tests/testthat/_snaps/dplyr.md

In ropenscilabs/skimr: Compact and Flexible Summaries of Data

dplyr::filter works as expected

Code
  dplyr::filter(skimmed_iris, skim_type == "numeric")
Output
  -- Data Summary ------------------------
                             Values
  Name                       iris  
  Number of rows             150   
  Number of columns          5     
  _______________________          
  Column type frequency:           
    numeric                  4     
  ________________________         
  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 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
  dplyr::filter(skimmed_iris, skim_type == "no_type")
Output
  # A tibble: 0 x 15
  # i 15 variables: skim_type <chr>, skim_variable <chr>, n_missing <int>,
  #   complete_rate <dbl>, factor.ordered <lgl>, factor.n_unique <int>,
  #   factor.top_counts <chr>, numeric.mean <dbl>, numeric.sd <dbl>,
  #   numeric.p0 <dbl>, numeric.p25 <dbl>, numeric.p50 <dbl>, numeric.p75 <dbl>,
  #   numeric.p100 <dbl>, numeric.hist <chr>

dplyr::select works as expected

Code
  with_type
Output
  # A tibble: 5 x 2
    skim_type skim_variable
    <chr>     <chr>        
  1 factor    Species      
  2 numeric   Sepal.Length 
  3 numeric   Sepal.Width  
  4 numeric   Petal.Length 
  5 numeric   Petal.Width

Code
  without_type
Output
  # A tibble: 5 x 1
    numeric.mean
           <dbl>
  1        NA   
  2         5.84
  3         3.06
  4         3.76
  5         1.20

dplyr::mutate works as expected

Code
  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 ▇▁▇▅▃
    mean2
  1 34.1 
  2  9.35
  3 14.1 
  4  1.44

dplyr::slice works as expected

Code
  input
Output
  -- Data Summary ------------------------
                             Values
  Name                       iris  
  Number of rows             150   
  Number of columns          5     
  _______________________          
  Column type frequency:           
    factor                   1     
    numeric                  2     
  ________________________         
  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 ▁▆▇▂▁

dplyr::arrange works as expected

Code
  dplyr::arrange(skimmed_iris, desc(numeric.mean))
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 Petal.Length          0             1 3.76 1.77  1   1.6 4.35 5.1  6.9 ▇▁▆▇▂
  3 Sepal.Width           0             1 3.06 0.436 2   2.8 3    3.3  4.4 ▁▆▇▂▁
  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.