Running examples in ‘skimr-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: skim
> ### Title: Skim a data frame, getting useful summary statistics
> ### Aliases: skim skim_tee
> 
> ### ** Examples
> 
> skim(iris)
Skim summary statistics
 n obs: 150 
 n variables: 5 

── Variable type:factor ────────────────────────────────────────────────────────
 variable missing complete   n n_unique                       top_counts
  Species       0      150 150        3 set: 50, ver: 50, vir: 50, NA: 0
 ordered
   FALSE

── Variable type:numeric ───────────────────────────────────────────────────────
     variable missing complete   n mean   sd  p0 p25  p50 p75 p100     hist
 Petal.Length       0      150 150 3.76 1.77 1   1.6 4.35 5.1  6.9 ▇▁▁▂▅▅▃▁
  Petal.Width       0      150 150 1.2  0.76 0.1 0.3 1.3  1.8  2.5 ▇▁▁▅▃▃▂▂
 Sepal.Length       0      150 150 5.84 0.83 4.3 5.1 5.8  6.4  7.9 ▂▇▅▇▆▅▂▂
  Sepal.Width       0      150 150 3.06 0.44 2   2.8 3    3.3  4.4 ▁▂▅▇▃▂▁▁
> 
> # Use tidyselect
> skim(iris, Species)
Skim summary statistics
 n obs: 150 
 n variables: 5 

── Variable type:factor ────────────────────────────────────────────────────────
 variable missing complete   n n_unique                       top_counts
  Species       0      150 150        3 set: 50, ver: 50, vir: 50, NA: 0
 ordered
   FALSE
> skim(iris, starts_with("Sepal"))
Skim summary statistics
 n obs: 150 
 n variables: 5 

── Variable type:numeric ───────────────────────────────────────────────────────
     variable missing complete   n mean   sd  p0 p25 p50 p75 p100     hist
 Sepal.Length       0      150 150 5.84 0.83 4.3 5.1 5.8 6.4  7.9 ▂▇▅▇▆▅▂▂
  Sepal.Width       0      150 150 3.06 0.44 2   2.8 3   3.3  4.4 ▁▂▅▇▃▂▁▁
> 
> # Skim also works groupwise
> dplyr::group_by(iris, Species) %>% skim()
Skim summary statistics
 n obs: 150 
 n variables: 5 
 group variables: Species 

