For this particular shiny example I am trying to apply a circular model and display and summarize it within the ggplot and a summary table. This is straightforward up until trying to add in reactive 'brushplot' capabilities. Each of the data points represent a date and the point of the selective graph is to be able to discard undesirable dates. As far as I've figured out, this requires the filtering and model fitting to be within a renderPlot which then leads to complications (unable to find the data/model) trying to call the filtered data and the circular model's statistical outputs outside the function and/or within another reactive function. This yields the Error: object 'k_circ.lm' not found So my questions are:
How can I read the filtered data from the renderPlot function
to the summarytable matrix?
How could I similarly add the fitted model values and residuals from k_circ.lm?
Is there a better or simpler way to arrange app to avoid this?
Alternatative code lines are commented out for a working (if poorly formatted) summary table.
library(dplyr) # For data manipulation
library(ggplot2) # For drawing plots
library(shiny) # For running the app
library(plotly) # For data manipulation
library(circular) # For Circular regressions
library(gridExtra)
# Define UI ----
ui <- fluidPage(
# App title ----
titlePanel("Circular Brushplot Demo"),
# Sidebar layout with input and output definitions ----
sidebarLayout(
sidebarPanel(
actionButton("exclude_toggle", "Toggle points"),
actionButton("exclude_reset", "Reset")
),
# Main panel for displaying outputs ----
mainPanel(
#reactive plot output with point and 'brush' selection
fluidRow(plotOutput("k", height = 400,
click = "k_click",
brush = brushOpts(
id = "k_brush" ))),
plotOutput("s", height = 400)
)
)
)
# Define server logic
server <- function(input, output) {
psideg <- c(356,97,211,232,343,292,157,302,335,302,324,85,324,340,157,238,254,146,232,122,329)
thetadeg <- c(119,162,221,259,270,29,97,292,40,313,94,45,47,108,221,270,119,248,270,45,23)
## Data in radians then to "circular format"
psirad <- psideg*2*pi/360
thetarad <- thetadeg*2*pi/360
cpsirad <- circular(psirad)
cthetarad <- circular(thetarad)
cdat <- data.frame(cpsirad, cthetarad)
###### reactive brush plot ########
# For storing which rows have been excluded
vals <- reactiveValues(
keeprows = rep(TRUE, nrow(cdat)))
output$k <- renderPlot({
# Plot the kept and excluded points as two separate data sets
keep <- cdat[ vals$keeprows, , drop = FALSE]
exclude <- cdat[!vals$keeprows, , drop = FALSE]
## Fits circular model specifically for 'keeprows' of selected data
k_circlm <- lm.circular(type = "c-c", y = keep$cthetarad, x = keep$cpsirad, order = 1)
k_circlm
ggplot(keep, aes(cthetarad, cpsirad)) +
geom_point(aes(cthetarad, cpsirad, colour = keep$Vmag, size = 5))+
scale_colour_gradient(low ="blue", high = "red")+
geom_smooth(method = lm, fullrange = TRUE, color = "black") +
geom_point(data = exclude, shape = 13, size = 5, fill = NA, color = "black", alpha = 0.25) +
annotate("text", x = min(keep$cthetarad), y = Inf, hjust = .1, vjust = 1,
label = paste0("p value 1 = ", round(k_circlm$p.values[1], 2)), size = 7)+
annotate("text", x = min(keep$cthetarad), y = Inf, hjust = .1, vjust = 2.5,
label = paste0("p value 2 = ", round(k_circlm$p.values[2], 2)), size = 7)+
annotate("text", x = min(keep$cthetarad), y = Inf, hjust = .1, vjust = 4,
label = paste0("rho = ", round(k_circlm$rho, 2)), size = 7)+
xlab("Lighthouse Direction (radians)")+ ylab("ADCP site direction (radians)")+
theme(axis.title.x = element_text(size = 20), axis.title.y = element_text(size = 20))
})
# Toggle points that are clicked
observeEvent(input$k_click, {
res <- nearPoints(cdat, input$k_click, allRows = TRUE)
vals$keeprows <- xor(vals$keeprows, res$selected_)})
# Toggle points that are brushed, when button is clicked
observeEvent(input$exclude_toggle, {
