I try to display interactive plots by using R shiny. I can successfully make the GUI and published, but the plots in tabPanel shows nothing, just like the picture shows below. There is the data I used (have been downloaded into my laptop).
I think problem may caused by the way how I preprocessing my data in server.R, but whatever I tried, it still display nothing. No Error shows when I run the app.
enter image description here
My code in ui.R:
library(shiny)
shinyUI(fluidPage(
titlePanel("Data Viz Lab"),
sidebarLayout(
sidebarPanel(
## Add X-Variable select element
selectInput(inputId = "var_x",
label = h5("X-Variable"),
choices = c("Structure.Cost", "Land.Value", "Home.Value", "Home.Price.index"),
selected = "Land.Value"),
## Add Fill Color select element
selectInput(inputId = "color",
label = h5("Fill Color"),
choices = c("brown", "yellow", "green", "blue", "red"),
selected = "brown"),
## Add log-scale check box
checkboxInput(inputId = "log",
label = "log-sclae for X-variable in Scatterplot?",
value = FALSE),
## Add Y-Variable select element
selectInput(inputId = "var_y",
label = h5("Y-Variable"),
choices = c("Structure.Cost", "Land.Value", "Home.Value", "Home.Price.index"),
selected = "Structure.Cost"),
## Add Circle-Size side bar
sliderInput(inputId = "size",
label = h5("Circle-Size"),
min = 1,
max = 10,
value = 3),
## Add Outlier color select element
selectInput(inputId = "color_out",
label = h5("Outlier Color"),
choices = c("white", "yellow", "green", "blue", "red"),
selected = "white")
),
mainPanel(
tabsetPanel( # Establish tabset panel
tabPanel(
# Tab1
title = "Histogram",
value = plotOutput(outputId = "hist") # Add an figure in tab1
),
tabPanel(
# Tab2
title = "Scatterplot",
value = plotOutput(outputId = "scatter") # Add an figure in tab2
)
)
)
)
))
My code in server.R:
library(shiny)
library(ggplot2)
library(sp)
library(dplyr)
# setwd()
landdata = read.csv("landdata.csv")
options(scipen = 999)
shinyServer(function(input, output) {
## Plotting Histogram
output$hist = renderPlot({
# Plotting
if (input$log == FALSE){
ggplot(landdata, aes_string(x = input$var_x)) +
geom_histogram(color = input$color)
}else{
ggplot(landdata, aes_string(x = input$var_x)) +
geom_histogram(color = input$color) +
scale_x_log10(input$var_x)
}
})
## Plotting Scatter plot
output$scatter = renderPlot({
# Data pre-processing
p = ggplot(data = landdata, aes_string(x = input$var_x, y = input$var_y)) +
geom_point() +
stat_ellipse(type = "norm", level = 0.95, color = "black")
build = ggplot_build(p)$data
pts = build[[1]]
elli = build[[2]]
Outlier = point.in.polygon(pts$x, pts$y, elli$x, elli$y)
landdata = cbind(landdata, Outlier)
landdata$Outlier = ifelse(landdata$Outlier == 0, yes = "Y", no = "N") %>% factor(level = c("Y", "N"))
# Plotting
if (input$log == FALSE){
ggplot(landdata, aes_string(x = input$var_x, y = input$var_y)) +
geom_point(aes(color = Outlier), size = input$size) +
scale_color_manual(values = c(input$color, input$color_out))
}else{
ggplot(landdata, aes_string(x = input$var_x, y = input$var_y)) +
geom_point(aes(color = Outlier), size = input$size) +
scale_color_manual(values = c(input$color, input$color_out)) +
scale_x_log10(input$var_x)
}
})
})
The mistake lies in the tabPanel setup. value is not the correct argument for the plot. value is "the value that should be sent when tabsetPanel reports that this tab is selected" (taken from the manual). That means, value has the role of an id (like id argument of tabsetPanel or outputId of plotOutput).
