impute_median imputes the median for a vector. To only impute many
variables at once, we recommend that you use the across function
workflow, shown in the examples for impute_median(). You can use the
scoped variants, impute_median_all.impute_below_at, and
impute_below_if to impute all, some, or just those variables meeting
some condition, respectively. To use _at effectively, you must know
that _at affects variables selected with a character vector, or with
vars().
![[Superseded]](figures/lifecycle-superseded.svg)
Examples
# select variables starting with a particular string.
impute_median_all(airquality)
#> Ozone Solar.R Wind Temp Month Day
#> 1 41.0 190 7.4 67 5 1
#> 2 36.0 118 8.0 72 5 2
#> 3 12.0 149 12.6 74 5 3
#> 4 18.0 313 11.5 62 5 4
#> 5 31.5 205 14.3 56 5 5
#> 6 28.0 205 14.9 66 5 6
#> 7 23.0 299 8.6 65 5 7
#> 8 19.0 99 13.8 59 5 8
#> 9 8.0 19 20.1 61 5 9
#> 10 31.5 194 8.6 69 5 10
#> 11 7.0 205 6.9 74 5 11
#> 12 16.0 256 9.7 69 5 12
#> 13 11.0 290 9.2 66 5 13
#> 14 14.0 274 10.9 68 5 14
#> 15 18.0 65 13.2 58 5 15
#> 16 14.0 334 11.5 64 5 16
#> 17 34.0 307 12.0 66 5 17
#> 18 6.0 78 18.4 57 5 18
#> 19 30.0 322 11.5 68 5 19
#> 20 11.0 44 9.7 62 5 20
#> 21 1.0 8 9.7 59 5 21
#> 22 11.0 320 16.6 73 5 22
#> 23 4.0 25 9.7 61 5 23
#> 24 32.0 92 12.0 61 5 24
#> 25 31.5 66 16.6 57 5 25
#> 26 31.5 266 14.9 58 5 26
#> 27 31.5 205 8.0 57 5 27
#> 28 23.0 13 12.0 67 5 28
#> 29 45.0 252 14.9 81 5 29
#> 30 115.0 223 5.7 79 5 30
#> 31 37.0 279 7.4 76 5 31
#> 32 31.5 286 8.6 78 6 1
#> 33 31.5 287 9.7 74 6 2
#> 34 31.5 242 16.1 67 6 3
#> 35 31.5 186 9.2 84 6 4
#> 36 31.5 220 8.6 85 6 5
#> 37 31.5 264 14.3 79 6 6
#> 38 29.0 127 9.7 82 6 7
#> 39 31.5 273 6.9 87 6 8
#> 40 71.0 291 13.8 90 6 9
#> 41 39.0 323 11.5 87 6 10
#> 42 31.5 259 10.9 93 6 11
#> 43 31.5 250 9.2 92 6 12
#> 44 23.0 148 8.0 82 6 13
#> 45 31.5 332 13.8 80 6 14
#> 46 31.5 322 11.5 79 6 15
#> 47 21.0 191 14.9 77 6 16
#> 48 37.0 284 20.7 72 6 17
#> 49 20.0 37 9.2 65 6 18
#> 50 12.0 120 11.5 73 6 19
#> 51 13.0 137 10.3 76 6 20
#> 52 31.5 150 6.3 77 6 21
#> 53 31.5 59 1.7 76 6 22
#> 54 31.5 91 4.6 76 6 23
#> 55 31.5 250 6.3 76 6 24
#> 56 31.5 135 8.0 75 6 25
#> 57 31.5 127 8.0 78 6 26
#> 58 31.5 47 10.3 73 6 27
#> 59 31.5 98 11.5 80 6 28