res <- brushedPoints(cdat, input$k_brush, allRows = TRUE)
vals$keeprows <- xor(vals$keeprows, res$selected_)})
# Reset all points
observeEvent(input$exclude_reset, {
vals$keeprows <- rep(TRUE, nrow(cdat))})
output$s <- renderPlot({
# Create Summary table
summarytable <- data.frame(matrix(ncol = 4, nrow = nrow(keep)))
colnames(summarytable) <- c( "Psi_dir", "Theta_dir", "Fitted_values", "Residuals")
# Un-comment lines below to read from non-reactive data for working summary table
#summarytable$Psi_dir <- round(cdat$cpsirad, 2)
#summarytable$Theta_dir <- round(cdat$cthetarad, 2)
# attempting to pull from circlm within render plot
# comment out for summarytable to work
summarytable$Psi_dir <- round(keep$cpsirad, 2)
summarytable$Theta_dir <- round(keep$cthetarad, 2)
summarytable$Fitted_values <- round(k_circ.lm$fitted)
summarytable$Residuals <- round(k_circ.lm$residuals)
# outputing table with minimal formatting
summarytable <-na.omit(summarytable)
t <- tableGrob(summarytable)
Q <- grid.arrange(t, nrow = 1)
Q
}
)
}
shinyApp(ui = ui, server = server)
Here's a few ideas - but there are multiple approaches to handling this, and you probably want to restructure your server function a bit more after working with this further.
First, you probably want a reactive expression that will update your model based on vals$keeprows as this changes with your clicks. Then, you can access the model results from this expression from both your plot and data table.
Here is an example:
fit_model <- reactive({
## Keep and exclude based on reactive value keeprows
keep = cdat[ vals$keeprows, , drop = FALSE]
exclude = cdat[!vals$keeprows, , drop = FALSE]
## Fits circular model specifically for 'keeprows' of selected data
k_circlm <- lm.circular(type = "c-c", y = keep$cthetarad, x = keep$cpsirad, order = 1)
## Returns list of items including what to keep, exclude, and model
list(k_circlm = k_circlm, keep = keep, exclude = exclude)
})
It will return a list that you can access from the plot:
output$k <- renderPlot({
exclude <- fit_model()[["exclude"]]
keep <- fit_model()[["keep"]]
k_circlm <- fit_model()[["k_circlm"]]
ggplot(keep, aes(cthetarad, cpsirad)) +
...
And can access the same from your table (though you have as renderPlot?):
output$s <- renderPlot({
keep = fit_model()[["keep"]]
k_circ.lm <- fit_model()[["k_circlm"]]
# Create Summary table
summarytable <- data.frame(matrix(ncol = 4, nrow = nrow(keep)))
...
Note that because the table length changes with rows kept, you might want to use nrow(keep) as I have above, rather than nrow(cdat), unless I am mistaken.
I also loaded gridExtra library for testing this.
I suspect there are a number of other improvements you could consider, but thought this might help you get to a functional state first.
I would like to include a 3D dynamic (i.e. one can change its perspective just by moving the plot) histogram widget in a R Shiny application.
Unfortunately I didn't find any until now.
So far the results of my searches: with threejs (e.g. here on CRAN and there on GitHub) one can use many different representations (scatterplots, surfaces, etc.) but no 3D histogram. plot3D and plot3Drgl don't have any R Shiny counterpart.
Unless something already exists my intention is to create an HTMLWidget from one of the sub-libraries of vis.js, namely graph3d.
What are your views on this issue?
Best regards,
Olivier
It's possible with plot3Drgl. Here is an example.
library(plot3Drgl)
library(shiny)
options(rgl.useNULL = TRUE)
ui <- fluidPage(
rglwidgetOutput("myWebGL")
)
server <- function(input, output) {
save <- options(rgl.inShiny = TRUE)
on.exit(options(save))
output$myWebGL <- renderRglwidget({
try(rgl.close())
V <- volcano[seq(1, nrow(volcano), by = 5),
seq(1, ncol(volcano), by = 5)] # lower resolution
hist3Drgl(z = V, col = "grey", border = "black", lighting = TRUE)
rglwidget()
})
}
shinyApp(ui, server)
My package graph3d is on CRAN now.
