I'm trying to make a multipanel figure with networks in the igraph package. I'd like 2 rows, each with 3 networks. I need to be able to save the figure as a PNG and I'd like to label them each A:F in one of the corners. I've tried to do this in a loop but only one network appears in the figures. I need the V(nw)$x<- y and E(nw)$x<- y code in the loop to make my networks come out properly. My networks are in a list().
I've made a small sample of the code I've tried, I would like to avoid doing it without a loop if I can. Thanks in advance.
srs_1nw <- graph("Zachary")
srs_2nw <- graph("Heawood")
srs_3nw <- graph("Folkman")
srs_1c <- cluster_fast_greedy(srs_1nw)
srs_2c <- cluster_fast_greedy(srs_2nw)
srs_3c <- cluster_fast_greedy(srs_3nw)
listofsrs_nws <- list(srs_1nw,srs_2nw,srs_3nw)
listofsrs_cs <- list(srs_1c,srs_2c,srs_3c)
colours <- c("red","blue","green","yellow")
par(mfrow=c(2,3))
for (i in length(listofsrs_nws)) {
c<-listofsrs_cs[[i]]
nw<-listofsrs_nws[[i]]
V(nw)$size <- log(strength(nw))*6 # weighted nodes
E(nw)$arrow.size <- 2 # arrow size
c.colours <- colours[membership(c)]
plot(c, nw, col = c.colours,
mark.col = adjustcolor(colours, alpha.f = 0.4),
mark.border = adjustcolor(colours, alpha.f = 1),
vertex.frame.width = 5, edge.curved = .15)
}
We can use mapply like below
mapply(function(c, nw) {
V(nw)$size <- log(strength(nw)) * 6 # weighted nodes
E(nw)$arrow.size <- 2 # arrow size
c.colours <- colours[membership(c)]
plot(c, nw,
col = c.colours,
mark.col = adjustcolor(colours, alpha.f = 0.4),
mark.border = adjustcolor(colours, alpha.f = 1),
vertex.frame.width = 5, edge.curved = .15
)
}, listofsrs_cs, listofsrs_nws)
Related
I want to identify 3d cylinders in an rgl plot to obtain one attribute of the nearest / selected cylinder. I tried using labels to simply spell out the attribute, but I work on data with more than 10.000 cylinders. Therefore, it gets so crowded that the labels are unreadable and it takes ages to render.
I tried to understand the documentation of rgl and I guess the solution to my issue is selecting the cylinder in the plot manually. I believe the function selectpoints3d() is probably the way to go. I believe it returns all vertices within the drawn rectangle, but I don't know how to go back to the cylinder data? I could calculate which cylinder is closest to the mean of the selected vertices, but this seems like a "quick & dirty" way to do the job.
Is there a better way to go? I noticed the argument value=FALSE to get the indices only, but I don't know how to go back to the cylinders.
