I am using biwavelet package to conduct wavelet coherence analysis. When I want to set my own x ticklabel, I find axisis not working. The following gives a reproducible example. Thanks.
require(biwavelet)
t1 <- cbind(1:100, rnorm(100))
t2 <- cbind(1:100, rnorm(100))
wtc.t1t2 <- wtc(t1,t2,nrands = 10)
plot(wtc.t1t2, plot.cb = TRUE, plot.phase = TRUE,xaxt='n')
axis(1,at = seq(10,100,10),labels = seq(1,10,1))
The thing that was breaking your plot was plot.cb = TRUE.
In the source code for plot.biwavelet the author notes the following about the plot.cb option:
## Add color bar: this must happen after everything, otherwise chaos
ensues!
So that was the problem -- you invoked axis() after plot.cb and chaos ensued. However, you can manually add back the color bar using image.plot from the fields package, after having run plot without plot.cb then having added your axis().
pacman::p_load(biwavelet,fields)
t1 <- cbind(1:100, rnorm(100))
t2 <- cbind(1:100, rnorm(100))
wtc.t1t2 <- wtc(t1,t2,nrands = 10)
plot(wtc.t1t2, plot.phase = TRUE,xaxt='n')
axis(1,at = seq(10,100,10),labels = seq(1,20,2))
image.plot( zlim=c(0,25), legend.only=TRUE)
You can customize the ticks and the color bar to your liking this way!
Related
I have 11 plots and used a looping function to plot them see my code below. However, I can't get them to fit in just 1 page or less. The plots are actually too big. I am using R software and writing my work in RMarkdown. I have spent almost an entire week trying to resolve this.
group_by(Firm_category) %>%
doo(
~ggboxplot(
data =., x = "Means.type", y = "means",
fill ="grey", palette = "npg", legend = "none",
ggtheme = theme_pubr()
),
result = "plots"
)
graph3
# Add statistical tests to each corresponding plot
Firm_category <- graph3$Firm_category
xx <- for(i in 1:length(Firm_category)){
graph3.i <- graph3$plots[[i]] +
labs(title = Firm_category[i]) +
stat_pvalue_manual(stat.test[i, ], label = "p.adj.signif")
print(graph3.i)
}
#output3.long data sample below as comments
#Firm_category billmonth Means.type means
#Agric 1 Before 38.4444
#Agric 1 After 51.9
Complete data is on my github: https://github.com/Fridahnyakundi/Descriptives-in-R/blob/master/Output3.csv
This code prints all the graphs but in like 4 pages. I want to group them into a grid. I have tried to add all these codes below just before my last curly bracket and none is working, please help me out.
library(cowplot)
print(plot_grid(plotlist = graph3.i[1:11], nrow = 4, ncol = 3))
library(ggpubr)
print(ggarrange(graph3.i[1:11], nrow = 4, ncol = 3))
I tried the gridExtra command as well (they all seem to do the same thing). I am the one with a mistake and I guess it has to do with my list. I read a lot of similar work here, some suggested
dev.new()
dev.off()
I still didn't get what they do. But adding either of them caused my code to stop.
I tried defining my 'for' loop function say call it 'XX', then later call it to make a list of graph but it returned NULL output.
I have tried defining an empty list (as I read in some answers here) then counting them to make a list that can be printed but I got so many errors.
I have done this for almost 3 days and will appreciate your help in resolving this.
Thanks!
I tried to complete your code ... and this works (but I don't have your 'stat.test' object). Basically, I added a graph3.i <- list() and replaced graph3.i in the loop ..
Is it what you wanted to do ?
library(magrittr)
library(dplyr)
library(rstatix)
library(ggplot2)
library(ggpubr)
data <- read.csv(url('http://raw.githubusercontent.com/Fridahnyakundi/Descriptives-in-R/master/Output3.csv'))
graph3 <- data %>% group_by(Firm_category) %>%
doo(
~ggboxplot(
data =., x = "Means.type", y = "means",
fill ="grey", palette = "npg", legend = "none",
ggtheme = theme_pubr()
),
result = "plots"
)
graph3
# Add statistical tests to each corresponding plot
graph3.i <- list()
Firm_category <- graph3$Firm_category
xx <- for(i in 1:length(Firm_category)){
graph3.i[[i]] <- graph3$plots[[i]] +
labs(title = Firm_category[i]) # +
# stat_pvalue_manual(stat.test[i, ], label = "p.adj.signif")
print(graph3.i)
}
library(cowplot)
print(plot_grid(plotlist = graph3.i[1:11], nrow = 4, ncol = 3))
I was using Seurat to analyse single cell RNA-seq data and I managed to draw a heatmap plot with DoHeatmap() after clustering and marker selection, but got a bunch of random characters appearing in the legend. They are random characters as they will change every time you run the code. I was worrying over it's something related to my own dataset, so I then tried the test Seurat object 'ifnb' but still got the same issue (see the red oval in the example plot).
example plot
I also tried importing the Seurat object in R in the terminal (via readRDS) and ran the plotting function, but got the same issue there, so it's not a Rstudio thing.
