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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()`.

Usage

impute_below_at(.tbl, .vars, prop_below = 0.1, jitter = 0.05, ...)

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

Value

an dataset with values imputed

Details

[Superseded]

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()
}