shadow_shift transforms missing values to facilitate visualisation, and has different behaviour for different types of variables. For numeric variables, the values are shifted to 10 variable plus some jittered noise, to separate repeated values, so that missing values can be visualised along with the rest of the data.

shadow_shift(x, ...)

Arguments

x

a variable of interest to shift

...

extra arguments to pass

See also

Examples

airquality$Ozone
#> [1] 41 36 12 18 NA 28 23 19 8 NA 7 16 11 14 18 14 34 6 #> [19] 30 11 1 11 4 32 NA NA NA 23 45 115 37 NA NA NA NA NA #> [37] NA 29 NA 71 39 NA NA 23 NA NA 21 37 20 12 13 NA NA NA #> [55] NA NA NA NA NA NA NA 135 49 32 NA 64 40 77 97 97 85 NA #> [73] 10 27 NA 7 48 35 61 79 63 16 NA NA 80 108 20 52 82 50 #> [91] 64 59 39 9 16 78 35 66 122 89 110 NA NA 44 28 65 NA 22 #> [109] 59 23 31 44 21 9 NA 45 168 73 NA 76 118 84 85 96 78 73 #> [127] 91 47 32 20 23 21 24 44 21 28 9 13 46 18 13 24 16 13 #> [145] 23 36 7 14 30 NA 14 18 20
shadow_shift(airquality$Ozone)
#> [1] 41.00000 36.00000 12.00000 18.00000 -19.72321 28.00000 23.00000 #> [8] 19.00000 8.00000 -18.51277 7.00000 16.00000 11.00000 14.00000 #> [15] 18.00000 14.00000 34.00000 6.00000 30.00000 11.00000 1.00000 #> [22] 11.00000 4.00000 32.00000 -17.81863 -19.43853 -15.14310 23.00000 #> [29] 45.00000 115.00000 37.00000 -16.17315 -14.65883 -17.85609 -13.29299 #> [36] -16.16323 -19.60935 29.00000 -19.65780 71.00000 39.00000 -13.40961 #> [43] -13.53728 23.00000 -19.65993 -16.48342 21.00000 37.00000 20.00000 #> [50] 12.00000 13.00000 -17.17718 -16.74073 -13.65786 -16.78786 -12.30098 #> [57] -13.33171 -16.77414 -17.08225 -15.98818 -19.17558 135.00000 49.00000 #> [64] 32.00000 -14.27138 64.00000 40.00000 77.00000 97.00000 97.00000 #> [71] 85.00000 -13.51764 10.00000 27.00000 -13.48998 7.00000 48.00000 #> [78] 35.00000 61.00000 79.00000 63.00000 16.00000 -16.92150 -16.60335 #> [85] 80.00000 108.00000 20.00000 52.00000 82.00000 50.00000 64.00000 #> [92] 59.00000 39.00000 9.00000 16.00000 78.00000 35.00000 66.00000 #> [99] 122.00000 89.00000 110.00000 -14.78907 -16.19151 44.00000 28.00000 #> [106] 65.00000 -19.73591 22.00000 59.00000 23.00000 31.00000 44.00000 #> [113] 21.00000 9.00000 -18.92235 45.00000 168.00000 73.00000 -14.86296 #> [120] 76.00000 118.00000 84.00000 85.00000 96.00000 78.00000 73.00000 #> [127] 91.00000 47.00000 32.00000 20.00000 23.00000 21.00000 24.00000 #> [134] 44.00000 21.00000 28.00000 9.00000 13.00000 46.00000 18.00000 #> [141] 13.00000 24.00000 16.00000 13.00000 23.00000 36.00000 7.00000 #> [148] 14.00000 30.00000 -14.83089 14.00000 18.00000 20.00000
library(dplyr) airquality %>% mutate(Ozone_shift = shadow_shift(Ozone))
