I have a combined .csv file containing coordinates for multiple trajectories.
I would like to plot these trajectories in R on the same graph with each line having a different colour (preferably using a loop). How do I do this?
Please supply a minimal working example for your work.
But in general: If you have loaded your .csv into R using read.csv or similar methods and arranged your data in a dataframe or matrix its a matter of looping over the desired dimension with lines.
Example:
simdata <- function()
{
set.seed(1234)
data <- matrix(data=NA,nrow=10,ncol=100)
for(i in 1:10) data[i,] <- dnorm(1:100,runif(1,1,100),runif(1,5,20))
return(data)
}
Matrix <- simdata()
cols <- colorRampPalette(c("blue","red"))(10) #generate ramping colors
plot(NULL,xlim=c(0,ncol(Matrix)),ylim=range(Matrix)) #setup empty plot window
for(i in 1:nrow(Matrix)) lines(Matrix[i,],col=cols[i]) #plot
Related
I am using a for loop to loop through a dataset and produce a mice_plot per category. In the for loop, I would like to save the produced mice_plot so that I can later plot them all in a grid.
my code looks like this atm:
for center in unique(df$center){
dispatch_centers <- list()
mice_plot <- aggr(center, ...)
# save mice_plot some how in a way I can later open it with ggplot
as.grob()?
#add to list
center_plots[[center]] <- mice_plot
}
grid.arrange(grobs = center_plots, ncol = 2)
However, I do not understand the aggr() object nor how to save it so I can later handle it with ggplot2. Does anyone have any ideas?
I am running a function that returns a custom ggplot from an input data (it is in fact a plot with several layers on it). I run the function over several different input data and obtain a list of ggplots.
I want to create a grid with these plots to compare them but they all have different y axes.
I guess what I have to do is extract the maximum and minimum y axes limits from the ggplot list and apply those to each plot in the list.
How can I do that? I guess its through the use of ggbuild. Something like this:
test = ggplot_build(plot_list[[1]])
> test$layout$panel_scales_x
[[1]]
<ScaleContinuousPosition>
Range:
Limits: 0 -- 1
I am not familiar with the structure of a ggplot_build and maybe this one in particular is not a standard one as it comes from a "custom" ggplot.
For reference, these plots are created whit the gseaplot2 function from the enrichplot package.
I dont know how to "upload" an R object but if that would help, let me know how to do it.
Thanks!
edit after comments (thanks for your suggestions!)
Here is an example of the a gseaplot2 plot. GSEA stands for Gene Set Enrichment Analysis, it is a technique used in genomic studies. The gseaplot2 function calculates a running average and then plots it and another bar plot on the bottom.
and here is the grid I create to compare the plots generated from different data:
I would like to have a common scale for the "Running Enrichment Score" part.
I guess I could try to recreate the gseaplot2 function and input all of the datasets and then create the grid by facet_wrap, but I was wondering if there was an easy way of extracting parameters from a plot list.
As a reproducible example (from the enrichplot package):
library(clusterProfiler)
data(geneList, package="DOSE")
gene <- names(geneList)[abs(geneList) > 2]
wpgmtfile <- system.file("extdata/wikipathways-20180810-gmt-Homo_sapiens.gmt", package="clusterProfiler")
wp2gene <- read.gmt(wpgmtfile)
wp2gene <- wp2gene %>% tidyr::separate(term, c("name","version","wpid","org"), "%")
wpid2gene <- wp2gene %>% dplyr::select(wpid, gene) #TERM2GENE
wpid2name <- wp2gene %>% dplyr::select(wpid, name) #TERM2NAME
ewp2 <- GSEA(geneList, TERM2GENE = wpid2gene, TERM2NAME = wpid2name, verbose=FALSE)
gseaplot2(ewp2, geneSetID=1, subplots=1:2)
And this is how I generate the plot list (probably there is a much more elegant way):
plot_list = list()
for(i in 1:3) {
fig_i = gseaplot2(ewp2,
geneSetID=i,
subplots=1:2)
plot_list[[i]] = fig_i
}
ggarrange(plotlist=plot_list)
I would like to make histogram of columns 5-34 of my data set and save them for reference. This is what I have and the error 'x must be numeric' is what keeps coming up. All of these columns have numeric data.
[data screenshot][1]
dput(longbroca)
histograms = c()
GBhistograms = c()
RThistograms = c()
for (i in 5:34){
hist(longbroca)
hist(GBlongbroca)
hist(RTlongbroca)
histograms = c(histograms, hist(longbroca[,5:34]))
GBhistograms = c(GBhistograms, hist(GBlongbroca[,5:34]))
RThistograms = c(RThistograms, hist(RTlongbroca[,5:34]))
}
#reproducible
fakerow1 <- c(100,80,60,40,20)
fakerow2 <- c(100,80,60,40,20)
fakedata = rbind(fakerow1,fakerow2)
colnames(fakedata) = c('ant1','ant2','ant3','ant4','ant5')
You cannot plot all of the columns with a single hist() function. That is why you are getting the error message. You are plotting histograms and saving the output list from the histogram. Your code does not save any histograms, only the data for producing them. If you actually want to save the plotted histograms, you need to plot them to a device (e.g. pdf).
