tests/testthat/_snaps/skim_print.md
In ropenscilabs/skimr: Compact and Flexible Summaries of Data
Skim prints a header for the entire output and each type
Code
input <- skim(iris)
input
Output
-- 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
1 Species 0 1 FALSE 3
top_counts
1 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 ▇▁▇▅▃
Code
input$numeric.hist <- NULL
input
Output
-- 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
1 Species 0 1 FALSE 3
top_counts
1 set: 50, ver: 50, vir: 50
-- Variable type: numeric ------------------------------------------------------
skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100
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 prints a special header for grouped data frames
Code
skim(dplyr::group_by(iris, Species))
Output
-- Data Summary ------------------------
Values
Name dplyr::group_by(iris, Spe...
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
1 Sepal.Length setosa 0 1 5.01 0.352 4.3 4.8 5
2 Sepal.Length versicolor 0 1 5.94 0.516 4.9 5.6 5.9
3 Sepal.Length virginica 0 1 6.59 0.636 4.9 6.22 6.5
4 Sepal.Width setosa 0 1 3.43 0.379 2.3 3.2 3.4
5 Sepal.Width versicolor 0 1 2.77 0.314 2 2.52 2.8
6 Sepal.Width virginica 0 1 2.97 0.322 2.2 2.8 3
7 Petal.Length setosa 0 1 1.46 0.174 1 1.4 1.5
8 Petal.Length versicolor 0 1 4.26 0.470 3 4 4.35
9 Petal.Length virginica 0 1 5.55 0.552 4.5 5.1 5.55
10 Petal.Width setosa 0 1 0.246 0.105 0.1 0.2 0.2
11 Petal.Width versicolor 0 1 1.33 0.198 1 1.2 1.3
12 Petal.Width virginica 0 1 2.03 0.275 1.4 1.8 2
p75 p100 hist
1 5.2 5.8 ▃▃▇▅▁
2 6.3 7 ▂▇▆▃▃
3 6.9 7.9 ▁▃▇▃▂
4 3.68 4.4 ▁▃▇▅▂
5 3 3.4 ▁▅▆▇▂
6 3.18 3.8 ▂▆▇▅▁
7 1.58 1.9 ▁▃▇▃▁
8 4.6 5.1 ▂▂▇▇▆
9 5.88 6.9 ▃▇▇▃▂
10 0.3 0.6 ▇▂▂▁▁
11 1.5 1.8 ▅▇▃▆▁
12 2.3 2.5 ▂▇▆▅▇
Skim lists print as expected
Code
partition(skimmed)
Output
$factor
-- 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:~
$numeric
-- 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 ▇▁▇▅▃
knit_print produces expected results
Code
cat(input)
Output
Table: Data summary
| | |
|:------------------------|:----|
|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 |
|:-------------|---------:|-------------:|:-------|--------:|:-------------------------|
|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 |
|:-------------|---------:|-------------:|----:|----:|---:|---:|----:|---:|----:|:-----|
|Sepal.Length | 0| 1| 5.84| 0.83| 4.3| 5.1| 5.80| 6.4| 7.9|▆▇▇▅▂ |
|Sepal.Width | 0| 1| 3.06| 0.44| 2.0| 2.8| 3.00| 3.3| 4.4|▁▆▇▂▁ |
|Petal.Length | 0| 1| 3.76| 1.77| 1.0| 1.6| 4.35| 5.1| 6.9|▇▁▆▇▂ |
|Petal.Width | 0| 1| 1.20| 0.76| 0.1| 0.3| 1.30| 1.8| 2.5|▇▁▇▅▃ |
knit_print works with skim summaries
Code
cat(knitr::knit_print(summarized))
Output
Table: Data summary
| | |
|:------------------------|:----|
|Name |iris |
|Number of rows |150 |
|Number of columns |5 |
|_______________________ | |
|Column type frequency: | |
|factor |1 |
|numeric |4 |
|________________________ | |
|Group variables |None |
knit_print appropriately falls back to tibble printing
Code
input <- knitr::knit_print(reduced)
Output
# A tibble: 5 x 2
skim_variable numeric.mean
<chr> <dbl>
1 Species NA
2 Sepal.Length 5.84
3 Sepal.Width 3.06
4 Petal.Length 3.76
5 Petal.Width 1.20
Summaries can be suppressed within knitr
Code
cat(knitr::knit_print(skimmed, options = options))
Output
**Variable type: factor**
|skim_variable | n_missing| complete_rate|ordered | n_unique|top_counts |
|:-------------|---------:|-------------:|:-------|--------:|:-------------------------|