Here is some dummy data and my code:
# dummy data
cylinder <- data.frame(
start_X = rep(1:3, 2)*2,
start_Y = rep(1:2, each = 3)*2,
start_Z = 0,
end_X = rep(1:3, 2)*2 + round(runif(6, -1, 1), 2),
end_Y = rep(1:2, each = 3)*2 + round(runif(6, -1, 1), 2),
end_Z = 0.5,
radius = 0.25,
attribute = sample(letters[1:6], 6)
)
# calculate centers
cylinder$center_X <- rowMeans(cylinder[,c("start_X", "end_X")])
cylinder$center_Y <- rowMeans(cylinder[,c("start_Y", "end_Y")])
cylinder$center_Z <- rowMeans(cylinder[,c("start_Z", "end_Z")])
# create cylinders
cylinder_list <- list()
for (i in 1:nrow(cylinder)) {
cylinder_list[[i]] <- cylinder3d(
center = cbind(
c(cylinder$start_X[i], cylinder$end_X[i]),
c(cylinder$start_Y[i], cylinder$end_Y[i]),
c(cylinder$start_Z[i], cylinder$end_Z[i])),
radius = cylinder$radius[i],
closed = -2)
}
# plot cylinders
open3d()
par3d()
shade3d(shapelist3d(cylinder_list, plot = FALSE), col = "blue")
text3d(cylinder$center_X+0.5, cylinder$center_Y+0.5, cylinder$center_Z+0.5, cylinder$attribute, color="red")
# get attribute
nearby <- selectpoints3d(value=TRUE, button = "right")
nearby <- colMeans(nearby)
cylinder$dist <- sqrt(
(nearby["x"]-cylinder$center_X)**2 +
(nearby["y"]-cylinder$center_Y)**2 +
(nearby["z"]-cylinder$center_Z)**2)
cylinder$attribute[which.min(cylinder$dist)]
If you call selectpoints3d(value = FALSE), you get two columns. The first column is the id of the object that was found. Your cylinders get two ids each. One way to mark the cylinders is to use "tags". For example, this modification of your code:
# dummy data
cylinder <- data.frame(
start_X = rep(1:3, 2)*2,
start_Y = rep(1:2, each = 3)*2,
start_Z = 0,
end_X = rep(1:3, 2)*2 + round(runif(6, -1, 1), 2),
end_Y = rep(1:2, each = 3)*2 + round(runif(6, -1, 1), 2),
end_Z = 0.5,
radius = 0.25,
attribute = sample(letters[1:6], 6)
)
# calculate centers
cylinder$center_X <- rowMeans(cylinder[,c("start_X", "end_X")])
cylinder$center_Y <- rowMeans(cylinder[,c("start_Y", "end_Y")])
cylinder$center_Z <- rowMeans(cylinder[,c("start_Z", "end_Z")])
# create cylinders
cylinder_list <- list()
for (i in 1:nrow(cylinder)) {
cylinder_list[[i]] <- cylinder3d(
center = cbind(
c(cylinder$start_X[i], cylinder$end_X[i]),
c(cylinder$start_Y[i], cylinder$end_Y[i]),
c(cylinder$start_Z[i], cylinder$end_Z[i])),
radius = cylinder$radius[i],
closed = -2)
# Add tag here:
cylinder_list[[i]]$material$tag <- cylinder$attribute[i]
}
# plot cylinders
open3d()
par3d()
shade3d(shapelist3d(cylinder_list, plot = FALSE), col = "blue")
text3d(cylinder$center_X+0.5, cylinder$center_Y+0.5, cylinder$center_Z+0.5, cylinder$attribute, color="red")
# Don't get values, get the ids
nearby <- selectpoints3d(value=FALSE, button = "right", closest = FALSE)
ids <- nearby[, "id"]
# Convert them to tags. If you select one of the labels, you'll get
# a blank in the list of tags, because we didn't tag the text.
unique(tagged3d(id = ids))
When I was trying this, I found that using closest = TRUE in selectpoints3d seemed to get too many ids; there may be a bug there.
I have vertices and indices data for human face here. I have a post one year ago on plotting 3D facial surface mesh based on these data. Now, I want to plot only the right half and mid-facial vertices while ignoring the left side vertices. Based on my earlier plot, I tried the following code:
library(tidyverse)
library(readxl)
library(rgl)
vb <- read_excel("...\\vb.xlsx", sheet = "Sheet1", col_names = F)
it <- read_excel("...\\it.xlsx", sheet = "Sheet1", col_names = F)
# Extract vertices for the right side
lm_right_ind <- which(vb[,1] < 0)
vb_mat_right <- t(vb[lm_right_ind, ])
vb_mat_right <- rbind(vb_mat_right, 1)
rownames(vb_mat_right) <- c("xpts", "ypts", "zpts", "")
vertices1_right <- c(vb_mat_right)
# Extract `it` whose rows do not contain vertices on the left side
# Left-side vertices have vb[,1] greater than 0
lm_left_ind <- which(vb[,1] > 0)
leftContain <- NULL
for (i in 1: dim(it)[1]) {
if (T %in% (it[i,] %in% lm_left_ind)) {
leftContain[i] <- i
} else {leftContain[i] <- NA}
}
leftContain <- leftContain[!is.na(leftContain)]
# Remove indices that involve left-side vertices
it_rightMid <- it[-leftContain,]
it_mat_right <- t(as.matrix(it_rightMid))
rownames(it_mat_right) <- NULL
indices_right <- c(it_mat_right)
# Plot
try1_right <- tmesh3d(vertices = vertices1_right, indices = indices_right, homogeneous = TRUE,
material = NULL, normals = NULL, texcoords = NULL)
# Use addNormals to smooth the plot. See my Stackoverflow question:
# https://stackoverflow.com/questions/53918849/smooth-3d-trangular-mesh-in-r
try12_right <- addNormals(try1_right)
shade3d(try12_right, col="#add9ec", specular = "#202020", alpha = 0.8)
I got an error whing trying to obtain try12_right:
Error in v[, it[3, i]] : subscript out of bounds.
