impute_below
imputes missing values to a set percentage below the range
of the data. To impute many variables at once, we recommend that you use the
across
function workflow, shown in the examples for impute_below()
.
impute_below_all
operates on all variables. To only impute variables
that satisfy a specific condition, use the scoped variants,
impute_below_at
, and impute_below_if
. To use _at
effectively,
you must know that _at`` affects variables selected with a character vector, or with
vars()`.
Arguments
- .tbl
a data.frame
- .vars
variables to impute
- prop_below
the degree to shift the values. default is
- jitter
the amount of jitter to add. default is 0.05
- ...
extra arguments
Examples
# select variables starting with a particular string.
impute_below_at(airquality,
.vars = c("Ozone", "Solar.R"))
#> Ozone Solar.R Wind Temp Month Day
#> 1 41.00000 190.00000 7.4 67 5 1
#> 2 36.00000 118.00000 8.0 72 5 2
#> 3 12.00000 149.00000 12.6 74 5 3
#> 4 18.00000 313.00000 11.5 62 5 4
#> 5 -19.72321 -33.57778 14.3 56 5 5
#> 6 28.00000 -33.07810 14.9 66 5 6
#> 7 23.00000 299.00000 8.6 65 5 7
#> 8 19.00000 99.00000 13.8 59 5 8
#> 9 8.00000 19.00000 20.1 61 5 9
#> 10 -18.51277 194.00000 8.6 69 5 10
#> 11 7.00000 -21.37719 6.9 74 5 11
#> 12 16.00000 256.00000 9.7 69 5 12
#> 13 11.00000 290.00000 9.2 66 5 13
#> 14 14.00000 274.00000 10.9 68 5 14
#> 15 18.00000 65.00000 13.2 58 5 15
#> 16 14.00000 334.00000 11.5 64 5 16
#> 17 34.00000 307.00000 12.0 66 5 17
#> 18 6.00000 78.00000 18.4 57 5 18
#> 19 30.00000 322.00000 11.5 68 5 19
#> 20 11.00000 44.00000 9.7 62 5 20
#> 21 1.00000 8.00000 9.7 59 5 21
#> 22 11.00000 320.00000 16.6 73 5 22
#> 23 4.00000 25.00000 9.7 61 5 23
#> 24 32.00000 92.00000 12.0 61 5 24
#> 25 -17.81863 66.00000 16.6 57 5 25
#> 26 -19.43853 266.00000 14.9 58 5 26
#> 27 -15.14310 -24.60954 8.0 57 5 27
#> 28 23.00000 13.00000 12.0 67 5 28
#> 29 45.00000 252.00000 14.9 81 5 29
#> 30 115.00000 223.00000 5.7 79 5 30
#> 31 37.00000 279.00000 7.4 76 5 31
#> 32 -16.17315 286.00000 8.6 78 6 1
#> 33 -14.65883 287.00000 9.7 74 6 2
#> 34 -17.85609 242.00000 16.1 67 6 3
#> 35 -13.29299 186.00000 9.2 84 6 4
#> 36 -16.16323 220.00000 8.6 85 6 5
#> 37 -19.60935 264.00000 14.3 79 6 6
#> 38 29.00000 127.00000 9.7 82 6 7
#> 39 -19.65780 273.00000 6.9 87 6 8
#> 40 71.00000 291.00000 13.8 90 6 9
#> 41 39.00000 323.00000 11.5 87 6 10
#> 42 -13.40961 259.00000 10.9 93 6 11
#> 43 -13.53728 250.00000 9.2 92 6 12
#> 44 23.00000 148.00000 8.0 82 6 13
#> 45 -19.65993 332.00000 13.8 80 6 14
