Creates a character vector describing presence/absence of missing values

label_missings(data)

Arguments

data

a dataframe or set of vectors of the same length

Value

character vector of "Missing" and "Not Missing".

See also

Examples

label_missings(airquality)
#> [1] "Not Missing" "Not Missing" "Not Missing" "Not Missing" "Missing" #> [6] "Missing" "Not Missing" "Not Missing" "Not Missing" "Missing" #> [11] "Missing" "Not Missing" "Not Missing" "Not Missing" "Not Missing" #> [16] "Not Missing" "Not Missing" "Not Missing" "Not Missing" "Not Missing" #> [21] "Not Missing" "Not Missing" "Not Missing" "Not Missing" "Missing" #> [26] "Missing" "Missing" "Not Missing" "Not Missing" "Not Missing" #> [31] "Not Missing" "Missing" "Missing" "Missing" "Missing" #> [36] "Missing" "Missing" "Not Missing" "Missing" "Not Missing" #> [41] "Not Missing" "Missing" "Missing" "Not Missing" "Missing" #> [46] "Missing" "Not Missing" "Not Missing" "Not Missing" "Not Missing" #> [51] "Not Missing" "Missing" "Missing" "Missing" "Missing" #> [56] "Missing" "Missing" "Missing" "Missing" "Missing" #> [61] "Missing" "Not Missing" "Not Missing" "Not Missing" "Missing" #> [66] "Not Missing" "Not Missing" "Not Missing" "Not Missing" "Not Missing" #> [71] "Not Missing" "Missing" "Not Missing" "Not Missing" "Missing" #> [76] "Not Missing" "Not Missing" "Not Missing" "Not Missing" "Not Missing" #> [81] "Not Missing" "Not Missing" "Missing" "Missing" "Not Missing" #> [86] "Not Missing" "Not Missing" "Not Missing" "Not Missing" "Not Missing" #> [91] "Not Missing" "Not Missing" "Not Missing" "Not Missing" "Not Missing" #> [96] "Missing" "Missing" "Missing" "Not Missing" "Not Missing" #> [101] "Not Missing" "Missing" "Missing" "Not Missing" "Not Missing" #> [106] "Not Missing" "Missing" "Not Missing" "Not Missing" "Not Missing" #> [111] "Not Missing" "Not Missing" "Not Missing" "Not Missing" "Missing" #> [116] "Not Missing" "Not Missing" "Not Missing" "Missing" "Not Missing" #> [121] "Not Missing" "Not Missing" "Not Missing" "Not Missing" "Not Missing" #> [126] "Not Missing" "Not Missing" "Not Missing" "Not Missing" "Not Missing" #> [131] "Not Missing" "Not Missing" "Not Missing" "Not Missing" "Not Missing" #> [136] "Not Missing" "Not Missing" "Not Missing" "Not Missing" "Not Missing" #> [141] "Not Missing" "Not Missing" "Not Missing" "Not Missing" "Not Missing" #> [146] "Not Missing" "Not Missing" "Not Missing" "Not Missing" "Missing" #> [151] "Not Missing" "Not Missing" "Not Missing"
library(dplyr) airquality %>% mutate(is_missing = label_missings(airquality))