Remove value = to make it work (the code snippet below gave me an output on my system).
tabsetPanel( # Establish tabset panel
tabPanel(
# Tab1
title = "Histogram",
plotOutput(outputId = "hist") # Add an figure in tab1
),
tabPanel(
# Tab2
title = "Scatterplot",
plotOutput(outputId = "scatter") # Add an figure in tab2
)
)
Related
I have a shiny app which creates a scatter plot between selected variables of the mtcars dataset. As you can see I have modified the data labels in order to display the car type in every point instead of the x-y coordinates. The problem is that when I click on my trendline, on spots where there are no data -so the coordinates are displayed-the app is breaking down. Here is a reproducible example:
#ui.r
library(shiny)
library(ggplot2)
library(plotly)
library(dplyr)
fluidPage(
# App title ----
titlePanel(div("CROSS CORRELATION",style = "color:blue")),
# Sidebar layout with input and output definitions ----
sidebarLayout(
# Sidebar panel for inputs ----
sidebarPanel(
),
# Main panel for displaying outputs ----
mainPanel(
tabsetPanel(type = "tabs",
tabPanel("Correlation Plot",
fluidRow(
column(3, uiOutput("lx1")),
column(3,uiOutput("lx2"))),
hr(),
fluidRow(
tags$style(type="text/css",
".shiny-output-error { visibility: hidden; }",
".shiny-output-error:before { visibility: hidden; }"
)
),
fluidRow(
plotlyOutput("sc"))
)
)
)))
#server.r
function(input, output) {
output$lx1<-renderUI({
selectInput("lx1", label = h4("Select 1st Expression Profile"),
choices = colnames(mtcars[,2:5]),
selected = "Lex1")
})
output$lx2<-renderUI({
selectInput("lx2", label = h4("Select 2nd Expression Profile"),
choices = colnames(mtcars[,2:5]),
selected = "Lex2")
})
# 1. create reactive values
vals <- reactiveValues()
# 2. create df to store clicks
vals$click_all <- data.frame(x = numeric(),
y = numeric(),
label = character())
# 3. add points upon plot click
observe({
# get clicked point
click_data <- event_data("plotly_click", source = "select")
# get data for current point
label_data <- data.frame(x = click_data[["x"]],
y = click_data[["y"]],
label = click_data[["key"]],
stringsAsFactors = FALSE)
# add current point to df of all clicks
vals$click_all <- merge(vals$click_all,
label_data,
all = TRUE)
})
output$sc<-renderPlotly({
mtcars$car <- row.names(mtcars)
p1 <- ggplot(mtcars, aes_string(x = input$lx1, y = input$lx2,key = "car",group="car"))+
# Change the point options in geom_point
geom_point(color = "darkblue") +
# Change the title of the plot (can change axis titles
# in this option as well and add subtitle)
labs(title = "Cross Correlation") +
# Change where the tick marks are
# Change how the text looks for each element
theme_bw()+
geom_smooth(aes(group = 1))+
# 4. add labels for clicked points
geom_text(data = vals$click_all,
aes(x = x, y = y, label = label),
inherit.aes = FALSE, nudge_x = 0.25)
ggplotly(p1,source = "select", tooltip = c("key")) %>%
layout(hoverlabel = list(bgcolor = "white",
font = list(family = "Calibri",
size = 9,
color = "black")))
})
}
As you said, the app breaks down after clicking on the trend line where there is no point that corresponds to a car. Let us stick to that scenario. You get following error:
Warning: Error in data.frame: arguments imply differing number of rows: 1, 0
The reason for this error is that after clicking on the trend line the data frame stored in click_data variable does not contain variable key.
You try to access this variable anyway via click_data[["key"]] and the output of it is NULL as it is not existent.
In the next step you want to build a new data.frame label_data, where label is assigned to NULL and hence the error.
