,
Vesna Memisevic [ctb],
Jonathan Chung [ctb]| Title: | 2D and 3D Hive Plots for R |
|---|---|
| Description: | Creates and plots 2D and 3D hive plots. Hive plots are a unique method of displaying networks of many types in which node properties are mapped to axes using meaningful properties rather than being arbitrarily positioned. The hive plot concept was invented by Martin Krzywinski at the Genome Science Center (www.hiveplot.net/). Keywords: networks, food webs, linnet, systems biology, bioinformatics. |
| Authors: | Bryan A. Hanson [aut, cre] ,
Vesna Memisevic [ctb],
Jonathan Chung [ctb] |
| Maintainer: | Bryan A. Hanson <[email protected]> |
| License: | GPL-3 |
| Version: | 0.4.0 |
| Built: | 2025-02-14 05:27:19 UTC |
| Source: | https://github.com/bryanhanson/hiver |
Creates and plots 2D and 3D hive plots. Hive plots are a unique method of displaying networks of many types in which node properties are mapped to axes using meaningful properties rather than being arbitrarily positioned. The hive plot concept was invented by Martin Krzywinski at the Genome Science Center (www.hiveplot.net/). Keywords: networks, food webs, linnet, systems biology, bioinformatics.
Bryan A. Hanson, DePauw University, Greencastle Indiana USA
Useful links:
This function will take an adjacency graph and convert it into a basic
HivePlotData object. Further manipulation by
mineHPD will almost certainly be required before the data can
be plotted.
adj2HPD(M = NULL, axis.cols = NULL, type = "2D", desc = NULL, ...)adj2HPD(M = NULL, axis.cols = NULL, type = "2D", desc = NULL, ...)
M |
A matrix with named dimensions. The names should be the node names. Should not be symmetric. If it is, only the lower triangle is used and a message is given. |
axis.cols |
A character vector giving the colors desired for the axes. |
type |
One of |
desc |
Character. A description of the data set. |
... |
Other parameters to be passed downstream. |
This function produces a "bare bones" HivePlotData object. The names
of the dimensions of M are used as the node names. All nodes are
given size 1, an id number (1:number of nodes), are colored black and
are assigned to axis 1. The edges are all gray, and the weight is M[i,j].
The user will likely have to manually make some changes to the resulting
HivePlotData object before plotting. Alternatively,
mineHPD may be able to extract some information buried in the
data, but even then, the user will probably need to make some adjustments.
See the examples.
A HivePlotData object.
Bryan A. Hanson, DePauw University. [email protected] Vesna Memisevic contributed a fix that limited this function to bipartite networks (changed in v. 0.2-12).
### Example 1: a bipartite network ### Note: this first example has questionable scientific value! ### The purpose is to show how to troubleshoot and ### manipulate a HivePlotData object. if (require("bipartite")) { data(Safariland, package = "bipartite") # This is a bipartite network # You may wish to do ?Safariland or ?Safari for background hive1 <- adj2HPD(Safariland, desc = "Safariland data set from bipartite") sumHPD(hive1) # Note that all nodes are one axis with radius 1. Process further: hive2 <- mineHPD(hive1, option = "rad <- tot.edge.count") sumHPD(hive2) # All nodes still on 1 axis but degree has been used to set radius # Process further: hive3 <- mineHPD(hive2, option = "axis <- source.man.sink") sumHPD(hive3, chk.all = TRUE) # Note that mineHPD is generating some warnings, telling us # that the first 9 nodes were not assigned to an axis. Direct # inspection of the data shows that these nodes are insects # that did not visit any of the flowers in this particular study. # Pretty up a few things, then plot: hive3$edges$weight <- sqrt(hive3$edges$weight) * 0.5 hive3$nodes$size <- 0.5 plotHive(hive3) # This is a one-sided hive plot of 2 axes, which results # from the curvature of the splines. We can manually fix # this by reversing the ends of edges as follows: for (n in seq(1, length(hive3$edges$id1), by = 2)) { a <- hive3$edges$id1[n] b <- hive3$edges$id2[n] hive3$edges$id1[n] <- b hive3$edges$id2[n] <- a } plotHive(hive3) ### Example 2, a simple random adjacency matrix set.seed(31) nr <- 20 nc <- 15 M <- matrix(floor(runif(nc * nr, 0, 10)), ncol = nc) colnames(M) <- sample(c(letters, LETTERS), nc, replace = FALSE) rownames(M) <- sample(c(letters, LETTERS), nr, replace = FALSE) hive4 <- adj2HPD(M) sumHPD(hive4) }### Example 1: a bipartite network ### Note: this first example has questionable scientific value! ### The purpose is to show how to troubleshoot and ### manipulate a HivePlotData object. if (require("bipartite")) { data(Safariland, package = "bipartite") # This is a bipartite network # You may wish to do ?Safariland or ?Safari for background hive1 <- adj2HPD(Safariland, desc = "Safariland data set from bipartite") sumHPD(hive1) # Note that all nodes are one axis with radius 1. Process further: hive2 <- mineHPD(hive1, option = "rad <- tot.edge.count") sumHPD(hive2) # All nodes still on 1 axis but degree has been used to set radius # Process further: hive3 <- mineHPD(hive2, option = "axis <- source.man.sink") sumHPD(hive3, chk.all = TRUE) # Note that mineHPD is generating some warnings, telling us # that the first 9 nodes were not assigned to an axis. Direct # inspection of the data shows that these nodes are insects # that did not visit any of the flowers in this particular study. # Pretty up a few things, then plot: hive3$edges$weight <- sqrt(hive3$edges$weight) * 0.5 hive3$nodes$size <- 0.5 plotHive(hive3) # This is a one-sided hive plot of 2 axes, which results # from the curvature of the splines. We can manually fix # this by reversing the ends of edges as follows: for (n in seq(1, length(hive3$edges$id1), by = 2)) { a <- hive3$edges$id1[n] b <- hive3$edges$id2[n] hive3$edges$id1[n] <- b hive3$edges$id2[n] <- a } plotHive(hive3) ### Example 2, a simple random adjacency matrix set.seed(31) nr <- 20 nc <- 15 M <- matrix(floor(runif(nc * nr, 0, 10)), ncol = nc) colnames(M) <- sample(c(letters, LETTERS), nc, replace = FALSE) rownames(M) <- sample(c(letters, LETTERS), nr, replace = FALSE) hive4 <- adj2HPD(M) sumHPD(hive4) }
This function takes a list of HivePlotData objects of type =
"3D" and plots each in its own rgl window using its own arguments,
then adds a controller which handles rotation and scaling.
animateHive(hives = list(), cmds = list(), xy = 400, ...)animateHive(hives = list(), cmds = list(), xy = 400, ...)
hives |
A list of |
cmds |
A list of arguments corresponding to how you want each hive plotted. |
xy |
An integer giving the size of the |
... |
Other parameters to be passed downstream to |
None. Side effect is one or more plots.
If you click the 'continue rotating' box on the controller window, be sure to unclick it and wait for the system to halt before closing any of the windows. If you close the controller w/o doing this, the remaining open windows with the hive plots will continue rotating endlessly and it seems you can't get their attention to close the windows.
Bryan A. Hanson, DePauw University. [email protected]
## Not run: require("rgl") # Sillyness: let's draw different hives with different settings # List of hives t4 <- ranHiveData(type = "3D", nx = 4) t5 <- ranHiveData(type = "3D", nx = 5) t6 <- ranHiveData(type = "3D", nx = 6) myhives <- list(t4, t5, t6) # List of arguments to plot in different coordinate systems cmd1 <- list(method = "abs", LA = TRUE, dr.nodes = FALSE, ch = 10) cmd2 <- list(method = "rank", LA = TRUE, dr.nodes = FALSE, ch = 2) cmd3 <- list(method = "norm", LA = TRUE, dr.nodes = FALSE, ch = 0.1) mycmds <- list(cmd1, cmd2, cmd3) # animateHive(hives = myhives, cmds = mycmds) ## End(Not run)## Not run: require("rgl") # Sillyness: let's draw different hives with different settings # List of hives t4 <- ranHiveData(type = "3D", nx = 4) t5 <- ranHiveData(type = "3D", nx = 5) t6 <- ranHiveData(type = "3D", nx = 6) myhives <- list(t4, t5, t6) # List of arguments to plot in different coordinate systems cmd1 <- list(method = "abs", LA = TRUE, dr.nodes = FALSE, ch = 10) cmd2 <- list(method = "rank", LA = TRUE, dr.nodes = FALSE, ch = 2) cmd3 <- list(method = "norm", LA = TRUE, dr.nodes = FALSE, ch = 0.1) mycmds <- list(cmd1, cmd2, cmd3) # animateHive(hives = myhives, cmds = mycmds) ## End(Not run)
Plant-pollinator data sets which were derived ultimately from Vasquez and
Simberloff, 2003. These are two-trophic level systems that have almost
exactly the same plants and pollinators. Safari is from an
undisturbed area, while Arroyo is from a nearby location grazed by
cattle. In the original publication, the data sets are called Safariland
and Arroyo Goye. See Details for how the original data was converted.
