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Build a data frame

Source: R/dendrogram.R
fortify_data_frame.dendrogram.Rd

[Stable]

This function converts various objects to a data frame.

Usage

# S3 method for class 'dendrogram'
fortify_data_frame(
  data,
  ...,
  priority = "right",
  center = FALSE,
  type = "rectangle",
  leaf_pos = NULL,
  leaf_braches = NULL,
  reorder_branches = TRUE,
  branch_gap = NULL,
  root = NULL,
  double = TRUE,
  data_arg = caller_arg(data),
  call = NULL
)

# S3 method for class 'hclust'
fortify_data_frame(data, ...)

Arguments

data

A hclust or a dendrogram object.

...

Additional arguments passed to dendrogram method.

priority

A string of "left" or "right". if we draw from right to left, the left will override the right, so we take the "left" as the priority. If we draw from left to right, the right will override the left, so we take the "right" as priority. This is used by align_dendro() to provide support of facet operation in ggplot2.

center

A boolean value. if TRUE, nodes are plotted centered with respect to all leaves/tips in the branch. Otherwise (default), plot them in the middle of the direct child nodes.

type

A string indicates the plot type, "rectangle" or "triangle".

leaf_pos

The x-coordinates of the leaf node. Must be the same length of the number of observations in tree.

leaf_braches

Branches of the leaf node. Must be the same length of the number of observations in tree. Usually come from cutree.

reorder_branches

A single boolean value, indicates whether reorder the provided leaf_braches based on the actual index.

branch_gap

A single numeric value indicates the gap between different branches.

root

A length one string or numeric indicates the root branch.

double

A single logical value indicating whether horizontal lines should be doubled when segments span multiple branches. If TRUE, the horizontal lines will be repeated for each branch that the segment spans. If FALSE, only one horizontal line will be drawn. This is used by align_dendro() to provide support of facet operation in ggplot2.

data_arg

The argument name for data. Developers can use it to improve messages. Not used by the user.

call

The execution environment where data and other arguments for the method are collected, e.g., caller_env(). Developers can use it to improve messages. Not used by the user.

Value

A data frame with the node coordinates:

  • .panel: Similar with panel column, but always give the correct branch for usage of the ggplot facet.

  • .index: the original index in the tree for the the node

  • label: node label text

  • x and y: x-axis and y-axis coordinates for the node

  • branch: which branch the node is. You can use this column to color different groups.

  • panel: which panel the node is, if we split the plot into panel using facet_grid, this column will show which panel the node is from. Note: some nodes may fall outside panel (between two panels), so there are possible NA values in this column.

  • leaf: A logical value indicates whether the node is a leaf.

ggalign attributes

edge: A data frame for edge coordinates:

  • .panel: Similar with panel column, but always give the correct branch for usage of the ggplot facet.

  • x and y: x-axis and y-axis coordinates for the start node of the edge.

  • xend and yend: the x-axis and y-axis coordinates of the terminal node for edge.

  • branch: which branch the edge is. You can use this column to color different groups.

  • panel1 and panel2: The panel1 and panel2 columns have the same functionality as panel, but they are specifically for the edge data and correspond to both nodes of each edge.

See also

Other fortify_data_frame methods: fortify_data_frame.character(), fortify_data_frame.default(), fortify_data_frame.matrix(), fortify_data_frame.phylo()

