R Shiny recoloring of points - r

I would like click-select points and group them based on color.
I can save selected points with color information into a new data frame and plot it, however I would like to keep track and see what was already selected on the interactive plot.
How can I show/label already selected points or make it permanent after "Add selection"?
library(shiny)
library(tidyverse)
library(DT)
library(colourpicker)
ui = fluidPage(
colourInput("col", "Select colour", "purple"),
actionButton("addToDT", "Add selection", icon = icon("plus")), hr(),
plotOutput("plot", click = "plot_click", dblclick = "plot_reset"),
DT::dataTableOutput('plot_DT'), hr(),
textOutput("clickcoord"),
DT::dataTableOutput('final_DT'),
plotOutput("plotSelected")
)
server = function(input, output, session) {
selectedPoint = reactiveVal(rep(FALSE, nrow(mtcars)))
output$clickcoord <- renderPrint({
print(input$plot_click)
})
observeEvent(input$plot_click, {
clicked = nearPoints(mtcars, input$plot_click, allRows = TRUE)$selected_
selectedPoint(clicked | selectedPoint())
})
observeEvent(input$plot_reset, {
selectedPoint(rep(FALSE, nrow(mtcars)))
})
output$plot_DT = DT::renderDataTable({
mtcars$sel = selectedPoint()
mtcars = dplyr::filter(mtcars, sel == TRUE) %>% mutate(group_color = input$col)
})
final_DT = reactiveValues()
final_DT$df = data.frame()
FinalData = eventReactive(input$addToDT, {
mtcars$sel = selectedPoint()
mtcars = dplyr::filter(mtcars, sel == TRUE) %>% mutate(group_color = input$col)
final_DT$df = bind_rows(final_DT$df, mtcars)
})
output$final_DT = renderDataTable({FinalData()})
output$plot = renderPlot({
mtcars$sel = selectedPoint()
ggplot(mtcars, aes(wt, mpg, color = mtcars$sel, fill=mpg)) +
geom_point(shape = 21, size = 6, stroke = 2) +
scale_color_manual(values = c("#ffffff00", input$col)) +
scale_fill_viridis_c() +
theme_bw()
})
output$plotSelected = renderPlot({
sel_df = FinalData()
ggplot(sel_df, aes(wt, mpg, fill = group_color, colour = group_color)) +
geom_point(shape = 21, size = 6, stroke = 2) +
scale_color_manual(values = unique(sel_df$group_color)) +
scale_fill_manual(values = unique(sel_df$group_color)) +
theme_bw()
})
observeEvent(input$addToDT, {
selectedPoint(rep(FALSE, nrow(mtcars)))
})
}
shinyApp(ui, server)

I think this is the "crux" of what your are looking for. I used a very similar example that I found in the help for entitled:
A demonstration of clicking, hovering, and brushing
(https://shiny.rstudio.com/reference/shiny/0.13.1/plotoutput)
It is very similar to your example.
I create a matrix of T/F elements where the rows are the observations and the columns are in which batch the observation is selected. So when you launch the whole matrix is False, but as you click on observations the switch to positive in the first column. Then if you click addSelection and continue you start switching the observations in the next column.
Could you confirm that this what you are looking for?
Below is the code.