These data sets are HivePlotData objects. They were created
from the datasets Safariland and vazarr in the package
bipartite. The process was the same for each: 1. Plants were placed
on one axis, pollinators on the other. 2. A radius was assigned by
calculating d' using function dfun in package bipartite. d'
is an index of specialization; higher values mean the plant or pollinator is
more specialized. 3. Edge weights were assigned proportional to the square
root of the normalized number of visits of a pollinator to a plant. Thus
the width of the edge drawn is an indication of the visitation rate. 4.
The number of visits were divided manually into 4 groups and used to assign
edge colors ranging from white to red. The redder colors represent greater
numbers of visits, and the color-coding is comparable for each data set.
Bryan A. Hanson, DePauw University, Greencastle Indiana USA
This function inspects the classes of each part of a HPD as a
means of verifying its integrity. A few other characteristics are checked
as well.
chkHPD(HPD, confirm = FALSE)chkHPD(HPD, confirm = FALSE)
HPD |
An object of S3 class |
confirm |
Logical; if |
A logical value; TRUE is there is a problem, otherwise
FALSE.
Bryan A. Hanson, DePauw University. [email protected]
sumHPD which allows inspection (checking) of many
properties of your HPD.
test4 <- ranHiveData(nx = 4) good <- chkHPD(test4, confirm = TRUE) # mess it up and do again # next test is not run as it halts execution ## Not run: test4$nodes$color <- as.factor(test4$nodes$color) bad <- chkHPD(test4) ## End(Not run)test4 <- ranHiveData(nx = 4) good <- chkHPD(test4, confirm = TRUE) # mess it up and do again # next test is not run as it halts execution ## Not run: test4$nodes$color <- as.factor(test4$nodes$color) bad <- chkHPD(test4) ## End(Not run)
This function will read a .dot file containing a graph specification in the
DOT language, and (optionally) using two other files, convert the
information into a HivePlotData object.
dot2HPD( file = NULL, node.inst = NULL, edge.inst = NULL, axis.cols = NULL, type = "2D", desc = NULL, ... )dot2HPD( file = NULL, node.inst = NULL, edge.inst = NULL, axis.cols = NULL, type = "2D", desc = NULL, ... )
file |
The path to the .dot file to be processed. |
node.inst |
The path to a .csv file containing instructions about how
to map node tags in the .dot file to parameters in the |
edge.inst |
The path to a .csv file containing instructions about how
to map edge tags in the .dot file to parameters in the |
axis.cols |
A character vector giving the colors desired for the axes. |
type |
One of |
desc |
Character. A description of the data set. |
... |
Other parameters to be passed downstream. |
This function is currently agnostic with respect to whether or not the .dot
graph is directed or not. Either type will be processed, but if the graph
is directed, this will only be indirectly stored in the HivePlotData
object (in that the first node of an edge in the .dot file will be in
HPD$nodes$id1 and the second node of an edge will be in
HPD$nodes$id2. This fact can be used; see the vignette and
mineHPD. Keep in mind the .dot standard is fairly loose.
This function has been tested to work with several .dot files, include those
with multiple tag=value attributes (in such cases, a typical line in the dot
file should be formatted like this: node_name [tag1 = value1, tag2 =
value2];). If you have trouble, please file a issue at Github so I can
track it down.
A HivePlotData object.
Bryan A. Hanson, DePauw University. [email protected]
See the vignette for an example of using this function. Use
browseVignettes("HiveR") to produce the vignette. adj2HPD for a means of importing adjacency matrices.
This function analyzes the edges of a HivePlotData object in order to
draw 3D splines representing those edges. Each pair of nodes at the ends of
an edge is identified, and a control point is computed. This information is
passed to rcsr to work out the details.
drawHiveSpline(HPD, L_A = FALSE, ...)drawHiveSpline(HPD, L_A = FALSE, ...)
HPD |
An object of S3 class |
L_A |
Logical: should splines be drawn with |
... |
Parameters to be passed downstream. |
None. A spline is added to the 3D hive plot in progress.
Bryan A. Hanson, DePauw University. [email protected]
plot3dHive which calls this function and is the user
interface.
This function will take an edge list and convert it into a basic
HivePlotData object. Further manipulation by
mineHPD will almost certainly be required before the data can
be plotted.
edge2HPD(edge_df = NULL, axis.cols = NULL, type = "2D", desc = NULL, ...)edge2HPD(edge_df = NULL, axis.cols = NULL, type = "2D", desc = NULL, ...)
edge_df |
A data frame containing edge list information. Columns should be node1, node2, edge weight (column names are arbitrary). Edge weight information is optional. If missing, edge weights will be set to 1. |
axis.cols |
A character vector giving the colors desired for the axes. |
type |
One of |
desc |
Character. A description of the data set. |
... |
Other parameters to be passed downstream. |
This function produces a "bare bones" HivePlotData object. The user
will likely have to make some changes manually to the resulting
HivePlotData object before plotting. Alternatively,
mineHPD may be able to extract some information buried in the
data, but even then, the user might need to make some adjustments. See the
examples.
A HivePlotData object.
Jonathan H. Chung, with minor changes for consistency by Bryan A. Hanson.
# Create a simple edge list & process it edges <- data.frame( lab1 = LETTERS[c(1:8, 7)], lab2 = LETTERS[c(2:4, 1:3, 4, 2, 2)], weight = c(1, 1, 2, 2, 3, 1, 2, 3, 1) ) td <- edge2HPD(edge_df = edges, desc = "Test of edge2HPD") td.out <- sumHPD(td, plot.list = TRUE) # compare: edges td.out[, c(3, 7, 8)]# Create a simple edge list & process it edges <- data.frame( lab1 = LETTERS[c(1:8, 7)], lab2 = LETTERS[c(2:4, 1:3, 4, 2, 2)], weight = c(1, 1, 2, 2, 3, 1, 2, 3, 1) ) td <- edge2HPD(edge_df = edges, desc = "Test of edge2HPD") td.out <- sumHPD(td, plot.list = TRUE) # compare: edges td.out[, c(3, 7, 8)]
This is an HPD (HivePlotData object) derived from the
built-in hair eye color data set (see ?HairEyeColor). It serves as a
test 2D data set, and the example below shows how it was built. While every
data set is different and will require a different approach, the example
illustrates the general approach to building a hive plot from scratch,
step-by-step.
The format is described in detail at HPD.