#> 60 31.5 31 14.9 77 6 29
#> 61 31.5 138 8.0 83 6 30
#> 62 135.0 269 4.1 84 7 1
#> 63 49.0 248 9.2 85 7 2
#> 64 32.0 236 9.2 81 7 3
#> 65 31.5 101 10.9 84 7 4
#> 66 64.0 175 4.6 83 7 5
#> 67 40.0 314 10.9 83 7 6
#> 68 77.0 276 5.1 88 7 7
#> 69 97.0 267 6.3 92 7 8
#> 70 97.0 272 5.7 92 7 9
#> 71 85.0 175 7.4 89 7 10
#> 72 31.5 139 8.6 82 7 11
#> 73 10.0 264 14.3 73 7 12
#> 74 27.0 175 14.9 81 7 13
#> 75 31.5 291 14.9 91 7 14
#> 76 7.0 48 14.3 80 7 15
#> 77 48.0 260 6.9 81 7 16
#> 78 35.0 274 10.3 82 7 17
#> 79 61.0 285 6.3 84 7 18
#> 80 79.0 187 5.1 87 7 19
#> 81 63.0 220 11.5 85 7 20
#> 82 16.0 7 6.9 74 7 21
#> 83 31.5 258 9.7 81 7 22
#> 84 31.5 295 11.5 82 7 23
#> 85 80.0 294 8.6 86 7 24
#> 86 108.0 223 8.0 85 7 25
#> 87 20.0 81 8.6 82 7 26
#> 88 52.0 82 12.0 86 7 27
#> 89 82.0 213 7.4 88 7 28
#> 90 50.0 275 7.4 86 7 29
#> 91 64.0 253 7.4 83 7 30
#> 92 59.0 254 9.2 81 7 31
#> 93 39.0 83 6.9 81 8 1
#> 94 9.0 24 13.8 81 8 2
#> 95 16.0 77 7.4 82 8 3
#> 96 78.0 205 6.9 86 8 4
#> 97 35.0 205 7.4 85 8 5
#> 98 66.0 205 4.6 87 8 6
#> 99 122.0 255 4.0 89 8 7
#> 100 89.0 229 10.3 90 8 8
#> 101 110.0 207 8.0 90 8 9
#> 102 31.5 222 8.6 92 8 10
#> 103 31.5 137 11.5 86 8 11
#> 104 44.0 192 11.5 86 8 12
#> 105 28.0 273 11.5 82 8 13
#> 106 65.0 157 9.7 80 8 14
#> 107 31.5 64 11.5 79 8 15
#> 108 22.0 71 10.3 77 8 16
#> 109 59.0 51 6.3 79 8 17
#> 110 23.0 115 7.4 76 8 18
#> 111 31.0 244 10.9 78 8 19
#> 112 44.0 190 10.3 78 8 20
#> 113 21.0 259 15.5 77 8 21
#> 114 9.0 36 14.3 72 8 22
#> 115 31.5 255 12.6 75 8 23
#> 116 45.0 212 9.7 79 8 24
#> 117 168.0 238 3.4 81 8 25
#> 118 73.0 215 8.0 86 8 26
#> 119 31.5 153 5.7 88 8 27
#> 120 76.0 203 9.7 97 8 28
#> 121 118.0 225 2.3 94 8 29
#> 122 84.0 237 6.3 96 8 30
#> 123 85.0 188 6.3 94 8 31
#> 124 96.0 167 6.9 91 9 1
#> 125 78.0 197 5.1 92 9 2
#> 126 73.0 183 2.8 93 9 3
#> 127 91.0 189 4.6 93 9 4
#> 128 47.0 95 7.4 87 9 5
#> 129 32.0 92 15.5 84 9 6
#> 130 20.0 252 10.9 80 9 7
#> 131 23.0 220 10.3 78 9 8
#> 132 21.0 230 10.9 75 9 9
#> 133 24.0 259 9.7 73 9 10
#> 134 44.0 236 14.9 81 9 11
#> 135 21.0 259 15.5 76 9 12
#> 136 28.0 238 6.3 77 9 13
#> 137 9.0 24 10.9 71 9 14
#> 138 13.0 112 11.5 71 9 15
#> 139 46.0 237 6.9 78 9 16
#> 140 18.0 224 13.8 67 9 17