library(graph3d)
dat <- data.frame(x = c(1,1,2,2), y = c(1,2,1,2), z = c(1,2,3,4))
graph3d(dat, type = "bar", zMin = 0, tooltip = TRUE)
You can customize the tooltips:
graph3d(dat, type = "bar", zMin = 0,
tooltip = JS(c("function(xyz){",
" var x = 'X: ' + xyz.x.toFixed(2);",
" var y = 'Y: ' + xyz.y.toFixed(2);",
" var z = 'Z: ' + xyz.z.toFixed(2);",
" return x + '<br/>' + y + '<br/>' + z;",
"}"))
)
I realize I have to add an option to control the size of the axes labels...
Many thanks, DSGym. I didn't know this library.
In my initial message (now amended) I actually forgot to mention the dynamic feature, i.e. the ability to change the perspective of the plot just by moving it with the mouse, like with vis.js-graph3d.
It seems plots from highcharter cannot do that, or am I mistaken?
[EDIT]: I just checked with Shiny: it is static.
I am trying to create an interactive histogram in R whose bin width can be adjusted either by moving a slider or entering a value in the text box. In addition to this, I would also like to provide the user with an option of saving the plot for a particular bin width.
To this end, I found the 'gslider' function of 'aplpack' library to be a good starting point. I tried to modify it to meet my purpose as well as learn more about Tcl/Tk constructs. But I am now stuck and can't proceed, mostly because I haven't completely understood how a slider value is captured and transferred between functions.
Following are the snippets of code that I haven't really understood. These are from the source code of the 'gslider' function.
# What is the rationale behind using the 'assign' function here and at
# other instances in the code?
img <- tkrplot::tkrplot(gr.frame, newpl, vscale = 1, hscale = 1)
tkpack(img, side = "top")
assign("img", img, envir = slider.env)
# I understand the below lines when considered individually. But collectively,
# I am having a difficult time comprehending them. Most importantly, where
# exactly is the slider movement captured here?
sc <- tkscale(fr, from = sl.min, to = sl.max,
showvalue = TRUE, resolution = sl.delta, orient = "horiz")
assign("sc", sc, envir = slider.env)
eval(parse(text = "tkconfigure(sc, variable=inputbw1)"), envir = slider.env)
sl.fun <- sl.function
if (!is.function(sl.fun))
sl.fun <- eval(parse(text = paste("function(...){",
sl.fun, "}")))
fname <- 'tkrrsl.fun1'
eval(parse(text = c(paste(fname, " <-"), " function(...){",
"tkrreplot(get('img',envir=slider.env),fun=function()",
deparse(sl.fun)[-1], ")", "}")))
eval(parse(text = paste("environment(", fname, ")<-parent.env")))
if (prompt)
tkconfigure(sc, command = get(fname))
else tkbind(sc, "<ButtonRelease>", get(fname))
if (exists("tkrrsl.fun1")) {
get("tkrrsl.fun1")()
}
assign("slider.values.old", sl.default, envir = slider.env)
Thanks to everyone for the varied scope of answers. Juba's and Greg's answers were the ones I could work upon to write the following code:
slider_txtbox <- function (x, col=1, sl.delta, title)
{
## Validations
require(tkrplot)
pos.of.panel <- 'bottom'
if(is.numeric(col))
col <- names(x)[col]
x <- x[,col, drop=FALSE]
if (missing(x) || is.null(dim(x)))
return("Error: insufficient x values")
sl.min <- sl.delta # Smarter initialization required
sl.max <- max(x)
xrange <- (max(x)-min(x))
sl.default <- xrange/30
if (!exists("slider.env")) {
slider.env <<- new.env(parent = .GlobalEnv)
}
if (missing(title))
title <- "Adjust parameters"
## Creating initial dialogs
require(tcltk)
nt <- tktoplevel()
tkwm.title(nt, title)
if(.Platform$OS.type == 'windows')
tkwm.geometry(nt, "390x490+0+10")
else if(.Platform$OS.type == 'unix')
tkwm.geometry(nt, "480x600+0+10")