shinyApp(
ui = basicPage(
fluidRow(
column(
width = 4,
plotOutput("plot",
height = 300,
click = "plot_click", # Equiv, to click=clickOpts(id='plot_click')
),
actionButton("addToDT", "Add selection", icon = icon("plus")), hr(),
h4("Clicked points"),
tableOutput("plot_clickedpoints"),
),
column(
width = 4,
verbatimTextOutput("counter"),
),
)
),
server = function(input, output, session) {
data <- reactive({
input$newplot
# Add a little noise to the cars data so the points move
cars + rnorm(nrow(cars))
})
output$plot <- renderPlot({
d <- data()
plot(d$speed, d$dist, main = paste("No of Sets Chosen", input$addToDT))
})
output$plot_clickinfo <- renderPrint({
cat("Click:
")
str(input$plot_click)
})
selectedPoints <- reactiveVal(rep(FALSE, nrow(cars)))
selectionMatrix <- reactiveVal(matrix(data = F, nrow = nrow(cars), ncol = 7))
observeEvent(input$plot_click, {
clicked <- nearPoints(data(), input$plot_click, "speed", "dist", allRows = TRUE)$selected
selectedPoints(clicked | selectedPoints())
tmp <- unlist(selectionMatrix())
tmp[, (input$addToDT + 1)] <- selectedPoints()
selectionMatrix(tmp)
})
observeEvent(input$addToDT, {
selectedPoints(rep(FALSE, nrow(cars)))
})
output$plot_clickedpoints <- renderTable({
# if (input$addToDT==0) {
res <- selectionMatrix()
return(res)
})
}
)

Related

Render different plots based on if/else on existence of reactive variable

I'm trying to create an app that allows 1) a user to hover over scatter plot points, which displays a label associated with the point, and 2) search for a specific point by label.
Currently, to highlight a point, I re-plot the graph each time the user submits a search term, adding that single point on top of rest of the points that have been plotted.
In order to avoid the server re-plotting before the user finishes typing the label, I require that the "search" button be pressed. However, I'm struggling to get the plot to display BEFORE the user has searched for any labels.
Here's what I have so far:
library(shiny)
library(ggplot2)
library(ggiraph)
df <- data.frame(x = rnorm(100), y = rnorm(100), label = paste("gene", seq(100)))
head(df)
x y label
1 -0.3383215 0.91212341 gene 1
2 -0.5318215 -0.63273778 gene 2
3 1.1281345 -0.01296204 gene 3
4 -1.2964345 -2.21689946 gene 4
5 1.5877938 -0.24993362 gene 5
6 0.6385419 0.07849135 gene 6
gg_scatter <- ggplot(data = df, aes(x, y)) +
geom_point_interactive(aes(tooltip = label, data_id = label))
ui <- fluidPage(
textInput(inputId = "gene_symbol",
label = "Search for a gene",
placeholder = "gene 1"),
actionButton(inputId = "go",
label = "Search"),
girafeOutput("scatterplot"),
textOutput("message")
)
server <- function(input, output) {
gene_search <- eventReactive(input$go, {
input$gene_symbol
})
output$scatterplot <-
renderGirafe({
gg_scatter_highlight <- gg_scatter +
geom_point_interactive(data = subset(df, label == gene_search()),
tooltip = gene_search(),
size = 3,
color = "red")
girafe(code = print(gg_scatter_highlight),
options = list(opts_selection(type = "single")))
})
output$message <- renderText({
if(sum(is.element(df$label, req(gene_search()))) == 0) {
paste("Gene not found")
}
})
}
shinyApp(ui = ui, server = server)
I'm trying to add something like this to output$scatterplot:
output$scatterplot <-
renderGirafe({
## If the user has not searched for anything, plot without any points highlighted
if(!isTruthy(gene_search)) {
girafe(code = print(gg_scatter),
options = list(opts_selection(type = "single")))
}
## Highlight the point that the user searched for
else {
gg_scatter_highlight <- gg_scatter +
geom_point_interactive(data = subset(df, label == gene_search()),
tooltip = gene_search(),
size = 3,
color = "red")
girafe(code = print(gg_scatter_highlight),
options = list(opts_selection(type = "single")))
}
})
... Unfortunately this still results in no plot being displayed until a label is searched.
Any help would be much appreciated.
