Running examples in ‘skimr-Ex.R’ failed
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> ### Name: skim
> ### Title: Skim a data frame, getting useful summary statistics
> ### Aliases: skim skim_tee skim_without_charts
> 
> ### ** Examples
> 
> 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
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 ▇▁▇▅▃
> 
> # Use tidyselect
> skim(iris, Species)
── Data Summary ────────────────────────
                           Values
Name                       iris  
Number of rows             150   
Number of columns          5     
_______________________          
Column type frequency:           
  factor                   1     
________________________         
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
> skim(iris, starts_with("Sepal"))
── 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 Sepal.Width           0             1 3.06 0.436 2   2.8 3   3.3  4.4 ▁▆▇▂▁
> skim(iris, where(is.numeric))
── 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 ▇▁▇▅▃
> 
> # Skim also works groupwise
> 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
 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 ▂▇▆▅▇
> 
> # Which five numeric columns have the greatest mean value?
> # Look in the `numeric.mean` column.
> iris %>%
+   skim() %>%
+   dplyr::select(numeric.mean) %>%
+   dplyr::top_n(5)
Selecting by numeric.mean
# A tibble: 4 × 1
  numeric.mean
         <dbl>
1         5.84
2         3.06
3         3.76
4         1.20
> 
> # Which of my columns have missing values? Use the base skimmer n_missing.
> iris %>%
+   skim() %>%
+   dplyr::filter(n_missing > 0)
# A tibble: 0 × 15
# … with 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>
> 
> # Use skim_tee to view the skim results and
> # continue using the original data.
> chickwts %>%
+   skim_tee() %>%
+   dplyr::filter(feed == "sunflower")
── Data Summary ────────────────────────
                           Values
Name                       data  
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
1 feed                  0             1 FALSE          6
  top_counts                        
1 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 ▆▆▇▇▃
   weight      feed
1     423 sunflower
2     340 sunflower
3     392 sunflower
4     339 sunflower
5     341 sunflower
6     226 sunflower
7     320 sunflower
8     295 sunflower
9     334 sunflower
10    322 sunflower
11    297 sunflower
12    318 sunflower
> 
> # Produce a summary without spark graphs
> iris %>%
+   skim_without_charts()
Error in `dplyr::summarize()`:
! Problem while computing `skimmed = purrr::map2(...)`.
Caused by error in `purrr::map2()`:
ℹ In index: 1.
Caused by error in `dplyr::summarize()`:
! Problem while computing `..1 = dplyr::across(variable_names,
  mangled_skimmers$funs)`.
Caused by error in `as_across_fn_call()`:
! Can't convert `fn`, NULL, to a function.
Backtrace:
     ▆
  1. ├─iris %>% skim_without_charts()
  2. ├─skimr::skim_without_charts(.)
  3. │ ├─dplyr::summarize(...)
  4. │ └─dplyr:::summarise.grouped_df(...)
  5. │   └─dplyr:::summarise_cols(.data, dplyr_quosures(...), caller_env = caller_env())
  6. │     ├─base::withCallingHandlers(...)
  7. │     └─dplyr:::map(quosures, summarise_eval_one, mask = mask)
  8. │       └─base::lapply(.x, .f, ...)
  9. │         └─dplyr (local) FUN(X[[i]], ...)
 10. │           └─mask$eval_all_summarise(quo)
 11. └─purrr::map2(...)
 12.   └─purrr:::map2_("list", .x, .y, .f, ..., .progress = .progress)
 13.     ├─purrr:::with_indexed_errors(...)
 14.     │ └─base::withCallingHandlers(...)
 15.     ├─skimr (local) .f(.x[[i]], .y[[i]], ...)
 16.     └─skimr:::skim_by_type.data.frame(.x[[i]], .y[[i]], ...)
 17.       ├─dplyr::summarize(data, dplyr::across(variable_names, mangled_skimmers$funs))
 18.       └─dplyr:::summarise.data.frame(data, dplyr::across(variable_names, mangled_skimmers$funs))
 19.         └─dplyr:::summarise_cols(.data, dplyr_quosures(...), caller_env = caller_env())
 20.           ├─base::withCallingHandlers(...)
 21.           └─dplyr:::expand_across(dots[[i]])
 22.             └─dplyr:::as_across_fn_call(fns[[j]], var, env, mask)
 23.               ├─rlang::call2(as_function(fn), sym(var))
 24.               └─rlang::as_function(fn)
 25.                 └─rlang:::abort_coercion(x, "a function", arg = arg)
 26.                   └─rlang::abort(msg, call = call)
Execution halted