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Segment keys

Source: R/key-segment.R
key_segments.Rd

These functions are helper functions for working with segment data as keys in guides. They all share the goal of creating a guide key, but have different methods:

  • key_segment_manual() directly uses user-provided vectors to set segments.

  • key_segment_map() makes mappings from a <data.frame> to set segments.

  • key_dendro() is a specialty case for coercing dendrogram data to segments. Be aware that setting the key alone cannot affect the scale limits, and will give misleading results when used incorrectly!

Usage

key_segment_manual(value, oppo, value_end = value, oppo_end = oppo, ...)

key_segment_map(data, ..., .call = caller_env())

key_dendro(dendro = NULL, type = "rectangle")

Arguments

value, value_end

A vector that is interpreted to be along the scale that the guide codifies.

oppo, oppo_end

A vector that is interpreted to be orthogonal to the value and value_end variables.

...

<data-masking> A set of mappings similar to those provided to aes(), which will be evaluated in the data argument. For key_segments_map(), these must contain value and oppo mappings.

data

A <data.frame> or similar object coerced by fortify() to a <data.frame>, in which the mapping argument is evaluated.

.call

A call to display in messages.

dendro

A data structure that can be coerced to a dendrogram through the as.dendrogram() function. When NULL (default) an attempt is made to search for such data in the scale.

type

A string, either "rectangle" or "triangle", indicating the shape of edges between nodes of the dendrogram.

Value

For key_segments_manual() and key_segments_map(), a <data.frame> with the <key_range> class.

See also

Other keys: key_group, key_range, key_specialty, key_standard

Examples

# Giving vectors directly
key_segment_manual(
  value = 0:1, value_end = 2:3,
  oppo  = 1:0, oppo_end  = 3:2
)
#>   value oppo value_end oppo_end
#> 1     0    1         2        3
#> 2     1    0         3        2

# Taking columns of a data frame
data <- data.frame(x = 0:1, y = 1:0, xend = 2:3, yend = 3:2)
key_segment_map(data, value = x, oppo = y, value_end = xend, oppo_end = yend)
#>   value oppo value_end oppo_end
#> 1     0    1         2        3
#> 2     1    0         3        2