#> 46 -16.48342 322.00000 11.5 79 6 15
#> 47 21.00000 191.00000 14.9 77 6 16
#> 48 37.00000 284.00000 20.7 72 6 17
#> 49 20.00000 37.00000 9.2 65 6 18
#> 50 12.00000 120.00000 11.5 73 6 19
#> 51 13.00000 137.00000 10.3 76 6 20
#> 52 -17.17718 150.00000 6.3 77 6 21
#> 53 -16.74073 59.00000 1.7 76 6 22
#> 54 -13.65786 91.00000 4.6 76 6 23
#> 55 -16.78786 250.00000 6.3 76 6 24
#> 56 -12.30098 135.00000 8.0 75 6 25
#> 57 -13.33171 127.00000 8.0 78 6 26
#> 58 -16.77414 47.00000 10.3 73 6 27
#> 59 -17.08225 98.00000 11.5 80 6 28
#> 60 -15.98818 31.00000 14.9 77 6 29
#> 61 -19.17558 138.00000 8.0 83 6 30
#> 62 135.00000 269.00000 4.1 84 7 1
#> 63 49.00000 248.00000 9.2 85 7 2
#> 64 32.00000 236.00000 9.2 81 7 3
#> 65 -14.27138 101.00000 10.9 84 7 4
#> 66 64.00000 175.00000 4.6 83 7 5
#> 67 40.00000 314.00000 10.9 83 7 6
#> 68 77.00000 276.00000 5.1 88 7 7
#> 69 97.00000 267.00000 6.3 92 7 8
#> 70 97.00000 272.00000 5.7 92 7 9
#> 71 85.00000 175.00000 7.4 89 7 10
#> 72 -13.51764 139.00000 8.6 82 7 11
#> 73 10.00000 264.00000 14.3 73 7 12
#> 74 27.00000 175.00000 14.9 81 7 13
#> 75 -13.48998 291.00000 14.9 91 7 14
#> 76 7.00000 48.00000 14.3 80 7 15
#> 77 48.00000 260.00000 6.9 81 7 16
#> 78 35.00000 274.00000 10.3 82 7 17
#> 79 61.00000 285.00000 6.3 84 7 18
#> 80 79.00000 187.00000 5.1 87 7 19
#> 81 63.00000 220.00000 11.5 85 7 20
#> 82 16.00000 7.00000 6.9 74 7 21
#> 83 -16.92150 258.00000 9.7 81 7 22
#> 84 -16.60335 295.00000 11.5 82 7 23
#> 85 80.00000 294.00000 8.6 86 7 24
#> 86 108.00000 223.00000 8.0 85 7 25
#> 87 20.00000 81.00000 8.6 82 7 26
#> 88 52.00000 82.00000 12.0 86 7 27
#> 89 82.00000 213.00000 7.4 88 7 28
#> 90 50.00000 275.00000 7.4 86 7 29
#> 91 64.00000 253.00000 7.4 83 7 30
#> 92 59.00000 254.00000 9.2 81 7 31
#> 93 39.00000 83.00000 6.9 81 8 1
#> 94 9.00000 24.00000 13.8 81 8 2
#> 95 16.00000 77.00000 7.4 82 8 3
#> 96 78.00000 -30.94374 6.9 86 8 4
#> 97 35.00000 -33.38707 7.4 85 8 5
#> 98 66.00000 -21.48980 4.6 87 8 6
#> 99 122.00000 255.00000 4.0 89 8 7
#> 100 89.00000 229.00000 10.3 90 8 8
#> 101 110.00000 207.00000 8.0 90 8 9
#> 102 -14.78907 222.00000 8.6 92 8 10
#> 103 -16.19151 137.00000 11.5 86 8 11
#> 104 44.00000 192.00000 11.5 86 8 12
#> 105 28.00000 273.00000 11.5 82 8 13
#> 106 65.00000 157.00000 9.7 80 8 14
#> 107 -19.73591 64.00000 11.5 79 8 15
#> 108 22.00000 71.00000 10.3 77 8 16
#> 109 59.00000 51.00000 6.3 79 8 17
#> 110 23.00000 115.00000 7.4 76 8 18
#> 111 31.00000 244.00000 10.9 78 8 19
#> 112 44.00000 190.00000 10.3 78 8 20
#> 113 21.00000 259.00000 15.5 77 8 21