Examples

fortify_data_frame(hclust(dist(USArrests), "ave"))
#>    .index          label         x         y branch  leaf panel .panel
#> 1       9        Florida  1.000000  0.000000   root  TRUE  <NA>   <NA>
#> 2      33 North Carolina  2.000000  0.000000   root  TRUE  <NA>   <NA>
#> 3      NA           <NA>  1.500000 38.527912   root FALSE  <NA>   <NA>
#> 4       5     California  3.000000  0.000000   root  TRUE  <NA>   <NA>
#> 5      20       Maryland  4.000000  0.000000   root  TRUE  <NA>   <NA>
#> 6       3        Arizona  5.000000  0.000000   root  TRUE  <NA>   <NA>
#> 7      31     New Mexico  6.000000  0.000000   root  TRUE  <NA>   <NA>
#> 8      NA           <NA>  5.500000 13.896043   root FALSE  <NA>   <NA>
#> 9      NA           <NA>  4.750000 15.453120   root FALSE  <NA>   <NA>
#> 10     NA           <NA>  3.875000 28.012211   root FALSE  <NA>   <NA>
#> 11      8       Delaware  7.000000  0.000000   root  TRUE  <NA>   <NA>
#> 12      1        Alabama  8.000000  0.000000   root  TRUE  <NA>   <NA>
#> 13     18      Louisiana  9.000000  0.000000   root  TRUE  <NA>   <NA>
#> 14     NA           <NA>  8.500000 15.454449   root FALSE  <NA>   <NA>
#> 15     NA           <NA>  7.750000 16.891499   root FALSE  <NA>   <NA>
#> 16     13       Illinois 10.000000  0.000000   root  TRUE  <NA>   <NA>
#> 17     32       New York 11.000000  0.000000   root  TRUE  <NA>   <NA>
#> 18     NA           <NA> 10.500000  6.236986   root FALSE  <NA>   <NA>
#> 19     22       Michigan 12.000000  0.000000   root  TRUE  <NA>   <NA>
#> 20     28         Nevada 13.000000  0.000000   root  TRUE  <NA>   <NA>
#> 21     NA           <NA> 12.500000 13.297368   root FALSE  <NA>   <NA>
#> 22     NA           <NA> 11.500000 18.417331   root FALSE  <NA>   <NA>
#> 23     NA           <NA>  9.625000 26.363428   root FALSE  <NA>   <NA>
#> 24      2         Alaska 14.000000  0.000000   root  TRUE  <NA>   <NA>
#> 25     24    Mississippi 15.000000  0.000000   root  TRUE  <NA>   <NA>
#> 26     40 South Carolina 16.000000  0.000000   root  TRUE  <NA>   <NA>
#> 27     NA           <NA> 15.500000 21.167192   root FALSE  <NA>   <NA>
#> 28     NA           <NA> 14.750000 28.095803   root FALSE  <NA>   <NA>
#> 29     NA           <NA> 12.187500 39.394633   root FALSE  <NA>   <NA>
#> 30     NA           <NA>  8.031250 44.283922   root FALSE  <NA>   <NA>
#> 31     NA           <NA>  4.765625 77.605024   root FALSE  <NA>   <NA>
#> 32     47     Washington 17.000000  0.000000   root  TRUE  <NA>   <NA>
#> 33     37         Oregon 18.000000  0.000000   root  TRUE  <NA>   <NA>
#> 34     50        Wyoming 19.000000  0.000000   root  TRUE  <NA>   <NA>
#> 35     36       Oklahoma 20.000000  0.000000   root  TRUE  <NA>   <NA>
#> 36     46       Virginia 21.000000  0.000000   root  TRUE  <NA>   <NA>
#> 37     NA           <NA> 20.500000  7.355270   root FALSE  <NA>   <NA>
#> 38     NA           <NA> 19.750000 10.736739   root FALSE  <NA>   <NA>
#> 39     NA           <NA> 18.875000 12.878100   root FALSE  <NA>   <NA>
#> 40     NA           <NA> 17.937500 16.425489   root FALSE  <NA>   <NA>
#> 41     39   Rhode Island 22.000000  0.000000   root  TRUE  <NA>   <NA>
#> 42     21  Massachusetts 23.000000  0.000000   root  TRUE  <NA>   <NA>
#> 43     30     New Jersey 24.000000  0.000000   root  TRUE  <NA>   <NA>
#> 44     NA           <NA> 23.500000 11.456439   root FALSE  <NA>   <NA>
#> 45     NA           <NA> 22.750000 22.595978   root FALSE  <NA>   <NA>
#> 46     NA           <NA> 20.343750 26.713777   root FALSE  <NA>   <NA>
#> 47     25       Missouri 25.000000  0.000000   root  TRUE  <NA>   <NA>
#> 48      4       Arkansas 26.000000  0.000000   root  TRUE  <NA>   <NA>
