Skip to contents

tidyselect helpers

Source: R/tidyselect-helpers.R
tidyselectors.Rd

#' [Experimental] Predicate functions simplifying picks specification.

Usage 

is_categorical(min.len, max.len)

Arguments

min.len

(integer(1)) minimal number of unique values

max.len

(integer(1)) maximal number of unique values

Value

A tidyselector that can be used directly in choices or selected of variables() in picks().

Examples 

# select factor column but exclude foreign keys
variables(choices = is_categorical(min.len = 2, max.len = 10))
#>  <variables>
#>    choices: <fn>
#>    selected: 1L
#>    multiple=FALSE, ordered=FALSE, fixed=FALSE, allow-clear=FALSE

# Supports tidyselect helpers, e.g. to select all categorical variables with 2 to 10 unique values
dplyr::select(iris, dplyr::where(is_categorical(2, 10)))
#>        Species
#> 1       setosa
#> 2       setosa
#> 3       setosa
#> 4       setosa
#> 5       setosa
#> 6       setosa
#> 7       setosa
#> 8       setosa
#> 9       setosa
#> 10      setosa
#> 11      setosa
#> 12      setosa
#> 13      setosa
#> 14      setosa
#> 15      setosa
#> 16      setosa
#> 17      setosa
#> 18      setosa
#> 19      setosa
#> 20      setosa
#> 21      setosa
#> 22      setosa
#> 23      setosa
#> 24      setosa
#> 25      setosa
#> 26      setosa
#> 27      setosa
#> 28      setosa
#> 29      setosa
#> 30      setosa
#> 31      setosa
#> 32      setosa
#> 33      setosa
#> 34      setosa
#> 35      setosa
#> 36      setosa
#> 37      setosa
#> 38      setosa
#> 39      setosa
#> 40      setosa
#> 41      setosa
#> 42      setosa
#> 43      setosa
#> 44      setosa
#> 45      setosa
#> 46      setosa
#> 47      setosa
#> 48      setosa
#> 49      setosa
#> 50      setosa
#> 51  versicolor
#> 52  versicolor
#> 53  versicolor
#> 54  versicolor
#> 55  versicolor
#> 56  versicolor
#> 57  versicolor
#> 58  versicolor
#> 59  versicolor
#> 60  versicolor
#> 61  versicolor
#> 62  versicolor
#> 63  versicolor
#> 64  versicolor
#> 65  versicolor
#> 66  versicolor
#> 67  versicolor
#> 68  versicolor
#> 69  versicolor
#> 70  versicolor
#> 71  versicolor
#> 72  versicolor
#> 73  versicolor
#> 74  versicolor
#> 75  versicolor
#> 76  versicolor
#> 77  versicolor
#> 78  versicolor
#> 79  versicolor
#> 80  versicolor
#> 81  versicolor
#> 82  versicolor
#> 83  versicolor
#> 84  versicolor
#> 85  versicolor
#> 86  versicolor
#> 87  versicolor
#> 88  versicolor
#> 89  versicolor
#> 90  versicolor
#> 91  versicolor
#> 92  versicolor
#> 93  versicolor
#> 94  versicolor
#> 95  versicolor
#> 96  versicolor
#> 97  versicolor
#> 98  versicolor
#> 99  versicolor
#> 100 versicolor
#> 101  virginica
#> 102  virginica
#> 103  virginica
#> 104  virginica
#> 105  virginica
#> 106  virginica
#> 107  virginica
#> 108  virginica
#> 109  virginica
#> 110  virginica
#> 111  virginica
#> 112  virginica
#> 113  virginica
#> 114  virginica
#> 115  virginica
#> 116  virginica
#> 117  virginica
#> 118  virginica
#> 119  virginica
#> 120  virginica
#> 121  virginica
#> 122  virginica
#> 123  virginica
#> 124  virginica
#> 125  virginica
#> 126  virginica
#> 127  virginica
#> 128  virginica
#> 129  virginica
#> 130  virginica
#> 131  virginica
#> 132  virginica
#> 133  virginica
#> 134  virginica
#> 135  virginica
#> 136  virginica
#> 137  virginica
#> 138  virginica
#> 139  virginica
#> 140  virginica
#> 141  virginica
#> 142  virginica
#> 143  virginica
#> 144  virginica
#> 145  virginica
#> 146  virginica
#> 147  virginica
#> 148  virginica
#> 149  virginica
#> 150  virginica

p <- picks(
  datasets(is.data.frame, 2L),
  variables(is_categorical(2, 10))
)
resolver(data = list(mtcars = mtcars, iris = iris), x = p)
#>  <picks>
#>    <datasets>:
#>      choices: mtcars, iris
#>      selected: iris
#>      multiple=FALSE, ordered=FALSE, fixed=FALSE
#>    <variables>:
#>      choices: Species
#>      selected: Species
#>      multiple=FALSE, ordered=FALSE, fixed=FALSE, allow-clear=FALSE