We can use the iris dataset which comes with R (data(iris)) as some example data. The first 4 columns are numeric. If you just want the data for the histograms from the iris data set (columns 1 through 4):
# R will plot all four but you will only see the last one.
histograms <- lapply(iris[, 1:4], hist)
The variable histograms is a list that contains 6 elements. These are documented on the manual page for the function (?hist).
# To plot one of the histograms with a title and x-axis label:
lbl <- names(histograms)
plot(histograms[[1]], main=lbl[1], xlab=lbl[1])
# To plot them all
pdf("histograms.pdf")
lapply(1:4, function(x) plot(histograms[[x]], main=lbl[x], xlab=lbl[x]))
dev.off()
The file "histograms.pdf" will have all four histograms, one per page.
In desperate need of a sanity check. I am struggling to see why the result of plot_grid (cowplot) of N plots in my code is producing N identical plots. From the list I provide, I've taken out each data frame to verify that each plot should be different, however, when I pass in the complete list to plot_grid they all look identical.
p <- vector("list",length(dataList))
for(i in 1:length(dataList)) {
df <- dataList[[i]]
p[[i]] <- ggplot(df, aes(df$base)) + geom_bar()
}
multi <- plot_grid(plotlist=p, align="hv")
save_plot(paste("data_freqs.tiff",sep=""), multi, dpi=300, base_aspect_ratio=1.5)
For example, when type the following I can see the data is different:
a<-dataList[[1]]
b<-dataList[[2]]
sum(a$base=="T")
>1245
sum(b$base=="T")
>1034
However, I end up with multiple plots of identical T values (all fixed to 1245).
Any help is much appreciated.
Thanks
library(affy)
microarrays <- ReadAffy() # 98 CEL files are read into the same object
RNAdeg <- AffyRNAdeg(microarrays)
Now I want to plot subsets of RNAdeg
plotAffyRNAdeg(RNAdeg[.......?]) # What can I do?
I've tried various 'for' loops without success.
But if plot line colors are specified then plotAffyRNAdeg plots a subset of 1:(number of colors specified), but I haven't thought of a way to use that effectively. For example, below plots the first through the sixth AffyRNAdeg'd set of microarray data (first through sixth .CEL file read in by ReadAffy() )
plotAffyRNAdeg(RNAdeg,col=c(2,2,2,3,3,3))
OK, one way was found by running AffyRNAdeg() on subsets of the object the CEL files are in and putting the resulting data in a list of lists organized by experiment, then plotting the list elements. Maybe there is an easier way, but this worked (I'm quite new to R).
library(affy)
library(RColorBrewer)
> sampleNames(ARTHwoundMA[,11:14])
[1] "GSE18960_05_GSM469416_trt_rep2.CEL" "GSE18960_06_GSM469418_trt_rep3.CEL"
[3] "GSE5525_GSM128715_ctrl12h.CEL" "GSE5525_GSM128716_ctrl24h.CEL
# RNA DEG
# Indices to subset by experiment
cel_names <- substr(sampleNames(ARTHwoundMA),1,7)
unique_exp <- unique(substr(sampleNames(ARTHwoundMA),1,7))
exp_ind <- list()
for (i in 1:length(unique_exp))
{
tempvec <- vector()
for (j in 1:length(cel_names))
{
if (cel_names[j]==unique_exp[i])
{
tempvec <- append(tempvec,j)
}
}
exp_ind[[(length(exp_ind)+1)]] <- tempvec
}
# Calculating
RNAdeg_exp <- list()
for(i in 1:length(exp_ind))
{
RNAdeg_exp[[i]] <- AffyRNAdeg(ARTHwoundMA[,exp_ind[[i]]])
}
# Plotting
colors <- colorRampPalette(rev(brewer.pal(9, "Reds")))(length(exp_ind[[i]])
pdf(file="C:\\R working directory\\TEST\\RNAdeg_plots.pdf")
for(i in 1:length(exp_ind))
{
par(bg="gray")
colors <- colorRampPalette(rev(brewer.pal(9, "Reds")))(length(exp_ind[[i]]))
plotAffyRNAdeg(RNAdeg_exp[[i]], col=colors)
plot.new()
legend("topleft", lty=1, lwd=2,col=colors,
legend=paste(sampleNames(ARTHwoundMA[,exp_ind[[i]]])))
}
dev.off()