Related
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'm trying to include a stacked bar chart in shiny that depends on a select input. It works fine outside of shiny but in shiny it is not displaying multiple bars.
Code:
library(shiny)
library(ggplot2)
# Define UI ----
ui <- fluidPage(
# Application title
titlePanel("Group fairness analysis"),
# Sidebar
sidebarLayout(
sidebarPanel(
selectInput("group", "Group:",
c("Age" = "age",
"Gender" = "gender",
"Region" = "region",
"Ethnicity"="ethnicity"))
),
# Show a plot of the generated distribution
mainPanel(
h3("Accuracy bar chart"),
plotOutput("accPlot")
)
)
)
# Define server logic ----
server <- function(input, output) {
output$accPlot <- renderPlot({
g2 <- ggplot(df %>% count(get(input$group),correct) , aes(x=c(input$group),y=n,fill=as.factor(correct))) +
geom_bar(stat="identity",position=position_fill())+
scale_y_continuous(labels = scales::percent) +
geom_text(aes(label = paste0((n/nrow(df))*100,"%")), position = position_fill(vjust = 0.5), size = 5)+
theme_bw()+
ylab("")+
coord_flip()
g2
})
}
shinyApp(ui, server)
Sample data
# data -----------------------------------------------------------
n<-20 #number of users
threshold <- 60 #threshold in risk score for referral to YS
df <- data.frame(age = rep(0,n),
gender = rep(0,n),
ethnicity = rep(0,n),
region = rep(0,n),
score = rep(0,n),
referred = rep(0,n),
target = rep(0,n))
df$age <- as.factor(sample(c(15,16,17),size=n,replace=TRUE))
df$gender <- as.factor(sample(c('M','F'),size=n,replace=TRUE))
df$ethnicity<- as.factor(sample(c('European','Maori','Pacific','other'),size=n,replace=TRUE))
df$region<-as.factor(sample(c('North','Mid','South'),size=n,replace=TRUE))
df$score<-runif(n,min=0,max=100)
df$target<-sample(c(0,1),size=n,replace = TRUE)
df[which(df$score>=threshold),"referred"]<-1
df$colour<-rep(0,n)
df[which(df$referred==1 & df$target==1),"colour"]<-1
df[which(df$referred==1 & df$target==0),"colour"]<-2
df[which(df$referred==0 & df$target==1),"colour"]<-3
df[which(df$referred==0 & df$target==0),"colour"]<-4
df$correct<-rep(0,n)
df[which(df$referred==0 & df$target==0),"correct"]<-1
df[which(df$referred==1 & df$target==1),"correct"]<-1
df[which(df$referred==0 & df$target==1),"correct"]<-0
df[which(df$referred==1 & df$target==0),"correct"]<-0
It should look like
Your input$group from selectInput is a string, not a variable symbol. You can convert it to a symbol for your ggplot with rlang::sym and evaluate with !!.
In addition, your aesthetic for ggplot can use aes_string and refer to your column names as strings.
And would convert your correct column to a factor separately.
df$correct <- as.factor(df$correct)
...
g2 <- ggplot(df %>% count(!!rlang::sym(input$group), correct), aes_string(x=c(input$group), y="n", fill="correct")) +
...
I am trying to create a shiny app with multiple sections, the section I am having trouble with right now displays calculated values on a ggplot graph. The user selects one Target gene from a dropdown list, and the graph displays calculated analysis values against a selection of other, Control genes. So far so simple.
I have a few default Control genesets, which I have preselected and that are always displayed, and then I have an option for the user to specify their own Control genes to perform analysis against. There is a checkbox that can be ticked if the user wants to select their own Controls. The user can also select different numbers of custom Controls, whereas the default controls each have sets of 3 Control genes.
Code for the default genesets as so:
ABC_control <- reactive(
Analysis_function(c("ACTB", "GAPDH", "TUBB")))
And code for the custom genesets is as so:
CUSTOM_control1 <- reactive( if (input$custom_checkbox1) {
Analysis_function(input$custom_controls1)
} else { NA } )
I have an if command in the Custom genesets so that they are not calculated and displayed if the tickbox is not checked.
First problem: The plot still displays an x axis label for the Custom control even if none is selected and the checkbox is not ticked. This is not a major problem, just an annoying one.