#> 141 13.0 27 10.3 76 9 18
#> 142 24.0 238 10.3 68 9 19
#> 143 16.0 201 8.0 82 9 20
#> 144 13.0 238 12.6 64 9 21
#> 145 23.0 14 9.2 71 9 22
#> 146 36.0 139 10.3 81 9 23
#> 147 7.0 49 10.3 69 9 24
#> 148 14.0 20 16.6 63 9 25
#> 149 30.0 193 6.9 70 9 26
#> 150 31.5 145 13.2 77 9 27
#> 151 14.0 191 14.3 75 9 28
#> 152 18.0 131 8.0 76 9 29
#> 153 20.0 223 11.5 68 9 30
impute_median_at(airquality,
.vars = c("Ozone", "Solar.R"))
#> Ozone Solar.R Wind Temp Month Day
#> 1 41.0 190 7.4 67 5 1
#> 2 36.0 118 8.0 72 5 2
#> 3 12.0 149 12.6 74 5 3
#> 4 18.0 313 11.5 62 5 4
#> 5 31.5 205 14.3 56 5 5
#> 6 28.0 205 14.9 66 5 6
#> 7 23.0 299 8.6 65 5 7
#> 8 19.0 99 13.8 59 5 8
#> 9 8.0 19 20.1 61 5 9
#> 10 31.5 194 8.6 69 5 10
#> 11 7.0 205 6.9 74 5 11
#> 12 16.0 256 9.7 69 5 12
#> 13 11.0 290 9.2 66 5 13
#> 14 14.0 274 10.9 68 5 14
#> 15 18.0 65 13.2 58 5 15
#> 16 14.0 334 11.5 64 5 16
#> 17 34.0 307 12.0 66 5 17
#> 18 6.0 78 18.4 57 5 18
#> 19 30.0 322 11.5 68 5 19
#> 20 11.0 44 9.7 62 5 20
#> 21 1.0 8 9.7 59 5 21
#> 22 11.0 320 16.6 73 5 22
#> 23 4.0 25 9.7 61 5 23
#> 24 32.0 92 12.0 61 5 24
#> 25 31.5 66 16.6 57 5 25
#> 26 31.5 266 14.9 58 5 26
#> 27 31.5 205 8.0 57 5 27
#> 28 23.0 13 12.0 67 5 28
#> 29 45.0 252 14.9 81 5 29
#> 30 115.0 223 5.7 79 5 30
#> 31 37.0 279 7.4 76 5 31
#> 32 31.5 286 8.6 78 6 1
#> 33 31.5 287 9.7 74 6 2
#> 34 31.5 242 16.1 67 6 3
#> 35 31.5 186 9.2 84 6 4
#> 36 31.5 220 8.6 85 6 5
#> 37 31.5 264 14.3 79 6 6
#> 38 29.0 127 9.7 82 6 7
#> 39 31.5 273 6.9 87 6 8
#> 40 71.0 291 13.8 90 6 9
#> 41 39.0 323 11.5 87 6 10
#> 42 31.5 259 10.9 93 6 11
#> 43 31.5 250 9.2 92 6 12
#> 44 23.0 148 8.0 82 6 13
#> 45 31.5 332 13.8 80 6 14
#> 46 31.5 322 11.5 79 6 15
#> 47 21.0 191 14.9 77 6 16
#> 48 37.0 284 20.7 72 6 17
#> 49 20.0 37 9.2 65 6 18
#> 50 12.0 120 11.5 73 6 19
#> 51 13.0 137 10.3 76 6 20
#> 52 31.5 150 6.3 77 6 21
#> 53 31.5 59 1.7 76 6 22
#> 54 31.5 91 4.6 76 6 23
#> 55 31.5 250 6.3 76 6 24
#> 56 31.5 135 8.0 75 6 25
#> 57 31.5 127 8.0 78 6 26
#> 58 31.5 47 10.3 73 6 27
#> 59 31.5 98 11.5 80 6 28
#> 60 31.5 31 14.9 77 6 29
#> 61 31.5 138 8.0 83 6 30
#> 62 135.0 269 4.1 84 7 1
#> 63 49.0 248 9.2 85 7 2
#> 64 32.0 236 9.2 81 7 3
#> 65 31.5 101 10.9 84 7 4