label_data <- data.frame(x = click_data[["x"]], # it is fine because it is number
y = click_data[["y"]], # also fine
label = NULL, # label gets NULL
stringsAsFactors = FALSE)
We can simply reproduce this error with
> data.frame(x = 1, y = 1, label = NULL)
Error in data.frame(x = 1, y = 1, label = NULL) :
arguments imply differing number of rows: 1, 0
Now that we know why we get the error, we can find multiple solutions to it. One of them would be to require first that
click_data <- event_data("plotly_click", source = "select")
returns a data frame and then if it does not contain key variable, we set the value of label to "" with
label_ <- ifelse(is.null(click_data[["key"]]),
yes = "",
no = click_data[["key"]])
That is
observe({
# get clicked point
click_data <- event_data("plotly_click", source = "select")
# Require that click_data is available (does not return NULL)
req(click_data)
label_ <- ifelse(is.null(click_data[["key"]]),
yes = "",
no = click_data[["key"]])
# get data for current point
label_data <- data.frame(x = click_data[["x"]],
y = click_data[["y"]],
label = label_,
stringsAsFactors = FALSE)
# add current point to df of all clicks
vals$click_all <- merge(vals$click_all,
label_data,
all = TRUE)
})
Full code:
library(shiny)
library(ggplot2)
library(plotly)
library(dplyr)
ui <- fluidPage(
# App title ----
titlePanel(div("CROSS CORRELATION",style = "color:blue")),
# Sidebar layout with input and output definitions ----
sidebarLayout(
# Sidebar panel for inputs ----
sidebarPanel(
),
# Main panel for displaying outputs ----
mainPanel(
tabsetPanel(type = "tabs",
tabPanel("Correlation Plot",
fluidRow(
column(3, uiOutput("lx1")),
column(3,uiOutput("lx2"))),
hr(),
fluidRow(
tags$style(type="text/css",
".shiny-output-error { visibility: hidden; }",
".shiny-output-error:before { visibility: hidden; }"
)
),
fluidRow(
plotlyOutput("sc"))
)
)
)))
#server.r
server <- function(input, output) {
output$lx1<-renderUI({
selectInput("lx1", label = h4("Select 1st Expression Profile"),
choices = colnames(mtcars[,2:5]),
selected = "Lex1")
})
output$lx2<-renderUI({
selectInput("lx2", label = h4("Select 2nd Expression Profile"),
choices = colnames(mtcars[,2:5]),
selected = "Lex2")
})
# 1. create reactive values
vals <- reactiveValues()
# 2. create df to store clicks
vals$click_all <- data.frame(x = numeric(),
y = numeric(),
label = character())
# 3. add points upon plot click
observe({
# get clicked point
click_data <- event_data("plotly_click", source = "select")
# Require that click_data is available (does not return NULL)
req(click_data)
label_ <- ifelse(is.null(click_data[["key"]]),
yes = "",
no = click_data[["key"]])
# get data for current point
label_data <- data.frame(x = click_data[["x"]],
y = click_data[["y"]],
label = label_,
stringsAsFactors = FALSE)
# add current point to df of all clicks
vals$click_all <- merge(vals$click_all,
label_data,
all = TRUE)
})
output$sc<-renderPlotly({
mtcars$car <- row.names(mtcars)
p1 <- ggplot(mtcars, aes_string(x = input$lx1, y = input$lx2,key = "car",group="car"))+
# Change the point options in geom_point
geom_point(color = "darkblue") +
# Change the title of the plot (can change axis titles
# in this option as well and add subtitle)
labs(title = "Cross Correlation") +
# Change where the tick marks are
# Change how the text looks for each element
theme_bw()+
geom_smooth(aes(group = 1))+
# 4. add labels for clicked points
geom_text(data = vals$click_all,
aes(x = x, y = y, label = label),
inherit.aes = FALSE, nudge_x = 0.25)
ggplotly(p1,source = "select", tooltip = c("key")) %>%
layout(hoverlabel = list(bgcolor = "white",
font = list(family = "Calibri",
size = 9,
color = "black")))
})
}
shinyApp(ui, server)
Hello I have a simple shiny app that creates a scatter plot of mtcars' variables given as inputs. What I want to achieve is to give user the choice to display and hide the trendline created with geom_smooth(). I tried with if statement as you can see below but with no result. Any suggestions?