#> Ozone Solar.R Wind Temp Month Day is_missing #> 1 41 190 7.4 67 5 1 Not Missing #> 2 36 118 8.0 72 5 2 Not Missing #> 3 12 149 12.6 74 5 3 Not Missing #> 4 18 313 11.5 62 5 4 Not Missing #> 5 NA NA 14.3 56 5 5 Missing #> 6 28 NA 14.9 66 5 6 Missing #> 7 23 299 8.6 65 5 7 Not Missing #> 8 19 99 13.8 59 5 8 Not Missing #> 9 8 19 20.1 61 5 9 Not Missing #> 10 NA 194 8.6 69 5 10 Missing #> 11 7 NA 6.9 74 5 11 Missing #> 12 16 256 9.7 69 5 12 Not Missing #> 13 11 290 9.2 66 5 13 Not Missing #> 14 14 274 10.9 68 5 14 Not Missing #> 15 18 65 13.2 58 5 15 Not Missing #> 16 14 334 11.5 64 5 16 Not Missing #> 17 34 307 12.0 66 5 17 Not Missing #> 18 6 78 18.4 57 5 18 Not Missing #> 19 30 322 11.5 68 5 19 Not Missing #> 20 11 44 9.7 62 5 20 Not Missing #> 21 1 8 9.7 59 5 21 Not Missing #> 22 11 320 16.6 73 5 22 Not Missing #> 23 4 25 9.7 61 5 23 Not Missing #> 24 32 92 12.0 61 5 24 Not Missing #> 25 NA 66 16.6 57 5 25 Missing #> 26 NA 266 14.9 58 5 26 Missing #> 27 NA NA 8.0 57 5 27 Missing #> 28 23 13 12.0 67 5 28 Not Missing #> 29 45 252 14.9 81 5 29 Not Missing #> 30 115 223 5.7 79 5 30 Not Missing #> 31 37 279 7.4 76 5 31 Not Missing #> 32 NA 286 8.6 78 6 1 Missing #> 33 NA 287 9.7 74 6 2 Missing #> 34 NA 242 16.1 67 6 3 Missing #> 35 NA 186 9.2 84 6 4 Missing #> 36 NA 220 8.6 85 6 5 Missing #> 37 NA 264 14.3 79 6 6 Missing #> 38 29 127 9.7 82 6 7 Not Missing #> 39 NA 273 6.9 87 6 8 Missing #> 40 71 291 13.8 90 6 9 Not Missing #> 41 39 323 11.5 87 6 10 Not Missing #> 42 NA 259 10.9 93 6 11 Missing #> 43 NA 250 9.2 92 6 12 Missing #> 44 23 148 8.0 82 6 13 Not Missing #> 45 NA 332 13.8 80 6 14 Missing #> 46 NA 322 11.5 79 6 15 Missing #> 47 21 191 14.9 77 6 16 Not Missing #> 48 37 284 20.7 72 6 17 Not Missing #> 49 20 37 9.2 65 6 18 Not Missing #> 50 12 120 11.5 73 6 19 Not Missing #> 51 13 137 10.3 76 6 20 Not Missing #> 52 NA 150 6.3 77 6 21 Missing #> 53 NA 59 1.7 76 6 22 Missing #> 54 NA 91 4.6 76 6 23 Missing #> 55 NA 250 6.3 76 6 24 Missing #> 56 NA 135 8.0 75 6 25 Missing #> 57 NA 127 8.0 78 6 26 Missing #> 58 NA 47 10.3 73 6 27 Missing #> 59 NA 98 11.5 80 6 28 Missing #> 60 NA 31 14.9 77 6 29 Missing #> 61 NA 138 8.0 83 6 30 Missing #> 62 135 269 4.1 84 7 1 Not Missing #> 63 49 248 9.2 85 7 2 Not Missing #> 64 32 236 9.2 81 7 3 Not Missing #> 65 NA 101 10.9 84 7 4 Missing #> 66 64 175 4.6 83 7 5 Not Missing #> 67 40 314 10.9 83 7 6 Not Missing #> 68 77 276 5.1 88 7 7 Not Missing #> 69 97 267 6.3 92 7 8 Not Missing #> 70 97 272 5.7 92 7 9 Not Missing #> 71 85 175 7.4 89 7 10 Not Missing #> 72 NA 139 8.6 82 7 11 Missing #> 73 10 264 14.3 73 7 12 Not Missing #> 74 27 175 14.9 81 7 13 Not Missing #> 75 NA 291 14.9 91 7 14 Missing #> 76 7 48 14.3 80 7 15 Not Missing #> 77 48 260 6.9 81 7 16 Not Missing #> 78 35 274 10.3 82 7 17 Not Missing #> 79 61 285 6.3 84 7 18 Not Missing #> 80 79 187 5.1 87 7 19 Not