Set ignoreNULL = FALSE in eventReactive to have a default value at the beginning.
library(shiny)
library(ggplot2)
library(ggiraph)
gg_scatter <- ggplot(data = df, aes(x, y)) +
geom_point_interactive(aes(tooltip = label, data_id = label))
ui <- fluidPage(
textInput(inputId = "gene_symbol",
label = "Search for a gene",
placeholder = "gene 1"),
actionButton(inputId = "go",
label = "Search"),
girafeOutput("scatterplot"),
textOutput("message")
)
server <- function(input, output) {
gene_search <- eventReactive(input$go, {
input$gene_symbol
}, ignoreNULL = FALSE)
output$scatterplot <-
renderGirafe({
gg_scatter_highlight <- gg_scatter +
geom_point_interactive(data = subset(df, label == gene_search()),
tooltip = gene_search(),
size = 3,
color = "red")
girafe(code = print(gg_scatter_highlight),
options = list(opts_selection(type = "single")))
})
output$message <- renderText({
if(sum(is.element(df$label, req(gene_search()))) == 0) {
paste("Gene not found")
}
})
}
shinyApp(ui = ui, server = server)

Saving Dynamically Generated Plots in Shiny

so I have recently adapted some code that I found on StackOverflow to create a dynamic number of plots based on user input. However, I now cannot figure out how to save all of those dynamic plots in one file; when I use ggsave() in downloadHandler, it only saves the last plot generated, as the plots are created inside of a for loop, inside of an observe function. I have tried saving the for loop as a separate function and saving that instead of last plot, I have tried saving the observe() as a function and calling that inside ggsave(), but nothing works. Any idea how I can save all of the generated plots to one file?
ui <- fluidPanel(
sidebarLayout(
sidebarPanel(
#this is the input widget for dataset selection
selectInput(inputId = "dataset_selec",
label = "Choose which Dataset to explore:",
choices = list("NK AD Dataset (Zhang, 2020)",
"APPPS1 Dataset (Van Hove, 2019)",
"Aging T Cell Dataset (Dulken, 2019)"),
selected = "APPPS1 Dataset (Van Hove, 2019)"))
mainPanel(
fluidRow(
column(4,
textInput(inputId = "gene_fp",
label = "Enter gene(s) of interest here, separated by commas: ")
),
column(4,
br(),
checkboxInput("split_fp", "Split the graph?")
),
column(4,
conditionalPanel(condition = "input.split_fp == true",
#display choices to split by
selectInput(inputId = "metadata_split_fp",
label = "Choose how to split the Seurat data: ",
choices = list("Genotype", "Timepoint")))
)
),
#ask users if they want to split the graphs
br(),
fluidRow(
column(4,
textInput("save_name_fp",
label = "Enter a file name: ")
),
column(4,
conditionalPanel(condition = "input.save_name_fp.length > 0",
selectInput("fp_device",
label = "Select file type: ",
choices = list("PNG", "JPEG", "PDF", "TIFF",
"BMP", "SVG")))
),
column(4,
br(),
conditionalPanel(condition = "input.save_name_fp.length > 0",
downloadButton("fp_save", label = "Save Feature Plot"))
)
),
#plot the actual plot
uiOutput("fp_plots")
)
)
)
server <- function(input, output, session) {
datasetInput <- reactive({
switch(input$dataset_selec,
"NK AD Dataset (Zhang, 2020)" = nk_data,
"APPPS1 Dataset (Van Hove, 2019)" = appps1_data,
"Aging T Cell Dataset (Dulken, 2019)" = tcellinfil_data)
})
output$fp_plots <- renderUI({
#validate is to prevent an error message from being displayed when a gene hasn't been entered yet
validate(
need(input$gene_fp !="", "Please enter a gene.")
)
fp_genes <- input$gene_fp
fp_genes <- gsub(" ", "", fp_genes)
fp_genes <- unlist(strsplit(fp_genes, split = ","))
n <- length(fp_genes)
plot_output_list <- lapply(1:n, function(i) {
plotname <- paste("plot", i, sep = "")
if (input$split_fp == TRUE) {plotOutput(plotname, height = 580, width = 1100)}
else {plotOutput(plotname, height = 580, width = 550)}
})
do.call(tagList, plot_output_list)
})
#Here, we take the input of genes, and turn it into a character vector, so that we can iterate
#over it. This needs to be under observe({}) because it involves an input.
#Next, we iterate through the list of genes using a for loop, and within that for loop we assign
#the plots that we want to be displayed to each plotname, which is also sequentially created within
#this for loop, and assign it to the tagList we generated earlier. Basically, we're adding objects to
#list of names we made earlier.