#> 114 9.00000 36.00000 14.3 72 8 22
#> 115 -18.92235 255.00000 12.6 75 8 23
#> 116 45.00000 212.00000 9.7 79 8 24
#> 117 168.00000 238.00000 3.4 81 8 25
#> 118 73.00000 215.00000 8.0 86 8 26
#> 119 -14.86296 153.00000 5.7 88 8 27
#> 120 76.00000 203.00000 9.7 97 8 28
#> 121 118.00000 225.00000 2.3 94 8 29
#> 122 84.00000 237.00000 6.3 96 8 30
#> 123 85.00000 188.00000 6.3 94 8 31
#> 124 96.00000 167.00000 6.9 91 9 1
#> 125 78.00000 197.00000 5.1 92 9 2
#> 126 73.00000 183.00000 2.8 93 9 3
#> 127 91.00000 189.00000 4.6 93 9 4
#> 128 47.00000 95.00000 7.4 87 9 5
#> 129 32.00000 92.00000 15.5 84 9 6
#> 130 20.00000 252.00000 10.9 80 9 7
#> 131 23.00000 220.00000 10.3 78 9 8
#> 132 21.00000 230.00000 10.9 75 9 9
#> 133 24.00000 259.00000 9.7 73 9 10
#> 134 44.00000 236.00000 14.9 81 9 11
#> 135 21.00000 259.00000 15.5 76 9 12
#> 136 28.00000 238.00000 6.3 77 9 13
#> 137 9.00000 24.00000 10.9 71 9 14
#> 138 13.00000 112.00000 11.5 71 9 15
#> 139 46.00000 237.00000 6.9 78 9 16
#> 140 18.00000 224.00000 13.8 67 9 17
#> 141 13.00000 27.00000 10.3 76 9 18
#> 142 24.00000 238.00000 10.3 68 9 19
#> 143 16.00000 201.00000 8.0 82 9 20
#> 144 13.00000 238.00000 12.6 64 9 21
#> 145 23.00000 14.00000 9.2 71 9 22
#> 146 36.00000 139.00000 10.3 81 9 23
#> 147 7.00000 49.00000 10.3 69 9 24
#> 148 14.00000 20.00000 16.6 63 9 25
#> 149 30.00000 193.00000 6.9 70 9 26
#> 150 -14.83089 145.00000 13.2 77 9 27
#> 151 14.00000 191.00000 14.3 75 9 28
#> 152 18.00000 131.00000 8.0 76 9 29
#> 153 20.00000 223.00000 11.5 68 9 30
impute_below_at(airquality, .vars = 1:2)
#> Ozone Solar.R Wind Temp Month Day
#> 1 41.00000 190.00000 7.4 67 5 1
#> 2 36.00000 118.00000 8.0 72 5 2
#> 3 12.00000 149.00000 12.6 74 5 3
#> 4 18.00000 313.00000 11.5 62 5 4
#> 5 -19.72321 -33.57778 14.3 56 5 5
#> 6 28.00000 -33.07810 14.9 66 5 6
#> 7 23.00000 299.00000 8.6 65 5 7
#> 8 19.00000 99.00000 13.8 59 5 8
#> 9 8.00000 19.00000 20.1 61 5 9
#> 10 -18.51277 194.00000 8.6 69 5 10
#> 11 7.00000 -21.37719 6.9 74 5 11
#> 12 16.00000 256.00000 9.7 69 5 12
#> 13 11.00000 290.00000 9.2 66 5 13
#> 14 14.00000 274.00000 10.9 68 5 14
#> 15 18.00000 65.00000 13.2 58 5 15
#> 16 14.00000 334.00000 11.5 64 5 16
#> 17 34.00000 307.00000 12.0 66 5 17
#> 18 6.00000 78.00000 18.4 57 5 18
#> 19 30.00000 322.00000 11.5 68 5 19
#> 20 11.00000 44.00000 9.7 62 5 20
#> 21 1.00000 8.00000 9.7 59 5 21
#> 22 11.00000 320.00000 16.6 73 5 22
#> 23 4.00000 25.00000 9.7 61 5 23
#> 24 32.00000 92.00000 12.0 61 5 24
#> 25 -17.81863 66.00000 16.6 57 5 25
#> 26 -19.43853 266.00000 14.9 58 5 26