── Variable type:numeric ───────────────────────────────────────────────────────
    Species     variable missing complete  n mean   sd  p0  p25  p50  p75 p100
     setosa Petal.Length       0       50 50 1.46 0.17 1   1.4  1.5  1.58  1.9
     setosa  Petal.Width       0       50 50 0.25 0.11 0.1 0.2  0.2  0.3   0.6
     setosa Sepal.Length       0       50 50 5.01 0.35 4.3 4.8  5    5.2   5.8
     setosa  Sepal.Width       0       50 50 3.43 0.38 2.3 3.2  3.4  3.68  4.4
 versicolor Petal.Length       0       50 50 4.26 0.47 3   4    4.35 4.6   5.1
 versicolor  Petal.Width       0       50 50 1.33 0.2  1   1.2  1.3  1.5   1.8
 versicolor Sepal.Length       0       50 50 5.94 0.52 4.9 5.6  5.9  6.3   7  
 versicolor  Sepal.Width       0       50 50 2.77 0.31 2   2.52 2.8  3     3.4
  virginica Petal.Length       0       50 50 5.55 0.55 4.5 5.1  5.55 5.88  6.9
  virginica  Petal.Width       0       50 50 2.03 0.27 1.4 1.8  2    2.3   2.5
  virginica Sepal.Length       0       50 50 6.59 0.64 4.9 6.23 6.5  6.9   7.9
  virginica  Sepal.Width       0       50 50 2.97 0.32 2.2 2.8  3    3.18  3.8
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> 
> # Skim pipelines; now we work with the tall format
> skim(iris) %>% as.data.frame()
       variable    type       stat      level       value formatted
1  Sepal.Length numeric    missing       .all   0.0000000         0
2  Sepal.Length numeric   complete       .all 150.0000000       150
3  Sepal.Length numeric          n       .all 150.0000000       150
4  Sepal.Length numeric       mean       .all   5.8433333      5.84
5  Sepal.Length numeric         sd       .all   0.8280661      0.83
6  Sepal.Length numeric         p0       .all   4.3000000       4.3
7  Sepal.Length numeric        p25       .all   5.1000000       5.1
8  Sepal.Length numeric        p50       .all   5.8000000       5.8
9  Sepal.Length numeric        p75       .all   6.4000000       6.4
10 Sepal.Length numeric       p100       .all   7.9000000       7.9
11 Sepal.Length numeric       hist       .all          NA  ▂▇▅▇▆▅▂▂
12  Sepal.Width numeric    missing       .all   0.0000000         0
13  Sepal.Width numeric   complete       .all 150.0000000       150
14  Sepal.Width numeric          n       .all 150.0000000       150
15  Sepal.Width numeric       mean       .all   3.0573333      3.06
16  Sepal.Width numeric         sd       .all   0.4358663      0.44
17  Sepal.Width numeric         p0       .all   2.0000000         2
18  Sepal.Width numeric        p25       .all   2.8000000       2.8
19  Sepal.Width numeric        p50       .all   3.0000000         3
20  Sepal.Width numeric        p75       .all   3.3000000       3.3
21  Sepal.Width numeric       p100       .all   4.4000000       4.4
22  Sepal.Width numeric       hist       .all          NA  ▁▂▅▇▃▂▁▁
23 Petal.Length numeric    missing       .all   0.0000000         0
24 Petal.Length numeric   complete       .all 150.0000000       150
25 Petal.Length numeric          n       .all 150.0000000       150
26 Petal.Length numeric       mean       .all   3.7580000      3.76
27 Petal.Length numeric         sd       .all   1.7652982      1.77
28 Petal.Length numeric         p0       .all   1.0000000         1
29 Petal.Length numeric        p25       .all   1.6000000       1.6
30 Petal.Length numeric        p50       .all   4.3500000      4.35
31 Petal.Length numeric        p75       .all   5.1000000       5.1
32 Petal.Length numeric       p100       .all   6.9000000       6.9
33 Petal.Length numeric       hist       .all          NA  ▇▁▁▂▅▅▃▁
34  Petal.Width numeric    missing       .all   0.0000000         0
35  Petal.Width numeric   complete       .all 150.0000000       150
36  Petal.Width numeric          n       .all 150.0000000       150
37  Petal.Width numeric       mean       .all   1.1993333       1.2
38  Petal.Width numeric         sd       .all   0.7622377      0.76
39  Petal.Width numeric         p0       .all   0.1000000       0.1
40  Petal.Width numeric        p25       .all   0.3000000       0.3
41  Petal.Width numeric        p50       .all   1.3000000       1.3
42  Petal.Width numeric        p75       .all   1.8000000       1.8
43  Petal.Width numeric       p100       .all   2.5000000       2.5
44  Petal.Width numeric       hist       .all          NA  ▇▁▁▅▃▃▂▂
45      Species  factor    missing       .all   0.0000000         0
46      Species  factor   complete       .all 150.0000000       150
47      Species  factor          n       .all 150.0000000       150
48      Species  factor   n_unique       .all   3.0000000         3
49      Species  factor top_counts     setosa  50.0000000   set: 50
50      Species  factor top_counts versicolor  50.0000000   ver: 50
51      Species  factor top_counts  virginica  50.0000000   vir: 50
52      Species  factor top_counts       <NA>   0.0000000     NA: 0
53      Species  factor    ordered       .all   0.0000000     FALSE
> skim(iris) %>% dplyr::filter(type == "factor")
Skim summary statistics
 n obs: 150 
 n variables: 5 

── Variable type:factor ────────────────────────────────────────────────────────
 variable missing complete   n n_unique                       top_counts
  Species       0      150 150        3 set: 50, ver: 50, vir: 50, NA: 0
 ordered
   FALSE
> 
> # Which column as the greatest mean value?
> skim(iris) %>%
+   dplyr::filter(stat == "mean") %>%
+   dplyr::arrange(dplyr::desc(value))
Skim summary statistics
 n obs: 150 
 n variables: 5 
Error: Can't find columns `missing`, `complete`, `n`, `sd`, `p0`, … (and 5 more) in `.data`.
Execution halted