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$obj)})
plotbutton <- gbutton('Plot', cont = group1, handler=function(h,...){
fplot(i, data)})
graphicspane1 <- ggraphics(cont = group1)
Related
I am analyzing tests with the itan package which turns out to be an incredible weapon to analyze item and of the few that I know it will be possible to shape the graphics that this package returns, I will paste the codes as they are shown on your page
library(itan)
datos<-data(datos) #data that is already part of the itan package
clave<-data(clave)
respuestas <- datos[,-1]
alternativas <- LETTERS[1:5]
#Alternative frequency chart
g <- graficarFrecuenciaAlternativas(respuestas, alternativas, clave)
g$i01
g$i02
g$i03
g$i04
The general question is whether it is possible to change the aesthetics of these graphics to fit them to my project?
Doing some research I found the source code of the packet on the next page:
enter link description here
With which it will be enough to simply modify the following code
graficarFrecuenciaAlternativas <- function(respuestas, alternativas, clave=NULL) {
item <- ncol(respuestas)
fa <-calcularFrecuenciaAlternativas(respuestas, alternativas)
names <- colnames(fa)
output <- c();
for (i in 1:item) {
colnames(fa) <- ifelse(colnames(fa) == clave[[i]],
paste(c("*"), colnames(fa), sep = ""),
colnames(fa))
fam <- melt(fa[i,], id.vars = "item")
output[[i]] <- ggplot2::ggplot(fam, aes_string(x="variable", y="value", fill="variable")) +
ggplot2::geom_col(show.legend = F) +
ggplot2::labs(title = paste("\u00CDtem ", i),
x="Alternativa",
y="Frecuencia") +
ggplot2::theme(plot.title = element_text(size=18, face="bold" ,hjust=0.5))
colnames(fa) <- names
}
names(output) <- colnames(respuestas)
return(output);
}
I have 11 plots and used a looping function to plot them see my code below. However, I can't get them to fit in just 1 page or less. The plots are actually too big. I am using R software and writing my work in RMarkdown. I have spent almost an entire week trying to resolve this.
group_by(Firm_category) %>%
doo(
~ggboxplot(
data =., x = "Means.type", y = "means",
fill ="grey", palette = "npg", legend = "none",
ggtheme = theme_pubr()
),
result = "plots"
)
graph3
# Add statistical tests to each corresponding plot
Firm_category <- graph3$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$gene, group.by = 'stim', assay = 'RNA') +
scale_color_manual(
values = my_colors,
limits = c('CTRL', 'STIM'))
Let me know if this works! It doesn't solve the source of the odd text values but it does the job! If you haven't already, I would recommend creating a forum question on the Seurat forums to see where these characters are coming from!
When I use seurat4.0, I met the same problem.
While I loaded 4.1, it disappeared
I am creating Benford plots for all the numeric variables in my dataset. https://en.wikipedia.org/wiki/Benford%27s_law
Running a single variable
#install.packages("benford.analysis")
library(benford.analysis)
plot(benford(iris$Sepal.Length))
Looks great. And the legend says "Dataset: iris$Sepal.Length", perfect!.
Using apply to run 4 variables,
apply(iris[1:4], 2, function(x) plot(benford(x)))
Creates four plots, however, each plot's legend says "Dataset: x"
I attempted to use a for loop,
for (i in colnames(iris[1:4])){
plot(benford(iris[[i]]))
}
This creates four plots, but now the legends says "Dataset: iris[[i]]". And I would like the name of the variable on each chart.
I tried a different loop, hoping to get titles with an evaluated parsed string like "iris$Sepal.Length":
for (i in colnames(iris[1:4])){
plot(benford(eval(parse(text=paste0("iris$", i)))))
}
But now the legend says "Dataset: eval(parse(text=paste0("iris$", i)))".
AND, Now I've run into the infamous eval(parse(text=paste0( (eg: How to "eval" results returned by "paste0"? and R: eval(parse(...)) is often suboptimal )
I would like labels such as "Dataset: iris$Sepal.Length" or "Dataset: Sepal.Length". How can I create multiple plots with meaningfully variable names in the legend?
This is happening because of the first line within the benford function=:
benford <- function(data, number.of.digits = 2, sign = "positive", discrete=TRUE, round=3){
data.name <- as.character(deparse(substitute(data)))
Source: https://github.com/cran/benford.analysis/blob/master/R/functions-new.R
data.name is then used to name your graph. Whatever variable name or expression you pass to the function will unfortunately be caught by the deparse(substitute()) call, and will be used as the name for your graph.
