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I am creating two interactive plots in R Shiny and while I can get one plot to show up and work, the second plot keeps giving me the "Warning: Error in [.data.frame: undefined columns selected" and will not appear.
I have looked at many solutions online and none so far have been able to help me or fix my issue.
I am having a hard time seeing how my columns are undefined, but I am also relatively new to R Shiny and could be easily overlooking something, so I was hoping someone could help me figure this out.
Here is my code:
library(shiny)
library(dplyr)
library(readr)
library(ggplot2)
library(tidyverse)
age <- c(1, 4, 7,10, 15)
v_m_1 <- c(10, 14, 17, 20, 25)
v_m_2 <- c(9, 13, 16, 19, 24)
sex <- c("F", "M","U", "F", "M")
P_v_rn <- c(0.11, 0.51, 0.61, 0.91, 1)
C_v_rn <- c(11.1, 15.1, 16.1, 19.1, 20.1)
P_v_rk <- c(0.11, 0.51, 0.61, 0.91, 1)
B_v_rk <- c("Low", "Medium", "Medium", "High", "High")
df_test <- data.frame(age, v_m_1, v_m_2, sex, P_v_rn, C_v_rn, P_v_rk, B_v_rk)
# Define UI for application that draws a histogram
ui <- fluidPage(
# Application title
titlePanel("Test"),
# Sidebar with a slider input for number of bins
verticalLayout(
sidebarLayout(
sidebarPanel(
selectInput(inputId = "xvar",
label = "Choose X variable", #All variables are numeric
c("Age" = 1),
selected = 1),
selectInput(inputId = "yvar",
label = "Choose bone variable", #All variables are numeric
c("v_m_1" = 2,
"v_m_2" = 3),
selected = 2),
checkboxInput(inputId = "regression",
label = "Fit LOESS - By Sex",
value = FALSE)),
mainPanel(
plotOutput('dataplot1')
)
),
tags$hr(),
sidebarLayout(
sidebarPanel(
selectInput(inputId = "xvar_name",
label = "Choose X variable", #All variables are numeric
c("Age" = 1),
selected = 1),
selectInput(inputId = "yvar_name",
label = "Choose Y variable", #The first variable option is numeric, the rest are factors
c("P_v_rk" = 7,
"B_v_rk" = 8),
selected = 7),
selectInput(inputId = "zvar_name",
label = "Choose Z variable", #All variables are numeric
c("C_v_rn" = 6,
"P_v_rn" = 5),
selected = 6)),
# Show a plot of the generated distribution
mainPanel(
plotOutput('dataplot2')
)
),
tags$hr(),
))
# Define server logic required to draw a scatterplot
server <- function(input, output) {
df <- df_test %>%
select(age, v_m_1, v_m_2, sex, P_v_rn, C_v_rn, P_v_rk, B_v_rk)
df$B_v_rk <- as.factor(df$B_v_rk)
#Growth Curve
output$dataplot1 <- renderPlot({
xvar <- as.numeric(input$xvar)
yvar <- as.numeric(input$yvar)
Sex <- as.factor(df$sex)
p <- ggplot() +
aes(x = df[ ,xvar],
y = df[ ,yvar],
col = sex) +
geom_point(alpha = 0.5, aes(size = 1.5)) + # 50% transparent
labs(x = names(df[xvar]),
y = names(df[yvar])) +
theme_classic()
if(input$regression) {
# add a line to the plot
p <- p + geom_smooth()
}
p # The plot ('p') is the "return value" of the renderPlot function
})
#Environmental metrics
output$dataplot2 <- renderPlot({
xvar_name <- input$xvar_name
yvar_name <- input$yvar_name
zvar_name <- input$zvar_name
#Color palette for ggplots as blue color range was difficult for me
fun_color_range <- colorRampPalette(c("yellow", "red"))
my_colors <- fun_color_range(20)
p2 <- ggplot() +
aes(x = df[ ,xvar_name],
y = df[ ,yvar_name],
col = df[ ,zvar_name]) +
geom_point(alpha = 0.5, aes(size = 1.5)) + # 50% transparent
scale_colour_gradientn(colors = my_colors) +
labs(x = names(df[xvar_name]),
y = names(df[yvar_name])) +
theme_classic()
p2 # The plot ('p2') is the "return value" of the renderPlot function
})
}
# Run the application
shinyApp(ui = ui, server = server)
Again the first plot works fine, it is the second plot that is producing an error code.
I guess I am confused as the code for the first plot works fine but it won't work for the second plot.
For reference, this is the layout I want, except I want another plot in the error code location.