I did exactly as what I did in my earlier plot but why something went wrong here? Thank you.
Here's an example of using a clipping plane to leave off the left hand side of a mesh object:
library(rgl)
open3d()
root <- currentSubscene3d()
newSubscene3d("inherit", "inherit", "inherit", parent = root) # Clipping limited to this subscene
shade3d(addNormals(subdivision3d(icosahedron3d(), 2)), col = "pink")
clipplanes3d(a = 1, b = 0, c = 0, d = 0)
useSubscene3d(root)
decorate3d()
The fiddling with subscenes limits the clipping to just the shaded sphere, not everything else in the picture.
This produces this output:
If there's nothing else there, it's simpler:
library(rgl)
open3d()
shade3d(addNormals(subdivision3d(icosahedron3d(), 2)), col = "pink")
clipplanes3d(a = 1, b = 0, c = 0, d = 0)
which produces
I was wondering if anyone knows of a package that allows partial row labeling of heatmaps. I am currently using pheatmap() to construct my heatmaps, but I can use any package that has this functionality.
I have plots with many rows of differentially expressed genes and I would like to label a subset of them. There are two main things to consider (that I can think of):
The placement of the text annotation depends on the height of the row. If the rows are too narrow, then the text label will be ambiguous without some sort of pointer.
If multiple adjacent rows are significant (i.e. will be labelled), then these will need to be offset, and again, a pointer will be needed.
Below is an example of a partial solution that really only gets maybe halfway there, but I hope illustrates what I'd like to be able to do.
set.seed(1)
require(pheatmap)
require(RColorBrewer)
require(grid)
### Data to plot
data_mat <- matrix(sample(1:10000, 300), nrow = 50, ncol = 6)
rownames(data_mat) <- paste0("Gene", 1:50)
colnames(data_mat) <- c(paste0("A", 1:3), paste0("B", 1:3))
### Set how many genes to annotate
### TRUE - make enough labels that some overlap
### FALSE - no overlap
tooMany <- T
### Select a few genes to annotate
if (tooMany) {
sigGenes_v <- paste0("Gene", c(5,20,26,42,47,16,28))
newMain_v <- "Too Many Labels"
} else {
sigGenes_v <- paste0("Gene", c(5,20,26,42))
newMain_v <- "OK Labels"
}
### Make color list
colors_v <- brewer.pal(8, "Dark2")
colors_v <- colors_v[c(1:length(sigGenes_v), 8)]
names(colors_v) <- c(sigGenes_v, "No")
annColors_lsv <- list("Sig" = colors_v)
### Column Metadata
colMeta_df <- data.frame(Treatment = c(rep("A", 3), rep("B", 3)),
Replicate = c(rep(1:3, 2)),
stringsAsFactors = F,
row.names = colnames(data_mat))
### Row metadata
rowMeta_df <- data.frame(Sig = rep("No", 50),
stringsAsFactors = F,
row.names = rownames(data_mat))
for (gene_v in sigGenes_v) rowMeta_df[rownames(rowMeta_df) == gene_v, "Sig"] <- gene_v
### Heatmap
heat <- pheatmap(data_mat,
annotation_row = rowMeta_df,
annotation_col = colMeta_df,
annotation_colors = annColors_lsv,
cellwidth = 10,
main = "Original Heat")
### Get order of genes after clustering
genesInHeatOrder_v <- heat$tree_row$labels[heat$tree_row$order]
whichSigInHeatOrder_v <- which(genesInHeatOrder_v %in% sigGenes_v)
whichSigInHeatOrderLabels_v <- genesInHeatOrder_v[whichSigInHeatOrder_v]
sigY <- 1 - (0.02 * whichSigInHeatOrder_v)
### Change title
whichMainGrob_v <- which(heat$gtable$layout$name == "main")
heat$gtable$grobs[[whichMainGrob_v]] <- textGrob(label = newMain_v,
gp = gpar(fontsize = 16))
### Remove rows
whichRowGrob_v <- which(heat$gtable$layout$name == "row_names")
