50 |
51 | ``` r
52 | ggparallel(list("gear", "cyl"), data=mtcars, method="hammock", ratio=0.25)
53 | ```
54 |
55 | 
56 |
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1 | url: https://heike.github.io/ggparallel/
2 | template:
3 | bootstrap: 5
4 |
5 |
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1 | comment: false
2 |
3 | coverage:
4 | status:
5 | project:
6 | default:
7 | target: auto
8 | threshold: 1%
9 | informational: true
10 | patch:
11 | default:
12 | target: auto
13 | threshold: 1%
14 | informational: true
15 |
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1 | ## Test environments
2 | * local OS X install, R 4.3.2
3 | * ubuntu 22.04 (on Github actions), R 4.3.2
4 | * win-builder (devel and release)
5 |
6 | ## R CMD check results
7 | There were no ERRORs, WARNINGs or NOTEs.
8 |
9 | ## Downstream dependencies
10 | There are no reverse dependencies.
11 |
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1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 | YEAR: 2012-2024 48 | COPYRIGHT HOLDER: Heike Hofmann; Marie Vendettuoli 49 |50 | 51 |
Source: inst/CITATION
Hofmann H, Vendettuoli M (2013). 64 | “Common Angle Plots as Perception-True Visualizations of Categorical Associations.” 65 | IEEE Transactions on Visualization & Computer Graphics, 2297–2305. 66 | doi:10.1109/TVCG.2013.140. 67 |
68 |@Article{,
69 | title = {Common Angle Plots as Perception-True Visualizations of Categorical Associations},
70 | author = {Heike Hofmann and Marie Vendettuoli},
71 | journal = {IEEE Transactions on Visualization & Computer Graphics},
72 | year = {2013},
73 | number = {12},
74 | pages = {2297--2305},
75 | doi = {10.1109/TVCG.2013.140},
76 | }
77 | The R package ggparallel implements and combines different types of parallel coordinate plots for categorical data: hammock plots, parallel sets plots, and common angle plots, as well as common angle plots with a hammock-like adjustment for line widths.
The package is available on CRAN:
75 | 76 |You can install the development version of ggparallel from GitHub with:
77 | 79 |
84 | library(ggparallel)
85 | #> Loading required package: ggplot2
86 | data(mtcars)
87 |
88 | ggparallel(list("gear", "cyl"), data=mtcars)
91 | ggparallel(list("gear", "cyl"), data=mtcars, method="hammock", ratio=0.25)
Table knownGene from track UCSC Genes was downloaded from the UCSC table 58 | browser for the human genome assembly (hg18, May 2006) and filtered for a 59 | selection of pathways associated with human metabolism was obtained from KEGG 60 | PATHWAY database. Bioconductor package KEGG.db was used to provide mappings 61 | between gene and pathway identifiers.
62 |Fujita PA, Rhead B, Zweig AS, Hinrichs AS, Karolchik D, Cline MS, 76 | Goldman M, Barber GP, Clawson H, Coelho A, Diekhans M, Dreszer TR, Giardine 77 | BM, Harte RA, Hillman-Jackson J, Hsu F, Kirkup V, Kuhn RM, Learned K, Li CH, 78 | Meyer LR, Pohl A, Raney BJ, Rosenbloom KR, Smith KE, Haussler D, Kent WJ. The 79 | UCSC Genome Browser database: update 2011. Nucleic Acids Res. 2010 Oct 18. 80 | http://genome.ucsc.edu/index.html?org=Human&db=hg19&hgsid=289810087
81 |Marc Carlson, Seth Falcon, Herve Pages and Nianhua Li (). KEGG.db: A set 82 | of annotation maps for KEGG. R package version 2.6.1.
83 |Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., and Tanabe, M.; KEGG for 84 | integration and interpretation of large-scale molecular datasets. Nucleic 85 | Acids Res. 40, D109-D114 (2012)
86 |Kanehisa, M. and Goto, S.; KEGG: Kyoto Encyclopedia of Genes and Genomes. 87 | Nucleic Acids Res. 28, 27-30 (2000).