# An example of building an HPD from scratch ### Step 0. Get to know your data. data(HairEyeColor) # see ?HairEyeColor for background df <- data.frame(HairEyeColor) # str(df) is useful # Frequencies of the colors can be found with: eyeF <- aggregate(Freq ~ Eye, data = df, FUN = "sum") hairF <- aggregate(Freq ~ Hair, data = df, FUN = "sum") es <- eyeF$Freq / eyeF$Freq[4] # node sizes for eye hs <- hairF$Freq / hairF$Freq[3] # node sizes for hair ### Step 1. Assemble a data frame of the nodes. # There are 32 rows in the data frame, but we are going to # separate the hair color from the eye color and thus # double the number of rows in the node data frame nodes <- data.frame( id = 1:64, lab = paste(rep(c("hair", "eye"), each = 32), 1:64, sep = "_"), axis = rep(1:2, each = 32), radius = rep(NA, 64) ) for (n in 1:32) { # assign node radius based most common colors if (df$Hair[n] == "Black") nodes$radius[n] <- 2 if (df$Hair[n] == "Brown") nodes$radius[n] <- 4 if (df$Hair[n] == "Red") nodes$radius[n] <- 1 if (df$Hair[n] == "Blond") nodes$radius[n] <- 3 if (df$Eye[n] == "Brown") nodes$radius[n + 32] <- 1 if (df$Eye[n] == "Blue") nodes$radius[n + 32] <- 2 if (df$Eye[n] == "Hazel") nodes$radius[n + 32] <- 3 if (df$Eye[n] == "Green") nodes$radius[n + 32] <- 4 # now do node sizes if (df$Hair[n] == "Black") nodes$size[n] <- hs[1] if (df$Hair[n] == "Brown") nodes$size[n] <- hs[2] if (df$Hair[n] == "Red") nodes$size[n] <- hs[3] if (df$Hair[n] == "Blond") nodes$size[n] <- hs[4] if (df$Eye[n] == "Brown") nodes$size[n + 32] <- es[4] if (df$Eye[n] == "Blue") nodes$size[n + 32] <- es[3] if (df$Eye[n] == "Hazel") nodes$size[n + 32] <- es[2] if (df$Eye[n] == "Green") nodes$size[n + 32] <- es[1] } nodes$color <- rep("black", 64) nodes$lab <- as.character(nodes$lab) # clean up some data types nodes$radius <- as.numeric(nodes$radius) ### Step 2. Assemble a data frame of the edges. edges <- data.frame( # There will be 32 edges, corresponding to the original 32 rows id1 = c(1:16, 49:64), # This will set up edges between each eye/hair pair id2 = c(33:48, 17:32), # & put the males above and the females below weight = df$Freq, color = rep(c("lightblue", "pink"), each = 16) ) edges$color <- as.character(edges$color) # Scale the edge weight (det'd by trial & error to emphasize differences) edges$weight <- 0.25 * log(edges$weight)^2.25 ### Step 3. Now assemble the HivePlotData (HPD) object. HEC <- list() HEC$nodes <- nodes HEC$edges <- edges HEC$type <- "2D" HEC$desc <- "HairEyeColor data set" HEC$axis.cols <- c("grey", "grey") class(HEC) <- "HivePlotData" ### Step 4. Check it & summarize chkHPD(HEC) # answer of FALSE means there are no problems sumHPD(HEC) ### Step 5. Plot it. # A minimal plot plotHive(HEC, ch = 0.1, bkgnd = "white") # See ?plotHive for fancier options# An example of building an HPD from scratch ### Step 0. Get to know your data. data(HairEyeColor) # see ?HairEyeColor for background df <- data.frame(HairEyeColor) # str(df) is useful # Frequencies of the colors can be found with: eyeF <- aggregate(Freq ~ Eye, data = df, FUN = "sum") hairF <- aggregate(Freq ~ Hair, data = df, FUN = "sum") es <- eyeF$Freq / eyeF$Freq[4] # node sizes for eye hs <- hairF$Freq / hairF$Freq[3] # node sizes for hair ### Step 1. Assemble a data frame of the nodes. # There are 32 rows in the data frame, but we are going to # separate the hair color from the eye color and thus # double the number of rows in the node data frame nodes <- data.frame( id = 1:64, lab = paste(rep(c("hair", "eye"), each = 32), 1:64, sep = "_"), axis = rep(1:2, each = 32), radius = rep(NA, 64) ) for (n in 1:32) { # assign node radius based most common colors if (df$Hair[n] == "Black") nodes$radius[n] <- 2 if (df$Hair[n] == "Brown") nodes$radius[n] <- 4 if (df$Hair[n] == "Red") nodes$radius[n] <- 1 if (df$Hair[n] == "Blond") nodes$radius[n] <- 3 if (df$Eye[n] == "Brown") nodes$radius[n + 32] <- 1 if (df$Eye[n] == "Blue") nodes$radius[n + 32] <- 2 if (df$Eye[n] == "Hazel") nodes$radius[n + 32] <- 3 if (df$Eye[n] == "Green") nodes$radius[n + 32] <- 4 # now do node sizes if (df$Hair[n] == "Black") nodes$size[n] <- hs[1] if (df$Hair[n] == "Brown") nodes$size[n] <- hs[2] if (df$Hair[n] == "Red") nodes$size[n] <- hs[3] if (df$Hair[n] == "Blond") nodes$size[n] <- hs[4] if (df$Eye[n] == "Brown") nodes$size[n + 32] <- es[4] if (df$Eye[n] == "Blue") nodes$size[n + 32] <- es[3] if (df$Eye[n] == "Hazel") nodes$size[n + 32] <- es[2] if (df$Eye[n] == "Green") nodes$size[n + 32] <- es[1] } nodes$color <- rep("black", 64) nodes$lab <- as.character(nodes$lab) # clean up some data types nodes$radius <- as.numeric(nodes$radius) ### Step 2. Assemble a data frame of the edges. edges <- data.frame( # There will be 32 edges, corresponding to the original 32 rows id1 = c(1:16, 49:64), # This will set up edges between each eye/hair pair id2 = c(33:48, 17:32), # & put the males above and the females below weight = df$Freq, color = rep(c("lightblue", "pink"), each = 16) ) edges$color <- as.character(edges$color) # Scale the edge weight (det'd by trial & error to emphasize differences) edges$weight <- 0.25 * log(edges$weight)^2.25 ### Step 3. Now assemble the HivePlotData (HPD) object. HEC <- list() HEC$nodes <- nodes HEC$edges <- edges HEC$type <- "2D" HEC$desc <- "HairEyeColor data set" HEC$axis.cols <- c("grey", "grey") class(HEC) <- "HivePlotData" ### Step 4. Check it & summarize chkHPD(HEC) # answer of FALSE means there are no problems sumHPD(HEC) ### Step 5. Plot it. # A minimal plot plotHive(HEC, ch = 0.1, bkgnd = "white") # See ?plotHive for fancier options
From time-to-time is useful to compare several hive plots based on related data (and you might wish to plot them side-by-side to facilitate comparison). Depending the nature of the data set and how it changes under the experimental design, some data sets may not have any nodes on a particular axis (and therefore, they don't participate in edges either). Let's say your system fundamentally has three axes, but in some data sets one of the axes has no nodes. When you plot them side-by-side, for visual comparison it is nice if all the plots, including the one with an empty axis, have the same general orientation. In other words, even if the data only requires two axes, you might want it plotted as if it had three axes for consistency in overall appearance.
When an axis is present but doesn't have a node on it, this makes
plotHive unhappy, but there is a simple solution. You simply put a
dummy or phantom node on the empty axis. This is illustrated in the example
below. Also demonstrated is a simple grid-based function for putting
more than one plot on a device.
Bryan A. Hanson, DePauw University. [email protected]