heat$gtable$grobs[[whichRowGrob_v]] <- textGrob(label = whichSigInHeatOrderLabels_v,
y = sigY,
vjust = 1)
grid.newpage()
grid.draw(heat)
Here are a few outputs:
original heatmap:
ok labels:
ok labels, with flags:
too many labels
too many labels, with flags
The "with flags" outputs are the desired final results.
I just saved these as images from the Rstudio plot viewer. I recognize that I could save them as pdfs and provide a larger file size to get rid of the label overlap, but then the individual cells would be larger than I want.
Based on your code, you seem fairly comfortable with gtables & grobs. A (relatively) straightforward way to achieve the look you want is to zoom in on the row label grob, & make some changes there:
replace unwanted labels with "";
evenly spread out labels within the available space;
add line segments joining the old and new label positions.
I wrote a wrapper function for this, which works as follows:
# heat refers to the original heatmap produced from the pheatmap() function
# kept.labels should be a vector of labels you wish to show
# repel.degree is a number in the range [0, 1], controlling how much the
# labels are spread out from one another
add.flag(heat,
kept.labels = sigGenes_v,
repel.degree = 0)
add.flag(heat,
kept.labels = sigGenes_v,
repel.degree = 0.5)
add.flag(heat,
kept.labels = sigGenes_v,
repel.degree = 1)
Function (explanations in annotations):
add.flag <- function(pheatmap,
kept.labels,
repel.degree) {
# repel.degree = number within [0, 1], which controls how much
# space to allocate for repelling labels.
## repel.degree = 0: spread out labels over existing range of kept labels
## repel.degree = 1: spread out labels over the full y-axis
heatmap <- pheatmap$gtable
new.label <- heatmap$grobs[[which(heatmap$layout$name == "row_names")]]
# keep only labels in kept.labels, replace the rest with ""
new.label$label <- ifelse(new.label$label %in% kept.labels,
new.label$label, "")
# calculate evenly spaced out y-axis positions
repelled.y <- function(d, d.select, k = repel.degree){
# d = vector of distances for labels
# d.select = vector of T/F for which labels are significant
# recursive function to get current label positions
# (note the unit is "npc" for all components of each distance)
strip.npc <- function(dd){
if(!"unit.arithmetic" %in% class(dd)) {
return(as.numeric(dd))
}
d1 <- strip.npc(dd$arg1)
d2 <- strip.npc(dd$arg2)
fn <- dd$fname
return(lazyeval::lazy_eval(paste(d1, fn, d2)))
}
full.range <- sapply(seq_along(d), function(i) strip.npc(d[i]))
selected.range <- sapply(seq_along(d[d.select]), function(i) strip.npc(d[d.select][i]))
return(unit(seq(from = max(selected.range) + k*(max(full.range) - max(selected.range)),
to = min(selected.range) - k*(min(selected.range) - min(full.range)),
length.out = sum(d.select)),
"npc"))
}
new.y.positions <- repelled.y(new.label$y,
d.select = new.label$label != "")
new.flag <- segmentsGrob(x0 = new.label$x,
x1 = new.label$x + unit(0.15, "npc"),
y0 = new.label$y[new.label$label != ""],
y1 = new.y.positions)
# shift position for selected labels
new.label$x <- new.label$x + unit(0.2, "npc")
new.label$y[new.label$label != ""] <- new.y.positions
# add flag to heatmap
heatmap <- gtable::gtable_add_grob(x = heatmap,
grobs = new.flag,
t = 4,
l = 4
)
# replace label positions in heatmap
heatmap$grobs[[which(heatmap$layout$name == "row_names")]] <- new.label
# plot result
grid.newpage()
grid.draw(heatmap)
# return a copy of the heatmap invisibly
invisible(heatmap)
}
I created a Sankey diagram using the plotly package.
