Quickly set all missing values to indicated value.
set_missing(data, value, exclude = NULL)
| data | input data, in data.table format only. |
|---|---|
| value | a single value or a list of two values to be set to. See 'Details'. |
| exclude | column index or name to be excluded. |
The class of value will determine what type of columns to be set, e.g., if value is 0, then missing values for continuous features will be set.
When supplying a list of two values, only one numeric and one non-numeric is allowed.
This function updates data.table object directly. Otherwise, output data will be returned matching input object class.
# Load packages library(data.table) # Generate missing values in iris data dt <- data.table(iris) for (j in 1:4) set(dt, i = sample.int(150, j * 30), j, value = NA_integer_) set(dt, i = sample.int(150, 25), 5L, value = NA_character_) # Set all missing values to 0L and unknown dt2 <- copy(dt) set_missing(dt2, list(0L, "unknown"))#>#>#>#>#>#>#>#>#>#>#>#>#>#># Return from non-data.table input set_missing(airquality, 999999L)#>#>#> Ozone Solar.R Wind Temp Month Day #> 1 41 190 7.4 67 5 1 #> 2 36 118 8.0 72 5 2 #> 3 12 149 12.6 74 5 3 #> 4 18 313 11.5 62 5 4 #> 5 999999 999999 14.3 56 5 5 #> 6 28 999999 14.9 66 5 6 #> 7 23 299 8.6 65 5 7 #> 8 19 99 13.8 59 5 8 #> 9 8 19 20.1 61 5 9 #> 10 999999 194 8.6 69 5 10 #> 11 7 999999 6.9 74 5 11 #> 12 16 256 9.7 69 5 12 #> 13 11 290 9.2 66 5 13 #> 14 14 274 10.9 68 5 14 #> 15 18 65 13.2 58 5 15 #> 16 14 334 11.5 64 5 16 #> 17 34 307 12.0 66 5 17 #> 18 6 78 18.4 57 5 18 #> 19 30 322 11.5 68 5 19 #> 20 11 44 9.7 62 5 20 #> 21 1 8 9.7 59 5 21 #> 22 11 320 16.6 73 5 22 #> 23 4 25 9.7 61 5 23 #> 24 32 92 12.0 61 5 24 #> 25 999999 66 16.6 57 5 25 #> 26 999999 266 14.9 58 5 26 #> 27 999999 999999 8.0 57 5 27 #> 28 23 13 12.0 67 5 28 #> 29 45 252 14.9 81 5 29 #> 30 115 223 5.7 79 5 30 #> 31 37 279 7.4 76 5 31 #> 32 999999 286 8.6 78 6 1 #> 33 999999 287 9.7 74 6 2 #> 34 999999 242 16.1 67 6 3 #> 35 999999 186 9.2 84 6 4 #> 36 999999 220 8.6 85 6 5 #> 37 999999 264 14.3 79 6 6 #> 38 29 127 9.7 82 6 7 #> 39 999999 273 6.9 87 6 8 #> 40 71 291 13.8 90 6 9 #> 41 39 323 11.5 87 6 10 #> 42 999999 259 10.9 93 6 11 #> 43 999999 250 9.2 92 6 12 #> 44 23 148 8.0 82 6 13 #> 45 999999 332 13.8 80 6 14 #> 46 999999 322 11.5 79 6 15 #> 47 21 191 14.9 77 6 16 #> 48 37 284 20.7 72 6 17 #> 49 20 37 9.2 65 6 18 #> 50 12 120 11.5 73 6 19 #> 51 13 137 10.3 76 6 20 #> 52 999999 150 6.3 77 6 21 #> 53 999999 59 1.7 76 6 22 #> 54 999999 91 4.6 76 6 23 #> 55 999999 250 6.3 76 6 24 #> 56 999999 135 8.0 75 6 25 #> 57 999999 127 8.0 78 6 26 #> 58 999999 47 10.3 73 6 27 #> 59 999999 98 11.5 80 6 28 #> 60 999999 31 14.9 77 6 29 #> 61 999999 138 8.0 83 6 30 #> 62 135 269 4.1 84 7 1 #> 63 49 248 9.2 85 7 2 #> 64 32 236 9.2 81 7 3 #> 65 999999 101 10.9 84 7 4 #> 66 64 175 4.6 83 7 5 #> 67 40 314 10.9 83 7 6 #> 68 77 276 5.1 88 7 7 #> 69 97 267 6.3 92 7 8 #> 70 97 272 5.7 92 7 9 #> 71 85 175 7.4 89 7 10 #> 72 999999 139 8.6 82 7 11 #> 73 10 264 14.3 73 7 12 #> 74 27 175 14.9 81 7 13 #> 75 999999 291 14.9 91 7 14 #> 76 7 48 14.3 80 7 15 #> 77 48 260 6.9 81 7 16 #> 78 35 274 10.3 82 7 17 #> 79 61 285 6.3 84 7 18 #> 80 79 187 5.1 87 7 19 #> 81 63 220 11.5 85 7 20 #> 82 16 7 6.9 74 7 21 #> 83 999999 258 9.7 81 7 22 #> 84 999999 295 11.5 82 7 23 #> 85 80 294 8.6 86 7 24 #> 86 108 223 8.0 85 7 25 #> 87 20 81 8.6 82 7 26 #> 88 52 82 12.0 86 7 27 #> 89 82 213 7.4 88 7 28 #> 90 50 275 7.4 86 7 29 #> 91 64 253 7.4 83 7 30 #> 92 59 254 9.2 81 7 31 #> 93 39 83 6.9 81 8 1 #> 94 9 24 13.8 81 8 2 #> 95 16 77 7.4 82 8 3 #> 96 78 999999 6.9 86 8 4 #> 97 35 999999 7.4 85 8 5 #> 98 66 999999 4.6 87 8 6 #> 99 122 255 4.0 89 8 7 #> 100 89 229 10.3 90 8 8 #> 101 110 207 8.0 90 8 9 #> 102 999999 222 8.6 92 8 10 #> 103 999999 137 11.5 86 8 11 #> 104 44 192 11.5 86 8 12 #> 105 28 273 11.5 82 8 13 #> 106 65 157 9.7 80 8 14 #> 107 999999 64 11.5 79 8 15 #> 108 22 71 10.3 77 8 16 #> 109 59 51 6.3 79 8 17 #> 110 23 115 7.4 76 8 18 #> 111 31 244 10.9 78 8 19 #> 112 44 190 10.3 78 8 20 #> 113 21 259 15.5 77 8 21 #> 114 9 36 14.3 72 8 22 #> 115 999999 255 12.6 75 8 23 #> 116 45 212 9.7 79 8 24 #> 117 168 238 3.4 81 8 25 #> 118 73 215 8.0 86 8 26 #> 119 999999 153 5.7 88 8 27 #> 120 76 203 9.7 97 8 28 #> 121 118 225 2.3 94 8 29 #> 122 84 237 6.3 96 8 30 #> 123 85 188 6.3 94 8 31 #> 124 96 167 6.9 91 9 1 #> 125 78 197 5.1 92 9 2 #> 126 73 183 2.8 93 9 3 #> 127 91 189 4.6 93 9 4 #> 128 47 95 7.4 87 9 5 #> 129 32 92 15.5 84 9 6 #> 130 20 252 10.9 80 9 7 #> 131 23 220 10.3 78 9 8 #> 132 21 230 10.9 75 9 9 #> 133 24 259 9.7 73 9 10 #> 134 44 236 14.9 81 9 11 #> 135 21 259 15.5 76 9 12 #> 136 28 238 6.3 77 9 13 #> 137 9 24 10.9 71 9 14 #> 138 13 112 11.5 71 9 15 #> 139 46 237 6.9 78 9 16 #> 140 18 224 13.8 67 9 17 #> 141 13 27 10.3 76 9 18 #> 142 24 238 10.3 68 9 19 #> 143 16 201 8.0 82 9 20 #> 144 13 238 12.6 64 9 21 #> 145 23 14 9.2 71 9 22 #> 146 36 139 10.3 81 9 23 #> 147 7 49 10.3 69 9 24 #> 148 14 20 16.6 63 9 25 #> 149 30 193 6.9 70 9 26 #> 150 999999 145 13.2 77 9 27 #> 151 14 191 14.3 75 9 28 #> 152 18 131 8.0 76 9 29 #> 153 20 223 11.5 68 9 30