One short-term solution is to copy and rewrite the function:
#install.packages("benford.analysis")
library(benford.analysis)
#install.packages("data.table")
library(data.table) # needed for function
# load hidden functions into namespace - needed for function
r <- unclass(lsf.str(envir = asNamespace("benford.analysis"), all = T))
for(name in r) eval(parse(text=paste0(name, '<-benford.analysis:::', name)))
benford_rev <- function{} # see below
for (i in colnames(iris[1:4])){
plot(benford_rev(iris[[i]], data.name = i))
}
This has negative side effects of:
Not being maintainable with package revisions
Fills your GlobalEnv with normally hidden functions in the package
So hopefully someone can propose a better way!
benford_rev <- function(data, number.of.digits = 2, sign = "positive", discrete=TRUE, round=3, data.name = as.character(deparse(substitute(data)))){ # changed
# removed line
benford.digits <- generate.benford.digits(number.of.digits)
benford.dist <- generate.benford.distribution(benford.digits)
empirical.distribution <- generate.empirical.distribution(data, number.of.digits,sign, second.order = FALSE, benford.digits)
n <- length(empirical.distribution$data)
second.order <- generate.empirical.distribution(data, number.of.digits,sign, second.order = TRUE, benford.digits, discrete = discrete, round = round)
n.second.order <- length(second.order$data)
benford.dist.freq <- benford.dist*n
## calculating useful summaries and differences
difference <- empirical.distribution$dist.freq - benford.dist.freq
squared.diff <- ((empirical.distribution$dist.freq - benford.dist.freq)^2)/benford.dist.freq
absolute.diff <- abs(empirical.distribution$dist.freq - benford.dist.freq)
### chi-squared test
chisq.bfd <- chisq.test.bfd(squared.diff, data.name)
### MAD
mean.abs.dev <- sum(abs(empirical.distribution$dist - benford.dist)/(length(benford.dist)))
if (number.of.digits > 3) {
MAD.conformity <- NA
} else {
digits.used <- c("First Digit", "First-Two Digits", "First-Three Digits")[number.of.digits]
MAD.conformity <- MAD.conformity(MAD = mean.abs.dev, digits.used)$conformity
}
### Summation
summation <- generate.summation(benford.digits,empirical.distribution$data, empirical.distribution$data.digits)
abs.excess.summation <- abs(summation - mean(summation))
### Mantissa
mantissa <- extract.mantissa(empirical.distribution$data)
mean.mantissa <- mean(mantissa)
var.mantissa <- var(mantissa)
ek.mantissa <- excess.kurtosis(mantissa)
sk.mantissa <- skewness(mantissa)
### Mantissa Arc Test
mat.bfd <- mantissa.arc.test(mantissa, data.name)
### Distortion Factor
distortion.factor <- DF(empirical.distribution$data)
## recovering the lines of the numbers
if (sign == "positive") lines <- which(data > 0 & !is.na(data))
if (sign == "negative") lines <- which(data < 0 & !is.na(data))
if (sign == "both") lines <- which(data != 0 & !is.na(data))
#lines <- which(data %in% empirical.distribution$data)
## output
output <- list(info = list(data.name = data.name,
n = n,
n.second.order = n.second.order,
number.of.digits = number.of.digits),
data = data.table(lines.used = lines,
data.used = empirical.distribution$data,
data.mantissa = mantissa,
data.digits = empirical.distribution$data.digits),
s.o.data = data.table(second.order = second.order$data,
data.second.order.digits = second.order$data.digits),
bfd = data.table(digits = benford.digits,
data.dist = empirical.distribution$dist,
data.second.order.dist = second.order$dist,
benford.dist = benford.dist,
data.second.order.dist.freq = second.order$dist.freq,
data.dist.freq = empirical.distribution$dist.freq,
benford.dist.freq = benford.dist.freq,
benford.so.dist.freq = benford.dist*n.second.order,
data.summation = summation,
abs.excess.summation = abs.excess.summation,
difference = difference,
squared.diff = squared.diff,
absolute.diff = absolute.diff),
mantissa = data.table(statistic = c("Mean Mantissa",
"Var Mantissa",
"Ex. Kurtosis Mantissa",
"Skewness Mantissa"),
values = c(mean.mantissa = mean.mantissa,
var.mantissa = var.mantissa,
ek.mantissa = ek.mantissa,
sk.mantissa = sk.mantissa)),
MAD = mean.abs.dev,
MAD.conformity = MAD.conformity,
distortion.factor = distortion.factor,
stats = list(chisq = chisq.bfd,
mantissa.arc.test = mat.bfd)
)
class(output) <- "Benford"
return(output)
}
I have just updated the package (GitHub version) to allow for a user supplied name.