Here are the codes I ran:
'''
library(Seurat)
library(SeuratData)
library(patchwork)
InstallData("ifnb")
LoadData("ifnb")
ifnb.list <- SplitObject(ifnb, split.by = "stim")
ifnb.list <- lapply(X = ifnb.list, FUN = function(x) {
x <- NormalizeData(x)
x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
features <- SelectIntegrationFeatures(object.list = ifnb.list)
immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, anchor.features = features)
immune.combined <- IntegrateData(anchorset = immune.anchors)
immune.combined <- ScaleData(immune.combined, verbose = FALSE)
immune.combined <- RunPCA(immune.combined, npcs = 30, verbose = FALSE)
immune.combined <- RunUMAP(immune.combined, reduction = "pca", dims = 1:30)
immune.combined <- FindNeighbors(immune.combined, reduction = "pca", dims = 1:30)
immune.combined <- FindClusters(immune.combined, resolution = 0.5)
DefaultAssay(immune.combined) <- 'RNA'
immune_markers <- FindAllMarkers(immune.combined, latent.vars = "stim", test.use = "MAST", assay = 'RNA')
immune_markers %>%
group_by(cluster) %>%
top_n(n = 10, wt = avg_log2FC) -> top10_immune
DoHeatmap(immune.combined, slot = 'data',features = top10_immune$gene, group.by = 'stim', assay = 'RNA')
'''
Does anyone have any idea how to solve this issue other than reinstalling everything?
I have been having the same issue myself and while I have solved it by not needing the legend, I think you could use this approach and use a similar solution:
DoHeatmap(immune.combined, slot = 'data',features = top10_immune$gene, group.by = 'stim', assay = 'RNA') +
scale_color_manual(
values = my_colors,
limits = c('CTRL', 'STIM'))
Let me know if this works! It doesn't solve the source of the odd text values but it does the job! If you haven't already, I would recommend creating a forum question on the Seurat forums to see where these characters are coming from!
When I use seurat4.0, I met the same problem.
While I loaded 4.1, it disappeared
I am plotting a dendrogram of the moduleeigengenes in the WGCNA package and I want to order/swap the branches. I use the plotEigengeneNetworks function to plot it, but cannot define the order of the branches. I know that there is the dendextend package for modifying dendrograms, but this does not work on the output that plotEigengeneNetworks function produces. I would be helpful for any suggestions on how to achieve this.
Example:
library(WGCNA)
set.seed(123)
ME <- data.frame(replicate(15, sample(1:10, 11, rep=TRUE)))
ME[,c(1:11)] <- sapply(ME[, c(1:11)], as.numeric)
plotEigengeneNetworks(ME, plotAdjacency = TRUE, setLabels = colnames(ME), plotDendrograms = TRUE, plotHeatmaps = FALSE)
Looking at the code of plotEigengeneNetworks, You will not be able to do what you want using it. However, what you can do is reproduce the way it creates the cluster, and then use the dendextend package to directly update the dendrogram (produced from the hclust), by using the following:
# we need to run all of this to get the relevant packages...
source("http://bioconductor.org/biocLite.R")
biocLite("S4Vectors")
biocLite("IRanges")
biocLite("GenomeInfoDb")
biocLite("AnnotationDbi")
biocLite("GO.db")
biocLite("WGCNA")
# what the author wanted:
library(WGCNA)
set.seed(123)
ME <- data.frame(replicate(15, sample(1:10, 11, rep=TRUE)))
ME[,c(1:11)] <- sapply(ME[, c(1:11)], as.numeric)
plotEigengeneNetworks(ME, plotAdjacency = TRUE, setLabels = colnames(ME), plotDendrograms = TRUE, plotHeatmaps = FALSE)
# =================================
# Reproduce the above plot:
corME = cor(ME)
disME = as.dist(1 - corME)
clust = fastcluster::hclust(disME, method = "average") # you could also use stats::hclust just as well...
plot(clust)
# Now that we got what we wanted, let's move to dendrogram land
dend <- as.dendrogram(clust)
# get dendextend
if(!require(dendextend)) install.packages("dendextend")
library(dendextend)
dend <- hang.dendrogram(dend)
# plot(dend) # it now looks similar to the hclust plot
# we can now rotate the labels:
dend <- color_labels(dend)
dend2 <- rotate(dend, order = sort(labels(dend)))
par(mfrow = c(1,2))
plot(dend, main = "Original dend plot")
plot(dend2, main = "Dend plot after rotating the labels")
#
Result:
According to the comments from others, this post has been separated into several
smaller questions from the previous version of this OP.
In the graph below, will you help me to (Newbie to R)
Custom legends according to the data they represent like filled for variable 1, circle points for variable 2 and line for variable 3 and their colors.
same letter size for the legend and axis-names.
The graph below is produced with the data in pdf device with following layout.
m <- matrix(c(1,2,3,3,4,5),nrow = 3,ncol = 2,byrow = TRUE)
layout(mat = m,heights = c(0.47,0.06,0.47))
par(mar=c(4,4.2,3,4.2))
#Codes for Fig A and B
...