|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 |
|:-------------|---------:|-------------:|----:|----:|---:|---:|----:|---:|----:|:-----|
|Sepal.Length | 0| 1| 5.84| 0.83| 4.3| 5.1| 5.80| 6.4| 7.9|▆▇▇▅▂ |
|Sepal.Width | 0| 1| 3.06| 0.44| 2.0| 2.8| 3.00| 3.3| 4.4|▁▆▇▂▁ |
|Petal.Length | 0| 1| 3.76| 1.77| 1.0| 1.6| 4.35| 5.1| 6.9|▇▁▆▇▂ |
|Petal.Width | 0| 1| 1.20| 0.76| 0.1| 0.3| 1.30| 1.8| 2.5|▇▁▇▅▃ |
Skim lists have a separate knit_print method
Code
cat(knit_print(skim_list))
Output
**Variable type: factor**
|skim_variable | n_missing| complete_rate|ordered | n_unique|top_counts |
|:-------------|---------:|-------------:|:-------|--------:|:-------------------------|
|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 |
|:-------------|---------:|-------------:|----:|----:|---:|---:|----:|---:|----:|:-----|
|Sepal.Length | 0| 1| 5.84| 0.83| 4.3| 5.1| 5.80| 6.4| 7.9|▆▇▇▅▂ |
|Sepal.Width | 0| 1| 3.06| 0.44| 2.0| 2.8| 3.00| 3.3| 4.4|▁▆▇▂▁ |
|Petal.Length | 0| 1| 3.76| 1.77| 1.0| 1.6| 4.35| 5.1| 6.9|▇▁▆▇▂ |
|Petal.Width | 0| 1| 1.20| 0.76| 0.1| 0.3| 1.30| 1.8| 2.5|▇▁▇▅▃ |
You can yank a type from a skim_df and call knit_print
Code
cat(knit_print(skim_one))
Output
**Variable type: factor**
|skim_variable | n_missing| complete_rate|ordered | n_unique|top_counts |
|:-------------|---------:|-------------:|:-------|--------:|:-------------------------|
|Species | 0| 1|FALSE | 3|set: 50, ver: 50, vir: 50 |
Skim falls back to tibble::print.tbl() appropriately
Code
input <- skim(iris)
dplyr::select(input, numeric.mean)
Output
# A tibble: 5 x 1
numeric.mean
<dbl>
1 NA
2 5.84
3 3.06
4 3.76
5 1.20
Print focused objects appropriately
Code
focus(skimmed, n_missing)
Output
-- 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
1 Species 0
-- Variable type: numeric ------------------------------------------------------
skim_variable n_missing
1 Sepal.Length 0
2 Sepal.Width 0
3 Petal.Length 0
4 Petal.Width 0
Support for smaller consoles can be set with the width option
Code
skim(iris)
Output
-- 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
1 Species 0 1 FALSE 3
top_counts
1 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 ▇▁▇▅▃
Table header width can be controlled by an option
Code
skimmed
Output
-- 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
1 Species 0 1 FALSE 3
top_counts
1 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 ▇▁▇▅▃
skimr creates appropriate output for Jupyter
Code
skimmed
Output
-- 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
1 Species 0 1 FALSE 3
top_counts
1 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 ▇▁▇▅▃
ropenscilabs/skimr documentation built on Feb. 2, 2025, 12:14 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Skim prints a header for the entire output and each type
Code
input <- skim(iris)
input
Output
-- 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
1 Species 0 1 FALSE 3
top_counts
1 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 ▇▁▇▅▃
Code
input$numeric.hist <- NULL
input
Output
-- 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
1 Species 0 1 FALSE 3
top_counts
1 set: 50, ver: 50, vir: 50
-- Variable type: numeric ------------------------------------------------------
skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100
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 prints a special header for grouped data frames
Code
skim(dplyr::group_by(iris, Species))
Output
-- Data Summary ------------------------
Values
Name dplyr::group_by(iris, Spe...