#> Ozone Solar.R Wind Temp Month Day Ozone_shift #> 1 41 190 7.4 67 5 1 41.00000 #> 2 36 118 8.0 72 5 2 36.00000 #> 3 12 149 12.6 74 5 3 12.00000 #> 4 18 313 11.5 62 5 4 18.00000 #> 5 NA NA 14.3 56 5 5 -19.72321 #> 6 28 NA 14.9 66 5 6 28.00000 #> 7 23 299 8.6 65 5 7 23.00000 #> 8 19 99 13.8 59 5 8 19.00000 #> 9 8 19 20.1 61 5 9 8.00000 #> 10 NA 194 8.6 69 5 10 -18.51277 #> 11 7 NA 6.9 74 5 11 7.00000 #> 12 16 256 9.7 69 5 12 16.00000 #> 13 11 290 9.2 66 5 13 11.00000 #> 14 14 274 10.9 68 5 14 14.00000 #> 15 18 65 13.2 58 5 15 18.00000 #> 16 14 334 11.5 64 5 16 14.00000 #> 17 34 307 12.0 66 5 17 34.00000 #> 18 6 78 18.4 57 5 18 6.00000 #> 19 30 322 11.5 68 5 19 30.00000 #> 20 11 44 9.7 62 5 20 11.00000 #> 21 1 8 9.7 59 5 21 1.00000 #> 22 11 320 16.6 73 5 22 11.00000 #> 23 4 25 9.7 61 5 23 4.00000 #> 24 32 92 12.0 61 5 24 32.00000 #> 25 NA 66 16.6 57 5 25 -17.81863 #> 26 NA 266 14.9 58 5 26 -19.43853 #> 27 NA NA 8.0 57 5 27 -15.14310 #> 28 23 13 12.0 67 5 28 23.00000 #> 29 45 252 14.9 81 5 29 45.00000 #> 30 115 223 5.7 79 5 30 115.00000 #> 31 37 279 7.4 76 5 31 37.00000 #> 32 NA 286 8.6 78 6 1 -16.17315 #> 33 NA 287 9.7 74 6 2 -14.65883 #> 34 NA 242 16.1 67 6 3 -17.85609 #> 35 NA 186 9.2 84 6 4 -13.29299 #> 36 NA 220 8.6 85 6 5 -16.16323 #> 37 NA 264 14.3 79 6 6 -19.60935 #> 38 29 127 9.7 82 6 7 29.00000 #> 39 NA 273 6.9 87 6 8 -19.65780 #> 40 71 291 13.8 90 6 9 71.00000 #> 41 39 323 11.5 87 6 10 39.00000 #> 42 NA 259 10.9 93 6 11 -13.40961 #> 43 NA 250 9.2 92 6 12 -13.53728 #> 44 23 148 8.0 82 6 13 23.00000 #> 45 NA 332 13.8 80 6 14 -19.65993 #> 46 NA 322 11.5 79 6 15 -16.48342 #> 47 21 191 14.9 77 6 16 21.00000 #> 48 37 284 20.7 72 6 17 37.00000 #> 49 20 37 9.2 65 6 18 20.00000 #> 50 12 120 11.5 73 6 19 12.00000 #> 51 13 137 10.3 76 6 20 13.00000 #> 52 NA 150 6.3 77 6 21 -17.17718 #> 53 NA 59 1.7 76 6 22 -16.74073 #> 54 NA 91 4.6 76 6 23 -13.65786 #> 55 NA 250 6.3 76 6 24 -16.78786 #> 56 NA 135 8.0 75 6 25 -12.30098 #> 57 NA 127 8.0 78 6 26 -13.33171 #> 58 NA 47 10.3 73 6 27 -16.77414 #> 59 NA 98 11.5 80 6 28 -17.08225 #> 60 NA 31 14.9 77 6 29 -15.98818 #> 61 NA 138 8.0 83 6 30 -19.17558 #> 62 135 269 4.1 84 7 1 135.00000 #> 63 49 248 9.2 85 7 2 49.00000 #> 64 32 236 9.2 81 7 3 32.00000 #> 65 NA 101 10.9 84 7 4 -14.27138 #> 66 64 175 4.6 83 7 5 64.00000 #> 67 40 314 10.9 83 7 6 40.00000 #> 68 77 276 5.1 88 7 7 77.00000 #> 69 97 267 6.3 92 7 8 97.00000 #> 70 97 272 5.7 92 7 9 97.00000 #> 71 85 175 7.4 89 7 10 85.00000 #> 72 NA 139 8.6 82 7 11 -13.51764 #> 73 10 264 14.3 73 7 12 10.00000 #> 74 27 175 14.9 81 7 13 27.00000 #> 75 NA 291 14.9 91 7 14 -13.48998 #> 76 7 48 14.3 80 7 15 7.00000 #> 77 48 260 