#This needs to be under local({}) control, otherwise each graph doesn't get its own number,
#because of when renderPlot is evaluated
observe({
fp_genes <- input$gene_fp
fp_genes <- gsub(" ", "", fp_genes)
fp_genes <- unlist(strsplit(fp_genes, split = ","))
for (i in 1:length(fp_genes)) {
local({
plotname <- paste("plot", i, sep = "")
gene <- fp_genes[i]
output[[plotname]] <- renderPlot({
if (input$split_fp == TRUE) {FeaturePlot(datasetInput(), features = gene, split.by = input$metadata_split_fp)}
else {FeaturePlot(datasetInput(), features = gene)}
})
})
}
})
output$fp_save <- downloadHandler(
filename = function() {
paste(input$save_name_fp, tolower(input$fp_device), sep = ".")
},
content = function(file) {
ggsave(file, device = tolower(input$fp_device))
}
)
}
Create a list of plots, use grid.arrange to save it in a format you wish, and then save it. Perhaps you can adapt this code.
library(shiny)
library(dplyr)
library(tidyr)
library(ggplot2)
library(gridExtra)
mean_data <- data.frame(
Name = c(paste0("Group_", LETTERS[1:20])),
matx <- matrix(sample(1:1000, 1000, replace = T), nrow = 20)
)
names(mean_data)[-1] <- c(paste0("Gene_", 1:50))
sd_data <- data.frame(
Name = c(paste0("Group_", LETTERS[1:20])),
matx <- matrix(runif(1000, 5, 10), nrow = 20)
)
names(sd_data)[-1] <- c(paste0("Gene_", 1:50))
# Prepare dataset.
# 1. Bind mean and sd data
# 2. Reshape
data <- bind_rows(list(
mean = mean_data,
sd = sd_data
), .id = "stat")
data_mean_sd1 <- data %>%
pivot_longer(-c(Name, stat), names_to = "Gene", values_to = "value") %>%
pivot_wider(names_from = "stat", values_from = "value")
###
ui <- fluidPage(
pageWithSidebar(
headerPanel("Gene_FPKM Value Barplot"),
sidebarPanel(
selectInput(
"selectGeneSymbol",
"Select Gene Symbol:",
choices = unique(data_mean_sd1$Gene),
multiple =F,
width = 400,
selected = 1 #"Igfbp7"
),
selectInput(
"selectGeneSymbol2",
"Select Gene Symbol2:",
choices = unique(data_mean_sd1$Gene),
multiple =F,
width = 400,
selected = 1 #"Igfbp7"
),
selectInput("fp_device",
label = "Select file type: ",
choices = list("PNG", "JPEG", "PDF", "TIFF","BMP", "SVG")
),
actionButton(inputId = "plot1", label = "FPKM"),
actionButton(inputId = "plot2", label = "logFC"),
actionButton(inputId = "all",label = "logFC&FPKM"),br(),
downloadButton("fp_save", label = "Save Feature Plot")
),
mainPanel(
uiOutput("all")
)
)
)
server <- function(input, output, session) {
plot_data1 <- reactive({
subset(data_mean_sd1, Gene %in% input$selectGeneSymbol)
})
plot_data2 <- reactive({
subset(data_mean_sd1, Gene %in% input$selectGeneSymbol2)
})
global <- reactiveValues(out = NULL)
observeEvent(input$plot1, {
global$out <- plotOutput("plot1", height=750)
})
observeEvent(input$plot2, {
global$out <- plotOutput("plot2", height=750)
})
observeEvent(input$all, {
global$out <- plotOutput("plot3", height=1150)
})
output$all <- renderUI({
global$out
})
p1 <- eventReactive(list(input$plot1,
input$all), {
ggplot(data = plot_data1(), aes(x = Name, y = mean,fill=Name)) +
geom_bar(stat = "identity", position = position_dodge(0.9), width = 0.9) +
geom_errorbar(aes(ymin = mean - sd, ymax = mean + sd), width = .2, position = position_dodge(0.9)) +
theme_classic2() +
rotate_x_text(angle = 45) +
theme(legend.position = "none") +
labs(title = input$selectGeneSymbol, x = NULL, y = "FPKM_value") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(plot.margin = unit(c(20, 5, 1, 5), "mm"))+
theme(axis.text.x=element_text(vjust=1,size=12))
})
p2 <- eventReactive(list(input$plot2,
input$all), {
ggplot(data = plot_data2(), aes(x = Name, y = mean,fill=Name)) +
geom_bar(stat = "identity", position = position_dodge(0.9), width = 0.9) +
geom_errorbar(aes(ymin = mean - sd, ymax = mean + sd), width = .2, position = position_dodge(0.9)) +
theme_classic2() +
rotate_x_text(angle = 45) +
theme(legend.position = "none") +