Missing #> 81 63 220 11.5 85 7 20 Not Missing #> 82 16 7 6.9 74 7 21 Not Missing #> 83 NA 258 9.7 81 7 22 Missing #> 84 NA 295 11.5 82 7 23 Missing #> 85 80 294 8.6 86 7 24 Not Missing #> 86 108 223 8.0 85 7 25 Not Missing #> 87 20 81 8.6 82 7 26 Not Missing #> 88 52 82 12.0 86 7 27 Not Missing #> 89 82 213 7.4 88 7 28 Not Missing #> 90 50 275 7.4 86 7 29 Not Missing #> 91 64 253 7.4 83 7 30 Not Missing #> 92 59 254 9.2 81 7 31 Not Missing #> 93 39 83 6.9 81 8 1 Not Missing #> 94 9 24 13.8 81 8 2 Not Missing #> 95 16 77 7.4 82 8 3 Not Missing #> 96 78 NA 6.9 86 8 4 Missing #> 97 35 NA 7.4 85 8 5 Missing #> 98 66 NA 4.6 87 8 6 Missing #> 99 122 255 4.0 89 8 7 Not Missing #> 100 89 229 10.3 90 8 8 Not Missing #> 101 110 207 8.0 90 8 9 Not Missing #> 102 NA 222 8.6 92 8 10 Missing #> 103 NA 137 11.5 86 8 11 Missing #> 104 44 192 11.5 86 8 12 Not Missing #> 105 28 273 11.5 82 8 13 Not Missing #> 106 65 157 9.7 80 8 14 Not Missing #> 107 NA 64 11.5 79 8 15 Missing #> 108 22 71 10.3 77 8 16 Not Missing #> 109 59 51 6.3 79 8 17 Not Missing #> 110 23 115 7.4 76 8 18 Not Missing #> 111 31 244 10.9 78 8 19 Not Missing #> 112 44 190 10.3 78 8 20 Not Missing #> 113 21 259 15.5 77 8 21 Not Missing #> 114 9 36 14.3 72 8 22 Not Missing #> 115 NA 255 12.6 75 8 23 Missing #> 116 45 212 9.7 79 8 24 Not Missing #> 117 168 238 3.4 81 8 25 Not Missing #> 118 73 215 8.0 86 8 26 Not Missing #> 119 NA 153 5.7 88 8 27 Missing #> 120 76 203 9.7 97 8 28 Not Missing #> 121 118 225 2.3 94 8 29 Not Missing #> 122 84 237 6.3 96 8 30 Not Missing #> 123 85 188 6.3 94 8 31 Not Missing #> 124 96 167 6.9 91 9 1 Not Missing #> 125 78 197 5.1 92 9 2 Not Missing #> 126 73 183 2.8 93 9 3 Not Missing #> 127 91 189 4.6 93 9 4 Not Missing #> 128 47 95 7.4 87 9 5 Not Missing #> 129 32 92 15.5 84 9 6 Not Missing #> 130 20 252 10.9 80 9 7 Not Missing #> 131 23 220 10.3 78 9 8 Not Missing #> 132 21 230 10.9 75 9 9 Not Missing #> 133 24 259 9.7 73 9 10 Not Missing #> 134 44 236 14.9 81 9 11 Not Missing #> 135 21 259 15.5 76 9 12 Not Missing #> 136 28 238 6.3 77 9 13 Not Missing #> 137 9 24 10.9 71 9 14 Not Missing #> 138 13 112 11.5 71 9 15 Not Missing #> 139 46 237 6.9 78 9 16 Not Missing #> 140 18 224 13.8 67 9 17 Not Missing #> 141 13 27 10.3 76 9 18 Not Missing #> 142 24 238 10.3 68 9 19 Not Missing #> 143 16 201 8.0 82 9 20 Not Missing #> 144 13 238 12.6 64 9 21 Not Missing #> 145 23 14 9.2 71 9 22 Not Missing #> 146 36 139 10.3 81 9 23 Not Missing #> 147 7 49 10.3 69 9 24 Not Missing #> 148 14 20 16.6 63 9 25 Not Missing #> 149 30 193 6.9 70 9 26 Not Missing #> 150 NA 145 13.2 77 9 27 Missing #> 151 14 191 14.3 75 9 28 Not Missing #> 152 18 131 8.0 76 9 29 Not Missing #> 153 20 223 11.5 68 9 30 Not Missing