#> 66 64.0 175 4.6 83 7 5
#> 67 40.0 314 10.9 83 7 6
#> 68 77.0 276 5.1 88 7 7
#> 69 97.0 267 6.3 92 7 8
#> 70 97.0 272 5.7 92 7 9
#> 71 85.0 175 7.4 89 7 10
#> 72 31.5 139 8.6 82 7 11
#> 73 10.0 264 14.3 73 7 12
#> 74 27.0 175 14.9 81 7 13
#> 75 31.5 291 14.9 91 7 14
#> 76 7.0 48 14.3 80 7 15
#> 77 48.0 260 6.9 81 7 16
#> 78 35.0 274 10.3 82 7 17
#> 79 61.0 285 6.3 84 7 18
#> 80 79.0 187 5.1 87 7 19
#> 81 63.0 220 11.5 85 7 20
#> 82 16.0 7 6.9 74 7 21
#> 83 31.5 258 9.7 81 7 22
#> 84 31.5 295 11.5 82 7 23
#> 85 80.0 294 8.6 86 7 24
#> 86 108.0 223 8.0 85 7 25
#> 87 20.0 81 8.6 82 7 26
#> 88 52.0 82 12.0 86 7 27
#> 89 82.0 213 7.4 88 7 28
#> 90 50.0 275 7.4 86 7 29
#> 91 64.0 253 7.4 83 7 30
#> 92 59.0 254 9.2 81 7 31
#> 93 39.0 83 6.9 81 8 1
#> 94 9.0 24 13.8 81 8 2
#> 95 16.0 77 7.4 82 8 3
#> 96 78.0 205 6.9 86 8 4
#> 97 35.0 205 7.4 85 8 5
#> 98 66.0 205 4.6 87 8 6
#> 99 122.0 255 4.0 89 8 7
#> 100 89.0 229 10.3 90 8 8
#> 101 110.0 207 8.0 90 8 9
#> 102 31.5 222 8.6 92 8 10
#> 103 31.5 137 11.5 86 8 11
#> 104 44.0 192 11.5 86 8 12
#> 105 28.0 273 11.5 82 8 13
#> 106 65.0 157 9.7 80 8 14
#> 107 31.5 64 11.5 79 8 15
#> 108 22.0 71 10.3 77 8 16
#> 109 59.0 51 6.3 79 8 17
#> 110 23.0 115 7.4 76 8 18
#> 111 31.0 244 10.9 78 8 19
#> 112 44.0 190 10.3 78 8 20
#> 113 21.0 259 15.5 77 8 21
#> 114 9.0 36 14.3 72 8 22
#> 115 31.5 255 12.6 75 8 23
#> 116 45.0 212 9.7 79 8 24
#> 117 168.0 238 3.4 81 8 25
#> 118 73.0 215 8.0 86 8 26
#> 119 31.5 153 5.7 88 8 27
#> 120 76.0 203 9.7 97 8 28
#> 121 118.0 225 2.3 94 8 29
#> 122 84.0 237 6.3 96 8 30
#> 123 85.0 188 6.3 94 8 31
#> 124 96.0 167 6.9 91 9 1
#> 125 78.0 197 5.1 92 9 2
#> 126 73.0 183 2.8 93 9 3
#> 127 91.0 189 4.6 93 9 4
#> 128 47.0 95 7.4 87 9 5
#> 129 32.0 92 15.5 84 9 6
#> 130 20.0 252 10.9 80 9 7
#> 131 23.0 220 10.3 78 9 8
#> 132 21.0 230 10.9 75 9 9
#> 133 24.0 259 9.7 73 9 10
#> 134 44.0 236 14.9 81 9 11
#> 135 21.0 259 15.5 76 9 12
#> 136 28.0 238 6.3 77 9 13
#> 137 9.0 24 10.9 71 9 14
#> 138 13.0 112 11.5 71 9 15
#> 139 46.0 237 6.9 78 9 16
#> 140 18.0 224 13.8 67 9 17
#> 141 13.0 27 10.3 76 9 18
#> 142 24.0 238 10.3 68 9 19
#> 143 16.0 201 8.0 82 9 20
#> 144 13.0 238 12.6 64 9 21
#> 145 23.0 14 9.2 71 9 22
#> 146 36.0 139 10.3 81 9 23