6.9 81 7 16 48.00000 #> 78 35 274 10.3 82 7 17 35.00000 #> 79 61 285 6.3 84 7 18 61.00000 #> 80 79 187 5.1 87 7 19 79.00000 #> 81 63 220 11.5 85 7 20 63.00000 #> 82 16 7 6.9 74 7 21 16.00000 #> 83 NA 258 9.7 81 7 22 -16.92150 #> 84 NA 295 11.5 82 7 23 -16.60335 #> 85 80 294 8.6 86 7 24 80.00000 #> 86 108 223 8.0 85 7 25 108.00000 #> 87 20 81 8.6 82 7 26 20.00000 #> 88 52 82 12.0 86 7 27 52.00000 #> 89 82 213 7.4 88 7 28 82.00000 #> 90 50 275 7.4 86 7 29 50.00000 #> 91 64 253 7.4 83 7 30 64.00000 #> 92 59 254 9.2 81 7 31 59.00000 #> 93 39 83 6.9 81 8 1 39.00000 #> 94 9 24 13.8 81 8 2 9.00000 #> 95 16 77 7.4 82 8 3 16.00000 #> 96 78 NA 6.9 86 8 4 78.00000 #> 97 35 NA 7.4 85 8 5 35.00000 #> 98 66 NA 4.6 87 8 6 66.00000 #> 99 122 255 4.0 89 8 7 122.00000 #> 100 89 229 10.3 90 8 8 89.00000 #> 101 110 207 8.0 90 8 9 110.00000 #> 102 NA 222 8.6 92 8 10 -14.78907 #> 103 NA 137 11.5 86 8 11 -16.19151 #> 104 44 192 11.5 86 8 12 44.00000 #> 105 28 273 11.5 82 8 13 28.00000 #> 106 65 157 9.7 80 8 14 65.00000 #> 107 NA 64 11.5 79 8 15 -19.73591 #> 108 22 71 10.3 77 8 16 22.00000 #> 109 59 51 6.3 79 8 17 59.00000 #> 110 23 115 7.4 76 8 18 23.00000 #> 111 31 244 10.9 78 8 19 31.00000 #> 112 44 190 10.3 78 8 20 44.00000 #> 113 21 259 15.5 77 8 21 21.00000 #> 114 9 36 14.3 72 8 22 9.00000 #> 115 NA 255 12.6 75 8 23 -18.92235 #> 116 45 212 9.7 79 8 24 45.00000 #> 117 168 238 3.4 81 8 25 168.00000 #> 118 73 215 8.0 86 8 26 73.00000 #> 119 NA 153 5.7 88 8 27 -14.86296 #> 120 76 203 9.7 97 8 28 76.00000 #> 121 118 225 2.3 94 8 29 118.00000 #> 122 84 237 6.3 96 8 30 84.00000 #> 123 85 188 6.3 94 8 31 85.00000 #> 124 96 167 6.9 91 9 1 96.00000 #> 125 78 197 5.1 92 9 2 78.00000 #> 126 73 183 2.8 93 9 3 73.00000 #> 127 91 189 4.6 93 9 4 91.00000 #> 128 47 95 7.4 87 9 5 47.00000 #> 129 32 92 15.5 84 9 6 32.00000 #> 130 20 252 10.9 80 9 7 20.00000 #> 131 23 220 10.3 78 9 8 23.00000 #> 132 21 230 10.9 75 9 9 21.00000 #> 133 24 259 9.7 73 9 10 24.00000 #> 134 44 236 14.9 81 9 11 44.00000 #> 135 21 259 15.5 76 9 12 21.00000 #> 136 28 238 6.3 77 9 13 28.00000 #> 137 9 24 10.9 71 9 14 9.00000 #> 138 13 112 11.5 71 9 15 13.00000 #> 139 46 237 6.9 78 9 16 46.00000 #> 140 18 224 13.8 67 9 17 18.00000 #> 141 13 27 10.3 76 9 18 13.00000 #> 142 24 238 10.3 68 9 19 24.00000 #> 143 16 201 8.0 82 9 20 16.00000 #> 144 13 238 12.6 64 9 21 13.00000 #> 145 23 14 9.2 71 9 22 23.00000 #> 146 36 139 10.3 81 9 23 36.00000 #> 147 7 49 10.3 69 9 24 7.00000 #> 148 14 20 16.6 63 9 25 14.00000 #> 149 30 193 6.9 70 9 26 30.00000 #> 150 NA 145 13.2 77 9 27 -14.83089 #> 151 14 191 14.3 75 9 28 14.00000 #> 152 18 131 8.0 76 9 29 18.00000 #> 153 20 223 11.5 68 9 30 20.00000