I am analysing some sequence data and wish to be able to see missing states within all of my sequence plots. However, I have noticed that TraMineR's state distribution plot function seqdplot automatically removes missing sequence states. I have included a reproducible example below. As you can see, the missing data is visible in the plot and legend of the sequence index plot seqIplot. However, it is automatically removed from the state distribution plot seqdplot.
How do I stop seqdplot from removing these missing values?
Create & Format Data
# Import required libraries
library(TraMineR)
library(tidyverse)
# Set seed for reproducibility
set.seed(123)
# Read in TraMineR sample data
data(mvad)
# For loop which generates missing data within the sequences
for (col in 17:86) {
mvad[sample(1:nrow(mvad),(round(nrow(mvad)*0.1))),col] <- NA
}
# Create sequence object
mvad.seq <- seqdef(mvad[, 17:86])
Sequence Index Plot (missing data visible)
# Create sequence index plot
seqIplot(mvad.seq, sortv = "from.start", with.legend = "right")
State Distribution Plot (missing data removed)
# Create state distribution plot
seqdplot(mvad.seq, sortv = "from.start", with.legend = "right")
To display missing values, simply use the argument with.missing=TRUE
seqdplot(mvad.seq, sortv = "from.start", with.legend = "right",
with.missing=TRUE, border=NA)
By default, seqdef sets right missings as voids, i.e., it assumes sequences end at the last valid state. If you want also to treat (display) right missings as missing tockens, set right=NA in the seqdef command (it is right="DEL" by default):
mvad.seq <- seqdef(mvad[, 17:86], right=NA)
Related
I have applied DBSCAN algorithm on built-in dataset iris in R. But I am getting error when tried to visualise the output using the plot( ).
Following is my code.
library(fpc)
library(dbscan)
data("iris")
head(iris,2)
data1 <- iris[,1:4]
head(data1,2)
set.seed(220)
db <- dbscan(data1,eps = 0.45,minPts = 5)
table(db$cluster,iris$Species)
plot(db,data1,main = 'DBSCAN')
Error: Error in axis(side = side, at = at, labels = labels, ...) :
invalid value specified for graphical parameter "pch"
How to rectify this error?
I have a suggestion below, but first I see two issues:
You're loading two packages, fpc and dbscan, both of which have different functions named dbscan(). This could create tricky bugs later (e.g. if you change the order in which you load the packages, different functions will be run).
It's not clear what you're trying to plot, either what the x- or y-axes should be or the type of plot. The function plot() generally takes a vector of values for the x-axis and another for the y-axis (although not always, consult ?plot), but here you're passing it a data.frame and a dbscan object, and it doesn't know how to handle it.
Here's one way of approaching it, using ggplot() to make a scatterplot, and dplyr for some convenience functions:
# load our packages
# note: only loading dbscacn, not loading fpc since we're not using it
library(dbscan)
library(ggplot2)
library(dplyr)
# run dbscan::dbscan() on the first four columns of iris
db <- dbscan::dbscan(iris[,1:4],eps = 0.45,minPts = 5)
# create a new data frame by binding the derived clusters to the original data
# this keeps our input and output in the same dataframe for ease of reference
data2 <- bind_cols(iris, cluster = factor(db$cluster))
# make a table to confirm it gives the same results as the original code
table(data2$cluster, data2$Species)
# using ggplot, make a point plot with "jitter" so each point is visible
# x-axis is species, y-axis is cluster, also coloured according to cluster
ggplot(data2) +
geom_point(mapping = aes(x=Species, y = cluster, colour = cluster),
position = "jitter") +
labs(title = "DBSCAN")
Here's the image it generates:
If you're looking for something else, please be more specific about what the final plot should look like.
I am running a function that returns a custom ggplot from an input data (it is in fact a plot with several layers on it). I run the function over several different input data and obtain a list of ggplots.
I want to create a grid with these plots to compare them but they all have different y axes.
I guess what I have to do is extract the maximum and minimum y axes limits from the ggplot list and apply those to each plot in the list.
How can I do that? I guess its through the use of ggbuild. Something like this:
test = ggplot_build(plot_list[[1]])
> test$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.