#ui.r
library(shiny)
library(ggplot2)
library(plotly)
library(dplyr)
fluidPage(
# App title ----
titlePanel(div("CROSS CORRELATION",style = "color:blue")),
# Sidebar layout with input and output definitions ----
sidebarLayout(
# Sidebar panel for inputs ----
sidebarPanel(
),
# Main panel for displaying outputs ----
mainPanel(
tabsetPanel(type = "tabs",
tabPanel("Correlation Plot",
fluidRow(
column(3, uiOutput("lx1")),
column(3,uiOutput("lx2"))),
hr(),
fluidRow(
tags$style(type="text/css",
".shiny-output-error { visibility: hidden; }",
".shiny-output-error:before { visibility: hidden; }"
),
column(3,uiOutput("td"))
),
fluidRow(
plotlyOutput("sc"))
)
)
)))
#server.r
function(input, output) {
output$lx1<-renderUI({
selectInput("lx1", label = h4("Select 1st Expression Profile"),
choices = colnames(mtcars[,2:5]),
selected = "Lex1")
})
output$lx2<-renderUI({
selectInput("lx2", label = h4("Select 2nd Expression Profile"),
choices = colnames(mtcars[,2:5]),
selected = "Lex2")
})
output$td<-renderUI({
radioButtons("td", label = h4("Trendline"),
choices = list("Add Trendline" = "lm", "Remove Trendline" = ""),
selected = "")
})
# 1. create reactive values
vals <- reactiveValues()
# 2. create df to store clicks
vals$click_all <- data.frame(x = numeric(),
y = numeric(),
label = character())
# 3. add points upon plot click
observe({
# get clicked point
click_data <- event_data("plotly_click", source = "select")
# get data for current point
label_data <- data.frame(x = click_data[["x"]],
y = click_data[["y"]],
label = click_data[["key"]],
stringsAsFactors = FALSE)
# add current point to df of all clicks
vals$click_all <- merge(vals$click_all,
label_data,
all = TRUE)
})
output$sc<-renderPlotly({
mtcars$car <- row.names(mtcars)
p1 <- ggplot(mtcars, aes_string(x = input$lx1, y = input$lx2,key="car",group='car'))+
# Change the point options in geom_point
geom_point(color = "darkblue") +
# Change the title of the plot (can change axis titles
# in this option as well and add subtitle)
labs(title = "Cross Correlation")
# Change where the tick marks are
# Change how the text looks for each element
if(input$td=="lm"){
geom_smooth(aes(group = 1))+
# 4. add labels for clicked points
geom_text(data = vals$click_all,
aes(x = x, y = y, label = label),
inherit.aes = FALSE, nudge_x = 0.25)
}
else{
# 4. add labels for clicked points
geom_text(data = vals$click_all,
aes(x = x, y = y, label = label),
inherit.aes = FALSE, nudge_x = 0.25)
}
ggplotly(p1,source = "select", tooltip = c("key"))
})
}
Based on comment above:
if(input$td=="lm"){
p1 <- p1+geom_smooth(aes(group = 1))+
# 4. add labels for clicked points
geom_text(data = vals$click_all,
aes(x = x, y = y, label = label),
inherit.aes = FALSE, nudge_x = 0.25)
}
else{
# 4. add labels for clicked points
p1 <- p1+geom_text(data = vals$click_all,
aes(x = x, y = y, label = label),
inherit.aes = FALSE, nudge_x = 0.25)
}
I would like to display the Species for each data point when the cursor is over the point rather than the than the x and y values. I use the iris dataset. Also I want to be able to click on a data point to make the label persistent and not get disapperaed when I choose a new spot in the plot. (if possible ). The basic is the label. The persistence issue is a plus. Here is my app:
## Note: extrafont is a bit finnicky on Windows,
## so be sure to execute the code in the order
## provided, or else ggplot won't find the font
# Use this to acquire additional fonts not found in R
install.packages("extrafont");library(extrafont)
# Warning: if not specified in font_import, it will
# take a bit of time to get all fonts
font_import(pattern = "calibri")
loadfonts(device = "win")
#ui.r
library(shiny)
library(ggplot2)
library(plotly)
library(extrafont)
library(ggrepel)
fluidPage(
# App title ----
titlePanel(div("CROSS CORRELATION",style = "color:blue")),
# Sidebar layout with input and output definitions ----
sidebarLayout(
# Sidebar panel for inputs ----
sidebarPanel(
# Input: Select a file ----
fileInput("file1", "Input CSV-File",
multiple = TRUE,
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".csv")),
# Horizontal line ----
tags$hr(),
# Input: Checkbox if file has header ----
checkboxInput("header", "Header", TRUE),
# Input: Select separator ----
radioButtons("sep", "Separator",
choices = c(Comma = ",",
Semicolon = ";",
Tab = "\t"),
selected = ","),