#> 147 7.0 49 10.3 69 9 24
#> 148 14.0 20 16.6 63 9 25
#> 149 30.0 193 6.9 70 9 26
#> 150 31.5 145 13.2 77 9 27
#> 151 14.0 191 14.3 75 9 28
#> 152 18.0 131 8.0 76 9 29
#> 153 20.0 223 11.5 68 9 30
library(dplyr)
impute_median_at(airquality,
.vars = vars(Ozone))
#> Ozone Solar.R Wind Temp Month Day
#> 1 41.0 190 7.4 67 5 1
#> 2 36.0 118 8.0 72 5 2
#> 3 12.0 149 12.6 74 5 3
#> 4 18.0 313 11.5 62 5 4
#> 5 31.5 NA 14.3 56 5 5
#> 6 28.0 NA 14.9 66 5 6
#> 7 23.0 299 8.6 65 5 7
#> 8 19.0 99 13.8 59 5 8
#> 9 8.0 19 20.1 61 5 9
#> 10 31.5 194 8.6 69 5 10
#> 11 7.0 NA 6.9 74 5 11
#> 12 16.0 256 9.7 69 5 12
#> 13 11.0 290 9.2 66 5 13
#> 14 14.0 274 10.9 68 5 14
#> 15 18.0 65 13.2 58 5 15
#> 16 14.0 334 11.5 64 5 16
#> 17 34.0 307 12.0 66 5 17
#> 18 6.0 78 18.4 57 5 18
#> 19 30.0 322 11.5 68 5 19
#> 20 11.0 44 9.7 62 5 20
#> 21 1.0 8 9.7 59 5 21
#> 22 11.0 320 16.6 73 5 22
#> 23 4.0 25 9.7 61 5 23
#> 24 32.0 92 12.0 61 5 24
#> 25 31.5 66 16.6 57 5 25
#> 26 31.5 266 14.9 58 5 26
#> 27 31.5 NA 8.0 57 5 27
#> 28 23.0 13 12.0 67 5 28
#> 29 45.0 252 14.9 81 5 29
#> 30 115.0 223 5.7 79 5 30
#> 31 37.0 279 7.4 76 5 31
#> 32 31.5 286 8.6 78 6 1
#> 33 31.5 287 9.7 74 6 2
#> 34 31.5 242 16.1 67 6 3
#> 35 31.5 186 9.2 84 6 4
#> 36 31.5 220 8.6 85 6 5
#> 37 31.5 264 14.3 79 6 6
#> 38 29.0 127 9.7 82 6 7
#> 39 31.5 273 6.9 87 6 8
#> 40 71.0 291 13.8 90 6 9
#> 41 39.0 323 11.5 87 6 10
#> 42 31.5 259 10.9 93 6 11
#> 43 31.5 250 9.2 92 6 12
#> 44 23.0 148 8.0 82 6 13
#> 45 31.5 332 13.8 80 6 14
#> 46 31.5 322 11.5 79 6 15
#> 47 21.0 191 14.9 77 6 16
#> 48 37.0 284 20.7 72 6 17
#> 49 20.0 37 9.2 65 6 18
#> 50 12.0 120 11.5 73 6 19
#> 51 13.0 137 10.3 76 6 20
#> 52 31.5 150 6.3 77 6 21
#> 53 31.5 59 1.7 76 6 22
#> 54 31.5 91 4.6 76 6 23
#> 55 31.5 250 6.3 76 6 24
#> 56 31.5 135 8.0 75 6 25
#> 57 31.5 127 8.0 78 6 26
#> 58 31.5 47 10.3 73 6 27
#> 59 31.5 98 11.5 80 6 28
#> 60 31.5 31 14.9 77 6 29
#> 61 31.5 138 8.0 83 6 30
#> 62 135.0 269 4.1 84 7 1
#> 63 49.0 248 9.2 85 7 2
#> 64 32.0 236 9.2 81 7 3
#> 65 31.5 101 10.9 84 7 4
#> 66 64.0 175 4.6 83 7 5
#> 67 40.0 314 10.9 83 7 6
#> 68 77.0 276 5.1 88 7 7
#> 69 97.0 267 6.3 92 7 8
#> 70 97.0 272 5.7 92 7 9
#> 71 85.0 175 7.4 89 7 10
#> 72 31.5 139 8.6 82 7 11