88 |library(ggplot2)
93 | library(RColorBrewer)
94 | genes$chrom <- factor(genes$chrom, levels=c(paste("chr", 1:22, sep=""),
95 | "chrX", "chrY"))
96 | ggparallel(
97 | list("path", "chrom"),
98 | text.offset = c(0.03, 0,-0.03),
99 | data = genes,
100 | width = 0.1,
101 | order = c(1, 0),
102 | angle = 0,
103 | color = "white",
104 | factorlevels = c(sapply(unique(genes$chrom), as.character),unique(genes$path))
105 | ) +
106 | scale_fill_manual(
107 | values = c(brewer.pal("YlOrRd", n = 9), rep("grey80", 24)),
108 | guide = "none"
109 | ) +
110 | scale_colour_manual(
111 | values = c(brewer.pal("YlOrRd", n = 9), rep("grey80", 24)),
112 | guide = "none"
113 | ) +
114 | coord_flip()
115 | 
116 | ggparallel implements and combines different types of parallel
56 | coordinate plots for categorical data: hammock plots, parallel sets plots,
57 | common angle plots, and common angle plots with a hammock-like adjustment
58 | for line widths.
ggparallel(
64 | vars = list(),
65 | data,
66 | weight = NULL,
67 | method = "angle",
68 | alpha = 0.5,
69 | width = 0.25,
70 | order = 1,
71 | ratio = NULL,
72 | asp = NULL,
73 | label = TRUE,
74 | label.colour = "grey90",
75 | label.size = 4,
76 | text.angle = 90,
77 | text.offset = NULL,
78 | same.level = FALSE,
79 | ...
80 | )list of variable names to be included in the plotting. Order of 87 | the variables is preserved in the display
data frame
weighting variable - use character string
plotting method to use - one of angle,
100 | adj.angle, parset, or hammock, for a hammock plot
101 | the aspect ratio needs to be fixed.
level of alpha blending for the fill color in ribbons, value 106 | has to be between 0 and 1, defaults to 0.5.
width of variables
flag variable with three levels -1, 0, 1 for levels in 115 | decreasing order, levels in increasing order and levels unchanged. This 116 | variable can be either a scalar or a vector
used for methods with angle adjustments (method =
121 | 'hammock', 'adj.angle'): specifies the height (width for horizontal
122 | displays) of the widest line as ratio of the overall display height (width
123 | for horizontal displays).
aspect ratio of the plot - it will be set to a default of 1 in 128 | the case of hammock plots.
binary variable (vector), whether labels should be shown.
character of colour in which the label should be shown. Ignored, if `label` is FALSE.
numeric value to determine the size in which labels are shown, defaults to 4
numeric value to determine the angle under which labels are shown.
(vector) of values for offsetting the labels
are all variables using the same levels? If yes, simplify the labelling
passed on directly to all of the ggplot2 commands
returns a ggplot2 object that can be plotted directly or used as base 164 | layer for additional modifications.
165 |Parallel sets have been suggested by kosara:2006 as a visualization 169 | technique to incorporate categorical variables into a parallel coordinate 170 | plot introduced by wegman:1990 and inselberg:1985. The parallel 171 | sets implemented here are reduced to representations of neighboring 172 | two-dimensional relationships only rather than the hierarchical version 173 | originally suggested.
174 |Both versions, however, show perceptual problems with interpreting line 175 | widths, leading to potentially wrong conclusions about the data. The hammock 176 | display, introduced by schonlau:2003, and the common angle plots are 177 | two approaches at fixing this problem: in Hammock plots the linewidth is 178 | adjusted by a factor countering the strength of the illusion, in the common 179 | angle plot all lines are adjusted to show the same angle - making line widths 180 | again comparable across ribbons.
181 |Additionally, we can also adjust ribbons in the common angle display for the
182 | angle, to make them appear having the same width (or height) across the
183 | display. We refer to this method as adj.angle.