assign("tktop.slider", nt, envir = slider.env)
"relax"
nt.bak <- nt
sl.frame <- tkframe(nt)
gr.frame <- tkframe(nt)
tx.frame <- tkframe(nt)
tkpack(sl.frame, tx.frame, gr.frame, side = pos.of.panel)
## Function to create and refresh the plot
library(ggplot2)
library(gridExtra)
makeplot <- function(bwidth, save) {
if(bwidth <= 0) {
df <- data.frame('x'=1:10, 'y'=1:10)
histplot <- ggplot(df, aes(x=x, y=y)) + geom_point(size=0) + xlim(0, 10) + ylim(0, 100) +
geom_text(aes(label='Invalid binwidth...', x=5, y=50), size=9)
} else {
histplot <- ggplot(data=x, aes_string(x=col)) +
geom_histogram(binwidth=bwidth, aes(y = ..density..), fill='skyblue') +
theme(axis.title.x=element_text(size=15), axis.title.y=element_text(size=15),
axis.text.x=element_text(size=10, colour='black'),
axis.text.y=element_text(size=10, colour='black'))
}
print(histplot)
if(save){
filename <- tkgetSaveFile(initialfile=paste('hist_bw_', bwidth, sep=''),
filetypes='{{PNG files} {.png}} {{JPEG files} {.jpg .jpeg}}
{{PDF file} {.pdf}} {{Postscript file} {.ps}}')
filepath <- as.character(filename)
splitpath <- strsplit(filepath, '/')[[1]]
flname <- splitpath[length(splitpath)]
pieces <- strsplit(flname, "\\.")[[1]]
ext <- tolower(pieces[length(pieces)])
if(ext != 'png' && ext != 'jpeg' && ext != 'jpg' && ext != 'pdf' && ext != 'ps') {
ext <- 'png'
filepath <- paste(filepath, '.png', sep='')
filename <- tclVar(filepath)
}
if(ext == 'ps')
ext <- 'postscript'
eval(parse(text=paste(ext, '(file=filepath)', sep='')))
eval(parse(text='print(histplot)'))
dev.off()
}
}
img <- tkrplot::tkrplot(gr.frame, makeplot(sl.default, FALSE), vscale = 1, hscale = 1)
tkpack(img, side = "top")
assign("img", img, envir = slider.env)
## Creating slider, textbox and labels
parent.env <- sys.frame(sys.nframe() - 1)
tkpack(fr <- tkframe(sl.frame), side = 'top')
sc <- tkscale(fr, from = sl.min, to = sl.max,
showvalue = TRUE, resolution = sl.delta,
orient = "horiz")
tb <- tkentry(fr, width=4)
labspace <- tklabel(fr, text='\t\t\t')
tkpack(sc, labspace, tb, side = 'left')
tkpack(textinfo <- tkframe(tx.frame), side = 'top')
lab <- tklabel(textinfo, text = ' Move slider', width = "20")
orlabel <- tklabel(textinfo, text=' OR', width='10')
txtboxmsg <- tklabel(textinfo, text = 'Enter binwidth', width='20')
tkpack(txtboxmsg, orlabel, lab, side='right')
tkpack(f.but <- tkframe(sl.frame))
tkpack(tklabel(f.but, text=''))
tkpack(tkbutton(f.but, text = "Exit", command = function() tkdestroy(nt)),
side='right')
tkpack(tkbutton(f.but, text = "Save", command = function(...) {
bwidth <- as.numeric(tclvalue(get('inputtb', envir=slider.env)))
tkrreplot(get('img',envir=slider.env),fun=function() { makeplot(bwidth, TRUE); sync_slider()})
}), side='right')
## Creating objects and variables associated with slider and textbox
assign("sc", sc, envir = slider.env)
eval(parse(text = "assign('inputsc', tclVar(sl.default), envir=slider.env)"))
eval(parse(text = "tkconfigure(sc, variable=inputsc)"), envir = slider.env)
assign("tb", tb, envir = slider.env)
eval(parse(text = "assign('inputtb', as.character(tclVar(sl.default)),
envir=slider.env)"))
eval(parse(text = "tkconfigure(tb, textvariable=inputtb)"), envir = slider.env)
## Function to update the textbox value when the slider has changed
sync_textbox <- function() {
bwidth_sl <- tclvalue(get('inputsc', envir=slider.env))
assign('inputtb', tclVar(bwidth_sl), envir=slider.env)
eval(parse(text = "tkconfigure(tb, textvariable=inputtb)"), envir = slider.env)
}
## Function to update the slider value when the textbox has changed
sync_slider <- function() {
bwidth_tb <- tclvalue(get('inputtb', envir=slider.env))
assign('inputsc', tclVar(bwidth_tb), envir=slider.env)