# Horizontal line ----
tags$hr(),
# Input: Select number of rows to display ----
radioButtons("disp", "Display",
choices = c(Head = "head",
All = "all"),
selected = "head")
),
# Main panel for displaying outputs ----
mainPanel(
tabsetPanel(type = "tabs",
tabPanel("Table",
shiny::dataTableOutput("contents")),
tabPanel("Correlation Plot",
tags$style(type="text/css", "
#loadmessage {
position: fixed;
top: 0px;
left: 0px;
width: 100%;
padding: 5px 0px 5px 0px;
text-align: center;
font-weight: bold;
font-size: 100%;
color: #000000;
background-color: #CCFF66;
z-index: 105;
}
"),conditionalPanel(condition="$('html').hasClass('shiny-busy')",
tags$div("Loading...",id="loadmessage")
),
fluidRow(
column(3, uiOutput("lx1")),
column(3,uiOutput("lx2"))),
hr(),
fluidRow(
tags$style(type="text/css",
".shiny-output-error { visibility: hidden; }",
".shiny-output-error:before { visibility: hidden; }"
),
column(3,uiOutput("td")),
column(3,uiOutput("an"))),
fluidRow(
plotlyOutput("sc"))
))
)))
#server.r
function(input, output) {
output$contents <- shiny::renderDataTable({
iris
})
output$lx1<-renderUI({
selectInput("lx1", label = h4("Select 1st Expression Profile"),
choices = colnames(iris[,1:4]),
selected = "Lex1")
})
output$lx2<-renderUI({
selectInput("lx2", label = h4("Select 2nd Expression Profile"),
choices = colnames(iris[,1:4]),
selected = "Lex2")
})
output$td<-renderUI({
radioButtons("td", label = h4("Trendline"),
choices = list("Add Trendline" = "lm", "Remove Trendline" = ""),
selected = "")
})
output$an<-renderUI({
radioButtons("an", label = h4("Correlation Coefficient"),
choices = list("Add Cor.Coef" = cor(subset(iris, select=c(input$lx1)),subset(iris, select=c(input$lx2))), "Remove Cor.Coef" = ""),
selected = "")
})
output$sc<-renderPlotly({
p1 <- ggplot(iris, aes_string(x = input$lx1, y = input$lx2))+
# Change the point options in geom_point
geom_point(color = "darkblue") +
# Change the title of the plot (can change axis titles
# in this option as well and add subtitle)
labs(title = "Cross Correlation") +
# Change where the tick marks are
scale_x_continuous(breaks = seq(0, 2.5, 30)) +
scale_y_continuous(breaks = seq(0, 2.5, 30)) +
# Change how the text looks for each element
theme(title = element_text(family = "Calibri",
size = 10,
face = "bold"),
axis.title = element_text(family = "Calibri Light",
size = 16,
face = "bold",
color = "darkgrey"),
axis.text = element_text(family = "Calibri",
size = 11))+
theme_bw()+
geom_smooth(method = input$td)+
annotate("text", x = 10, y = 10, label = as.character(input$an))
ggplotly(p1) %>%
layout(hoverlabel = list(bgcolor = "white",
font = list(family = "Calibri",
size = 9,
color = "black")))
})
}
1. Tooltip
You can change the tooltip in a number of ways, as described here. To just show Species in the tooltip, something like this should work:
library(ggplot2)
library(plotly)
p1 <- ggplot(iris, aes_string(x = "Sepal.Length",
y = "Sepal.Width",
key = "Species")) +
geom_point()
ggplotly(p1, source = "select", tooltip = c("key"))
2. Persistent Label
I'm not sure how to leave the plotly tooltip on the point upon clicking, but you could use a plotly click event to get the clicked point and then add a geom_text layer to your ggplot.
3. Minimal Example
I've adapated your code to make a simpler example. Generally, it's helpful if you create a minimal example and remove sections of your app that aren't needed to recreate your question (e.g. changing fonts).
library(shiny)
library(plotly)
library(ggplot2)
ui <- fluidPage(
plotlyOutput("iris")
)
server <- function(input, output, session) {
output$iris <- renderPlotly({
# set up plot
p1 <- ggplot(iris, aes_string(x = "Sepal.Length",
y = "Sepal.Width",
key = "Species")) +
geom_point()
# get clicked point
click_data <- event_data("plotly_click", source = "select")
# if a point has been clicked, add a label to the plot
if(!is.null(click_data)) {
label_data <- data.frame(x = click_data[["x"]],
y = click_data[["y"]],
label = click_data[["key"]],
stringsAsFactors = FALSE)
p1 <- p1 +
geom_text(data = label_data,
aes(x = x, y = y, label = label),
inherit.aes = FALSE, nudge_x = 0.25)
}
# return the plot
ggplotly(p1, source = "select", tooltip = c("key"))
})
}
shinyApp(ui, server)
Edit: Keep All Labels
You can store each click in a reactive data.frame using reactiveValues and use this data.frame for your geom_text layer.