require("grid") # Adjacency matrix describing the connectivity in 2-butanone # H's on a single carbon collapsed into a group. # Matrix entry is bond order. CH3 is coded so the # bond order between C & H is 3 (3 single C-H bonds) dnames <- c("C1", "C2", "C3", "C4", "O", "HC1", "HC3", "HC4") # C1, C2, C3, C4, O, HC1, HC3, HC4 butanone <- matrix(c( 0, 1, 0, 0, 0, 3, 0, 0, # C1 1, 0, 1, 0, 2, 0, 0, 0, # C2 0, 1, 0, 1, 0, 0, 2, 0, # C3 0, 0, 1, 0, 0, 0, 0, 3, # C4 0, 2, 0, 0, 0, 0, 0, 0, # O 3, 0, 0, 0, 0, 0, 0, 0, # HC1 0, 0, 2, 0, 0, 0, 0, 0, # HC3 0, 0, 0, 3, 0, 0, 0, 0 ), # HC4 ncol = 8, byrow = TRUE, dimnames = list(dnames, dnames) ) butanoneHPD <- adj2HPD( M = butanone, axis.col = c("black", "gray", "red"), desc = "2-butanone" ) # Fix up the nodes manually (carbon is on axis 1) butanoneHPD$nodes$axis[5] <- 3L # oxygen on axis 3 butanoneHPD$nodes$axis[6:8] <- 2L # hydrogen on axis 2 butanoneHPD$nodes$color[5] <- "red" butanoneHPD$nodes$color[6:8] <- "gray" # Exaggerate the edge weights, which are proportional to the number of bonds butanoneHPD$edges$weight <- butanoneHPD$edges$weight^2 butanoneHPD$edges$color <- rep("wheat3", 7) plotHive(butanoneHPD, method = "rank", bkgnd = "white", axLabs = c("carbon", "hydrogen", "oxygen"), axLab.pos = c(1, 1, 1), axLab.gpar = gpar(col = c("black", "gray", "red")) ) # Now repeat the process for butane dnames <- c("C1", "C2", "C3", "C4", "HC1", "HC2", "HC3", "HC4") # C1, C2, C3, C4, HC1, HC2, HC3, HC4 butane <- matrix(c( 0, 1, 0, 0, 3, 0, 0, 0, # C1 1, 0, 1, 0, 0, 2, 0, 0, # C2 0, 1, 0, 1, 0, 0, 2, 0, # C3 0, 0, 1, 0, 0, 0, 0, 3, # C4 3, 0, 0, 0, 0, 0, 0, 0, # HC1 0, 2, 0, 0, 0, 0, 0, 0, # HC2 0, 0, 2, 0, 0, 0, 0, 0, # HC3 0, 0, 0, 3, 0, 0, 0, 0 ), # HC4 ncol = 8, byrow = TRUE, dimnames = list(dnames, dnames) ) butaneHPD <- adj2HPD( M = butane, axis.col = c("black", "gray"), desc = "butane" ) butaneHPD$nodes$axis[5:8] <- 2L # hydrogen on axis 2 butaneHPD$nodes$color[5:8] <- "gray" butaneHPD$edges$weight <- butaneHPD$edges$weight^2 butaneHPD$edges$color <- rep("wheat3", 7) plotHive(butaneHPD, method = "rank", bkgnd = "white", axLabs = c("carbon", "hydrogen"), axLab.pos = c(1, 1), axLab.gpar = gpar(col = c("black", "gray")) ) # butaneHPD has 2 axes. If we wanted to compare to butanoneHPD effectively # we should add a third dummy axis where the oxygen axis was in butanone # You might want to look at str(butaneHPD) before beginning dummy <- c(9, "dummy", 3, 1.0, 1.0, "white") # mixed data types # but coerced to character butaneHPD$nodes <- rbind(butaneHPD$nodes, dummy) str(butaneHPD$nodes) # The data types are mangled from the rbind! # Now coerce the data types to the standard of the class, and check it butaneHPD$nodes$id <- as.integer(butaneHPD$nodes$id) butaneHPD$nodes$axis <- as.integer(butaneHPD$nodes$axis) butaneHPD$nodes$radius <- as.numeric(butaneHPD$nodes$radius) butaneHPD$nodes$size <- as.numeric(butaneHPD$nodes$size) str(butaneHPD$nodes) chkHPD(butaneHPD) # OK! (False means there were no problems) sumHPD(butaneHPD) # Plot it plotHive(butaneHPD, method = "rank", bkgnd = "white", axLabs = c("carbon", "hydrogen", "oxygen"), axLab.pos = c(1, 1, 1), axLab.gpar = gpar(col = c("black", "gray", "red")) ) # Put 2 plots side-by-side using a little helper function vplayout <- function(x, y) viewport(layout.pos.row = x, layout.pos.col = y) # pdf("Demo.pdf", width = 10, height = 5) # Aspect ratio better # default screen device grid.newpage() pushViewport(viewport(layout = grid.layout(1, 2))) pushViewport(vplayout(1, 1)) # left plot plotHive(butanoneHPD, method = "rank", bkgnd = "white", axLabs = c("carbon", "hydrogen", "oxygen"), axLab.pos = c(1, 1, 1), axLab.gpar = gpar(col = c("black", "gray", "red")), np = FALSE ) grid.text("butanone", x = 0.5, y = 0.1, default.units = "npc", gp = gpar(fontsize = 14, col = "black") ) popViewport(2) pushViewport(vplayout(1, 2)) # right plot grid.text("test2") plotHive(butaneHPD, method = "rank", bkgnd = "white", axLabs = c("carbon", "hydrogen", "oxygen"), axLab.pos = c(1, 1, 1), axLab.gpar = gpar(col = c("black", "gray", "red")), np = FALSE ) grid.text("butane", x = 0.5, y = 0.1, default.units = "npc", gp = gpar(fontsize = 14, col = "black") ) # dev.off()require("grid") # Adjacency matrix describing the connectivity in 2-butanone # H's on a single carbon collapsed into a group. # Matrix entry is bond order. CH3 is coded so the # bond order between C & H is 3 (3 single C-H bonds) dnames <- c("C1", "C2", "C3", "C4", "O", "HC1", "HC3", "HC4") # C1, C2, C3, C4, O, HC1, HC3, HC4 butanone <- matrix(c( 0, 1, 0, 0, 0, 3, 0, 0, # C1 1, 0, 1, 0, 2, 0, 0, 0, # C2 0, 1, 0, 1, 0, 0, 2, 0, # C3 0, 0, 1, 0, 0, 0, 0, 3, # C4 0, 2, 0, 0, 0, 0, 0, 0, # O 3, 0, 0, 0, 0, 0, 0, 0, # HC1 0, 0, 2, 0, 0, 0, 0, 0, # HC3 0, 0, 0, 3, 0, 0, 0, 0 ), # HC4 ncol = 8, byrow = TRUE, dimnames = list(dnames, dnames) ) butanoneHPD <- adj2HPD( M = butanone, axis.col = c("black", "gray", "red"), desc = "2-butanone" ) # Fix up the nodes manually (carbon is on axis 1) butanoneHPD$nodes$axis[5] <- 3L # oxygen on axis 3 butanoneHPD$nodes$axis[6:8] <- 2L # hydrogen on axis 2 butanoneHPD$nodes$color[5] <- "red" butanoneHPD$nodes$color[6:8] <- "gray" # Exaggerate the edge weights, which are proportional to the number of bonds butanoneHPD$edges$weight <- butanoneHPD$edges$weight^2 butanoneHPD$edges$color <- rep("wheat3", 7) plotHive(butanoneHPD, method = "rank", bkgnd = "white", axLabs = c("carbon", "hydrogen", "oxygen"), axLab.pos = c(1, 1, 1), axLab.gpar = gpar(col = c("black", "gray", "red")) ) # Now repeat the process for butane dnames <- c("C1", "C2", "C3", "C4", "HC1", "HC2", "HC3", "HC4") # C1, C2, C3, C4, HC1, HC2, HC3, HC4 butane <- matrix(c( 0, 1, 0, 0, 3, 0, 0, 0, # C1 1, 0, 1, 0, 0, 2, 0, 0, # C2 0, 1, 0, 1, 0, 0, 2, 0, # C3 0, 0, 1, 0, 0, 0, 0, 3, # C4 3, 0, 0, 0, 0, 0, 0, 0, # HC1 0, 2, 0, 0, 0, 0, 0, 0, # HC2 0, 0, 2, 0, 0, 0, 0, 0, # HC3 0, 0, 0, 3, 0, 0, 0, 0 ), # HC4 ncol = 8, byrow = TRUE, dimnames = list(dnames, dnames) ) butaneHPD <- adj2HPD( M = butane, axis.col = c("black", "gray"), desc = "butane" ) butaneHPD$nodes$axis[5:8] <- 2L # hydrogen on axis 2 butaneHPD$nodes$color[5:8] <- "gray" butaneHPD$edges$weight <- butaneHPD$edges$weight^2 butaneHPD$edges$color <- rep("wheat3", 7) plotHive(butaneHPD, method = "rank", bkgnd = "white", axLabs = c("carbon", "hydrogen"), axLab.pos = c(1, 1), axLab.gpar = gpar(col = c("black", "gray")) ) # butaneHPD has 2 axes. If we wanted to compare to butanoneHPD effectively # we should add a third dummy axis where the oxygen axis was in butanone # You might want to look at str(butaneHPD) before beginning dummy <- c(9, "dummy", 3, 1.0, 1.0, "white") # mixed data types # but coerced to character butaneHPD$nodes <- rbind(butaneHPD$nodes, dummy) str(butaneHPD$nodes) # The data types are mangled from the rbind! # Now coerce the data types to the standard of the class, and check it butaneHPD$nodes$id <- as.integer(butaneHPD$nodes$id) butaneHPD$nodes$axis <- as.integer(butaneHPD$nodes$axis) butaneHPD$nodes$radius <- as.numeric(butaneHPD$nodes$radius) butaneHPD$nodes$size <- as.numeric(butaneHPD$nodes$size) str(butaneHPD$nodes) chkHPD(butaneHPD) # OK! (False means there were no problems) sumHPD(butaneHPD) # Plot it plotHive(butaneHPD, method = "rank", bkgnd = "white", axLabs = c("carbon", "hydrogen", "oxygen"), axLab.pos = c(1, 1, 1), axLab.gpar = gpar(col = c("black", "gray", "red")) ) # Put 2 plots side-by-side using a little helper function vplayout <- function(x, y) viewport(layout.pos.row = x, layout.pos.col = y) # pdf("Demo.pdf", width = 10, height = 5) # Aspect ratio better # default screen device grid.newpage() pushViewport(viewport(layout = grid.layout(1, 2))) pushViewport(vplayout(1, 1)) # left plot plotHive(butanoneHPD, method = "rank", bkgnd = "white", axLabs = c("carbon", "hydrogen", "oxygen"), axLab.pos = c(1, 1, 1), axLab.gpar = gpar(col = c("black", "gray", "red")), np = FALSE ) grid.text("butanone", x = 0.5, y = 0.1, default.units = "npc", gp = gpar(fontsize = 14, col = "black") ) popViewport(2) pushViewport(vplayout(1, 2)) # right plot grid.text("test2") plotHive(butaneHPD, method = "rank", bkgnd = "white", axLabs = c("carbon", "hydrogen", "oxygen"), axLab.pos = c(1, 1, 1), axLab.gpar = gpar(col = c("black", "gray", "red")), np = FALSE ) grid.text("butane", x = 0.5, y = 0.1, default.units = "npc", gp = gpar(fontsize = 14, col = "black") ) # dev.off()
In package HiveR, hive plot data sets are stored as an S3 class
called HivePlotData, detailed below.