#> 27 -15.14310 -24.60954 8.0 57 5 27
#> 28 23.00000 13.00000 12.0 67 5 28
#> 29 45.00000 252.00000 14.9 81 5 29
#> 30 115.00000 223.00000 5.7 79 5 30
#> 31 37.00000 279.00000 7.4 76 5 31
#> 32 -16.17315 286.00000 8.6 78 6 1
#> 33 -14.65883 287.00000 9.7 74 6 2
#> 34 -17.85609 242.00000 16.1 67 6 3
#> 35 -13.29299 186.00000 9.2 84 6 4
#> 36 -16.16323 220.00000 8.6 85 6 5
#> 37 -19.60935 264.00000 14.3 79 6 6
#> 38 29.00000 127.00000 9.7 82 6 7
#> 39 -19.65780 273.00000 6.9 87 6 8
#> 40 71.00000 291.00000 13.8 90 6 9
#> 41 39.00000 323.00000 11.5 87 6 10
#> 42 -13.40961 259.00000 10.9 93 6 11
#> 43 -13.53728 250.00000 9.2 92 6 12
#> 44 23.00000 148.00000 8.0 82 6 13
#> 45 -19.65993 332.00000 13.8 80 6 14
#> 46 -16.48342 322.00000 11.5 79 6 15
#> 47 21.00000 191.00000 14.9 77 6 16
#> 48 37.00000 284.00000 20.7 72 6 17
#> 49 20.00000 37.00000 9.2 65 6 18
#> 50 12.00000 120.00000 11.5 73 6 19
#> 51 13.00000 137.00000 10.3 76 6 20
#> 52 -17.17718 150.00000 6.3 77 6 21
#> 53 -16.74073 59.00000 1.7 76 6 22
#> 54 -13.65786 91.00000 4.6 76 6 23
#> 55 -16.78786 250.00000 6.3 76 6 24
#> 56 -12.30098 135.00000 8.0 75 6 25
#> 57 -13.33171 127.00000 8.0 78 6 26
#> 58 -16.77414 47.00000 10.3 73 6 27
#> 59 -17.08225 98.00000 11.5 80 6 28
#> 60 -15.98818 31.00000 14.9 77 6 29
#> 61 -19.17558 138.00000 8.0 83 6 30
#> 62 135.00000 269.00000 4.1 84 7 1
#> 63 49.00000 248.00000 9.2 85 7 2
#> 64 32.00000 236.00000 9.2 81 7 3
#> 65 -14.27138 101.00000 10.9 84 7 4
#> 66 64.00000 175.00000 4.6 83 7 5
#> 67 40.00000 314.00000 10.9 83 7 6
#> 68 77.00000 276.00000 5.1 88 7 7
#> 69 97.00000 267.00000 6.3 92 7 8
#> 70 97.00000 272.00000 5.7 92 7 9
#> 71 85.00000 175.00000 7.4 89 7 10
#> 72 -13.51764 139.00000 8.6 82 7 11
#> 73 10.00000 264.00000 14.3 73 7 12
#> 74 27.00000 175.00000 14.9 81 7 13
#> 75 -13.48998 291.00000 14.9 91 7 14
#> 76 7.00000 48.00000 14.3 80 7 15
#> 77 48.00000 260.00000 6.9 81 7 16
#> 78 35.00000 274.00000 10.3 82 7 17
#> 79 61.00000 285.00000 6.3 84 7 18
#> 80 79.00000 187.00000 5.1 87 7 19
#> 81 63.00000 220.00000 11.5 85 7 20
#> 82 16.00000 7.00000 6.9 74 7 21
#> 83 -16.92150 258.00000 9.7 81 7 22
#> 84 -16.60335 295.00000 11.5 82 7 23
#> 85 80.00000 294.00000 8.6 86 7 24
#> 86 108.00000 223.00000 8.0 85 7 25
#> 87 20.00000 81.00000 8.6 82 7 26
#> 88 52.00000 82.00000 12.0 86 7 27
#> 89 82.00000 213.00000 7.4 88 7 28
#> 90 50.00000 275.00000 7.4 86 7 29
#> 91 64.00000 253.00000 7.4 83 7 30
#> 92 59.00000 254.00000 9.2 81 7 31
#> 93 39.00000 83.00000 6.9 81 8 1
