I am analyzing tests with the itan package which turns out to be an incredible weapon to analyze item and of the few that I know it will be possible to shape the graphics that this package returns, I will paste the codes as they are shown on your page
library(itan)
datos<-data(datos) #data that is already part of the itan package
clave<-data(clave)
respuestas <- datos[,-1]
alternativas <- LETTERS[1:5]
#Alternative frequency chart
g <- graficarFrecuenciaAlternativas(respuestas, alternativas, clave)
g$i01
g$i02
g$i03
g$i04
The general question is whether it is possible to change the aesthetics of these graphics to fit them to my project?
Doing some research I found the source code of the packet on the next page:
enter link description here
With which it will be enough to simply modify the following code
graficarFrecuenciaAlternativas <- function(respuestas, alternativas, clave=NULL) {
item <- ncol(respuestas)
fa <-calcularFrecuenciaAlternativas(respuestas, alternativas)
names <- colnames(fa)
output <- c();
for (i in 1:item) {
colnames(fa) <- ifelse(colnames(fa) == clave[[i]],
paste(c("*"), colnames(fa), sep = ""),
colnames(fa))
fam <- melt(fa[i,], id.vars = "item")
output[[i]] <- ggplot2::ggplot(fam, aes_string(x="variable", y="value", fill="variable")) +
ggplot2::geom_col(show.legend = F) +
ggplot2::labs(title = paste("\u00CDtem ", i),
x="Alternativa",
y="Frecuencia") +
ggplot2::theme(plot.title = element_text(size=18, face="bold" ,hjust=0.5))
colnames(fa) <- names
}
names(output) <- colnames(respuestas)
return(output);
}
Related
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'm trying to optimize the parameters for baseline in the R baseline package by changing each parameters in a loop and comparing plots to determine which parameters give me the best baseline.
I currently have the code written so that the loop produces each plot, but I'm having trouble with getting the plot saved as the class of each object I'm creating is a baseline package-specific (which I'm suspecting is the problem here).
foo <- data.frame(Date=seq.Date(as.Date("1957-01-01"), by = "day",
length.out = ncol(milk$spectra)),
Visits=milk$spectra[1,],
Old_baseline_visits=milk$spectra[1,], row.names = NULL)
foo.t <- t(foo$Visits)
#the lines above were copied from https://stackoverflow.com/questions/37346967/r-packagebaseline-application-to-sample-dataset to make a reproducible dataset
df <- expand.grid(lambda=seq(1,10,1), p=seq(0.01,0.1,0.01))
baselinediff <- list()
for(i in 1:nrow(df)){
thislambda <- df[i,]$lambda
thisp <- df[i,]$p
thisplot <- baseline(foo.t, lambda=thislambda, p=thisp, maxit=20, method='als')
print(paste0("lambda = ", thislambda))
print(paste0("p = ", thisp))
print(paste0("index = ", i))
baselinediff[[i]] <- plot(thisplot)
jpeg(file = paste(baselinediff[[i]], '.jpeg', sep = ''))
dev.off()
}
I know that I would be able to extract corrected spectra using baseline.als but I just want to save the plot images with the red baseline so that I can see how well the baselines are getting drawn. Any baseline users out there that can help?
I suggest you change your loop in the following way:
for(i in 1:nrow(df)){
thislambda <- df[i,]$lambda
thisp <- df[i,]$p
thisplot <- baseline(foo.t, lambda=thislambda, p=thisp, maxit=20, method='als')
print(paste0("lambda = ", thislambda))
print(paste0("p = ", thisp))
print(paste0("index = ", i))
baselinediff[[i]] <- thisplot
jpeg(file = paste('baseline', i, '.jpeg', sep = ''))
plot(baselinediff[[i]])
dev.off()
}
Note that this does not try to capture the already plotted element (thisplot) inside of the list. Instead, the plotting is done after you call the jpeg command. This solves your export issue. Another problem was the naming of the file. If you call baselinediff[[i]] inside of paste, you apparently end up with an error. So I switched it to a simpler name. To plot your resulting list, call:
lapply(baselinediff, plot)
If you are determined on storing the already plotted element, the capture.plotfunction from the imager package might be a good start.
I am new to R , I am trying to run example which is given in "rebmix-help pdf". It use galaxy dataset and here is the code
library(rebmix)
devAskNewPage(ask = TRUE)
data("galaxy")
write.table(galaxy, file = "galaxy.txt", sep = "\t",eol = "\n", row.names = FALSE, col.names = FALSE)
REBMIX <- array(list(NULL), c(3, 3, 3))
Table <- NULL
Preprocessing <- c("histogram", "Parzen window", "k-nearest neighbour")
InformationCriterion <- c("AIC", "BIC", "CLC")
pdf <- c("normal", "lognormal", "Weibull")
K <- list(7:20, 7:20, 2:10)
for (i in 1:3) {
for (j in 1:3) {
for (k in 1:3) {
REBMIX[[i, j, k]] <- REBMIX(Dataset = "galaxy.txt",
Preprocessing = Preprocessing[k], D = 0.0025,
cmax = 12, InformationCriterion = InformationCriterion[j],
pdf = pdf[i], K = K[[k]])
if (is.null(Table))
Table <- REBMIX[[i, j, k]]$summary
else Table <- merge(Table, REBMIX[[i, j,k]]$summary, all = TRUE, sort = FALSE)
}
}
}
It is giving me error ERROR:
unused argument (InformationCriterion = InformationCriterion[j])
Plz help
I'm running R 3.0.2 (Windows) and the library rebmix defines a function REBMIX where InformationCriterion is not listed as a named argument, but Criterion.