#> 73 10.0 264 14.3 73 7 12
#> 74 27.0 175 14.9 81 7 13
#> 75 31.5 291 14.9 91 7 14
#> 76 7.0 48 14.3 80 7 15
#> 77 48.0 260 6.9 81 7 16
#> 78 35.0 274 10.3 82 7 17
#> 79 61.0 285 6.3 84 7 18
#> 80 79.0 187 5.1 87 7 19
#> 81 63.0 220 11.5 85 7 20
#> 82 16.0 7 6.9 74 7 21
#> 83 31.5 258 9.7 81 7 22
#> 84 31.5 295 11.5 82 7 23
#> 85 80.0 294 8.6 86 7 24
#> 86 108.0 223 8.0 85 7 25
#> 87 20.0 81 8.6 82 7 26
#> 88 52.0 82 12.0 86 7 27
#> 89 82.0 213 7.4 88 7 28
#> 90 50.0 275 7.4 86 7 29
#> 91 64.0 253 7.4 83 7 30
#> 92 59.0 254 9.2 81 7 31
#> 93 39.0 83 6.9 81 8 1
#> 94 9.0 24 13.8 81 8 2
#> 95 16.0 77 7.4 82 8 3
#> 96 78.0 NA 6.9 86 8 4
#> 97 35.0 NA 7.4 85 8 5
#> 98 66.0 NA 4.6 87 8 6
#> 99 122.0 255 4.0 89 8 7
#> 100 89.0 229 10.3 90 8 8
#> 101 110.0 207 8.0 90 8 9
#> 102 31.5 222 8.6 92 8 10
#> 103 31.5 137 11.5 86 8 11
#> 104 44.0 192 11.5 86 8 12
#> 105 28.0 273 11.5 82 8 13
#> 106 65.0 157 9.7 80 8 14
#> 107 31.5 64 11.5 79 8 15
#> 108 22.0 71 10.3 77 8 16
#> 109 59.0 51 6.3 79 8 17
#> 110 23.0 115 7.4 76 8 18
#> 111 31.0 244 10.9 78 8 19
#> 112 44.0 190 10.3 78 8 20
#> 113 21.0 259 15.5 77 8 21
#> 114 9.0 36 14.3 72 8 22
#> 115 31.5 255 12.6 75 8 23
#> 116 45.0 212 9.7 79 8 24
#> 117 168.0 238 3.4 81 8 25
#> 118 73.0 215 8.0 86 8 26
#> 119 31.5 153 5.7 88 8 27
#> 120 76.0 203 9.7 97 8 28
#> 121 118.0 225 2.3 94 8 29
#> 122 84.0 237 6.3 96 8 30
#> 123 85.0 188 6.3 94 8 31
#> 124 96.0 167 6.9 91 9 1
#> 125 78.0 197 5.1 92 9 2
#> 126 73.0 183 2.8 93 9 3
#> 127 91.0 189 4.6 93 9 4
#> 128 47.0 95 7.4 87 9 5
#> 129 32.0 92 15.5 84 9 6
#> 130 20.0 252 10.9 80 9 7
#> 131 23.0 220 10.3 78 9 8
#> 132 21.0 230 10.9 75 9 9
#> 133 24.0 259 9.7 73 9 10
#> 134 44.0 236 14.9 81 9 11
#> 135 21.0 259 15.5 76 9 12
#> 136 28.0 238 6.3 77 9 13
#> 137 9.0 24 10.9 71 9 14
#> 138 13.0 112 11.5 71 9 15
#> 139 46.0 237 6.9 78 9 16
#> 140 18.0 224 13.8 67 9 17
#> 141 13.0 27 10.3 76 9 18
#> 142 24.0 238 10.3 68 9 19
#> 143 16.0 201 8.0 82 9 20
#> 144 13.0 238 12.6 64 9 21
#> 145 23.0 14 9.2 71 9 22
#> 146 36.0 139 10.3 81 9 23
#> 147 7.0 49 10.3 69 9 24
#> 148 14.0 20 16.6 63 9 25
#> 149 30.0 193 6.9 70 9 26
#> 150 31.5 145 13.2 77 9 27
#> 151 14.0 191 14.3 75 9 28
#> 152 18.0 131 8.0 76 9 29