#> 94 9.00000 24.00000 13.8 81 8 2
#> 95 16.00000 77.00000 7.4 82 8 3
#> 96 78.00000 -30.94374 6.9 86 8 4
#> 97 35.00000 -33.38707 7.4 85 8 5
#> 98 66.00000 -21.48980 4.6 87 8 6
#> 99 122.00000 255.00000 4.0 89 8 7
#> 100 89.00000 229.00000 10.3 90 8 8
#> 101 110.00000 207.00000 8.0 90 8 9
#> 102 -14.78907 222.00000 8.6 92 8 10
#> 103 -16.19151 137.00000 11.5 86 8 11
#> 104 44.00000 192.00000 11.5 86 8 12
#> 105 28.00000 273.00000 11.5 82 8 13
#> 106 65.00000 157.00000 9.7 80 8 14
#> 107 -19.73591 64.00000 11.5 79 8 15
#> 108 22.00000 71.00000 10.3 77 8 16
#> 109 59.00000 51.00000 6.3 79 8 17
#> 110 23.00000 115.00000 7.4 76 8 18
#> 111 31.00000 244.00000 10.9 78 8 19
#> 112 44.00000 190.00000 10.3 78 8 20
#> 113 21.00000 259.00000 15.5 77 8 21
#> 114 9.00000 36.00000 14.3 72 8 22
#> 115 -18.92235 255.00000 12.6 75 8 23
#> 116 45.00000 212.00000 9.7 79 8 24
#> 117 168.00000 238.00000 3.4 81 8 25
#> 118 73.00000 215.00000 8.0 86 8 26
#> 119 -14.86296 153.00000 5.7 88 8 27
#> 120 76.00000 203.00000 9.7 97 8 28
#> 121 118.00000 225.00000 2.3 94 8 29
#> 122 84.00000 237.00000 6.3 96 8 30
#> 123 85.00000 188.00000 6.3 94 8 31
#> 124 96.00000 167.00000 6.9 91 9 1
#> 125 78.00000 197.00000 5.1 92 9 2
#> 126 73.00000 183.00000 2.8 93 9 3
#> 127 91.00000 189.00000 4.6 93 9 4
#> 128 47.00000 95.00000 7.4 87 9 5
#> 129 32.00000 92.00000 15.5 84 9 6
#> 130 20.00000 252.00000 10.9 80 9 7
#> 131 23.00000 220.00000 10.3 78 9 8
#> 132 21.00000 230.00000 10.9 75 9 9
#> 133 24.00000 259.00000 9.7 73 9 10
#> 134 44.00000 236.00000 14.9 81 9 11
#> 135 21.00000 259.00000 15.5 76 9 12
#> 136 28.00000 238.00000 6.3 77 9 13
#> 137 9.00000 24.00000 10.9 71 9 14
#> 138 13.00000 112.00000 11.5 71 9 15
#> 139 46.00000 237.00000 6.9 78 9 16
#> 140 18.00000 224.00000 13.8 67 9 17
#> 141 13.00000 27.00000 10.3 76 9 18
#> 142 24.00000 238.00000 10.3 68 9 19
#> 143 16.00000 201.00000 8.0 82 9 20
#> 144 13.00000 238.00000 12.6 64 9 21
#> 145 23.00000 14.00000 9.2 71 9 22
#> 146 36.00000 139.00000 10.3 81 9 23
#> 147 7.00000 49.00000 10.3 69 9 24
#> 148 14.00000 20.00000 16.6 63 9 25
#> 149 30.00000 193.00000 6.9 70 9 26
#> 150 -14.83089 145.00000 13.2 77 9 27
#> 151 14.00000 191.00000 14.3 75 9 28
#> 152 18.00000 131.00000 8.0 76 9 29
#> 153 20.00000 223.00000 11.5 68 9 30
if (FALSE) {
library(dplyr)
impute_below_at(airquality,
.vars = vars(Ozone))
library(ggplot2)
airquality %>%
bind_shadow() %>%
impute_below_at(vars(Ozone, Solar.R)) %>%
add_label_shadow() %>%
ggplot(aes(x = Ozone,
y = Solar.R,
colour = any_missing)) +
geom_point()
}