eval(parse(text = "tkconfigure(sc, variable=inputsc)"), envir = slider.env)
}
## Bindings : association of certain functions to certain events for the slider
## and the textbox
tkbind(sc, "<ButtonRelease>", function(...) {
bwidth <- as.numeric(tclvalue(get('inputsc', envir=slider.env)))
tkrreplot(get('img',envir=slider.env),fun=function() { makeplot(bwidth, FALSE); sync_textbox()})
})
tkbind(tb, "<Return>", function(...) {
bwidth <- as.numeric(tclvalue(get('inputtb', envir=slider.env)))
if(bwidth > sl.max && !is.na(bwidth)) {
bwidth <- sl.max
assign('inputtb', tclVar(bwidth), envir=slider.env)
eval(parse(text = "tkconfigure(tb, textvariable=inputtb)"), envir = slider.env)
} else
if(bwidth < sl.min || is.na(bwidth)) {
bwidth <- sl.min
assign('inputtb', tclVar(bwidth), envir=slider.env)
eval(parse(text = "tkconfigure(tb, textvariable=inputtb)"), envir = slider.env)
}
tkrreplot(get('img',envir=slider.env),fun=function() { makeplot(bwidth, FALSE); sync_slider()})
})
}
library(ggplot2)
slider_txtbox(movies, 'rating', 0.1, 'Adjust binwidth')
Here is a minimal working example with comments, based on the complete code you first submit. As I'm far from an expert in tcl/tk, there may be cleaner or better ways to do it. And it is quite incomplete (for example the textbox values should be checked to be in the range of the slider, etc.) :
library(ggplot2)
library(gridExtra)
title <- "Default title"
data(movies)
## Init dialog
require(tkrplot)
if (!exists("slider.env")) slider.env <<- new.env(parent = .GlobalEnv)
require(tcltk)
nt <- tktoplevel()
tkwm.title(nt, title)
tkwm.geometry(nt, "480x600+0+10")
assign("tktop.slider", nt, envir = slider.env)
"relax"
nt.bak <- nt
sl.frame <- tkframe(nt)
gr.frame <- tkframe(nt)
tx.frame <- tkframe(nt)
tkpack(sl.frame, tx.frame, gr.frame, side = "bottom")
## First default plot
newpl <- function(...) {
dummydf <- data.frame('x'=1:10, 'y'=1:10)
dummy <- ggplot(dummydf, aes(x=x, y=y)) + geom_point(size=0) + xlim(0, 10) + ylim(0, 100) +
geom_text(aes(label='Generating plot...', x=5, y=50), size=9)
print(dummy)
}
img <- tkrplot::tkrplot(gr.frame, newpl, vscale = 1, hscale = 1)
tkpack(img, side = "top")
assign("img", img, envir = slider.env)
tkpack(fr <- tkframe(sl.frame), side = 'top')
## Creating slider, textbox and labels
sc <- tkscale(fr, from = 0, to = 5, showvalue = TRUE, resolution = 0.1, orient = "horiz")
tb <- tkentry(fr, width=4)
lab <- tklabel(fr, text = 'Select binwidth ', width = "16")
orlabel <- tklabel(fr, text=' or ', width='4')
tkpack(lab, sc, orlabel, tb, side = 'left')
tkpack(textinfo <- tkframe(tx.frame), side = 'top')
## Creating objects and variables associated with slider and textbox
assign("sc", sc, envir = slider.env)
assign("tb", tb, envir = slider.env)
assign('inputsc', tclVar(2.5), envir=slider.env)
assign('inputtb', tclVar('2.5'), envir=slider.env)
eval(parse(text = "tkconfigure(sc, variable=inputsc)"), envir = slider.env)
eval(parse(text = "tkconfigure(tb, textvariable=inputtb)"), envir = slider.env)
## Function to update the textbox value when the slider has changed
sync_textbox <- function() {
bwidth_sl <- tclvalue(get('inputsc', envir=slider.env))
assign('inputtb', tclVar(bwidth_sl), envir=slider.env)
eval(parse(text = "tkconfigure(tb, textvariable=inputtb)"), envir = slider.env)
}
## Function to update the slider value when the textbox has changed
sync_slider <- function() {
bwidth_tb <- tclvalue(get('inputtb', envir=slider.env))
assign('inputsc', tclVar(bwidth_tb), envir=slider.env)
eval(parse(text = "tkconfigure(sc, variable=inputsc)"), envir = slider.env)
}
## Function to refresh the plot
refresh <- function(bwidth) {