The second problem:
When displaying just the default genesets everything runs perfectly. And when the user selects their own Control genes, everything runs fine.
The problem is when the user ticks the CheckboxInput(), and the selectizeInput() for the custom control genes is still empty, the graph goes and re-orders its x-axis into alphabetical order, rather than the levels order that I have specified earlier. As soon as a Control gene is selected, it re-orders back into the levels order. The problem only occurs when the selectizeInput box is empty, or a new gene is being selected.
How can I force the plot to always display in the correct levels order, even when the reactive custom input is empty?
Also, preferably, how can I prevent the Custom input from being displayed on the graph at all unless the checkbox is ticked.
A full Shiny app data is below:
#### Load packages ####
library(shiny)
library(ggplot2)
library(dplyr)
#### Load data files ####
load("GeneNames.Rda")
load("Dataset.Rda")
#### Define UI ####
ui <- fluidPage(
#### Sidebar inputs ####
sidebarLayout(
sidebarPanel(width = 3,
#first wellpanel for selecting Target gene
h4("Target gene selection"),
wellPanel(
selectInput(
inputId = "gene_select",
label = NULL,
choices = GeneNames,
selected = "ESAM")),
#Second wellpanel for selecting custom Control genes
h4("Custom control genes"),
wellPanel(
checkboxInput(inputId = "custom_checkbox1",
label = "Custom 1:"),
conditionalPanel(condition = "input.custom_checkbox1 == true",
selectizeInput(inputId = "custom_controls1",
label = NULL,
choices = GeneNames,
multiple = TRUE,
options = list(openOnFocus = FALSE, closeAfterSelect = TRUE, maxOptions = 50, maxItems = 6))))
),
#### Mainpanel results Normal ####
mainPanel(width = 9,
#HTML code to have the last entry in any tables bolded (last entry is Mean in all tables)
#Results title and main bar plot graph
fluidRow(plotOutput(outputId = "celltype_bar_plot"),width = 9)
)))
#### Define server ####
server <- function(input, output) {
target_gene <- reactive({
input$gene_select
})
#### calculations ####
Analysis_function <- function(controls){
cor(Dataset[, target_gene()], Dataset[, controls])
}
ABC_control <- reactive(
Analysis_function(c("ACTB", "GAPDH", "TUBB")))
GHI_control <- reactive(
Analysis_function(c("ACTB", "GAPDH", "TUBB")))
DEF_control <- reactive(
Analysis_function(c("ACTB", "GAPDH", "TUBB")))
CUSTOM_control1 <- reactive( if (input$custom_checkbox1) {
Analysis_function(input$custom_controls1)
} else { NA } )
#### Analysis datatables Normal ####
control_list <- c("ABC_control", "GHI_control", "DEF_control", "CUSTOM_control1")
analysis_list <- reactive({ list(ABC_control(), GHI_control(), DEF_control(), CUSTOM_control1()) })
#generating melted data table of the induvidual analysed gene values, transposed to get in right format, and times = c(length()) to replicate titles the correct no of times
values_list <- reactive({
data.frame(Control_types2 = factor(rep(control_list, times = c(unlist(lapply(analysis_list(), length)))), levels =control_list),
values = c(unlist(lapply(analysis_list(), t))))
})
#Generating data table of the means of analysed values above
Mean_list <- reactive({
data.frame(Control_types = factor(control_list, levels =control_list),
Mean_correlation = c(unlist(lapply(analysis_list(), mean))))
})
#### Main Bar Plot Normal ####
output$celltype_bar_plot <- renderPlot({
ggplot() +
geom_point(data = values_list(),aes(x=Control_types2, y=values,size = 7, color = Control_types2), show.legend = FALSE, position=position_jitter(h=0, w=0.1), alpha = 0.7) +
geom_boxplot(data = Mean_list(), aes(Control_types, Mean_correlation), size = 0.5, colour = "black")
})
}
#### Run application ####
shinyApp(ui = ui, server = server)
I can't fully test this solution since the data you provided isn't available (so I can't run the app), but I suspect that the following should help.
First, by using ordered or factor(..., ordered = TRUE) you can tell the graph what order to put label in.