#> 153 20.0 223 11.5 68 9 30
impute_median_if(airquality,
.predicate = is.numeric)
#> Ozone Solar.R Wind Temp Month Day
#> 1 41.0 190 7.4 67 5 1
#> 2 36.0 118 8.0 72 5 2
#> 3 12.0 149 12.6 74 5 3
#> 4 18.0 313 11.5 62 5 4
#> 5 31.5 205 14.3 56 5 5
#> 6 28.0 205 14.9 66 5 6
#> 7 23.0 299 8.6 65 5 7
#> 8 19.0 99 13.8 59 5 8
#> 9 8.0 19 20.1 61 5 9
#> 10 31.5 194 8.6 69 5 10
#> 11 7.0 205 6.9 74 5 11
#> 12 16.0 256 9.7 69 5 12
#> 13 11.0 290 9.2 66 5 13
#> 14 14.0 274 10.9 68 5 14
#> 15 18.0 65 13.2 58 5 15
#> 16 14.0 334 11.5 64 5 16
#> 17 34.0 307 12.0 66 5 17
#> 18 6.0 78 18.4 57 5 18
#> 19 30.0 322 11.5 68 5 19
#> 20 11.0 44 9.7 62 5 20
#> 21 1.0 8 9.7 59 5 21
#> 22 11.0 320 16.6 73 5 22
#> 23 4.0 25 9.7 61 5 23
#> 24 32.0 92 12.0 61 5 24
#> 25 31.5 66 16.6 57 5 25
#> 26 31.5 266 14.9 58 5 26
#> 27 31.5 205 8.0 57 5 27
#> 28 23.0 13 12.0 67 5 28
#> 29 45.0 252 14.9 81 5 29
#> 30 115.0 223 5.7 79 5 30
#> 31 37.0 279 7.4 76 5 31
#> 32 31.5 286 8.6 78 6 1
#> 33 31.5 287 9.7 74 6 2
#> 34 31.5 242 16.1 67 6 3
#> 35 31.5 186 9.2 84 6 4
#> 36 31.5 220 8.6 85 6 5
#> 37 31.5 264 14.3 79 6 6
#> 38 29.0 127 9.7 82 6 7
#> 39 31.5 273 6.9 87 6 8
#> 40 71.0 291 13.8 90 6 9
#> 41 39.0 323 11.5 87 6 10
#> 42 31.5 259 10.9 93 6 11
#> 43 31.5 250 9.2 92 6 12
#> 44 23.0 148 8.0 82 6 13
#> 45 31.5 332 13.8 80 6 14
#> 46 31.5 322 11.5 79 6 15
#> 47 21.0 191 14.9 77 6 16
#> 48 37.0 284 20.7 72 6 17
#> 49 20.0 37 9.2 65 6 18
#> 50 12.0 120 11.5 73 6 19
#> 51 13.0 137 10.3 76 6 20
#> 52 31.5 150 6.3 77 6 21
#> 53 31.5 59 1.7 76 6 22
#> 54 31.5 91 4.6 76 6 23
#> 55 31.5 250 6.3 76 6 24
#> 56 31.5 135 8.0 75 6 25
#> 57 31.5 127 8.0 78 6 26
#> 58 31.5 47 10.3 73 6 27
#> 59 31.5 98 11.5 80 6 28
#> 60 31.5 31 14.9 77 6 29
#> 61 31.5 138 8.0 83 6 30
#> 62 135.0 269 4.1 84 7 1
#> 63 49.0 248 9.2 85 7 2
#> 64 32.0 236 9.2 81 7 3
#> 65 31.5 101 10.9 84 7 4
#> 66 64.0 175 4.6 83 7 5
#> 67 40.0 314 10.9 83 7 6
#> 68 77.0 276 5.1 88 7 7
#> 69 97.0 267 6.3 92 7 8
#> 70 97.0 272 5.7 92 7 9
#> 71 85.0 175 7.4 89 7 10
#> 72 31.5 139 8.6 82 7 11
#> 73 10.0 264 14.3 73 7 12
#> 74 27.0 175 14.9 81 7 13
#> 75 31.5 291 14.9 91 7 14
#> 76 7.0 48 14.3 80 7 15
#> 77 48.0 260 6.9 81 7 16
#> 78 35.0 274 10.3 82 7 17
#> 79 61.0 285 6.3 84 7 18
