments. The
behavior is the same as in purrr or dplyr,
with both . and .x as acceptable pronouns.
Setting the append = FALSE argument uses only those
functions that you’ve provided.
my_skim <- skim_with(
numeric = sfl(
iqr = IQR,
p01 = ~ quantile(.x, probs = .01)
p99 = ~ quantile(., probs = .99)
),
append = FALSE
)
my_skim(iris, Sepal.Length)And you can remove default skimmers by setting them to
NULL.
my_skim <- skim_with(numeric = sfl(hist = NULL))
my_skim(iris, Sepal.Length)skimr has summary functions for the following types of
data by default:
numeric (which includes both double and
integer)characterfactorlogicalcomplexDatePOSIXcttsAsIsskimr also provides a small API for writing packages
that provide their own default summary functions for data types not
covered above. It relies on R S3 methods for the
get_skimmers function. This function should return a
sfl, similar to customization within
skim_with(), but you should also provide a value for the
class argument. Here’s an example.
get_skimmers.my_data_type <- function(column) {
sfl(
.class = "my_data_type",
p99 = quantile(., probs = .99)
)
}We are aware that there are issues with rendering the inline histograms and line charts in various contexts, some of which are described below.
There are known issues with printing the spark-histogram characters
when printing a data frame. For example, "▂▅▇" is printed
as "<U+2582><U+2585><U+2587>". This
longstanding problem originates
in the low-level code for printing dataframes. While some cases have
been addressed, there are, for example, reports of this issue in Emacs
ESS. While this is a deep issue, there is ongoing
work to address it in base R.
This means that while skimr can render the histograms to
the console and in RMarkdown documents, it cannot in other
circumstances. This includes:
skimr data frame to a vanilla R data
frame, but tibbles render correctlyOne workaround for showing these characters in Windows is to set the
CTYPE part of your locale to Chinese/Japanese/Korean with
Sys.setlocale("LC_CTYPE", "Chinese"). The helper function
fix_windows_histograms() does this for you.
And last but not least, we provide skim_without_charts()
as a fallback. This makes it easy to still get summaries of your data,
even if unicode issues continue.
Spark-bar and spark-line work in the console, but may not work when
you knit them to a specific document format. The same session that
produces a correctly rendered HTML document may produce an incorrectly
rendered PDF, for example. This issue can generally be addressed by
changing fonts to one with good building block (for histograms) and
Braille support (for line graphs). For example, the open font “DejaVu
Sans” from the extrafont package supports these. You may
also want to try wrapping your results in knitr::kable().
Please see the vignette on using fonts for details.
Displays in documents of different types will vary. For example, one user found that the font “Yu Gothic UI Semilight” produced consistent results for Microsoft Word and Libre Office Write.
The earliest use of unicode characters to generate sparklines appears to be from 2009.
Exercising these ideas to their fullest requires a font with good support for block drawing characters. PragamataPro is one such font.
We welcome issue reports and pull requests, including potentially adding support for commonly used variable classes. However, in general, we encourage users to take advantage of skimr’s flexibility to add their own customized classes. Please see the contributing and conduct documents.