My guess is that the bug is in the line with names(df[xvar_name]). If df is a data frame, this will throw the error you quoted. To subset a data frame with indices or column names you either use double brackets (df[[...]]) or a comma (df[ ..., ... ]). I think you meant names(df[ , xvar_name ]). This error is repeated on the line below as well.
In general, to identify the place where the problem occurs, use browser() in your code.
I'm working on cancer data from TCGA.
Im new to shiny and creating web applications (learning it!!)
I'm working on a shiny tool to plot the volcanoplot using highcharter package.
sometimes I'm successfully able to plot the volcanoplot in the UI. but sometimes it fails to plot it and throws an error saying,
"An error has occurred!
could not find function "highchartOutput"
and one warning message is given for the error;
Listening on http://127.0.0.1:5335
Warning: Error in highchartOutput: could not find function "highchartOutput"
83: dots_list
82: div
81: tabPanel
I think there is some problem with the tabset panel.
is this error has anything to do with indentation? (wherever I adjust the brackets it works magically. not sure how it works for sometimes.)
I am attaching the UI and server files with this post.
code is attached for one type of comparison
UI file below:
library(shiny)
# Define UI for application
shinyUI(fluidPage(
# Application title
titlePanel("miR-Gyn-Explorer"),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
## select the count matrix
selectInput("file", label = h3("Count Matrix"),
choices = list("Stage I - Normal" = list("TCGA-BRCA" = "Data/TCGA-BRCASI_NT.rda", "TCGA-UCEC" = "Data/TCGA-UCECSI_NT.rda"))),
## select the phenodata of samples
selectInput("phenofile", label = h3("Sample Phenodata"),
choices = list("Stage I - Normal" = list("TCGA-BRCA" = "Data/TCGA-BRCA_phenoSI_NT.rda", "TCGA-UCEC" = "Data/TCGA-UCEC_phenoSI_NT.rda"))),
submitButton("Update View")
),
# Show a plot of the generated distribution
mainPanel(
tabsetPanel(
tabPanel("DEmiRNA", DT::dataTableOutput("DEmiRNA"),
"Volcano-Plot", highchartOutput("volcanoPlot", height = "500px"))
#tabPanel("miRNA-Targets", DT::dataTableOutput('miRTarget'),
#plotOutput("GO"))
)
)
)
)
)
server file:
library(shiny)
library(R.utils)
##function to find the DEmiRNA by edgeR method
library(limma)
library(edgeR)
library(DT)
library(dplyr)
library(multiMiR)
library(miRBaseConverter)
library(ggplot2)
#library(ggrepel)
library(tidyverse)
library(highcharter)
library(org.Hs.eg.db)
library(clusterProfiler)
library(purrr)
gdcDEmiRNA <- function(counts, groups, comparison, filter=TRUE) {
## method = edgeR
dge = DGEList(counts = counts, samples = groups)
group <- factor(groups$group)
design <- model.matrix(~0+group)
colnames(design) <- levels(group)
contrast.matrix <- makeContrasts(contrasts=comparison,
levels=design)
keep = filterByExpr(dge,design)
dge <- dge[keep,,keep.lib.sizes = TRUE]
dge <- calcNormFactors(dge)
dge <- estimateDisp(dge, design)
fit <- glmFit(dge, design)
lrt <- glmLRT(fit, contrast=contrast.matrix)
DEGAll <- lrt$table
DEGAll$FDR <- p.adjust(DEGAll$PValue, method = 'fdr')
o <- order(DEGAll$FDR)
DEGAll <- DEGAll[o,]
return (DEGAll)
}
# Define server logic required to perform the DEmiRNA analysis
server <- function(input, output) {
d <- reactive({
#DEmiRNA calculation
file <- load(input$file)
phenofile <- load(input$phenofile)
if(file == "SI_NT"){
if(phenofile == "phenoSI_NT"){
DEmiRNA <- gdcDEmiRNA(counts = SI_NT, groups = phenoSI_NT,
comparison = 'StageI-Normal')
}
}
})
output$DEmiRNA <- DT::renderDataTable({
mir <- d()
#mir <- mir[mir$FDR < input$FDR,]
})
output$volcanoPlot <- renderHighchart({
x <- d()
x$mirna <- rownames(x)
x$sig <- ifelse(x$PValue < 0.05 & abs(x$logFC) > 0.57, "DEmiRNA", "Not Regulated")
hc <- highchart() %>%
hc_add_series(x, "scatter", hcaes(logFC, -log10(PValue), group = sig, value = mirna),
color = c('rgba(67, 67, 72, 0.6)', 'rgba(124, 181, 236, 0.6)'),
enableMouseTracking = c(TRUE, TRUE),
showInLegend = TRUE, marker = list(radius = 4)) %>%
hc_tooltip(pointFormat = "{point.value}", headerFormat = "") %>%
hc_xAxis(title = list(text = "Log fold change"), gridLineWidth = 1,
tickLength = 0, startOnTick = "true", endOnTick = "true", min = -6, max = 6) %>%
hc_yAxis(title = list(text = "-Log10(p-value)")) %>%
hc_chart(zoomType = "xy", width=700) %>%
hc_exporting(enabled = TRUE, filename = "volcano")
hc
})
}
any comment and help from you guys is appreciated
Thank you in advance!