histplot <- ggplot(data=movies, aes_string(x="rating")) +
geom_histogram(binwidth=bwidth,
aes(y = ..density..), fill='skyblue') +
theme(axis.title.x=element_text(size=15), axis.title.y=element_text(size=15),
axis.text.x=element_text(size=10, colour='black'),
axis.text.y=element_text(size=10, colour='black'))
print(histplot)
}
## Bindings : association of certain functions to certain events for the slider
## and the textbox
tkbind(sc, "<ButtonRelease>", function(...) {
bwidth <- as.numeric(tclvalue(get('inputsc', envir=slider.env)))
tkrreplot(get('img',envir=slider.env),fun=function() { refresh(bwidth); sync_textbox()})
})
tkbind(tb, "<Return>", function(...) {
bwidth <- as.numeric(tclvalue(get('inputtb', envir=slider.env)))
tkrreplot(get('img',envir=slider.env),fun=function() { refresh(bwidth); sync_slider()})
})
If you do not insist on a local solution, you might give rapporter.net a try, which lets you specify such tasks easily with any number of tweakable sliders. Okay, enough of marketing :)
Here goes a quick demo: Interactive histogram on mtcars which looks like:
There you could choose one of the well-know variables of mtcars, but of course you could provide any data frame to be used here or tweak the above form after a free registration.
How it's done? I have just created a quick rapport template and let it rapplicate. The body of the template is written in brew-style (please see the above "rapport" URL for more details):
<%=
evalsOptions('width', width)
evalsOptions('height', height)
%>
# Histogram
<%=
set.caption(paste('Histogram of', var.name))
hist(var, breaks=seq(min(var), max(var), diff(range(var))/round(binwidth)), main = paste('Histogram of', var.name), xlab = '')
%>
## Parameters
Provided parameters were:
* variable: <%=var.name%> (<%=var.label%>)
* bin-width of histogram: <%=binwidth%>
* height of generated images: <%=height%>
* width of generated images: <%=width%>
# Kernel density plot
<%=
set.caption('A kernel density plot')
plot(density(var), main = '', xlab = '')
%>
But a bare-minimal example of the task could be also addressed by a simple one-liner template:
<%=hist(var, breaks=seq(min(var), max(var), diff(range(var))/round(binwidth)))%>
There you would only need to create a new template, add two input types with a click (one numeric variable of any data set and a number input field which would hold the binwidth of the histogram), and you are ready to go.
You might want to look at the R package 'rpanel' -- it uses tcltk under the hood but is much simpler to use:
rpanel
rpanel reference
I don't know the gslider function and cannot help you there, but here are some alternatives:
One simple option is to use the tkexamp function from the TeachingDemos package, here is one way:
library(TeachingDemos)
myhist <- function(x, s.width, e.width, ...) {
if( missing(e.width) || is.null(e.width) || is.na(e.width) ) {
e.width<- s.width
}
b <- seq( min(x)-e.width/2, max(x)+e.width, by=e.width )
hist(x, b, ...)
}
mylist <- list( s.width=list('slider', init=1, from=1, to=10, resolution=1),
e.width=list('numentry', init='', width=7)
)
sampdata <- rnorm(100, 50, 5)
tkexamp(myhist(sampdata), mylist)
This will create a quick GUI with your histogram and a slider and entry widget. The width of the bars are determined by the value in the entry widget, and if that is blank (default) then the value of the slider. Unfortunately the slider and entry widget do not update each other. There is a button that will print out the current call, so the same plot can be recreated from the command line in the default or current plotting device. You can edit the mylist variable above to make the controls fit your data better.