# Using dendrogram data
clust <- hclust(dist(USArrests), "ave")
key_dendro(clust)(scale_x_discrete())
#>         value       oppo value_end   oppo_end
#> 1   17.994141 152.313999  4.765625 152.313999
#> 2    4.765625 152.313999  4.765625  77.605024
#> 3   17.994141 152.313999 31.222656 152.313999
#> 4   31.222656 152.313999 31.222656  89.232093
#> 5    4.765625  77.605024  1.500000  77.605024
#> 6    1.500000  77.605024  1.500000  38.527912
#> 7    4.765625  77.605024  8.031250  77.605024
#> 8    8.031250  77.605024  8.031250  44.283922
#> 9    1.500000  38.527912  1.000000  38.527912
#> 10   1.000000  38.527912  1.000000   0.000000
#> 11   1.500000  38.527912  2.000000  38.527912
#> 12   2.000000  38.527912  2.000000   0.000000
#> 13   8.031250  44.283922  3.875000  44.283922
#> 14   3.875000  44.283922  3.875000  28.012211
#> 15   8.031250  44.283922 12.187500  44.283922
#> 16  12.187500  44.283922 12.187500  39.394633
#> 17   3.875000  28.012211  3.000000  28.012211
#> 18   3.000000  28.012211  3.000000   0.000000
#> 19   3.875000  28.012211  4.750000  28.012211
#> 20   4.750000  28.012211  4.750000  15.453120
#> 21   4.750000  15.453120  4.000000  15.453120
#> 22   4.000000  15.453120  4.000000   0.000000
#> 23   4.750000  15.453120  5.500000  15.453120
#> 24   5.500000  15.453120  5.500000  13.896043
#> 25   5.500000  13.896043  5.000000  13.896043
#> 26   5.000000  13.896043  5.000000   0.000000
#> 27   5.500000  13.896043  6.000000  13.896043
#> 28   6.000000  13.896043  6.000000   0.000000
#> 29  12.187500  39.394633  9.625000  39.394633
#> 30   9.625000  39.394633  9.625000  26.363428
#> 31  12.187500  39.394633 14.750000  39.394633
#> 32  14.750000  39.394633 14.750000  28.095803
#> 33   9.625000  26.363428  7.750000  26.363428
#> 34   7.750000  26.363428  7.750000  16.891499
#> 35   9.625000  26.363428 11.500000  26.363428
#> 36  11.500000  26.363428 11.500000  18.417331
#> 37   7.750000  16.891499  7.000000  16.891499
#> 38   7.000000  16.891499  7.000000   0.000000
#> 39   7.750000  16.891499  8.500000  16.891499
#> 40   8.500000  16.891499  8.500000  15.454449
#> 41   8.500000  15.454449  8.000000  15.454449
#> 42   8.000000  15.454449  8.000000   0.000000
#> 43   8.500000  15.454449  9.000000  15.454449
#> 44   9.000000  15.454449  9.000000   0.000000
#> 45  11.500000  18.417331 10.500000  18.417331
#> 46  10.500000  18.417331 10.500000   6.236986
#> 47  11.500000  18.417331 12.500000  18.417331
#> 48  12.500000  18.417331 12.500000  13.297368
#> 49  10.500000   6.236986 10.000000   6.236986
#> 50  10.000000   6.236986 10.000000   0.000000
#> 51  10.500000   6.236986 11.000000   6.236986
#> 52  11.000000   6.236986 11.000000   0.000000
#> 53  12.500000  13.297368 12.000000  13.297368
#> 54  12.000000  13.297368 12.000000   0.000000
#> 55  12.500000  13.297368 13.000000  13.297368
#> 56  13.000000  13.297368 13.000000   0.000000
#> 57  14.750000  28.095803 14.000000  28.095803
#> 58  14.000000  28.095803 14.000000   0.000000
#> 59  14.750000  28.095803 15.500000  28.095803
#> 60  15.500000  28.095803 15.500000  21.167192
#> 61  15.500000  21.167192 15.000000  21.167192
#> 62  15.000000  21.167192 15.000000   0.000000
#> 63  15.500000  21.167192 16.000000  21.167192
#> 64  16.000000  21.167192 16.000000   0.000000
#> 65  31.222656  89.232093 23.796875  89.232093
#> 66  23.796875  89.232093 23.796875  44.837933
#> 67  31.222656  89.232093 38.648438  89.232093
#> 68  38.648438  89.232093 38.648438  54.746831
#> 69  23.796875  44.837933 20.343750  44.837933
#> 70  20.343750  44.837933 20.343750  26.713777
#> 71  23.796875  44.837933 27.250000  44.837933
#> 72  27.250000  44.837933 27.250000  29.054195
#> 73  20.343750  26.713777 17.937500  26.713777
#> 74  17.937500  26.713777 17.937500  16.425489
#> 75  20.343750  26.713777 22.750000  26.713777
#> 76  22.750000  26.713777 22.750000  22.595978
#> 77  17.937500  16.425489 17.000000  16.425489
#> 78  17.000000  16.425489 17.000000   0.000000
#> 79  17.937500  16.425489 18.875000  16.425489
#> 80  18.875000  16.425489 18.875000  12.878100
#> 81  18.875000  12.878100 18.000000  12.878100
#> 82  18.000000  12.878100 18.000000   0.000000
#> 83  18.875000  12.878100 19.750000  12.878100
#> 84  19.750000  12.878100 19.750000  10.736739
#> 85  19.750000  10.736739 19.000000  10.736739
#> 86  19.000000  10.736739 19.000000   0.000000
#> 87  19.750000  10.736739 20.500000  10.736739
#> 88  20.500000  10.736739 20.500000   7.355270
#> 89  20.500000   7.355270 20.000000   7.355270
#> 90  20.000000   7.355270 20.000000   0.000000
#> 91  20.500000   7.355270 21.000000   7.355270
#> 92  21.000000   7.355270 21.000000   0.000000
#> 93  22.750000  22.595978 22.000000  22.595978
#> 94  22.000000  22.595978 22.000000   0.000000
#> 95  22.750000  22.595978 23.500000  22.595978
#> 96  23.500000  22.595978 23.500000  11.456439