library(shiny)
library(plotly)
library(ggplot2)
ui <- fluidPage(
plotlyOutput("iris")
)
server <- function(input, output, session) {
# 1. create reactive values
vals <- reactiveValues()
# 2. create df to store clicks
vals$click_all <- data.frame(x = numeric(),
y = numeric(),
label = character())
# 3. add points upon plot click
observe({
# get clicked point
click_data <- event_data("plotly_click", source = "select")
# get data for current point
label_data <- data.frame(x = click_data[["x"]],
y = click_data[["y"]],
label = click_data[["key"]],
stringsAsFactors = FALSE)
# add current point to df of all clicks
vals$click_all <- merge(vals$click_all,
label_data,
all = TRUE)
})
output$iris <- renderPlotly({
# set up plot
p1 <- ggplot(iris, aes_string(x = "Sepal.Length",
y = "Sepal.Width",
key = "Species")) +
geom_point() +
# 4. add labels for clicked points
geom_text(data = vals$click_all,
aes(x = x, y = y, label = label),
inherit.aes = FALSE, nudge_x = 0.25)
# return the plot
ggplotly(p1, source = "select", tooltip = c("key"))
})
}
shinyApp(ui, server)
Hello I have created a shiny app which creates a scatter plot between different variables of iris dataset. The problem is that when the plot is updated by choosing different variables the data label changes its position when clicked as it is affected by max value of the selected variable. I tried using hjust = -.05 which works for ggplot2 but not with plotly. I was wondering if instead of nudge_x=0.25I could set the distance to pixels or something else that would keep the distance between data point and data label unaffected by the the axes values.
#ui.r
library(shiny)
library(ggplot2)
library(plotly)
library(dplyr)
fluidPage(
useShinyjs(),
# App title ----
titlePanel(div("CROSS CORRELATION",style = "color:blue")),
# Sidebar layout with input and output definitions ----
sidebarLayout(
# Sidebar panel for inputs ----
sidebarPanel(
),
# Main panel for displaying outputs ----
mainPanel(
tabsetPanel(type = "tabs",
tabPanel("Correlation Plot",
fluidRow(
column(3, uiOutput("lx1")),
column(3,uiOutput("lx2")),
actionButton("hideshow", "Hide/show plot")),
hr(),
fluidRow(
tags$style(type="text/css",
".shiny-output-error { visibility: hidden; }",
".shiny-output-error:before { visibility: hidden; }"
)
),
fluidRow(
plotlyOutput("sc"))
)
)
)))
#server.r
function(input, output,session) {
output$lx1<-renderUI({
selectInput("lx1", label = h4("Select 1st Expression Profile"),
choices = colnames(iris[,1:4]),
selected = "Lex1")
})
output$lx2<-renderUI({
selectInput("lx2", label = h4("Select 2nd Expression Profile"),
choices = colnames(iris[,1:4]),
selected = "Lex2")
})
# 1. create reactive values
vals <- reactiveValues()
# 2. create df to store clicks
vals$click_all <- data.frame(x = numeric(),
y = numeric(),
label = character())
# 3. add points upon plot click
observe({
# get clicked point
click_data <- event_data("plotly_click", source = "select")
# get data for current point
label_data <- data.frame(x = click_data[["x"]],
y = click_data[["y"]],
label = click_data[["key"]],
stringsAsFactors = FALSE)
# add current point to df of all clicks
vals$click_all <- merge(vals$click_all,
label_data,
all = TRUE)
})
output$sc<-renderPlotly({
p1 <- ggplot(iris, aes_string(x = input$lx1, y = input$lx2,key = "Species"))+
# Change the point options in geom_point
geom_point(color = "darkblue") +
# Change the title of the plot (can change axis titles
# in this option as well and add subtitle)
labs(title = "Cross Correlation") +
# Change where the tick marks are
# Change how the text looks for each element
theme_bw()+
# 4. add labels for clicked points
geom_text(data = vals$click_all,
aes(x = x, y = y, label = label),
inherit.aes = FALSE, nudge_x = 0.25)
ggplotly(p1,source = "select", tooltip = c("key")) %>%
layout(hoverlabel = list(bgcolor = "white",
font = list(family = "Calibri",
size = 9,
color = "black")))
})
observeEvent(input$hideshow, {
# every time the button is pressed, alternate between hiding and showing the plot
toggle("sc")
})
}
Shiny newbie here.