labs(title = input$selectGeneSymbol2, x = NULL, y = "FPKM_value") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(plot.margin = unit(c(20, 5, 1, 5), "mm"))+
theme(axis.text.x=element_text(vjust=1,size=12))
})
#plotlist <- do.call(tagList, list(p1(),p2()))
output$plot1 <- renderPlot({ p1() })
output$plot2 <- renderPlot({ p2() })
output$plot3 <- renderPlot({ grid.arrange(p1(),p2(), ncol=1) })
observe({
plots <- list(p1(),p2())
myplots <- do.call(grid.arrange, c(plots, ncol = 1))
output$fp_save <- downloadHandler(
filename = function() {
paste("myplots", tolower(input$fp_device), sep = ".")
},
content = function(file) {
ggsave(file, plot=myplots, device = tolower(input$fp_device))
}
)
})
}
# Create Shiny app ----
shinyApp(ui = ui, server = server)

shiny click on plot update input

I have this very simple shiny app
When input changes, the graph changes accordingly
When a point is selected within the graph the corresponding model is displayed on the right of the input text box
I would like to see the selection to be displayed inside the text box
Can anyone please point me in the right direction
Thanks for any help
require(ggplot2)
require(dplyr)
require(Cairo)
require(dplyr)
mtcars2 <- datasets::mtcars %>% rownames_to_column(var = 'model')
ui <- fluidPage(
fluidRow(
column(width = 3,
selectInput(inputId = '.model', label = 'input model', choices = mtcars2$model, selected = NULL)),
br(),br(),
column(width = 3,
textOutput('click_1A'), label = 'selected model')
),
fluidRow(
column(width = 8,
plotOutput("plot1", height = 350, brush = "plot_brush", click = "plot_click")),
)
)
server <- function(input, output) {
global <- reactiveValues(.model = NULL)
# scatter plot
output$plot1 <- renderPlot({
selected_model <- input$.model
ggplot(mtcars2, aes(x=mpg,y=disp), color = 'red') +
geom_point(size = 3, col = 'red') +
geom_point(data = filter(mtcars2, model == selected_model), col = 'blue', size = 5) +
theme_bw() +
theme(legend.position = 'none')
})
# MODEL name
output$click_1A <- renderText({
near_out <- nearPoints(mtcars2, input$plot_click, addDist = TRUE)
global$.model <- near_out %>%
pull(model)
})
}
shinyApp(ui, server)
Thanks #Ben
Here is the clean version of what was trying to achieve:
require(ggplot2)
require(tidyr)
require(tibble)
require(lubridate)
require(Cairo)
require(dplyr)
mtcars2 <- datasets::mtcars %>% rownames_to_column(var = 'model')
ui <- fluidPage(
fluidRow(
column(width = 3,
selectInput(inputId = '.model', label = 'input model', choices = mtcars2$model, selected = NULL)),
),
fluidRow(
column(width = 8,
plotOutput("plot1", height = 350, brush = "plot_brush", click = "plot_click")),
)
)
server <- function(input, output, session) {
global <- reactiveValues(.model = NULL)
# scatter plot
output$plot1 <- renderPlot({
selected_model <- input$.model
ggplot(mtcars2, aes(x=mpg,y=disp, label = model), color = 'red') +
geom_point(size = 3, col = 'red') +
geom_point(data = filter(mtcars2, model == selected_model), col = 'blue', size = 5) +
#geom_text() +
theme_bw() +
theme(legend.position = 'none')
})
observeEvent(
eventExpr = input$plot_click,
handlerExpr = {
selected_model <- nearPoints(mtcars2, input$plot_click, maxpoints = 1, addDist = F) %>% pull(model)
updateSelectInput(session, inputId = ".model", choices = mtcars2$model, selected = selected_model)}
)
}
shinyApp(ui, server)

Using a (logical) vector from a reactive expression in a reactive context / attempt to apply non-function error

I am trying to use a logical vector from a reactive expression. This generates an error in a function xor() when I try to perform a logical operation on this vector in another reactive expression. I would like to generate a reactive expression (logical vector), and then use it in another reactive function. A toy example below. The error appears when points on the plot are clicked.