The structure of a HivePlotData object is a list
of 6 elements, some of which are data frames, and an attribute, as follows:
| element | (element) | type | description |
| $nodes | data frame | Data frame of node properties | |
| $id | int | Node identifier | |
| $lab | chr | Node label | |
| $axis | int | Axis to which node is assigned | |
| $radius | num | Radius (position) of node along the axis | |
| $size | num | Node size in pixels | |
| $color | chr | Node color | |
| $edges | data frame | Data frame of edge properties | |
| $id1 | int | Starting node id | |
| $id2 | int | Ending node id | |
| $weight | num | Width of edge in pixels | |
| $color | chr | Edge color | |
| $type | chr | Type of hive. See Note. | |
| $desc | chr | Description of data | |
| $axis.cols | chr | Colors for axes | |
| - attr | chr "HivePlotData" | The S3 class designation. | |
While $edges$id1 and $edges$id2 are defined as the
starting and ending nodes of a particular edge, hive plots as currently
implemented are not directed graphs (agnostic might be a better word). HPD$type indicates the type of hive data: If 2D, then the
data is intended to be plotted with hivePlot which is a 2D plot with
axes radially oriented, and (hopefully) no edges that cross axes. If
3D, then the data is intended to be plotted with plot3dHive
which gives an interactive 3D plot, with axes oriented in 3D.
Bryan A. Hanson, DePauw University. [email protected]
sumHPD to summarize a HivePlotData object.chkHPD to verify the integrity of a HivePlotData
object.ranHiveData to generate random HivePlotData
objects for testing and demonstration.
test4 <- ranHiveData(nx = 4) str(test4) sumHPD(test4) plotHive(test4)test4 <- ranHiveData(nx = 4) str(test4) sumHPD(test4) plotHive(test4)
This function modifies various aspects of a HivePlotData object. A
typical use is to convert the radii from the native/absolute values in the
original object to either a normalized value (0...1) or to a ranked
value. The order of nodes on an axis can also be inverted, and an axis can
be pruned (removed) from the HivePlotData object.
manipAxis(HPD, method, action = NULL, ...)manipAxis(HPD, method, action = NULL, ...)
HPD |
An object of S3 class |
method |
One of |
action |
For |
... |
Arguments to be passed downstream. Needed in this case for when
|
The rank method uses ties.method = "first" so that each node gets a
unique radius. For pruning, the nodes and edges are removed and then the
remaining axes are renumbered to start from one. Exercise caution!
For "scale" node radii will be multiplied by the corresponding value
in this argument. For "invert" a value of -1 will cause the
corresponding axis to be inverted. For "prune", a single value
specifying the axis to be pruned should be given. For "offset" the
values in "action" will be subtracted from the node radii. For
"stretch", node radii will first be offset so that the minimum value
is zero, then multiplied by the values in "action" to stretch the
axis. Depending upon the desired effect, one might use "stretch"
followed by "offset" or perhaps other combinations.
A modified HivePlotData object.
Bryan A. Hanson, DePauw University. [email protected]
data(HEC) # The first 3 examples take advantage of the argument '...' # in plotHive, which passes action through to manipAxis on the fly. # For this particular data, norm and absolute scaling appear the same. plotHive(HEC, bkgnd = "white") # default is absolute positioning of nodes plotHive(HEC, method = "rank", bkgnd = "white") plotHive(HEC, method = "norm", bkgnd = "white") # In these examples, we'll explicitly use manipAxis and then plot # in a separate step. This is because trying to plot on the fly in # these cases will result in absolute scaling (which we do use here, # but one might not want to be forced to do so). HEC2 <- manipAxis(HEC, method = "invert", action = c(-1, 1)) plotHive(HEC2, bkgnd = "white") HEC3 <- manipAxis(HEC, method = "stretch", action = c(2, 3)) plotHive(HEC3, bkgnd = "white") HEC4 <- manipAxis(HEC, method = "offset", action = c(0, 1.5)) plotHive(HEC4, bkgnd = "white")data(HEC) # The first 3 examples take advantage of the argument '...' # in plotHive, which passes action through to manipAxis on the fly. # For this particular data, norm and absolute scaling appear the same. plotHive(HEC, bkgnd = "white") # default is absolute positioning of nodes plotHive(HEC, method = "rank", bkgnd = "white") plotHive(HEC, method = "norm", bkgnd = "white") # In these examples, we'll explicitly use manipAxis and then plot # in a separate step. This is because trying to plot on the fly in # these cases will result in absolute scaling (which we do use here, # but one might not want to be forced to do so). HEC2 <- manipAxis(HEC, method = "invert", action = c(-1, 1)) plotHive(HEC2, bkgnd = "white") HEC3 <- manipAxis(HEC, method = "stretch", action = c(2, 3)) plotHive(HEC3, bkgnd = "white") HEC4 <- manipAxis(HEC, method = "offset", action = c(0, 1.5)) plotHive(HEC4, bkgnd = "white")
A HivePlotData object, especially one created fresh using
dot2HPD, generally contains a lot of hidden information about
the network described. This function can extract this hidden information.
This function has options which are quite specific as to what they
do. The user can easily write new options and incorporate them.
This function can be called multiple times
using different options to gradually modify the HivePlotData object.
mineHPD(HPD, option = "rad <- tot.edge.count")mineHPD(HPD, option = "rad <- tot.edge.count")
HPD |
A |
option |
A character string giving the option desired. See Details for current options. |
option = "rad <- tot.edge.count" This option looks through the
HivePlotData object and determines how many edges start or end on
each node (the "degree"). This value is then assigned to the radius for
that node.
option = "axis <- source.man.sink" This option
examines the nodes and corresponding edges in a HivePlotData object
to determine if the node is a source, manager or sink. A source node only
has outgoing edges. A sink node only has incoming edges. A manager has
both. Hence, this option treats the HivePlotData object as if it
were directed in that the first node of an edge in will be in
HPD$nodes$id1 and the second node of an edge will be in
HPD$nodes$id2. As a result, this option produces a hive plot with 3
axes (note: sources are on axis 1, sinks on axis 2, and managers on axis 3).
This concept is similar to the idea of FuncMap but
the internals are quite different. See also dot2HPD for some
details about processing .dot files in an agnostic fashion.
option = "remove orphans" removes nodes that have degree zero (no
incoming or outgoing edges).
option = "remove zero edge"
removes edges with length zero. Such edges cause an error because
the spline cannot be drawn. This option combines the next two options.
option = "remove self edge" removes edges that
start and end on the same node.
option = "remove virtual edge" removes virtual edges which are
edges which involve different nodes but the nodes happen to be on the
the same axis at the same radius.
option = "remove edges same axis" removes edges which start and
end on the same axis.
A modified HivePlotData object.
Bryan A. Hanson, DePauw University. [email protected]
See the vignette for an example of using this function. Use
browseVignettes("HiveR") to produce the vignette.
These functions plot a HivePlotData object in either 2D or 3D,
depending upon which function is called.
plot3dHive( HPD, ch = 1, dr.nodes = TRUE, method = "abs", axLabs = NULL, axLab.pos = NULL, LA = FALSE, ... ) plotHive( HPD, ch = 1, method = "abs", dr.nodes = TRUE, bkgnd = "black", axLabs = NULL, axLab.pos = NULL, axLab.gpar = NULL, anNodes = NULL, anNode.gpar = NULL, grInfo = NULL, arrow = NULL, np = TRUE, anCoord = "local", ... )plot3dHive( HPD, ch = 1, dr.nodes = TRUE, method = "abs", axLabs = NULL, axLab.pos = NULL, LA = FALSE, ... ) plotHive( HPD, ch = 1, method = "abs", dr.nodes = TRUE, bkgnd = "black", axLabs = NULL, axLab.pos = NULL, axLab.gpar = NULL, anNodes = NULL, anNode.gpar = NULL, grInfo = NULL, arrow = NULL, np = TRUE, anCoord = "local", ... )
HPD |
An object of S3 class |
ch |
Numeric; the size of the central hole in the hive plot. |
dr.nodes |
Logical; if |
method |
Character. Passed to |
axLabs |
A vector of character strings for the axis labels. |
axLab.pos |
Numeric; An offset from the end of the axis for label
placement. Either a single value or a vector of values. If a single value,
all labels are offset the same amount. If a vector of values, there should
be a value for each axis. This allows flexibility with long axis names.
The units depend upon the |
LA |
(Applies to |
... |
Additional parameters to be passed downstream. |
bkgnd |
Any valid color specification. Used for the background color
for |
axLab.gpar |
(Applies to |
anNodes |
(Applies to |
anNode.gpar |
(Applies to |
grInfo |
(Applies to |
arrow |
(Applies to |
np |
(Applies to |
anCoord |
(Applies to |
General. plotHive uses grid graphics to produce a 2D hive
plot in a style similar to the original concept. For a 2D plot, axis number
1 is vertical except in the case of 2 axes in which case it is to the right.
plot3dHive produces a 3D hive plot using rgl graphics.