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
1 Sepal.Length setosa 0 1 5.01 0.352 4.3 4.8 5
2 Sepal.Length versicolor 0 1 5.94 0.516 4.9 5.6 5.9
3 Sepal.Length virginica 0 1 6.59 0.636 4.9 6.22 6.5
4 Sepal.Width setosa 0 1 3.43 0.379 2.3 3.2 3.4
5 Sepal.Width versicolor 0 1 2.77 0.314 2 2.52 2.8
6 Sepal.Width virginica 0 1 2.97 0.322 2.2 2.8 3
7 Petal.Length setosa 0 1 1.46 0.174 1 1.4 1.5
8 Petal.Length versicolor 0 1 4.26 0.470 3 4 4.35
9 Petal.Length virginica 0 1 5.55 0.552 4.5 5.1 5.55
10 Petal.Width setosa 0 1 0.246 0.105 0.1 0.2 0.2
11 Petal.Width versicolor 0 1 1.33 0.198 1 1.2 1.3
12 Petal.Width virginica 0 1 2.03 0.275 1.4 1.8 2
p75 p100 hist
1 5.2 5.8 ▃▃▇▅▁
2 6.3 7 ▂▇▆▃▃
3 6.9 7.9 ▁▃▇▃▂
4 3.68 4.4 ▁▃▇▅▂
5 3 3.4 ▁▅▆▇▂
6 3.18 3.8 ▂▆▇▅▁
7 1.58 1.9 ▁▃▇▃▁
8 4.6 5.1 ▂▂▇▇▆
9 5.88 6.9 ▃▇▇▃▂
10 0.3 0.6 ▇▂▂▁▁
11 1.5 1.8 ▅▇▃▆▁
12 2.3 2.5 ▂▇▆▅▇
Skim lists print as expected
Code
partition(skimmed)
Output
$factor
-- 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:~
$numeric
-- 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 ▇▁▇▅▃
knit_print produces expected results
Code
cat(input)
Output
Table: Data summary
| | |
|:------------------------|:----|
|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 |
|:-------------|---------:|-------------:|:-------|--------:|:-------------------------|
|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 |
|:-------------|---------:|-------------:|----:|----:|---:|---:|----:|---:|----:|:-----|
|Sepal.Length | 0| 1| 5.84| 0.83| 4.3| 5.1| 5.80| 6.4| 7.9|▆▇▇▅▂ |
|Sepal.Width | 0| 1| 3.06| 0.44| 2.0| 2.8| 3.00| 3.3| 4.4|▁▆▇▂▁ |
|Petal.Length | 0| 1| 3.76| 1.77| 1.0| 1.6| 4.35| 5.1| 6.9|▇▁▆▇▂ |
|Petal.Width | 0| 1| 1.20| 0.76| 0.1| 0.3| 1.30| 1.8| 2.5|▇▁▇▅▃ |
knit_print works with skim summaries
Code
cat(knitr::knit_print(summarized))
Output
Table: Data summary
| | |
|:------------------------|:----|
|Name |iris |
|Number of rows |150 |
|Number of columns |5 |
|_______________________ | |
|Column type frequency: | |
|factor |1 |
|numeric |4 |
|________________________ | |
|Group variables |None |
knit_print appropriately falls back to tibble printing
Code
input <- knitr::knit_print(reduced)
Output
# A tibble: 5 x 2
skim_variable numeric.mean
<chr> <dbl>
1 Species NA
2 Sepal.Length 5.84
3 Sepal.Width 3.06
4 Petal.Length 3.76
5 Petal.Width 1.20
Summaries can be suppressed within knitr
Code
cat(knitr::knit_print(skimmed, options = options))
Output
**Variable type: factor**
|skim_variable | n_missing| complete_rate|ordered | n_unique|top_counts |
|:-------------|---------:|-------------:|:-------|--------:|:-------------------------|