-Ankita
I am trying to create a shiny app that has multiple plots from different data frames. What I want is to have a selectinput for each plot where the user's selection changes the dataframe that is being plotted, with some other user inputs depending on the plot. When I do this, only one plot will render while the other plots need to have a value changed before they render. I have the following reprex to help explain what I mean.
library(shiny)
library(shinyBS)
library (dplyr)
library(wordcloud)
library(wordcloud2)
library(ggplot2)
library(plotly)
Survey1_Together <- read.csv(text = '"Answer", "Score"
"A", 20
"B", 10
"C", 14')
Survey1_user1 <- read.csv(text = '"Answer", "Score"
"A", 10
"B", 5
"C", 4')
Survey1_user2 <- read.csv(text = '"Answer", "Score"
"A", 10
"B", 5
"C", 4')
Survey2_Together <- read.csv(text = '"Answer", "Score"
"A", 200
"B", 120
"C", 140')
Survey2_user1 <- read.csv(text = '"Answer", "Score"
"A", 150
"B", 50
"C", 40')
Survey2_user2 <- read.csv(text = '"Answer", "Score"
"A", 150
"B", 70
"C", 100')
ui <- fluidPage(
fluidRow(column(width =4),titlePanel(h2("Wordcloud Survey 1", align = "center"))),
fluidRow(HTML("<br>")),
fluidRow(HTML("<br>")),
fluidRow(flowLayout((tags$div(selectizeInput("which", "Choose a user", choices = c("Together", "User 1", "User 2")), align = "center")),
(tags$div(sliderInput(inputId = "num", label = "Range of word usage", value = c(0,10000), min = 1, max = 10000), align = "center")), align = "center")),
fluidRow(HTML("<br style = 'line-height: 100px'>")),
fluidRow(column = 12, (tags$div(wordcloud2Output("wordcloud1", height = "600px"), align = "center")))
,
fluidRow(column(width =4),titlePanel(h2("Wordcloud survey 2", align = "center"))),
fluidRow(HTML("<br>")),
fluidRow(HTML("<br>")),
fluidRow(flowLayout((tags$div(selectizeInput("which2", "Choose a user", choices = c("Together", "User 1", "User 2")), align = "center")),
(tags$div(sliderInput(inputId = "num2", label = "Range of word usage", value = c(0,1000), min = 1, max = 1000), align = "center")), align = "center")),
fluidRow(HTML("<br style = 'line-height: 100px'>")),
fluidRow(column = 12, (tags$div(wordcloud2Output("wordcloud2"), align = "center"))),
fluidRow(HTML("<br style = 'line-height: 200px'>")),
)
server <- function(input, output, session) {
datasetInput1 <- reactive({
switch(input$which,
"Together" = Survey1_Together,
"User 1" = Survey1_user1,
"User 2" = Survey1_user2)})
wc1 <- reactive(
datasetInput1() %>%
filter(Score %in% (input$num[1]:input$num[2])))
output$wordcloud1 <- renderWordcloud2({wordcloud2(wc1(), color = "random-light", widgetsize = 10, size = 2)
})
datasetInput2 <- reactive({
switch(input$which2,
"Together" = Survey2_Together,
"User 1" = Survey2_user1,
"User 2" = Survey2_user2)})
wc2 <- reactive(datasetInput2() %>%
filter(Score %in% (input$num2[1]:input$num2[2])))
output$wordcloud2 <- renderWordcloud2({wordcloud2(wc2(), size = 1, fontFamily = "Arial", fontWeight = "bold", color = brewer.pal(8, "Set1"), minSize = .5, gridSize = 15, minRotation = 0.01, maxRotation = 180, rotateRatio = 1 )})
}
shinyApp(ui = ui, server = server)
Here, the first wordcloud will render while the second one needs to have a value changed before it can render. Does anyone know what I can do to make both plots render with the default value?
So far I have tried: Using unique values, using req functions (which I've seen in similar forums but it did not work here), and rearranging the code blocks. I apologize for any unnecessary libraries or if the slider range don't make sense, I pulled this code from a much larger project I am working on and I am using dummy variables here. The same problem happens in the larger project with the first plot rendering and all others needing a variable to change before rendering. Am I missing something as to how to make them all render? Thank you
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 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)