Second, in order to prevent the column from showing up on the graph you must remove all datapoints for that column INCLUDING NA.
control_list <- c("ABC_control", "GHI_control", "DEF_control", "CUSTOM_control1")
# Some data to try out
values_list <- data.frame(
Control_types2 = ordered(rep(control_list, times = 4), levels =control_list),
values = c(0.25,0.50,0.75,NA)
)
Mean_list <- data.frame(
Control_types = ordered(control_list, levels =control_list),
Mean_correlation = c(0.25,0.50,0.75,NA)
)
# Original plot code
ggplot() +
geom_point(data = values_list,aes(x=Control_types2, y=values,size = 7, color = Control_types2), show.legend = FALSE, position=position_jitter(h=0, w=0.1), alpha = 0.7) +
geom_boxplot(data = Mean_list, aes(Control_types, Mean_correlation), size = 0.5, colour = "black")
# New plot with NA values removed
ggplot() +
geom_point(data = values_list %>% filter(!is.na(values)),
aes(x=Control_types2, y=values,size = 7, color = Control_types2),
show.legend = FALSE,
position=position_jitter(h=0, w=0.1),
alpha = 0.7) +
geom_boxplot(data = Mean_list %>% filter(!is.na(Mean_correlation)),
aes(Control_types, Mean_correlation),
size = 0.5,
colour = "black")
Hello Stackoverflow Commununity !
I am working on a Dashboard using Flexdashboard and Shiny.
I want to introduce a filter on a graph.
The dataframe used for the graph is the following :
date <- seq(as.Date("2017/1/1"), as.Date("2018/1/1"), by = "month")
date <- as.character(date[-1])
proto <- as.data.frame(matrix(c(1,1,1,2,2,3,3,3,3,4,4,4,
0,0,0,0,1,1,1,1,1,2,2,2,
5,5,5,6,6,7,7,7,7,7,8,8), ncol = 3, nrow = length(date), byrow = F))
names(proto)[1] <- "cap_1"
names(proto)[2] <- "cap_2"
names(proto)[3] <- "cap_3"
row.names(proto) <- date
proto$total <- proto[,1] + proto[,2] + proto[,3]
If I run the graph out of the shiny code, it works perfectly for each column and I obtain :
Ggplot Graph for the total column
Now, I try to put this graph into my dashboard with the objective to have a filter on the graph to select the column (cap_1, cap_2, cap_3 or total) to plot.
Here is the code I used :
ProtoUI <- function(id) {
ns <- NS(id)
fillCol(height = 600, flex = c(NA, 1),
inputPanel(
selectInput(ns("cap"), "Capabilities:", choices = colnames(proto))
),
plotOutput(ns("proto_plot"), height = "100%")
)
}
Proto_serve <- function(input, output, session) {
output$proto_plot <- renderPlot({
ggplot(proto, aes(row.names(proto), input$cap, group = 1)) +
geom_line(size=1.5, color="blue") +
labs(x = "Date", y = "Number of prototypes", title = " ") +
geom_rangeframe() +
theme_tufte() +
theme(axis.text=element_text(size=12),
axis.title=element_text(size=13,face="bold"))
})
}
ProtoUI("proto")
callModule(Proto_serve, "proto")
And I obtain this graph :
Graph in the dashboard
Normally, the line must not be constant but should follow the data of the dataframe for the column selected.
Thanks for sharing your knowledge to solve my issue =)
Flavien.
I am trying to create a Shiny app to explore a data frame with 4 variables/columns (A, B, C, D) and 10,000 rows. There is an input field where users must select 2 of the 4 variables/columns. Once they have done so, then a scatterplot is shown on the right. The scatterplot is a Plotly object with hexagon binning summarizing the values of the 10,000 rows between the two user-selected variables/columns.
At this point, the user can select a "Go!" button, which causes an orange dot corresponding to the first row of those 2 variables/columns to be superimposed onto the Plotly object. The user can sequentially select "Go!" and then the orange dot corresponding to the second, third, fourth, etc. row will be superimposed onto the Plotly object. The name of the row ID is output above the scatterplot matrix.
For the most part, the app is working. There are only 2 things I am trying to improve upon:
1) I would like the user to be able to select new pairs in the input field. This works for the most part. However, there is one specific situation where this will cause the app to close suddenly. It happens after an orange point has been overlaid onto the scatterplot. If the user then erases the two input pairs, the app suddenly closes. I would like the user to be able to erase both input pair values and input two new pair values without the app closing even after orange points have been plotted to the scatterplot.