|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 |
|:-------------|---------:|-------------:|----:|----:|---:|---:|----:|---:|----:|:-----|
|Sepal.Length | 0| 1| 5.84| 0.83| 4.3| 5.1| 5.80| 6.4| 7.9|▆▇▇▅▂ |
|Sepal.Width | 0| 1| 3.06| 0.44| 2.0| 2.8| 3.00| 3.3| 4.4|▁▆▇▂▁ |
|Petal.Length | 0| 1| 3.76| 1.77| 1.0| 1.6| 4.35| 5.1| 6.9|▇▁▆▇▂ |
|Petal.Width | 0| 1| 1.20| 0.76| 0.1| 0.3| 1.30| 1.8| 2.5|▇▁▇▅▃ |
Skim lists have a separate knit_print method
Code
cat(knit_print(skim_list))
Output
**Variable type: factor**
|skim_variable | n_missing| complete_rate|ordered | n_unique|top_counts |
|:-------------|---------:|-------------:|:-------|--------:|:-------------------------|
|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 |
|:-------------|---------:|-------------:|----:|----:|---:|---:|----:|---:|----:|:-----|
|Sepal.Length | 0| 1| 5.84| 0.83| 4.3| 5.1| 5.80| 6.4| 7.9|▆▇▇▅▂ |
|Sepal.Width | 0| 1| 3.06| 0.44| 2.0| 2.8| 3.00| 3.3| 4.4|▁▆▇▂▁ |
|Petal.Length | 0| 1| 3.76| 1.77| 1.0| 1.6| 4.35| 5.1| 6.9|▇▁▆▇▂ |
|Petal.Width | 0| 1| 1.20| 0.76| 0.1| 0.3| 1.30| 1.8| 2.5|▇▁▇▅▃ |
You can yank a type from a skim_df and call knit_print
Code
cat(knit_print(skim_one))
Output
**Variable type: factor**
|skim_variable | n_missing| complete_rate|ordered | n_unique|top_counts |
|:-------------|---------:|-------------:|:-------|--------:|:-------------------------|
|Species | 0| 1|FALSE | 3|set: 50, ver: 50, vir: 50 |
Skim falls back to tibble::print.tbl() appropriately
Code
input <- skim(iris)
dplyr::select(input, numeric.mean)
Output
# A tibble: 5 x 1
numeric.mean
<dbl>
1 NA
2 5.84
3 3.06
4 3.76
5 1.20
Print focused objects appropriately
Code
focus(skimmed, n_missing)
Output
-- 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
1 Species 0
-- Variable type: numeric ------------------------------------------------------
skim_variable n_missing
1 Sepal.Length 0
2 Sepal.Width 0
3 Petal.Length 0
4 Petal.Width 0
Support for smaller consoles can be set with the width option
Code
skim(iris)
Output
-- 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
1 Species 0 1 FALSE 3
top_counts
1 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 ▇▁▇▅▃
Table header width can be controlled by an option
Code
skimmed
Output
-- 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
1 Species 0 1 FALSE 3
top_counts
1 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 ▇▁▇▅▃
skimr creates appropriate output for Jupyter
Code
skimmed
Output
-- 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
1 Species 0 1 FALSE 3
top_counts
1 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 ▇▁▇▅▃
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.