Functions from either package can be used to make additional modifications
after the hive plot is drawn, either via the ... argument or by
subsequent function calls. See the examples.
Units and Annotations. If you add node labels, arrows or graphic decorations,
the units that you
must specify are those intrinsic to the data itself, modified by your
setting of ch and method. These generally cannot be known
precisely ahead of time, so some experimentation will be necessary to polish
the plots. For instance, if you have data with node radii that run from
4-23 then you have an idea of how to position your annotations if using
method = "abs". But the same data plotted with method =
"norm" or method = "rank" will require that you move your annotation
positions accordingly. In the first case no radius is larger than 23, but
the maximum radius is 1 when the data is normed and when it is ranked, the
maximum value will depend upon which axis has the most nodes on it, and the
number of unique radii values.
Positioning Node Labels and Graphics.
In addition to the nuances just above, there are two ways to specify the
location of node labels and graphic decorations. Polar coordinates are used
in both cases. If annCoord = "local" then the angle, radius and
offset arguments are relative to the node to be annotated. An angle of 0
positions the label horizontally to the right of the node. Thus the label
can be placed within a circular area around the node. If annCoord =
"global" then the specifications are relative to dead center on the plot.
These two methods give one lots of flexibility in lining up labels in
different ways. See the examples.
Size of Graphics. The size of
graphic decorations is controlled by the column 'width' in grInfo.
The ultimate call to display the graphic is done with as.raster.
Specifying only the width preserves the aspect ratio of the graphic. See
?as.raster for further discussion.
Colors. For any of the
gpar arguments, watch out: In grid graphics the default color for
text and arrows is black, so if are using the default bkgnd = "black"
in the hive plot be sure to specify col = "white" (or some other
non-black color) for the labels and arrows or you won't see them.
Speed and 3D Hive Plots. For most work with plot3dHive, use LA
= FALSE for speed of drawing. LA = TRUE is over 20 times slower,
and is more appropriate for high quality hive plots. These are probably
better made with R CMD BATCH script.R rather than interactive use.
None. Side effect is a plot.
plot3dHive(): Create a 3D Hive Plot
plotHive(): Create a 2D Hive Plot
Bryan A. Hanson, DePauw University. [email protected]
### 2D Hive Plots require("grid") # Generate some random data test2 <- ranHiveData(nx = 2) test3 <- ranHiveData(nx = 3) # First the nx = 2 case. # Note that gpar contains parameters that apply to both the # axis labels and arrow. A 6th value in arrow offsets the arrow vertically: plotHive(test2, ch = 5, axLabs = c("axis 1", "axis 2"), rot = c(-90, 90), axLab.pos = c(20, 20), axLab.gpar = gpar(col = "pink", fontsize = 14, lwd = 2), arrow = c("radius units", 0, 20, 60, 25, 40) ) # Now nx = 3: plotHive(test3) # default plot # Add axis labels & options to nx = 3 example. Note that rot is not part of gpar plotHive(test3, ch = 5, axLabs = c("axis 1", "axis 2", "axis 3"), axLab.pos = c(10, 15, 15), rot = c(0, 30, -30), axLab.gpar = gpar(col = "orange", fontsize = 14) ) # Call up a built-in data set to illustrate some plotting tricks data(HEC) require("grid") # for text additions outside of HiveR (grid.text) plotHive(HEC, ch = 0.1, bkgnd = "white", axLabs = c("hair\ncolor", "eye\ncolor"), axLab.pos = c(1, 1), axLab.gpar = gpar(fontsize = 14) ) grid.text("males", x = 0, y = 2.3, default.units = "native") grid.text("females", x = 0, y = -2.3, default.units = "native") grid.text("Pairing of Eye Color with Hair Color", x = 0, y = 4, default.units = "native", gp = gpar(fontsize = 18) ) # Add node labels and graphic decorations # The working directory has to include # not only the grInfo and anNodes files but also the jpgs. # So, we are going to move to such a directory and return you home afterwards. currDir <- getwd() setwd(system.file("extdata", "Misc", package = "HiveR")) plotHive(HEC, ch = 0.1, bkgnd = "white", axLabs = c("hair\ncolor", "eye\ncolor"), axLab.pos = c(1, 1), axLab.gpar = gpar(fontsize = 14), anNodes = "HECnodes.txt", anNode.gpar = gpar(col = "black"), grInfo = "HECgraphics.txt", arrow = c("more\ncommon", 0.0, 2, 4, 1, -2) ) grid.text("males", x = 0, y = 2.3, default.units = "native") grid.text("females", x = 0, y = -2.3, default.units = "native") grid.text("Pairing of Eye Color with Hair Color", x = 0, y = 3.75, default.units = "native", gp = gpar(fontsize = 18) ) grid.text("A test of plotHive annotation options", x = 0, y = 3.25, default.units = "native", gp = gpar(fontsize = 12) ) grid.text("Images from Wikipedia Commons", x = 0, y = -3.5, default.units = "native", gp = gpar(fontsize = 9) ) setwd(currDir) # Use the node label concept to create tick marks currDir <- getwd() setwd(system.file("extdata", "Misc", package = "HiveR")) plotHive(HEC, ch = 0.1, bkgnd = "white", axLabs = c("hair\ncolor", "eye\ncolor"), axLab.pos = c(1, 1), axLab.gpar = gpar(fontsize = 14), anNodes = "HECticks.txt", anNode.gpar = gpar(col = "black"), arrow = c("more\ncommon", 0.0, 2, 4, 1, -2), dr.nodes = FALSE ) grid.text("males", x = 0, y = 2.3, default.units = "native") grid.text("females", x = 0, y = -2.3, default.units = "native") grid.text("Pairing of Eye Color with Hair Color", x = 0, y = 3.75, default.units = "native", gp = gpar(fontsize = 18) ) grid.text("Adding tick marks to the nodes", x = 0, y = 3.25, default.units = "native", gp = gpar(fontsize = 12) ) setwd(currDir) ### 3D Hive Plots. The following must be run interactively. ## Not run: require("rgl") test4 <- ranHiveData(nx = 4, type = "3D") plot3dHive(test4) ## End(Not run)### 2D Hive Plots require("grid") # Generate some random data test2 <- ranHiveData(nx = 2) test3 <- ranHiveData(nx = 3) # First the nx = 2 case. # Note that gpar contains parameters that apply to both the # axis labels and arrow. A 6th value in arrow offsets the arrow vertically: plotHive(test2, ch = 5, axLabs = c("axis 1", "axis 2"), rot = c(-90, 90), axLab.pos = c(20, 20), axLab.gpar = gpar(col = "pink", fontsize = 14, lwd = 2), arrow = c("radius units", 0, 20, 60, 25, 40) ) # Now nx = 3: plotHive(test3) # default plot # Add axis labels & options to nx = 3 example. Note that rot is not part of gpar plotHive(test3, ch = 5, axLabs = c("axis 1", "axis 2", "axis 3"), axLab.pos = c(10, 15, 15), rot = c(0, 30, -30), axLab.gpar = gpar(col = "orange", fontsize = 14) ) # Call up a built-in data set to illustrate some plotting tricks data(HEC) require("grid") # for text additions outside of HiveR (grid.text) plotHive(HEC, ch = 0.1, bkgnd = "white", axLabs = c("hair\ncolor", "eye\ncolor"), axLab.pos = c(1, 1), axLab.gpar = gpar(fontsize = 14) ) grid.text("males", x = 0, y = 2.3, default.units = "native") grid.text("females", x = 0, y = -2.3, default.units = "native") grid.text("Pairing of Eye Color with Hair Color", x = 0, y = 4, default.units = "native", gp = gpar(fontsize = 18) ) # Add node labels and graphic decorations # The working directory has to include # not only the grInfo and anNodes files but also the jpgs. # So, we are going to move to such a directory and return you home afterwards. currDir <- getwd() setwd(system.file("extdata", "Misc", package = "HiveR")) plotHive(HEC, ch = 0.1, bkgnd = "white", axLabs = c("hair\ncolor", "eye\ncolor"), axLab.pos = c(1, 1), axLab.gpar = gpar(fontsize = 14), anNodes = "HECnodes.txt", anNode.gpar = gpar(col = "black"), grInfo = "HECgraphics.txt", arrow = c("more\ncommon", 0.0, 2, 4, 1, -2) ) grid.text("males", x = 0, y = 2.3, default.units = "native") grid.text("females", x = 0, y = -2.3, default.units = "native") grid.text("Pairing of Eye Color with Hair Color", x = 0, y = 3.75, default.units = "native", gp = gpar(fontsize = 18) ) grid.text("A test of plotHive annotation options", x = 0, y = 3.25, default.units = "native", gp = gpar(fontsize = 12) ) grid.text("Images from Wikipedia Commons", x = 0, y = -3.5, default.units = "native", gp = gpar(fontsize = 9) ) setwd(currDir) # Use the node label concept to create tick marks currDir <- getwd() setwd(system.file("extdata", "Misc", package = "HiveR")) plotHive(HEC, ch = 0.1, bkgnd = "white", axLabs = c("hair\ncolor", "eye\ncolor"), axLab.pos = c(1, 1), axLab.gpar = gpar(fontsize = 14), anNodes = "HECticks.txt", anNode.gpar = gpar(col = "black"), arrow = c("more\ncommon", 0.0, 2, 4, 1, -2), dr.nodes = FALSE ) grid.text("males", x = 0, y = 2.3, default.units = "native") grid.text("females", x = 0, y = -2.3, default.units = "native") grid.text("Pairing of Eye Color with Hair Color", x = 0, y = 3.75, default.units = "native", gp = gpar(fontsize = 18) ) grid.text("Adding tick marks to the nodes", x = 0, y = 3.25, default.units = "native", gp = gpar(fontsize = 12) ) setwd(currDir) ### 3D Hive Plots. The following must be run interactively. ## Not run: require("rgl") test4 <- ranHiveData(nx = 4, type = "3D") plot3dHive(test4) ## End(Not run)
This function generates random data sets which can be used to make a hive plot.