Now the function has a new parameter called data.name in which you can provide a character vector with the name of the data and override the default. Thus, for your example you can simply run the following code.
First install the GitHub version (I will submit this version to CRAN soon).
devtools::install_github("carloscinelli/benford.analysis") # install new version
Now you can provide the name of the data inside the for loop:
library(benford.analysis)
for (i in colnames(iris[1:4])){
plot(benford(iris[[i]], data.name = i))
}
And all the plots will have the correct naming as you wish (below).
Created on 2019-08-10 by the reprex package (v0.2.1)
I want to include math symbols in the panel titles for this stratigraphic plot:
library(analogue)
data(V12.122)
Depths <- as.numeric(rownames(V12.122))
names(V12.122)
(plt <- Stratiplot(Depths ~ O.univ + G.ruber + G.tenel + G.pacR,
data = V12.122,
type = c("h","l","g"),
zones = 400))
plt
For example, I want to have this text in place of "O.univ" etc.:
I used this code to make that text:
plot(1, type="n", axes=FALSE, ann=FALSE)
title(line = -1, main = expression(phantom()^14*C~years~BP))
title(line = -3, main = expression(delta^18*O))
title(line = -5, main = expression(paste("TP ", mu,"g l"^-1)))
title(line = -10, main = expression("very long title \n with \n line breaks"))
But if I try to update the colnames of the data frame passed to Stratiplot, the code is not parsed, and we do not get the correct text formatting:
V12.122 <- V12.122[, 1:4]
names(V12.122)[1] <- expression(phantom()^14*C~years~BP)
names(V12.122)[2] <- expression(delta^18*O)
names(V12.122)[3] <- expression(paste("TP ", mu,"g l"^-1))
(plt <- Stratiplot(Depths ~ .,
data = V12.122,
type = c("h","l","g"),
zones = 400))
plt
How can I get Stratiplot to parse the expressions in the colnames and format them correctly in the plot?
I've tried looking through str(plt) to see where the panel titles are stored, but no success:
text <- expression(phantom()^14*C~years~BP)
plt$condlevels$ind[1] <- text
names(plt$packet.sizes)[1] <- text
names(plt$par.settings$layout.widths$panel)[1] <- text
You can't actually do this in the current release of analogue; the function is doing too much messing around with data for the expressions to remain unevaluated prior to plotting. I could probably figure this out to allow expressions as the names of the data argument object, but it is easier to just allow users to pass a vector of labels that they want for the variables.
This is now implemented in the development version of the package on github, and I'll push this to CRAN early next week.
This change implements a new argument labelValues which takes a vector of labels for use in labelling the top axis. This can be a vector of expressions.
Here is an illustration of the usage:
library("analogue")
set.seed(1)
df <- setNames(data.frame(matrix(rnorm(200 * 3), ncol = 3)),
c("d13C", "d15N", "d18O"))
df <- transform(df, Age = 1:200)
exprs <- expression(delta^{13}*C, # label for 1st variable
delta^{15}*N, # label for 2nd variable
delta^{18}*O) # label for 3rd variable
Stratiplot(Age ~ ., data = df, labelValues = exprs, varTypes = "absolute", type = "h")
which produces
Note that this is just a first pass; I'm pretty sure I haven't accounted for any reordering that goes on with sort and svar etc. if they are used.
Never used lattice plots, but I thought a chance to learn something should be worth while. Took too long to figure out.
text <- "c( expression(phantom()^14*C~years~BP),expression(delta^18*O))"
strip = strip.custom(factor.levels=eval(parse(text=text)))
plt <- Stratiplot(Depths ~ .,
data = V12.122[, 1:4],
type = c("h","l","g"),
zones = 400,
strip = strip)
Hope this gets you started.