I am trying to write a R shiny script, and one of things I want to do is generate various plots.
I have a written this code for plotting by taking input from user but getting error of
Error in exists(name, envir = env, mode = mode) :
argument "env" is missing, with no default
Need Help to solve this
I am uploading my server and ui code.
Server.r
shinyServer(function(input,output){
data<-reactive({
file1<-input$file
if(is.null(file1)){return()}
read.table(file=file1$datapath,sep = input$sep,header = input$header,stringsAsFactors = input$stringAsFactors)
})
output$variable <- renderUI({
obj<-data()
if (is.null(obj))
return(NULL)
var.opts<-namel(colnames(obj))
selectInput("variable","Variable:", var.opts)
})
# y variable
output$group <- renderUI({
obj<-data()
if (is.null(obj))
return(NULL)
var.opts<-namel(colnames(obj))
selectInput("group","Groups:", var.opts)
})
#caption
output$caption<-renderText({
switch(input$plot.type,
"boxplot" = "Boxplot",
"histogram" = "Histogram",
"density" = "Density plot",
"bar" = "Bar graph")
})
#plot
output$plot <- renderUI({
plotOutput("p")
})
#plotting function using ggplot2
output$p <- renderPlot({
obj<-data()
plot.type<-switch(input$plot.type,
"boxplot" = geom_boxplot(),
"histogram" = geom_histogram(alpha=0.5,position="identity"),
"density" = geom_density(alpha=.75),
"bar" = geom_bar(position="dodge")
)
require(ggplot2)
#plotting theme
.theme<- theme(
axis.line = element_line(colour = 'gray', size = .75),
panel.background = element_blank(),
plot.background = element_blank()
)
if(input$plot.type=="boxplot") { #control for 1D or 2D graphs
p<-ggplot(data=obj,
aes(
x = obj$group,
y = obj$variable,
fill = as.factor(obj$group)
)
) + plot.type
if(input$show.points==TRUE)
{
p<-p+ geom_point(color='black',alpha=0.5, position = 'jitter')
}
} else {
p<-ggplot(data=obj,
aes(
x = obj$variable,
fill = as.factor(obj$group),
group = as.factor(obj$group)
#color = as.factor(plot.obj$group)
)
) + plot.type
}
p<-p+labs(
fill = input$group,
x = "",
y = input$variable
) +
.theme
print(p)
})
})
ui.R
shinyUI(fluidPage(
#Heading panel
titlePanel(title="Machine Learning and Statistics",),
#input data set
sidebarLayout(position = "right",
sidebarPanel(fileInput('file', 'Choose a File', multiple = T, accept=c('text/csv',
'text/comma-separated-values,text/plain',
'.csv')),
#default size for dataset
helpText("Default max. size is 7mb"),
#input number of observations
numericInput("obs", "Number of observations to view:", 10),
tags$hr(),
checkboxInput(inputId = 'header',label = 'Header',value = TRUE),
checkboxInput(inputId = "stringAsFactors","stringAsFactors",TRUE),
br(),
radioButtons(inputId = 'sep',label = 'Seprator',choices=c(comma=',',Semicolon=';',Tab='\t',Space=' '),selected = ','),
sliderInput("train_percent",
"Training Percentage:",
min = 10, max = 90,
value = 20, step = 10),
uiOutput("variable"), # depends on dataset ( set by output$variable in server.R)
uiOutput("group"), # depends on dataset ( set by output$group in server.R)
selectInput("plot.type","Plot Type:",
list(boxplot = "boxplot", histogram = "histogram", density = "density", bar = "bar")
),
checkboxInput("show.points", "show points", TRUE)
),
mainPanel(
("output"),
h3(textOutput("caption")),
uiOutput("plot")
)
)))
Help?Thanks.
My solution to this error message when using ggplot in Shiny is:
ggplot(data = obj, aes(...), environment = environment())
Would appreciate if someone could explain the reason behind the extra need for this in a Shiny app.