2) I notice that the output of the row ID lags somewhat after the orange dot is plotted. I wonder why this happens since I output the row ID before plotting the orange dot in the script. I would prefer for there to be less of a lag, but am uncertain how to approach that.
Any suggestions on how to solve either of these two issues would be greatly appreciated! My MWE showing this issue is below.
library(plotly)
library(GGally)
library(hexbin)
library(htmlwidgets)
library(tidyr)
library(shiny)
library(dplyr)
library(data.table)
library(ggplot2)
library(tibble)
myPairs <- c("A", "B", "C", "D")
ui <- shinyUI(fluidPage(
titlePanel("title panel"),
sidebarLayout(position = "left",
sidebarPanel(
selectizeInput("selPair", "Pairs:", choices = myPairs, multiple = TRUE, options = list(maxItems = 2)),
actionButton("goButton", "Go!"),
width = 3
),
mainPanel(
verbatimTextOutput("info"),
plotlyOutput("scatMatPlot")
)
)
))
server <- shinyServer(function(input, output, session) {
# Create data and subsets of data based on user selection of pairs
dat <- data.frame(ID = paste0("ID", 1:10000), A = rnorm(10000), B = rnorm(10000), C = rnorm(10000), D = rnorm(10000))
pairNum <- reactive(input$selPair)
group1 <- reactive(pairNum()[1])
group2 <- reactive(pairNum()[2])
sampleIndex <- reactive(which(colnames(dat) %in% c(group1(), group2())))
# Create data subset based on two letters user chooses
datSel <- eventReactive(sampleIndex(), {
datSel <- dat[, c(1, sampleIndex())]
datSel$ID <- as.character(datSel$ID)
datSel <- as.data.frame(datSel)
datSel
})
sampleIndex1 <- reactive(which(colnames(datSel()) %in% c(group1())))
sampleIndex2 <- reactive(which(colnames(datSel()) %in% c(group2())))
# Create background Plotly graph with hex binning all 100 rows of the two user-selected columns
ggPS <- eventReactive(datSel(), {
minVal = min(datSel()[,-1])
maxVal = max(datSel()[,-1])
maxRange = c(minVal, maxVal)
xbins=7
buffer = (maxRange[2]-maxRange[1])/xbins/2
x = unlist(datSel()[,(sampleIndex1())])
y = unlist(datSel()[,(sampleIndex2())])
h <- hexbin(x=x, y=y, xbins=xbins, shape=1, IDs=TRUE, xbnds=maxRange, ybnds=maxRange)
hexdf <- data.frame (hcell2xy (h), hexID = h#cell, counts = h#count)
attr(hexdf, "cID") <- h#cID
p <- ggplot(hexdf, aes(x=x, y=y, fill = counts, hexID=hexID)) + geom_hex(stat="identity") + geom_abline(intercept = 0, color = "red", size = 0.25) + coord_cartesian(xlim = c(maxRange[1]-1*buffer, maxRange[2]+buffer), ylim = c(maxRange[1]-1*buffer, maxRange[2]+buffer)) + coord_equal(ratio=1) + labs(x = colnames(datSel()[sampleIndex1()]), y = colnames(datSel()[sampleIndex2()]))
ggPS <- ggplotly(p)
ggPS})
# Output hex bin plot created just above
output$scatMatPlot <- renderPlotly({
# Each time user pushes Go! button, the next row of the data frame is selected
datInput <- eventReactive(input$goButton, {
g <- datSel()$ID[input$goButton]
# Output ID of selected row
output$info <- renderPrint({
g
})
# Get x and y values of seleced row
currGene <- datSel()[which(datSel()$ID==g),]
currGene1 <- unname(unlist(currGene[,sampleIndex1()]))
currGene2 <- unname(unlist(currGene[,sampleIndex2()]))
c(currGene1, currGene2)
})
# Send x and y values of selected row into onRender() function
observe({
session$sendCustomMessage(type = "points", datInput())
})
# Use onRender() function to draw x and y values of seleced row as orange point
ggPS() %>% onRender("
function(el, x, data) {
noPoint = x.data.length;
Shiny.addCustomMessageHandler('points', function(drawPoints) {
if (x.data.length > noPoint){
Plotly.deleteTraces(el.id, x.data.length-1);
}
var Traces = [];
var trace = {
x: drawPoints.slice(0, drawPoints.length/2),
y: drawPoints.slice(drawPoints.length/2, drawPoints.length),
mode: 'markers',
marker: {
color: 'orange',
size: 7
},
hoverinfo: 'none'
};
Traces.push(trace);
Plotly.addTraces(el.id, Traces);
});}")
})
})
shinyApp(ui, server)
As #HubertL mentioned, it's better to avoid nesting reactive functions. Your app will probably run more smoothely if you change that.