#> 80 79.0 187 5.1 87 7 19
#> 81 63.0 220 11.5 85 7 20
#> 82 16.0 7 6.9 74 7 21
#> 83 31.5 258 9.7 81 7 22
#> 84 31.5 295 11.5 82 7 23
#> 85 80.0 294 8.6 86 7 24
#> 86 108.0 223 8.0 85 7 25
#> 87 20.0 81 8.6 82 7 26
#> 88 52.0 82 12.0 86 7 27
#> 89 82.0 213 7.4 88 7 28
#> 90 50.0 275 7.4 86 7 29
#> 91 64.0 253 7.4 83 7 30
#> 92 59.0 254 9.2 81 7 31
#> 93 39.0 83 6.9 81 8 1
#> 94 9.0 24 13.8 81 8 2
#> 95 16.0 77 7.4 82 8 3
#> 96 78.0 205 6.9 86 8 4
#> 97 35.0 205 7.4 85 8 5
#> 98 66.0 205 4.6 87 8 6
#> 99 122.0 255 4.0 89 8 7
#> 100 89.0 229 10.3 90 8 8
#> 101 110.0 207 8.0 90 8 9
#> 102 31.5 222 8.6 92 8 10
#> 103 31.5 137 11.5 86 8 11
#> 104 44.0 192 11.5 86 8 12
#> 105 28.0 273 11.5 82 8 13
#> 106 65.0 157 9.7 80 8 14
#> 107 31.5 64 11.5 79 8 15
#> 108 22.0 71 10.3 77 8 16
#> 109 59.0 51 6.3 79 8 17
#> 110 23.0 115 7.4 76 8 18
#> 111 31.0 244 10.9 78 8 19
#> 112 44.0 190 10.3 78 8 20
#> 113 21.0 259 15.5 77 8 21
#> 114 9.0 36 14.3 72 8 22
#> 115 31.5 255 12.6 75 8 23
#> 116 45.0 212 9.7 79 8 24
#> 117 168.0 238 3.4 81 8 25
#> 118 73.0 215 8.0 86 8 26
#> 119 31.5 153 5.7 88 8 27
#> 120 76.0 203 9.7 97 8 28
#> 121 118.0 225 2.3 94 8 29
#> 122 84.0 237 6.3 96 8 30
#> 123 85.0 188 6.3 94 8 31
#> 124 96.0 167 6.9 91 9 1
#> 125 78.0 197 5.1 92 9 2
#> 126 73.0 183 2.8 93 9 3
#> 127 91.0 189 4.6 93 9 4
#> 128 47.0 95 7.4 87 9 5
#> 129 32.0 92 15.5 84 9 6
#> 130 20.0 252 10.9 80 9 7
#> 131 23.0 220 10.3 78 9 8
#> 132 21.0 230 10.9 75 9 9
#> 133 24.0 259 9.7 73 9 10
#> 134 44.0 236 14.9 81 9 11
#> 135 21.0 259 15.5 76 9 12
#> 136 28.0 238 6.3 77 9 13
#> 137 9.0 24 10.9 71 9 14
#> 138 13.0 112 11.5 71 9 15
#> 139 46.0 237 6.9 78 9 16
#> 140 18.0 224 13.8 67 9 17
#> 141 13.0 27 10.3 76 9 18
#> 142 24.0 238 10.3 68 9 19
#> 143 16.0 201 8.0 82 9 20
#> 144 13.0 238 12.6 64 9 21
#> 145 23.0 14 9.2 71 9 22
#> 146 36.0 139 10.3 81 9 23
#> 147 7.0 49 10.3 69 9 24
#> 148 14.0 20 16.6 63 9 25
#> 149 30.0 193 6.9 70 9 26
#> 150 31.5 145 13.2 77 9 27
#> 151 14.0 191 14.3 75 9 28
#> 152 18.0 131 8.0 76 9 29
#> 153 20.0 223 11.5 68 9 30
library(ggplot2)
airquality %>%
bind_shadow() %>%
impute_median_all() %>%
add_label_shadow() %>%
ggplot(aes(x = Ozone,
y = Solar.R,
colour = any_missing)) +
geom_point()