#Margin for legend
par(mar = c(0.2,0.2,0.1,0.1))
# Code for legend
...
#Codes for Fig C and D
...
Using doubleYScale from latticeExtra and the data in the long format (see my previous answer), you can simplify the work:
No need to create a custom layout to superpose many plots
No need to create the legend manually
The idea is to create 2 separates objects and then merge them using doubleYScale. The latter will create the second axes. I hope I get your ploygon idea since it is not very clear why do you invert it in your OP.
library(latticeExtra)
obj1 <- xyplot(Variable~TimeVariable|Type,type='l',
groups=time, scales=list(x=list(relation='free'),
y=list(relation='free')),
auto.key=list(columns = 3,lines = TRUE,points=FALSE) ,
data = subset(dat.l,time !=1))
obj2 <- xyplot(Variable~TimeVariable|Type,
data = subset(dat.l,time ==1),type='l',
scales=list(x=list(alternating=2),
auto.key=list(columns = 3,lines = TRUE,points=FALSE),
y=list(relation='free')),
panel=function(x,y,...){
panel.xyplot(x,y,...)
panel.polygon(x,y,col='violetred4',border=NA,alpha=0.3)
})
doubleYScale(obj1, obj2, add.axis = TRUE,style1 = 0, style2 = 1)
Try the following:
1) For the legend part
The data can be found on https://www.dropbox.com/s/4kgq8tyvuvq22ym/stackfig1_2.csv
The code I used is as follows:
data <- read.csv("stackfig1_2.csv")
library(Hmisc)
label1=c(0,100,200,300)
plot(data$TimeVariable2C,data$Variable2C,axes=FALSE,ylab="",xlab="",xlim=c(0,24),
ylim=c(0,2.4),xaxs="i",yaxs="i",pch=19)
lines(data$TimeVariable3C,data$Variable3C)
axis(2,tick=T,at=seq(0.0,2.4,by=0.6),label= seq(0.0,2.4,by=0.6))
axis(1,tick=T,at=seq(0,24,by=6),label=seq(0,24,by=6))
mtext("(C)",side=1,outer=F,line=-10,adj=0.8)
minor.tick(nx=5,ny=5)
par(new=TRUE)
plot(data$TimeVariable1C,data$Variable1C,axes=FALSE,xlab="",ylab="",type="l",
ylim=c(800,0),xaxs="i",yaxs="i")
axis(3,xlim=c(0,24),tick=TRUE,at= seq(0,24,by=6),label=seq(0,24,by=6),col.axis="violetred4",col="violetred4")
axis(4,tick=TRUE,at= label1,label=label1,col.axis="violetred4",col="violetred4")
polygon(data$TimeVariable1C,data$Variable1C,col='violetred4',border=NA)
legend("top", legend = c("Variable A","Variable B","Variable C"), col = c("black","violetred4","black"),
ncol = 2, lwd =c("","",2),pch=c(19,15,NA),cex=1)
The output is as follows:
2) To make the font size same use the parameter cex and make it same everywhere.
I'm trying to make very simple GUI for my script. In nutshell problem looks like that :
dataset is dataframe, I would like to plot one column as the time and use simple GUI for choosing next/previus column.
dataset <-data.frame(rnorm(10), rnorm(10), rnorm(10))
columnPlot <- function(dataset, i){
plot(dataset[, i])
}
how to use tcltk for calling fplot with different i's ?
Not what you asked for (not tcltkrelated), but I would advise you to have a look at the new shiny package from RStudio.
Are you particularly attached to the idea of using tcltk? I've been working on something similar using the gWidgets package and have had some success. According to it's CRAN site, "gWidgets provides a toolkit-independent API for building interactive GUIs". This package uses tcltk or GTK2 and I've been using the GTK2 portion. Here's a quick example of a GUI with a spinbutton for changing i. I also added a little fanciness to your function because you mentioned you would be plotting time series, so I made the x axis Time.
data<-data.frame(rnorm(11),rnorm(11),rnorm(11))
i = 1
fplot <- function(i, data = data){
library(ggplot2)
TimeStart <- as.Date('1/1/2012', format = '%m/%d/%Y')
plotdat <- data.frame(Value = data[ ,i], Time = seq(TimeStart,TimeStart + nrow(data) - 1, by = 1))
myplot <- ggplot(plotdat, aes(x = Time, y = Value))+
geom_line()
print(myplot)
}
library(gWidgets)
options(guiToolkit = 'RGtk2')
window <- gwindow ("Time Series Plots", visible = T)
notebook <- gnotebook (cont = window)
group1 <- ggroup(cont = notebook, label = "Choose i", horizontal=F)
ichooser <- gspinbutton(cont = group1, from = 1, to = ncol(data), by = 1, value = i, handler = function(h,...){
i <<- svalue(h$obj)})
plotbutton <- gbutton('Plot', cont = group1, handler=function(h,...){
fplot(i, data)})
graphicspane1 <- ggraphics(cont = group1)