I am making a heatmap, but I can't assign the result in a variable to check the result before plotting. Rstudio plot it automatically. I would like to get the list of rownames in the order of the heatmap. I'am not sure if this is possible. I'am using this code:
hm <- heatmap.2( assay(vsd)[ topVarGenes, ], scale="row",
trace="none", dendrogram="both",
col = colorRampPalette( rev(brewer.pal(9, "RdBu")) )(255),
ColSideColors = c(Controle="gray", Col1.7G2="darkgreen", JG="blue", Mix="orange")[
colData(vsd)$condition ] )
You can assign the plot to an object. The plot will still be drawn in the plot window, however, you'll also get a list with all the data for each plot element. Then you just need to extract the desired plot elements from the list. For example:
library(gplots)
p = heatmap.2(as.matrix(mtcars), dendrogram="both", scale="row")
p is a list with all the elements of the plot.
p # Outputs all the data in the list; lots of output to the console
str(p) # Struture of p; also lots of output to the console
names(p) # Names of all the list elements
p$rowInd # Ordering of the data rows
p$carpet # The heatmap values
You'll see all the other values associated with the dendrogram and the heatmap if you explore the list elements.
To others out there, a more complete description way to capture a matrix representation of the heatmap created by gplots:
matrix_map <- p$carpet
matrix_map <- t(matrix_map)
I use the following R code to generate a dendrogram (see attached picture) with labels based on TraMineR sequences:
library(TraMineR)
library(cluster)
clusterward <- agnes(twitter.om, diss = TRUE, method = "ward")
plot(clusterward, which.plots = 2, labels=colnames(twitter_sequences))
The full code (including dataset) can be found here.
As informative as the dendrogram is graphically, it would be handy to get the same information in text and/or table format. If I call any of the aspects of the object clusterward (created by agnes), such as "order" or "merge" I get everything labeled using numbers rather than the names I get from colnames(twitter_sequences). Also, I don't see how I can output the groupings represented graphically in the dendrogram.
To summarize: How can I get the cluster output in text/table format with the labels properly displayed using R and ideally the traminer/cluster libraries?
The question concerns the cluster package. The help page for the agnes.object returned by agnes
(See http://stat.ethz.ch/R-manual/R-devel/library/cluster/html/agnes.object.html ) states that this object contains an order.lab component "similar to order, but containing observation labels instead of observation numbers. This component is only available if the original observations were labelled."
The dissimilarity matrix (twitter.om in your case) produced by TraMineR does currently not retain the sequence labels as row and column names. To get the order.lab component you have to manually assign sequence labels as both the rownames and colnames of your twitter.om matrix. I illustrate here with the mvad data provided by the TraMineR package.
library(TraMineR)
data(mvad)
## attaching row labels
rownames(mvad) <- paste("seq",rownames(mvad),sep="")
mvad.seq <- seqdef(mvad[17:86])
## computing the dissimilarity matrix
dist.om <- seqdist(mvad.seq, method = "OM", indel = 1, sm = "TRATE")
## assigning row and column labels
rownames(dist.om) <- rownames(mvad)
colnames(dist.om) <- rownames(mvad)
dist.om[1:6,1:6]
## Hierarchical cluster with agnes library(cluster)
cward <- agnes(dist.om, diss = TRUE, method = "ward")
## here we can see that cward has an order.lab component
attributes(cward)
That is for getting order with sequence labels rather than numbers. But now it is not clear to me which cluster outcome you want in text/table form. From the dendrogram you decide of where you want to cut it, i.e., the number of groups you want and cut the dendrogram with cutree, e.g. cl.4 <- cutree(clusterward1, k = 4). The result cl.4 is a vector with the cluster membership for each sequence and you get the list of the members of group 1, for example, with rownames(mvad.seq)[cl.4==1].
Alternatively, you can use the identify method (see ?identify.hclust) to select the groups interactively from the plot, but need to pass the argument as as.hclust(cward). Here is the code for the example
## plot the dendrogram
plot(cward, which.plot = 2, labels=FALSE)
## and select the groups manually from the plot
x <- identify(as.hclust(cward)) ## Terminate with second mouse button
## number of groups selected
length(x)
## list of members of the first group
x[[1]]
Hope this helps.