In the original here, keeprows() is not reactive, but I would like to make this structured as on the schematic below (from Shiny website). The fist object is input for a reactive expression, and then a second (reactive) object (which is a user-subsetted table) is used for point selection, etc. The elements after bifurcation are the tables with the kept and excluded points. I have a problem in making this last subsetting to work.
Could someone explain to me the root of this problem?
library(ggplot2)
library(shiny)
library(dplyr)
ui <- fluidPage(
fluidRow(
column(width = 6,
plotOutput("plot1", height = 350,
click = "plot1_click",
brush = brushOpts(
id = "plot1_brush"
)
),
actionButton("exclude_toggle", "Toggle points"),
sliderInput(inputId = "efficiency", value = 20, label = "MPG", min = min(mtcars$mpg), max = max(mtcars$mpg))
)
)
)
server <- function(input, output) {
# For storing which rows have been excluded
mt_subset <- reactive(mtcars %>% filter(mpg > input$efficiency))
vals <- reactiveValues()
vals$keeprows <- reactive(rep(TRUE, nrow(mt_subset()), label = "TuProblem", quoted = FALSE))
output$plot1 <- renderPlot({
# Plot the kept and excluded points as two separate data sets
keep <- mt_subset()[ vals$keeprows(), , drop = FALSE]
exclude <- mt_subset()[!vals$keeprows(), , drop = FALSE]
ggplot(keep, aes(wt, mpg)) + geom_point() +
geom_smooth(method = lm, fullrange = TRUE, color = "black") +
geom_point(data = exclude, shape = 21, fill = NA, color = "black", alpha = 0.25) +
coord_cartesian(xlim = c(1.5, 5.5), ylim = c(5,35))
})
# Toggle points that are clicked
observeEvent(input$plot1_click, {
res <- nearPoints(mt_subset(), input$plot1_click, allRows = TRUE)
vals$keeprows <- xor(as.logical(vals$keeprows()), as.logical(res$selected_))
})
# Toggle points that are brushed, when button is clicked
observeEvent(input$exclude_toggle, {
res <- brushedPoints(mt_subset(), input$plot1_brush, allRows = TRUE)
vals$keeprows <- xor(vals$keeprows(), res$selected_)
})
}
shinyApp(ui, server)
I'm not sure if this is the output you're looking for, but this code reads in a local file and then performs the brushing point selection, greying out the brushed points after "toggle points" is hit and also adjusting the correlation.