Brief invoke REBMIX as :
REBMIX[[i, j, k]] <- REBMIX(Dataset = "galaxy.txt",
Preprocessing = Preprocessing[k], D = 0.0025,
cmax = 12, Criterion = InformationCriterion[j],
pdf = pdf[i], K = K[[k]])
It looks as though there have been substantial changes to the rebmix package since the example mentioned in the OP was created. Among the most noticable changes is the use of S4 classes.
There's also an updated demo in the rebmix package using the galaxy data (see demo("rebmix.galaxy"))
To get the above example to produce results (Note: I am not familiar with this package or the rebmix algorithm!!!):
Change the argument to Criterion as mentioned by #Giupo
Use the S4 slot access operator # instead of $
Don't name the results object REDMIX because that's already the function name
library(rebmix)
data("galaxy")
## Don't re-name the REBMIX object!
myREBMIX <- array(list(NULL), c(3, 3, 3))
Table <- NULL
Preprocessing <- c("histogram", "Parzen window", "k-nearest neighbour")
InformationCriterion <- c("AIC", "BIC", "CLC")
pdf <- c("normal", "lognormal", "Weibull")
K <- list(7:20, 7:20, 2:10)
for (i in 1:3) {
for (j in 1:3) {
for (k in 1:3) {
myREBMIX[[i, j, k]] <- REBMIX(Dataset = list(galaxy),
Preprocessing = Preprocessing[k], D = 0.0025,
cmax = 12, Criterion = InformationCriterion[j],
pdf = pdf[i], K = K[[k]])
if (is.null(Table)) {
Table <- myREBMIX[[i, j, k]]#summary
} else {
Table <- merge(Table, myREBMIX[[i, j,k]]#summary, all = TRUE, sort = FALSE)
}
}
}
}
I guess this is late. But I encountered a similar problem just a few minutes ago. And I realized the real scenario that you may face when you got this kind of error msg... It's just the version conflict.
You may use a different version of the R package from the tutorial, thus the argument names could be different between what you are running and what the real code use.
So please check the version first before you try to manually edit the file. Also, it happens that your old version package is still in the path and it overrides the new one. This was exactly what I had... since I manually installed the old and new version separately...
I have a large dataset (questionnaire results) of mostly categorical variables. I have tested for dependency between the variables using chi-square test. There are incomprehensible number of dependencies between variables. I used the chaid() function in the CHAID package to detect interactions and separate out (what I hope to be) the underlying structure of these dependencies for each variable. What typically happens is that the chi-square test will reveal a large number of dependencies (say 10-20) for a variable and the chaid function will reduce this to something much more comprehensible (say 3-5). What I want to do is to extract the names of those variable that were shown to be relevant in the chaid() results.
The chaid() output is in the form of a constparty object. My question is how to extract the variable names associated with the nodes in such an object.
Here is a self contained code example:
library(evtree) # for the ContraceptiveChoice dataset
library(CHAID)
library(vcd)
library(MASS)
data("ContraceptiveChoice")
longform = formula(contraceptive_method_used ~ wifes_education +
husbands_education + wifes_religion + wife_now_working +
husbands_occupation + standard_of_living_index + media_exposure)
z = chaid(longform, data = ContraceptiveChoice)
# plot(z)
z
# This is the part I want to do programatically
shortform = formula(contraceptive_method_used ~ wifes_education + husbands_occupation)
# The thing I want is a programatic way to extract 'shortform' from 'z'
# Examples of use of 'shortfom'
loglm(shortform, data = ContraceptiveChoice)
One possible sollution:
nn <- nodeapply(z)
n.names= names(unlist(nn[[1]]))
ext <- unlist(sapply(n.names, function(x) grep("split.varid.", x, value=T)))
ext <- gsub("kids.split.varid.", "", ext)
ext <- gsub("split.varid.", "", ext)
dep.var <- as.character(terms(z)[1][[2]]) # get the dependent variable
plus = paste(ext, collapse=" + ")
mul = paste(ext, collapse=" * ")
shortform <- as.formula(paste (dep.var, plus, sep = " ~ "))
satform <- as.formula(paste (dep.var, mul, sep = " ~ "))
mosaic(shortform, data = ContraceptiveChoice)
#stp <- step(glm(satform, data=ContraceptiveChoice, family=binomial), direction="both")
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)