About your first problem, req and validate are probably the best way to go. These functions check if the user inputs are valid and deal with the invalid ones.
I've adjusted your code a bit following these sugetions, but you still can change it more. If you take a closer look to ggPS you may notice that it only uses datSel() so you could turn it into a function.
library(plotly)
library(GGally)
library(hexbin)
library(htmlwidgets)
library(tidyr)
library(shiny)
library(dplyr)
library(data.table)
library(ggplot2)
library(tibble)
myPairs <- c("A", "B", "C", "D")
ui <- shinyUI(fluidPage(
titlePanel("title panel"),
sidebarLayout(
position = "left",
sidebarPanel(
selectizeInput("selPair", "Pairs:", choices = myPairs, multiple = TRUE,
options = list(maxItems = 2)),
actionButton("goButton", "Go!"),
width = 3
),
mainPanel(
verbatimTextOutput("info"),
plotlyOutput("scatMatPlot")
)
)
))
server <- shinyServer(function(input, output, session) {
# Create data and subsets of data based on user selection of pairs
dat <- data.frame(
ID = paste0("ID", 1:10000), A = rnorm(10000),
B = rnorm(10000), C = rnorm(10000), D = rnorm(10000),
stringsAsFactors = FALSE
)
# Create data subset based on two letters user chooses
datSel <- eventReactive(input$selPair, {
validate(need(length(input$selPair) == 2, "Select a pair."))
dat[c("ID", input$selPair)]
}, ignoreNULL = FALSE)
# Create background Plotly graph with hex binning all 100 rows of the two user-selected columns
ggPS <- eventReactive(datSel(), {
minVal = min(datSel()[,-1])
maxVal = max(datSel()[,-1])
maxRange = c(minVal, maxVal)
xbins=7
buffer = (maxRange[2]-maxRange[1])/xbins/2
x = unlist(datSel()[input$selPair[1]])
y = unlist(datSel()[input$selPair[2]])
h <- hexbin(x=x, y=y, xbins=xbins, shape=1, IDs=TRUE,
xbnds=maxRange, ybnds=maxRange)
hexdf <- data.frame (hcell2xy (h), hexID = h#cell, counts = h#count)
attr(hexdf, "cID") <- h#cID
p <- ggplot(hexdf, aes(x=x, y=y, fill = counts, hexID=hexID)) +
geom_hex(stat="identity") + geom_abline(intercept = 0, color = "red", size = 0.25) +
coord_cartesian(xlim = c(maxRange[1]-1*buffer, maxRange[2]+buffer),
ylim = c(maxRange[1]-1*buffer, maxRange[2]+buffer)) +
coord_equal(ratio = 1) +
labs(x = input$selPair[1], y = input$selPair[2])
ggPS <- ggplotly(p)
ggPS
})
# Output ID of selected row
output$info <- renderPrint({ datSel()$ID[req(input$goButton)] })
# Output hex bin plot created just above
output$scatMatPlot <- renderPlotly({
# Use onRender() function to draw x and y values of seleced row as orange point
ggPS() %>% onRender("
function(el, x, data) {
noPoint = x.data.length;
Shiny.addCustomMessageHandler('points', function(drawPoints) {
if (x.data.length > noPoint){
Plotly.deleteTraces(el.id, x.data.length-1);
}
var Traces = [];
var trace = {
x: drawPoints.slice(0, drawPoints.length/2),
y: drawPoints.slice(drawPoints.length/2, drawPoints.length),
mode: 'markers',
marker: {
color: 'orange',
size: 7
},
hoverinfo: 'none'
};
Traces.push(trace);
Plotly.addTraces(el.id, Traces);
});}")
})
observe({
# Get x and y values of seleced row
currGene <- datSel()[input$goButton, -1]
# Send x and y values of selected row into onRender() function
session$sendCustomMessage(type = "points", unname(unlist(currGene)))
})
})
shinyApp(ui, server)