ranHiveData( type = "2D", nx = 4, nn = nx * 15, ne = nx * 15, rad = 1:100, ns = c(0.5, 1, 1.5), ew = 1:3, nc = brewer.pal(5, "Set1"), ec = brewer.pal(5, "Set1"), axis.cols = brewer.pal(nx, "Set1"), desc = NULL, allow.same = FALSE, verbose = FALSE )ranHiveData( type = "2D", nx = 4, nn = nx * 15, ne = nx * 15, rad = 1:100, ns = c(0.5, 1, 1.5), ew = 1:3, nc = brewer.pal(5, "Set1"), ec = brewer.pal(5, "Set1"), axis.cols = brewer.pal(nx, "Set1"), desc = NULL, allow.same = FALSE, verbose = FALSE )
type |
The type of hive plot to be generated. One of |
nx |
An integer giving the number of axes to be created ( |
nn |
An integer giving the number of nodes to be created. This is an initial number which may be reduced during clean up. See Details. |
ne |
An integer giving the number of edges to be created. This is an initial number which may be reduced during clean up. See Details. |
rad |
Numeric; a range of values that will be used as node radius values (the position of the node along the axis). |
ns |
Numeric; a range of values that will be used as the node sizes. |
ew |
Numeric; a range of values that will be used as the edge weights. |
nc |
A vector of valid color names giving the node colors. |
ec |
A vector of valid color names giving the edge colors. |
axis.cols |
A vector of valid color names to be used to color the axes;
|
desc |
Character; a description of the data set. |
allow.same |
Logical; indicates if edges may begin and end on the same
axis. Only applies to |
verbose |
Logical; If |
For type = "2D", after the function creates an initial set of random
nodes, these are randomly chosen and connected between adjacent axes, so
that no edge crosses an axis.
For type = "3D", after the
function creates an initial set of random nodes and edges, these are cleaned
up by removing the following cases (which the rest of HiveR is not
intended to handle at this time): duplicated nodes, nodes that are not part
of any edge, edges that begin and end on the same point, edges that begin
and end on the same axis, and finally, for nx = 5 or 6, edges that
begin and end on colinear axes. Most of these don't cause an error, but
produce some ugly results.
For the arguments rad, ns, ew, nc
and ec, the values given are sampled randomly (with replacement) and
assigned to particular nodes or edges.
An object of S3 class HivePlotData.
If you create a very small data set with few nodes, there
may be no nodes assigned to some axes which will give an error when you try
to plot the data. It's up to the user to check for this possibility (you
can use sumHPD).
Bryan A. Hanson, DePauw University. [email protected]
test4 <- ranHiveData(nx = 4) str(test4) sumHPD(test4)test4 <- ranHiveData(nx = 4) str(test4) sumHPD(test4)
This is a wild bit of trigonometry! Three points in 3D space, two ends and
an control point, are rotated into 2D space. Then a spline curve is
computed. This is necessary because spline curves are only defined in
R as 2D objects. The new collection of points, which is the complete
spline curve and when drawn will be the edge of a hive plot, is rotated back
into the original 3D space. rcsr stands for rotate, compute spline,
rotate back.
rcsr(p0, cp, p1)rcsr(p0, cp, p1)
p0 |
A triple representing one end of the final curve (x, y, z). |
cp |
A triple representing the control point used to compute the final curve (x, y, z). |
p1 |
A triple representing the other end of the final curve (x, y, z). |
See the code for exactly how the function works. Based upon the process described at http://www.fundza.com/mel/axis_to_vector/index.html Timing tests show this function is fast and scales linearly (i.e. 10x more splines to draw takes 10x more time). Roughly 3 seconds were required to draw 1,000 spline curves in my testing.
A 3 column matrix with the x, y and z coordinates to be plotted to create a hive plot edge.
Bryan A. Hanson, DePauw University. [email protected]
# This is a lengthy example to prove it works. # Read it and then copy the whole thing to a blank script. # Parts of it require rgl and are interactive. # So none of the below is run during package build/check. ### First, a helper function ## Not run: drawUnitCoord <- function() { # Simple function to draw a unit 3D coordinate system # Draw a Coordinate System r <- c(0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1) # in polar coordinates theta <- c(0, 0, 0, 90, 0, 180, 0, 270, 0, 0, 0, 0) # start, end, start, end phi <- c(0, 90, 0, 90, 0, 90, 0, 90, 0, 0, 0, 180) cs <- data.frame(radius = r, theta, phi) ax.coord <- sph2cart(cs) segments3d(ax.coord, col = "gray", line_antialias = TRUE) points3d( x = 0, y = 0, z = 0, color = "black", size = 4, point_antialias = TRUE ) # plot origin # Label the axes r <- c(1.1, 1.1, 1.1, 1.1, 1.1, 1.1) # in polar coordinates theta <- c(0, 90, 180, 270, 0, 0) phi <- c(90, 90, 90, 90, 0, 180) l <- data.frame(radius = r, theta, phi) lab.coord <- sph2cart(l) text3d(lab.coord, texts = c("+x", "+y", "-x", "-y", "+z", "-z")) } ### Now, draw a reference coordinate system and demo the function in it. drawUnitCoord() ### Draw a bounding box box <- data.frame( x = c(1, -1, 1, 1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, -1, -1, -1, -1), y = c(1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, -1, 1), z = c(1, 1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, -1, 1, 1, 1, -1, -1) ) segments3d(box$x, box$y, box$z, line_antialias = TRUE, col = "red") ### Draw the midlines defining planes mid <- data.frame( x = c(0, 0, 0, 0, 0, 0, 0, 0, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, 1, 1, 1), y = c(-1, -1, -1, 1, 1, 1, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, 1, 1, 1, 1, -1), z = c(-1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0) ) segments3d(mid$x, mid$y, mid$z, line_antialias = TRUE, col = "blue") ### Generate two random points p <- runif(6, -1, 1) # Special case where p1 is on z axis # Uncomment line below to demo # p[4:5] <- 0 p0 <- c(p[1], p[2], p[3]) p1 <- c(p[4], p[5], p[6]) ### Draw the pts, label them, draw vectors to those pts from origin segments3d( x = c(0, p[1], 0, p[4]), y = c(0, p[2], 0, p[5]), z = c(0, p[3], 0, p[6]), line_antialias = TRUE, col = "black", lwd = 3 ) points3d( x = c(p[1], p[4]), y = c(p[2], p[5]), z = c(p[3], p[6]), point_antialias = TRUE, col = "black", size = 8 ) text3d( x = c(p[1], p[4]), y = c(p[2], p[5]), z = c(p[3], p[6]), col = "black", texts = c("p0", "p1"), adj = c(1, 1) ) ### Locate control point ### Compute and draw net vector from origin thru cp ### Connect p0 and p1 s <- p0 + p1 segments3d( x = c(0, s[1]), y = c(0, s[2]), z = c(0, s[3]), line_antialias = TRUE, col = "grey", lwd = 3 ) segments3d( x = c(p[1], p[4]), # connect p0 & p1 y = c(p[2], p[5]), z = c(p[3], p[6]), line_antialias = TRUE, col = "grey", lwd = 3 ) cp <- 0.6 * s # Now for the control point points3d( x = cp[1], # Plot the control point y = cp[2], z = cp[3], point_antialias = TRUE, col = "black", size = 8 ) text3d( x = cp[1], # Label the control point y = cp[2], z = cp[3], texts = c("cp"), col = "black", adj = c(1, 1) ) ### Now ready to work on the spline curve n2 <- rcsr(p0, cp, p1) # Compute the spline lines3d( x = n2[, 1], y = n2[, 2], z = n2[, 3], line_antialias = TRUE, col = "blue", lwd = 3 ) ### Ta-Da!!!!! ## End(Not run)# This is a lengthy example to prove it works. # Read it and then copy the whole thing to a blank script. # Parts of it require rgl and are interactive. # So none of the below is run during package build/check. ### First, a helper function ## Not run: drawUnitCoord <- function() { # Simple function to draw a unit 3D coordinate system # Draw a Coordinate System r <- c(0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1) # in polar coordinates theta <- c(0, 0, 0, 