data(mtcars)
189 |
190 | ggparallel(list("gear", "cyl"), data=mtcars)
191 | 
192 | ggparallel(list("gear", "cyl"), data=mtcars, method="hammock", ratio=0.25)
193 | 
194 |
195 | require(RColorBrewer)
196 | require(ggplot2)
197 | cols <- c(brewer.pal(4, "Reds")[-1], brewer.pal(4, "Blues")[-1])
198 | ggparallel(list("gear", "cyl"), ratio=0.2, data=mtcars,
199 | method="hammock", text.angle=0) +
200 | scale_fill_manual(values=cols) + scale_colour_manual(values=cols) +
201 | theme_bw()
202 | 
203 |
204 | ## combination of common angle plot and hammock adjustment:
205 | ggparallel(list("gear", "cyl"), data=mtcars, method="adj.angle",
206 | ratio=2)
207 | 
208 |
209 | ## compare with method='parset'
210 | ggparallel(list("gear", "cyl"), data=mtcars, method='parset')
211 | 
212 |
213 | ## flip plot and rotate text
214 | ggparallel(list("gear", "cyl"), data=mtcars, text.angle=0) +
215 | coord_flip()
216 | 
217 |
218 | ## change colour scheme
219 | ggparallel(list("gear", "cyl"), data=mtcars, text.angle=0) +
220 | coord_flip() +
221 | scale_fill_brewer(palette="Set1") +
222 | scale_colour_brewer(palette="Set1")
223 | 
224 |
225 | ## example with more than two variables:
226 | titanic <- as.data.frame(Titanic)
227 | ggparallel(names(titanic)[c(1,4,2,1)], order=0, titanic, weight="Freq") +
228 | scale_fill_brewer(palette="Paired", guide="none") +
229 | scale_colour_brewer(palette="Paired", guide="none")
230 | #> Warning: attributes are not identical across measure variables; they will be dropped
231 | 
232 |
233 | if (FALSE) {
234 | cols <- c(brewer.pal(5,"Blues")[-1], brewer.pal(3, "Oranges")[-1],
235 | brewer.pal(3, "Greens")[-1])
236 | ggparallel(names(titanic)[c(1,4,2,1)], order=0, titanic, weight="Freq") +
237 | scale_fill_manual(values=cols, guide="none") +
238 | scale_colour_manual(values=cols, guide="none") + theme_bw()
239 |
240 | ## hammock plot with same width lines
241 | ggparallel(names(titanic)[c(1,4,2,3)], titanic, weight=1, asp=0.5,
242 | method="hammock", ratio=0.2, order=c(0,0)) +
243 | theme( legend.position="none") +
244 | scale_fill_brewer(palette="Paired") +
245 | scale_colour_brewer(palette="Paired")
246 |
247 | ## hammock plot with line widths adjusted by frequency
248 | ggparallel(names(titanic)[c(1,4,2,3)], titanic, weight="Freq",
249 | asp=0.5, method="hammock", order=c(0,0), text.angle=0,
250 | width=0.45) +
251 | theme( legend.position="none")
252 |
253 |
254 | ## biological examples: genes and pathways
255 | data(genes)
256 | cols <- c(rep("grey80", 24), brewer.pal("YlOrRd", n = 9))
257 | genes$chrom <- factor(genes$chrom, levels=c(paste("chr", 1:22, sep=""), "chrX", "chrY"))
258 | ggparallel(list("path", "chrom"), text.offset=c(0.03, 0,-0.03),
259 | data = genes, width=0.1, order=c(1,0), text.angle=0,
260 | color="white",
261 | factorlevels = c(sapply(unique(genes$chrom), as.character),
262 | unique(genes$path))) +
263 | scale_fill_manual(values = cols, guide="none") +
264 | scale_colour_manual(values = cols, guide="none") +
265 | coord_flip()
266 | }
267 |
268 | data(Titanic)
269 | titanic <- as.data.frame(Titanic)
270 |
271 | titanic$SexSurvived <- with(titanic, interaction(Sex, Survived))
272 | titanic$SexClassSurvived <- with(titanic, interaction(Sex,Class, Survived))