If you want the entry and slider to update each other then you can program that more directly. Here is a basic function that uses tkrplot:
mytkhist <- function(x, ...) {
width <- tclVar()
tclvalue(width) <- 1
replot <- function(...) {
width <- as.numeric(tclvalue(width))
b <- seq( min(x) - width/2, max(x)+width, by=width )
hist(x,b,...)
}
tt <- tktoplevel()
img <- tkrplot(tt, replot)
tkpack(img, side='top')
tkpack( tkscale(tt, variable=width, from=1, to=10,
command=function(...) tkrreplot(img),
orient='horizontal'), side='top' )
tkpack( e <- tkentry(tt, textvariable=width), side='top' )
tkbind(e, "<KeyRelease>", function(...) tkrreplot(img))
}
mytkhist(sampdata)
The fact that both the slider (scale) and the entry widget use the same variable is what makes them automatically update each other (no calls to assign needed). The command argument in tkscale and the tkbind call mean that any changes to either the slider or the entry will update the plot. This does not have anything to save the current plot, but you should be able to add that part as well as any other controls that you want to use.
I am writing a GUI with the gwidgets package in R. I am implementing a tool that will allow the user to select a number of variables from one list, and drag them to an empty list for collection. The inspiration is the gui from SPSS, see link:
spss drag and drop
I was planning do this with two gtables, i.e. first create one gtable with the list of variables, and an empty gtable to collect the selected variables. Below is my sample code:
portfolioBuilder <- function(h,...){
## globals
widgets <- list()
varNames <- c("var1","var2","var3","var4" )#with(.GlobalEnv, names(data))
#window
win <- gwindow("Test")
#groups
g <- ggroup(horizontal = FALSE, container = win, expand = TRUE)
gg <- ggroup(horizontal = FALSE, container = win, expand = TRUE)
#graphics container
ggraphics(container = gg)
#paned group
pg <- gpanedgroup(container = g, expand = TRUE)
nb <- gnotebook(container = pg)
## main group
qpg <- ggroup(horizontal = FALSE, container = nb, label = "portfolio")
parg <- ggroup(horizontal = FALSE, container = nb, label = "portfolio args")
## qplot group
tbl <- glayout(container = qpg)
#variable list
tbl[1,1,anchor = c(1,0)] <- "Variables"
tbl[2:10,2] <- (widgets[["table"]] <- gtable(varNames, multiple = TRUE, container = tbl, expand = TRUE))
tbl[3,3, anchor = c(1,0)] <- "y"
tbl[3,4] <- (widgets[["y"]] <- gedit("", container = tbl))
tbl[4,3, anchor = c(1,0)] <- "x"
tbl[4,4] <- (widgets[["x"]] <- gtable(c(""),container = tbl))
## make table visible and set tab
visible(tbl) <- TRUE
svalue(nb) <- 1
##################################end layout#################################
}
However my sample code spits out an error because of the empty gtable widget. Does anyone know how to accomplish this with gwidgets?
You'll want to work on the layout, but the key to this is addDropSource and addDropTarget:
options(guiToolkit="RGtk2")
library(gWidgets)
w <- gwindow(visible=FALSE)
g <- gpanedgroup(cont=w)
tbl <- gtable(names(mtcars), cont=g)
fl <- gframe("variables", horizontal=FALSE, cont=g)
dep <- gedit(initial.msg="Dependent variable", label="Dependent", cont=fl)
ind <- gedit(initial.msg="Independent variable(s)", label="Independent", cont=fl)
addDropSource(tbl, handler=function(h,...) svalue(h$obj))
addDropTarget(dep, handler=function(h,...) svalue(h$obj) <- h$dropdata)
addDropTarget(ind, handler=function(h,...) {
cur <- svalue(h$obj)
new <- ifelse(nchar(cur) > 0, paste(cur, h$dropdata, sep=", "), h$dropdata)
svalue(h$obj) <- new
})
visible(w) <- TRUE
Drag and drop support in gWidgets is really variable. Of the 6 possibilities for this to work in: gWidgetsRGtk2, gWidgets2RGtk2, gWidgetstcltk, gWidgets2tcltk, gWidgetsQt and gWidgets2Qt this code only worked in gWidgetsRGtk2.