library(ggplot2)
library(Cairo) # For nicer ggplot2 output when deployed on Linux
library(shiny)
library(readxl)
data(iris)
write.xlsx(x = iris, file = "iris.xlsx")
ui <- fluidPage(
fluidRow(
fileInput(inputId = "file",
label = "Load file"),
column(width = 6,
plotOutput("plot1", height = 350,
click = "plot1_click",
brush = brushOpts(
id = "plot1_brush"
)
),
actionButton("exclude_toggle", "Toggle points"),
actionButton("exclude_reset", "Reset")
)
)
)
server <- function(input, output) {
# Get file
getFile <- reactive({ if (is.null(input$file)) {
return(NULL)
} else {
return(input$file)
}})
# Read data
data <- reactive({ if (is.null(getFile())) {
return(NULL)
} else {
as.data.frame(read_excel(getFile()$datapath))
}})
# For storing which rows have been excluded
vals <- reactiveValues()
observeEvent(data(), {
vals$keeprows <- rep(T, nrow(data()))
})
# Toggle points that are clicked
observeEvent(input$plot1_click, {
res <- nearPoints(data(), input$plot1_click, allRows = TRUE)
vals$keeprows <- xor(vals$keeprows, res$selected_)
})
# Toggle points that are brushed, when button is clicked
observeEvent(input$exclude_toggle, {
res <- brushedPoints(data(), input$plot1_brush, allRows = TRUE)
vals$keeprows <- xor(vals$keeprows, res$selected_)
})
# Reset all points
observeEvent(input$exclude_reset, {
vals$keeprows <- rep(TRUE, nrow(data()))
})
output$plot1 <- renderPlot({
if (is.null(data())) {
return(NULL)
} else {
# Indices for keep and exclude
keep_v <- which(vals$keeprows)
exclude_v <- which(!vals$keeprows)
# Subset data
keep <- data()[keep_v, , drop = F]
exclude <- data()[exclude_v, , drop = F]
ggplot(keep, aes(Sepal.Length, Sepal.Width)) + geom_point() +
geom_smooth(method = lm, fullrange = TRUE, color = "black") +
geom_point(data = exclude, shape = 21, fill = NA, color = "black", alpha = 0.25)
}
})
}
shinyApp(ui, server)
Solved:
library(ggplot2)
library(shiny)
library(dplyr)
ui <- fluidPage(
fluidRow(
column(width = 6,
plotOutput("plot1", height = 350,
click = "plot1_click",
brush = brushOpts(
id = "plot1_brush"
)
),
actionButton("exclude_toggle", "Toggle points"),
sliderInput(inputId = "efficiency", value = 20, label = "MPG", min = min(mtcars$mpg), max = max(mtcars$mpg))
)
)
)
server <- function(input, output) {
mt_subset <- reactive(mtcars %>% filter(mpg > input$efficiency))
vals <- reactiveValues()
observeEvent(mt_subset(), {
vals$keeprows <- rep(TRUE, nrow(mt_subset()), label = "TuProblem", quoted = FALSE)
})
output$plot1 <- renderPlot({
# Plot the kept and excluded points as two separate data sets
keep <- mt_subset()[ vals$keeprows, , drop = FALSE]
exclude <- mt_subset()[!vals$keeprows, , drop = FALSE]
ggplot(keep, aes(wt, mpg)) + geom_point() +
geom_smooth(method = lm, fullrange = TRUE, color = "black") +
geom_point(data = exclude, shape = 21, fill = NA, color = "black", alpha = 0.25) +
coord_cartesian(xlim = c(1.5, 5.5), ylim = c(5,35))
})
observeEvent(input$plot1_click, {
res <- nearPoints(mt_subset(), input$plot1_click, allRows = TRUE)
vals$keeprows <- xor(vals$keeprows, res$selected_)
})
observeEvent(input$exclude_toggle, {
res <- brushedPoints(mt_subset(), input$plot1_brush, allRows = TRUE)
vals$keeprows <- xor(vals$keeprows, res$selected_)
})
}
shinyApp(ui, server)

Shiny R: Interactive toggle output with `input` data selection

I have a question about the data selection of interactive toggle shiny app. I would like to make the data selected from selectInput but the error say: Operation not allowed without an active reactive context. (You tried to do something that can only be done from inside a reactive expression or observer.)
Is there any way to make the data interactive with the input?
Thank you!