273 |
274 | ggparallel(vars=list("Survived", "SexSurvived", "SexClassSurvived"), weight="Freq", data=titanic) +
275 | theme(legend.position="none") +
276 | scale_fill_manual(values = rep(c("Orange", "Steelblue"), 14)) +
277 | scale_colour_manual(values = rep(c("Orange", "Steelblue"), 14))
278 | 
279 |
280 | ###########
281 |
282 | p1 <- ggparallel(vars=list("Survived", "SexSurvived", "SexClassSurvived"),
283 | weight="Freq", data=titanic, label = FALSE) +
284 | theme(legend.position="none") +
285 | scale_fill_manual(values = rep(c("Orange", "Steelblue"), 14)) +
286 | scale_colour_manual(values = rep(c("Orange", "Steelblue"), 14))
287 | p2 <- ggparallel(vars=list("Survived", "SexSurvived", "SexClassSurvived"),
288 | weight="Freq", data=titanic, label = TRUE) +
289 | theme(legend.position="none") +
290 | scale_fill_manual(values = rep(c("Orange", "Steelblue"), 14)) +
291 | scale_colour_manual(values = rep(c("Orange", "Steelblue"), 14))
292 |
293 | ggplot_build(p2)$data[[4]]
294 | #> x y label PANEL group colour size angle hjust vjust alpha
295 | #> 1 1.01 355.49 Yes 1 -1 grey90 4 90 0.5 0.5 NA
296 | #> 2 1.01 1455.99 No 1 -1 grey90 4 90 0.5 0.5 NA
297 | #> 3 2.01 62.99 Female.No 1 -1 grey90 4 90 0.5 0.5 NA
298 | #> 4 2.01 297.99 Female.Yes 1 -1 grey90 4 90 0.5 0.5 NA
299 | #> 5 2.01 653.49 Male.Yes 1 -1 grey90 4 90 0.5 0.5 NA
300 | #> 6 2.01 1518.99 Male.No 1 -1 grey90 4 90 0.5 0.5 NA
301 | #> 7 3.01 1.49 Female.Crew.No 1 -1 grey90 4 90 0.5 0.5 NA
302 | #> 8 3.01 4.99 Female.1st.No 1 -1 grey90 4 90 0.5 0.5 NA
303 | #> 9 3.01 13.49 Female.2nd.No 1 -1 grey90 4 90 0.5 0.5 NA
304 | #> 10 3.01 29.99 Female.Crew.Yes 1 -1 grey90 4 90 0.5 0.5 NA
305 | #> 11 3.01 52.49 Male.2nd.Yes 1 -1 grey90 4 90 0.5 0.5 NA
306 | #> 12 3.01 95.99 Male.1st.Yes 1 -1 grey90 4 90 0.5 0.5 NA
307 | #> 13 3.01 170.99 Male.3rd.Yes 1 -1 grey90 4 90 0.5 0.5 NA
308 | #> 14 3.01 259.99 Female.3rd.Yes 1 -1 grey90 4 90 0.5 0.5 NA
309 | #> 15 3.01 351.49 Female.2nd.Yes 1 -1 grey90 4 90 0.5 0.5 NA
310 | #> 16 3.01 450.99 Female.3rd.No 1 -1 grey90 4 90 0.5 0.5 NA
311 | #> 17 3.01 562.99 Male.1st.No 1 -1 grey90 4 90 0.5 0.5 NA
312 | #> 18 3.01 692.49 Female.1st.Yes 1 -1 grey90 4 90 0.5 0.5 NA
313 | #> 19 3.01 839.99 Male.2nd.No 1 -1 grey90 4 90 0.5 0.5 NA
314 | #> 20 3.01 1012.99 Male.Crew.Yes 1 -1 grey90 4 90 0.5 0.5 NA
315 | #> 21 3.01 1319.99 Male.3rd.No 1 -1 grey90 4 90 0.5 0.5 NA
316 | #> 22 3.01 1865.99 Male.Crew.No 1 -1 grey90 4 90 0.5 0.5 NA
317 | #> family fontface lineheight
318 | #> 1 1 1.2
319 | #> 2 1 1.2
320 | #> 3 1 1.2
321 | #> 4 1 1.2
322 | #> 5 1 1.2
323 | #> 6 1 1.2
324 | #> 7 1 1.2
325 | #> 8 1 1.2
326 | #> 9 1 1.2
327 | #> 10 1 1.2
328 | #> 11 1 1.2
329 | #> 12 1 1.2
330 | #> 13 1 1.2
331 | #> 14 1 1.2
332 | #> 15 1 1.2
333 | #> 16 1 1.2
334 | #> 17 1 1.2
335 | #> 18 1 1.2
336 | #> 19 1 1.2
337 | #> 20 1 1.2
338 | #> 21 1 1.2
339 | #> 22 1 1.2
340 | p1 + geom_text(aes(x = x, y=y, label = label), data = ggplot_build(p2)$data[[4]])
341 | 
342 | genes
60 | ggparallel()
65 | package-ggparallel
70 | R/help.R
49 | package-ggparallel.RdThe main function ggparallel implements three types of
54 | parallel coordinate plots for categorical data: hammock plots, parallel
55 | sets plots, and common angle plots.