90, 0, 180, 0, 270, 0, 0, 0, 0) # start, end, start, end phi <- c(0, 90, 0, 90, 0, 90, 0, 90, 0, 0, 0, 180) cs <- data.frame(radius = r, theta, phi) ax.coord <- sph2cart(cs) segments3d(ax.coord, col = "gray", line_antialias = TRUE) points3d( x = 0, y = 0, z = 0, color = "black", size = 4, point_antialias = TRUE ) # plot origin # Label the axes r <- c(1.1, 1.1, 1.1, 1.1, 1.1, 1.1) # in polar coordinates theta <- c(0, 90, 180, 270, 0, 0) phi <- c(90, 90, 90, 90, 0, 180) l <- data.frame(radius = r, theta, phi) lab.coord <- sph2cart(l) text3d(lab.coord, texts = c("+x", "+y", "-x", "-y", "+z", "-z")) } ### Now, draw a reference coordinate system and demo the function in it. drawUnitCoord() ### Draw a bounding box box <- data.frame( x = c(1, -1, 1, 1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, -1, -1, -1, -1), y = c(1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, -1, 1), z = c(1, 1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, -1, 1, 1, 1, -1, -1) ) segments3d(box$x, box$y, box$z, line_antialias = TRUE, col = "red") ### Draw the midlines defining planes mid <- data.frame( x = c(0, 0, 0, 0, 0, 0, 0, 0, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, 1, 1, 1), y = c(-1, -1, -1, 1, 1, 1, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, 1, 1, 1, 1, -1), z = c(-1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0) ) segments3d(mid$x, mid$y, mid$z, line_antialias = TRUE, col = "blue") ### Generate two random points p <- runif(6, -1, 1) # Special case where p1 is on z axis # Uncomment line below to demo # p[4:5] <- 0 p0 <- c(p[1], p[2], p[3]) p1 <- c(p[4], p[5], p[6]) ### Draw the pts, label them, draw vectors to those pts from origin segments3d( x = c(0, p[1], 0, p[4]), y = c(0, p[2], 0, p[5]), z = c(0, p[3], 0, p[6]), line_antialias = TRUE, col = "black", lwd = 3 ) points3d( x = c(p[1], p[4]), y = c(p[2], p[5]), z = c(p[3], p[6]), point_antialias = TRUE, col = "black", size = 8 ) text3d( x = c(p[1], p[4]), y = c(p[2], p[5]), z = c(p[3], p[6]), col = "black", texts = c("p0", "p1"), adj = c(1, 1) ) ### Locate control point ### Compute and draw net vector from origin thru cp ### Connect p0 and p1 s <- p0 + p1 segments3d( x = c(0, s[1]), y = c(0, s[2]), z = c(0, s[3]), line_antialias = TRUE, col = "grey", lwd = 3 ) segments3d( x = c(p[1], p[4]), # connect p0 & p1 y = c(p[2], p[5]), z = c(p[3], p[6]), line_antialias = TRUE, col = "grey", lwd = 3 ) cp <- 0.6 * s # Now for the control point points3d( x = cp[1], # Plot the control point y = cp[2], z = cp[3], point_antialias = TRUE, col = "black", size = 8 ) text3d( x = cp[1], # Label the control point y = cp[2], z = cp[3], texts = c("cp"), col = "black", adj = c(1, 1) ) ### Now ready to work on the spline curve n2 <- rcsr(p0, cp, p1) # Compute the spline lines3d( x = n2[, 1], y = n2[, 2], z = n2[, 3], line_antialias = TRUE, col = "blue", lwd = 3 ) ### Ta-Da!!!!! ## End(Not run)
This function converts spherical to Cartesian coordinates.
sph2cart(df)sph2cart(df)
df |
A data frame with columns named r, theta and phi with the radius and angles (in spherical coordinates) to be converted to Cartesian coordinates. |
A data frame with named columns containing the converted coordinates.
Cobbled together from similar functions in other packages.
Bryan A. Hanson, DePauw University. [email protected]
This function summarizes a HivePlotData object in a convenient
form. Optionally, it can run some checks for certain conditions that may be
of interest. It can also output a summary of edges to be drawn, either as a
data frame or in a LaTeX ready form, or a data frame of orphaned nodes.
sumHPD( HPD, chk.all = FALSE, chk.sm.pt = FALSE, chk.ax.jump = FALSE, chk.sm.ax = FALSE, chk.orphan.node = FALSE, chk.virtual.edge = FALSE, plot.list = FALSE, tex = FALSE, orphan.list = FALSE )sumHPD( HPD, chk.all = FALSE, chk.sm.pt = FALSE, chk.ax.jump = FALSE, chk.sm.ax = FALSE, chk.orphan.node = FALSE, chk.virtual.edge = FALSE, plot.list = FALSE, tex = FALSE, orphan.list = FALSE )
HPD |
An object of S3 class |
chk.all |
Logical; should all the checks below be run? See Details. |
chk.sm.pt |
Logical; should the edges be checked to see if any of them start and end on the same axis with the same radius? See Details. |
chk.ax.jump |
Logical; should the edges be checked to see if any of them start and end on non-adjacent axes, e.g. axis 1 –> axis 3? See Details. |
chk.sm.ax |
Logical; should the edges be checked to see if any of them start and end on the same axis? |
chk.orphan.node |
Logical; should orphan nodes be identifed? Orphan nodes have degree 0 (no incoming or outgoing edges). |
chk.virtual.edge |
Logical; should the edges be checked to see if any of them start and end on different nodes which happen to be at the same radius on the same axis? See Details. |
plot.list |
Logical; should a data frame of edges to be drawn be returned? |
tex |
Logical; should the |
orphan.list |
Logical; should a data frame of orphaned nodes be returned? |
Argument chk.sm.pt applies only to hive plots of type = 2D.
It checks to see if any of the edges start and end at the same node id.
These by definition exist at the same radius on the same axis, which
causes an error in plotHive since you are trying to draw an edge of
length zero (the actual error message is Error in calcCurveGrob(x,
x$debug) : End points must not be identical. Some data sets may have such
cases intrinsically or due to data entry error, or the condition may arise
during processing. Either way, one needs to be able to detect such cases
for removal or modification. This argument will tell you which nodes cause
the problem.
Argument chk.virtual.edge applies only to hive plots of type = 2D
and is similiar to chk.sm.pt above except
that it checks for virtual edges. These are edges start and end on the
same axis at the same radius but at different node id's (in other words,
two nodes have the same radius on the same axis). This condition
gives the same error as above. It is checked for separately as it arises
via a different problem in the construction of the data.
Argument chk.ax.jump applies only to hive plots
of type = 2D. It checks to see if any of the edges jump an axis,
e.g. axis 1 –> axis 3. This argument will tell you which nodes are at
either end of the jumping edge. Jumping should should be avoided in hive
plots as it makes the plot aesthetically unpleasing. However, depending
upon how you process the data, this condition may arise and hence it is
useful to be able to locate jumps.
A summary of the HivePlotData object's key characteristics is
printed at the console, followed by the results of any checks set to
TRUE. The format of these results is identical to that of
plot.list described just below, except for the orphan node check.
This is formatted the same as HPD$nodes; see ?HPD for details.
If plot.list = TRUE, a data frame containing a list of the
edges to be drawn in a format suitable for troubleshooting a plot. If
tex = TRUE as well, the data frame will be in a format suitable for
pasting into a LaTeX document. The data frame will contain rows describing
each edge to be drawn with the following columns: node 1 id, node 1 axis,
node 1 label, node 1 radius, then the same info for node 2, then the edge
weight and the edge color.
If orphan.list = TRUE a data frame
giving the orphan nodes is returned. If you want both plot.list and
orphan.list you have to call this function twice.
Bryan A. Hanson, DePauw University. [email protected]
set.seed(55) test <- ranHiveData(nx = 4, ne = 5, desc = "Tiny 4D data set") out <- sumHPD(test, chk.all = TRUE, plot.list = TRUE) print(out)set.seed(55) test <- ranHiveData(nx = 4, ne = 5, desc = "Tiny 4D data set") out <- sumHPD(test, chk.all = TRUE, plot.list = TRUE) print(out)