Here is my app:
app.r:
ui <- fluidPage(
fluidRow(
column(width = 6,
selectInput("vsselection", "Choose a vs:",
choices = names(table(data.frame(mtcars$vs))),selected=0),
plotOutput("plot1", height = 350,
click = "plot1_click",
brush = brushOpts(
id = "plot1_brush"
)
),
actionButton("exclude_toggle", "Toggle points"),
actionButton("exclude_reset", "Reset")
)
)
)
server <- function(input, output) {
# For storing which rows have been excluded
vals <- reactiveValues(
keeprows = rep(TRUE, nrow(mtcars[which(mtcars$vs==input$vsselection),]))
)
output$plot1 <- renderPlot({
# Plot the kept and excluded points as two separate data sets
keep <- mtcars[which(mtcars$vs==input$vsselection),][ vals$keeprows, , drop = FALSE]
exclude <- mtcars[which(mtcars$vs==input$vsselection),][!vals$keeprows, , drop = FALSE]
ggplot(keep, aes(wt, mpg)) + geom_point() +
geom_smooth(method = lm, fullrange = TRUE, color = "black") +
geom_point(data = exclude, shape = 21, fill = NA, color = "black", alpha = 0.25) +
coord_cartesian(xlim = c(1.5, 5.5), ylim = c(5,35))
})
# Toggle points that are clicked
observeEvent(input$plot1_click, {
res <- nearPoints(mtcars[which(mtcars$vs==input$vsselection),], input$plot1_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(mtcars[which(mtcars$vs==input$vsselection),], input$plot1_brush, allRows = TRUE)
vals$keeprows <- xor(vals$keeprows, res$selected_)
})
# Reset all points
observeEvent(input$exclude_reset, {
vals$keeprows <- rep(TRUE, nrow(mtcars[which(mtcars$vs==input$vsselection),]))
})
}
shinyApp(ui, server)
I guess the first thing you should to, is to change vals to vals <- reactive({...}) and then when referring to it, add parenthesis, e.g. vals()$keeprows. This should solve the reactivity problem.
Finally, I solved this issue by removing the interactive part from the object of reactiveValues() by keep the interactive part of Vals.
Note that values taken from the reactiveValues object are reactive, but the reactiveValues object itself is not.
Here is my app:
app.r:
ui <- fluidPage(
fluidRow(
column(width = 6,
selectInput("vsselection", "Choose a vs:",
choices = names(table(data.frame(mtcars$vs))),selected=0),
plotOutput("plot1", height = 350,
click = "plot1_click",
brush = brushOpts(
id = "plot1_brush"
)
),
actionButton("exclude_toggle", "Toggle points"),
actionButton("exclude_reset", "Reset")
)
)
)
server <- function(input, output) {
# For storing which rows have been excluded
vals <- reactiveValues(
keeprows = rep(TRUE, nrow(mtcars))
)
output$plot1 <- renderPlot({
# Plot the kept and excluded points as two separate data sets
keep <- mtcars[which(mtcars$vs==input$vsselection),][ vals$keeprows, , drop = FALSE]
exclude <- mtcars[which(mtcars$vs==input$vsselection),][!vals$keeprows, , drop = FALSE]
ggplot(keep, aes(wt, mpg)) + geom_point(color = "blue") +
geom_smooth(method = lm, fullrange = TRUE, color = "black") +
geom_point(data = exclude, shape = 21, fill = NA, color = "black", alpha = 0.25)
})
# Toggle points that are clicked
observeEvent(input$plot1_click, {
res <- nearPoints(mtcars[which(mtcars$vs==input$vsselection),], input$plot1_click, allRows = TRUE)
vals$keeprows <- xor(vals$keeprows, res$selected_)
})
# Toggle points that are brushed, when button is clicked
observeEvent(input$exclude_toggle, {
res <- brushedPoints(mtcars[which(mtcars$vs==input$vsselection),], input$plot1_brush, allRows = TRUE)
vals$keeprows <- xor(vals$keeprows, res$selected_)
})
# Reset all points
observeEvent(input$exclude_reset, {
vals$keeprows <- rep(TRUE, nrow(mtcars[which(mtcars$vs==input$vsselection),]))
})
}

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