Please look at below example. I tried to make five streams, 1_6_7, 2_6_7, and so on. But two of five links between 6 and 7 disappeared. As far as I see, plotly allows to make only three or less links between two nodes.
Can I remove this restrictions ? Any help would be greatly appreciated.
Here is an example code and the outputs:
d <- expand.grid(1:5, 6, 7)
node_label <- 1:max(d)
node_colour <- scales::alpha(RColorBrewer::brewer.pal(7, "Set2"), 0.8)
link_source_nodeind <- c(d[,1], d[,2]) - 1
link_target_nodeind <- c(d[,2], d[,3]) - 1
link_value <- rep(100, nrow(d) * 2)
link_label <- rep(paste(d[,1], d[,2], d[,3], sep = "_"), 2)
link_colour <- rep(scales::alpha(RColorBrewer::brewer.pal(5, "Set2"), 0.2), 2)
p <- plotly::plot_ly(type = "sankey",
domain = c(x = c(0,1), y = c(0,1)),
orientation = "h",
node = list(label = node_label,
color = node_colour),
link = list(source = link_source_nodeind,
target = link_target_nodeind,
value = link_value,
label = link_label,
color = link_colour))
p
I am working with corrplot and following example here Plotting multiple corrplots (R) in the same graph I can display multiple corrplots(R) in the same graph. However I would like to save to a tiff file and because I working with loop I don't know how to achieve this. See code below.
I loop through several block of my experiments (Block1, block2) and plot the corrplot one next to another. This works. I don't understand how to direct to tiff file. In particular where to put
tiff(file = 'Figure4Plots.tiff', width = 12, height = 12, units = "in", res = 300) and dev.off() I tried after dflist and several others but does not work Thanks!
dflist<-c('Block1', 'Block2')
par(mfrow=c(2,2))
for (i in seq_along(dflist)) {
#Subset different Blocks
dataCorr<- subset(total , (block == dflist[i] ))
p.mat <- cor.mtest(dataCorr)
M<-cor(dataCorr)
col <- colorRampPalette(c("#BB4444", "#EE9988", "#FFFFFF", "#77AADD", "#4477AA"))
corrplot(M, method="color", col=col(200),
type="upper", title = title,
addCoef.col = "black", # Add coefficient of correlation
tl.col="red", tl.srt=45, #Text label color and rotation
# Combine with significance
p.mat = p.mat, sig.level = 0.05, insig = "blank",
diag=TRUE,
mar=c(0,0,1,0) )}
I don't have your original data, and I'm not familiar with the corrplot package, so I've made some dummy data and used just a simple plot() function. Unless there's something particular about the corrplot() function, you should be able to enclose most of your code in a tiff() block, like this:
dflist <- c('Block1', 'Block2', 'Block3', 'Block4')
total <- data.frame(block=sample(dflist, size=100, replace=TRUE), x=runif(100), y=runif(100)*2)
tiff(file = 'Figure4Plots.tiff', width = 12, height = 12, units = "cm", res = 72)
par(mfrow=c(2,2))
for (thisBlock in dflist) {
#Subset different Blocks
dataCorr <- subset(total , (block == thisBlock ))
dataCorr <- dataCorr[, c('x', 'y')]
plot(dataCorr)
}
dev.off()
This code produces Figure4Plots.tiff: