Quickly set all missing values to indicated value.
Arguments
- 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.
Details
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.
Examples
# 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"))
#> Column [Sepal.Length]: Set 30 missing values to 0
#> Column [Sepal.Width]: Set 60 missing values to 0
#> Column [Petal.Length]: Set 90 missing values to 0
#> Column [Petal.Width]: Set 120 missing values to 0
#> Column [Species]: Set 25 missing values to unknown
# Set missing numerical values to 0L
dt3 <- copy(dt)
set_missing(dt3, 0L)
#> Column [Sepal.Length]: Set 30 missing values to 0
#> Column [Sepal.Width]: Set 60 missing values to 0
#> Column [Petal.Length]: Set 90 missing values to 0
#> Column [Petal.Width]: Set 120 missing values to 0
# Set missing discrete values to unknown
dt4 <- copy(dt)
set_missing(dt4, "unknown")
#> Column [Species]: Set 25 missing values to unknown
# Set missing values excluding some columns
dt5 <- copy(dt)
set_missing(dt4, 0L, 1L:2L)
#> Column [Petal.Length]: Set 90 missing values to 0
#> Column [Petal.Width]: Set 120 missing values to 0
set_missing(dt4, 0L, names(dt5)[3L:4L])
#> Column [Sepal.Length]: Set 30 missing values to 0
#> Column [Sepal.Width]: Set 60 missing values to 0
# Return from non-data.table input
set_missing(airquality, 999999L)
#> Column [Ozone]: Set 37 missing values to 999999
#> Column [Solar.R]: Set 7 missing values to 999999
#> 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