#> 49     42      Tennessee 27.000000  0.000000   root  TRUE  <NA>   <NA>
#> 50     NA           <NA> 26.500000 12.614278   root FALSE  <NA>   <NA>
#> 51     NA           <NA> 25.750000 20.198479   root FALSE  <NA>   <NA>
#> 52     10        Georgia 28.000000  0.000000   root  TRUE  <NA>   <NA>
#> 53      6       Colorado 29.000000  0.000000   root  TRUE  <NA>   <NA>
#> 54     43          Texas 30.000000  0.000000   root  TRUE  <NA>   <NA>
#> 55     NA           <NA> 29.500000 14.501034   root FALSE  <NA>   <NA>
#> 56     NA           <NA> 28.750000 23.972143   root FALSE  <NA>   <NA>
#> 57     NA           <NA> 27.250000 29.054195   root FALSE  <NA>   <NA>
#> 58     NA           <NA> 23.796875 44.837933   root FALSE  <NA>   <NA>
#> 59     12          Idaho 31.000000  0.000000   root  TRUE  <NA>   <NA>
#> 60     27       Nebraska 32.000000  0.000000   root  TRUE  <NA>   <NA>
#> 61     17       Kentucky 33.000000  0.000000   root  TRUE  <NA>   <NA>
#> 62     26        Montana 34.000000  0.000000   root  TRUE  <NA>   <NA>
#> 63     NA           <NA> 33.500000  3.834058   root FALSE  <NA>   <NA>
#> 64     NA           <NA> 32.750000 12.438692   root FALSE  <NA>   <NA>
#> 65     NA           <NA> 31.875000 15.026107   root FALSE  <NA>   <NA>
#> 66     35           Ohio 35.000000  0.000000   root  TRUE  <NA>   <NA>
#> 67     44           Utah 36.000000  0.000000   root  TRUE  <NA>   <NA>
#> 68     NA           <NA> 35.500000  6.637771   root FALSE  <NA>   <NA>
#> 69     14        Indiana 37.000000  0.000000   root  TRUE  <NA>   <NA>
#> 70     16         Kansas 38.000000  0.000000   root  TRUE  <NA>   <NA>
#> 71     NA           <NA> 37.500000  3.929377   root FALSE  <NA>   <NA>
#> 72      7    Connecticut 39.000000  0.000000   root  TRUE  <NA>   <NA>
#> 73     38   Pennsylvania 40.000000  0.000000   root  TRUE  <NA>   <NA>
#> 74     NA           <NA> 39.500000  8.027453   root FALSE  <NA>   <NA>
#> 75     NA           <NA> 38.500000 13.352260   root FALSE  <NA>   <NA>
#> 76     NA           <NA> 37.000000 15.122897   root FALSE  <NA>   <NA>
#> 77     NA           <NA> 34.437500 20.598507   root FALSE  <NA>   <NA>
#> 78     11         Hawaii 41.000000  0.000000   root  TRUE  <NA>   <NA>
#> 79     48  West Virginia 42.000000  0.000000   root  TRUE  <NA>   <NA>
#> 80     19          Maine 43.000000  0.000000   root  TRUE  <NA>   <NA>
#> 81     41   South Dakota 44.000000  0.000000   root  TRUE  <NA>   <NA>
#> 82     NA           <NA> 43.500000  8.537564   root FALSE  <NA>   <NA>
#> 83     NA           <NA> 42.750000 10.771175   root FALSE  <NA>   <NA>
#> 84     34   North Dakota 45.000000  0.000000   root  TRUE  <NA>   <NA>
#> 85     45        Vermont 46.000000  0.000000   root  TRUE  <NA>   <NA>
#> 86     NA           <NA> 45.500000 13.044922   root FALSE  <NA>   <NA>
#> 87     23      Minnesota 47.000000  0.000000   root  TRUE  <NA>   <NA>
#> 88     49      Wisconsin 48.000000  0.000000   root  TRUE  <NA>   <NA>
#> 89     15           Iowa 49.000000  0.000000   root  TRUE  <NA>   <NA>
#> 90     29  New Hampshire 50.000000  0.000000   root  TRUE  <NA>   <NA>
#> 91     NA           <NA> 49.500000  2.291288   root FALSE  <NA>   <NA>
#> 92     NA           <NA> 48.750000 10.184218   root FALSE  <NA>   <NA>
#> 93     NA           <NA> 47.875000 18.993398   root FALSE  <NA>   <NA>
#> 94     NA           <NA> 46.687500 27.779904   root FALSE  <NA>   <NA>
#> 95     NA           <NA> 44.718750 33.117815   root FALSE  <NA>   <NA>
#> 96     NA           <NA> 42.859375 41.094765   root FALSE  <NA>   <NA>
#> 97     NA           <NA> 38.648438 54.746831   root FALSE  <NA>   <NA>
#> 98     NA           <NA> 31.222656 89.232093   root FALSE  <NA>   <NA>