#> 97  23.500000  11.456439 23.000000  11.456439
#> 98  23.000000  11.456439 23.000000   0.000000
#> 99  23.500000  11.456439 24.000000  11.456439
#> 100 24.000000  11.456439 24.000000   0.000000
#> 101 27.250000  29.054195 25.750000  29.054195
#> 102 25.750000  29.054195 25.750000  20.198479
#> 103 27.250000  29.054195 28.750000  29.054195
#> 104 28.750000  29.054195 28.750000  23.972143
#> 105 25.750000  20.198479 25.000000  20.198479
#> 106 25.000000  20.198479 25.000000   0.000000
#> 107 25.750000  20.198479 26.500000  20.198479
#> 108 26.500000  20.198479 26.500000  12.614278
#> 109 26.500000  12.614278 26.000000  12.614278
#> 110 26.000000  12.614278 26.000000   0.000000
#> 111 26.500000  12.614278 27.000000  12.614278
#> 112 27.000000  12.614278 27.000000   0.000000
#> 113 28.750000  23.972143 28.000000  23.972143
#> 114 28.000000  23.972143 28.000000   0.000000
#> 115 28.750000  23.972143 29.500000  23.972143
#> 116 29.500000  23.972143 29.500000  14.501034
#> 117 29.500000  14.501034 29.000000  14.501034
#> 118 29.000000  14.501034 29.000000   0.000000
#> 119 29.500000  14.501034 30.000000  14.501034
#> 120 30.000000  14.501034 30.000000   0.000000
#> 121 38.648438  54.746831 34.437500  54.746831
#> 122 34.437500  54.746831 34.437500  20.598507
#> 123 38.648438  54.746831 42.859375  54.746831
#> 124 42.859375  54.746831 42.859375  41.094765
#> 125 34.437500  20.598507 31.875000  20.598507
#> 126 31.875000  20.598507 31.875000  15.026107
#> 127 34.437500  20.598507 37.000000  20.598507
#> 128 37.000000  20.598507 37.000000  15.122897
#> 129 31.875000  15.026107 31.000000  15.026107
#> 130 31.000000  15.026107 31.000000   0.000000
#> 131 31.875000  15.026107 32.750000  15.026107
#> 132 32.750000  15.026107 32.750000  12.438692
#> 133 32.750000  12.438692 32.000000  12.438692
#> 134 32.000000  12.438692 32.000000   0.000000
#> 135 32.750000  12.438692 33.500000  12.438692
#> 136 33.500000  12.438692 33.500000   3.834058
#> 137 33.500000   3.834058 33.000000   3.834058
#> 138 33.000000   3.834058 33.000000   0.000000
#> 139 33.500000   3.834058 34.000000   3.834058
#> 140 34.000000   3.834058 34.000000   0.000000
#> 141 37.000000  15.122897 35.500000  15.122897
#> 142 35.500000  15.122897 35.500000   6.637771
#> 143 37.000000  15.122897 38.500000  15.122897
#> 144 38.500000  15.122897 38.500000  13.352260
#> 145 35.500000   6.637771 35.000000   6.637771
#> 146 35.000000   6.637771 35.000000   0.000000
#> 147 35.500000   6.637771 36.000000   6.637771
#> 148 36.000000   6.637771 36.000000   0.000000
#> 149 38.500000  13.352260 37.500000  13.352260
#> 150 37.500000  13.352260 37.500000   3.929377
#> 151 38.500000  13.352260 39.500000  13.352260
#> 152 39.500000  13.352260 39.500000   8.027453
#> 153 37.500000   3.929377 37.000000   3.929377
#> 154 37.000000   3.929377 37.000000   0.000000
#> 155 37.500000   3.929377 38.000000   3.929377
#> 156 38.000000   3.929377 38.000000   0.000000
#> 157 39.500000   8.027453 39.000000   8.027453
#> 158 39.000000   8.027453 39.000000   0.000000
#> 159 39.500000   8.027453 40.000000   8.027453
#> 160 40.000000   8.027453 40.000000   0.000000
#> 161 42.859375  41.094765 41.000000  41.094765
#> 162 41.000000  41.094765 41.000000   0.000000
#> 163 42.859375  41.094765 44.718750  41.094765
#> 164 44.718750  41.094765 44.718750  33.117815
#> 165 44.718750  33.117815 42.750000  33.117815
#> 166 42.750000  33.117815 42.750000  10.771175
#> 167 44.718750  33.117815 46.687500  33.117815
#> 168 46.687500  33.117815 46.687500  27.779904
#> 169 42.750000  10.771175 42.000000  10.771175
#> 170 42.000000  10.771175 42.000000   0.000000
#> 171 42.750000  10.771175 43.500000  10.771175
#> 172 43.500000  10.771175 43.500000   8.537564
#> 173 43.500000   8.537564 43.000000   8.537564
#> 174 43.000000   8.537564 43.000000   0.000000
#> 175 43.500000   8.537564 44.000000   8.537564
#> 176 44.000000   8.537564 44.000000   0.000000
#> 177 46.687500  27.779904 45.500000  27.779904
#> 178 45.500000  27.779904 45.500000  13.044922
#> 179 46.687500  27.779904 47.875000  27.779904
#> 180 47.875000  27.779904 47.875000  18.993398
#> 181 45.500000  13.044922 45.000000  13.044922
#> 182 45.000000  13.044922 45.000000   0.000000
#> 183 45.500000  13.044922 46.000000  13.044922
#> 184 46.000000  13.044922 46.000000   0.000000
#> 185 47.875000  18.993398 47.000000  18.993398
#> 186 47.000000  18.993398 47.000000   0.000000
#> 187 47.875000  18.993398 48.750000  18.993398
#> 188 48.750000  18.993398 48.750000  10.184218
#> 189 48.750000  10.184218 48.000000  10.184218
#> 190 48.000000  10.184218 48.000000   0.000000
#> 191 48.750000  10.184218 49.500000  10.184218
#> 192 49.500000  10.184218 49.500000   2.291288
#> 193 49.500000   2.291288 49.000000   2.291288
#> 194 49.000000   2.291288 49.000000   0.000000
#> 195 49.500000   2.291288 50.000000   2.291288
#> 196 50.000000   2.291288 50.000000   0.000000