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Hi,
I've applied the content of this example https://plot.ly/r/range-sliders-selectors/ to a stacked bar chart plot and the bottom chart with the complete preview is not appearing.
I have checked that changing the plot type to line chart works fine. Can you check the problem with the bar charts?
Use this NB http://nbviewer.ipython.org/gist/msund/d3e00e5e27dff31a7b6d instead.
Hello.
I'm trying to use plotly in R's shinydashboard package. Whenever I load the app and try to interact with the plot I just made, the buttons usually on the top right of the plot become so large that I can't even see/interact with said plot.
So far, I've only used the plotly's shiny tutorial (https://plot.ly/r/shiny-tutorial/), the movie ratings one, in my shinydashboard app.
Here is what my app looks like when I try to interact with the plot:
Here is my ui code (the renderPlotly can be found at the bottom of the code):
library(ggplot2)
library(plyr)
library(rCharts)
library(shiny)
library(xtable)
library(shinythemes)
library(shinydashboard)
library(dplyr)
library(plotly)
PopData <- read.csv("CountyPopLong.csv", header=T)
ChangeData <- read.csv("CountyChangeLong.csv", header=T)
CountyPop <- read.csv("CountyPopWide.csv", header=T)
CountyChange <- read.csv("CountyChangeWide.csv", header=T)
CntyInfo <- read.csv("CntyCensusInfo.csv", header=T)
CityPop <- read.csv("CityData.csv", header=T)
CityPopLong <- read.csv("CityPop.csv", header=T)
CityChange <- read.csv("CityChangeWide.csv", header=T)
CityChangeLong <- read.csv("CityChangeLong.csv", header=T)
CityInfo <- read.csv("CityCensusInfo.csv", header=T)
TwpInfo <- read.csv("TwpCensusInfo.csv", header=T)
TwpPop <- read.csv("TownshipPopWide.csv", header=T)
TwpPopLong <- read.csv("TownshipPopLongWithCounty.csv", header=T)
TwpChange <- read.csv("TwpChangeWide.csv", header=T)
TwpChangeLong <- read.csv("TwpChangeLong.csv", header=T)
MHIdata <- read.csv("CountyMHILong.csv", header=T)
PCIdata <- read.csv("CountyPCILong.csv", header=T)
CityACS <- read.csv("CityACSLong.csv", header=T)
TwpACS <- read.csv("TwpACSLong.csv", header=T)
CountyBGLong <- read.csv("CountyBG2000to2010long.csv", header=T)
CityBGLong <- read.csv("CityBG2000to2010long.csv", header=T)
TwpBGLong <- read.csv("TwpBrainGain2000to2010long.csv", header=T)
CountyBG90Long <- read.csv("CountyBG1990to2000long.csv", header=T)
CityBG90Long <- read.csv("CityBG1990to2000long.csv", header=T)
TwpBG90Long <- read.csv("TwpBrainGain1990to2000long.csv", header=T)
options(RCHART_WIDTH=500, RCHART_HEIGHT=500)
dashboardPage(
header <- dashboardHeader(
title = "CST Datapage"
),
dashboardSidebar(
sidebarMenu(
sidebarMenu(
menuItem("County Table", tabName="County-Table"),
menuItem("Township", tabName="Township",
menuSubItem("Table", tabName = "Table"),
menuSubItem("Chart", tabName = "Chart")
),
menuItem("Plotly Stuff", tabName = "Plotly-Stuff")
)),
tags$head(
tags$style(HTML("
.sidebar{height: 90vh; overflow-y: auto; }
.dataTables_wrapper { overflow-x: scroll; }
.rChart {height: 500px, width: 100%}
"))
)
),
dashboardBody(
tabItems(
tabItem(tabName = "Table",
tabBox(
title = "Township Table",
id= "TwpPopTable", height = "5000px", width = "1000px",
tabPanel("Download Data",
fluidPage(
absolutePanel(draggable=F, top=125, left = 250, width = "40.2%",
sidebarPanel(
helpText("The township data is organized by county. To view a specific township�s population, first select the county it is located. You can compare multiple townships by holding down �CTRL� while selecting your desired counties."),
selectInput("County4",
"Select a County:",
c("All",
unique(as.character(TwpPop$County))),
selected = "All",
multiple = TRUE
# , selectize = FALSE
),
helpText("To download the full dataset in .csv format, click the button below. Customized data can also be copied and pasted into an Excel spreadsheet with the formatting intact."),
downloadButton('twpPopDownload', label = "Download Data"),
width = 12)))),
tabPanel("Table",
mainPanel(dataTableOutput(outputId="TwpPopTable"),
p("Source: U.S. Census Bureau, Decennial Censuses 1900-2010"))
))),
tabItem((tabName = "Chart"),
fluidRow(
column(width=5,
box(
showOutput("TwpPopChart", "nvd3"), status= "primary",
p("Source: U.S. Census Bureau, Decennial Censuses 1900-2010")
, widths = c(1, 10))),
absolutePanel(draggable=T, top=200, left = 1000, width = "20%",
helpText("The township data is organized by county. To view a specific township�s population, first select the county it is located. You can compare multiple townships by holding down �CTRL� while selecting your desired counties. Then select the communities you want to compare by clicking on the legend."),
selectInput("County5",
"Select a County:",
c(unique(as.character(TwpPopLong$County))),
selected = "Aitkin",
multiple = TRUE
# , selectize = FALSE
)))
),
tabItem(
(tabName = "County-Table"),
h2("Ayyyyyy")
),
tabItem(
(tabName = "Plotly-Stuff"),
fluidRow(
mainPanel(plotlyOutput("trendPlot", width="100%")),
sliderInput("bins", "Numbner of bins:", min=1,max=50,value=10))
)),
#Below is for colors display of the
tags$head(tags$style(HTML('
.skin-blue .main-header .logo{
font-family: Georgia, Times, "Times New Roman", serif;
font-weight: bold;
font-size: 24px;
color: #FFFF1C;
}
.skin-blue .main-header .logo{
background-color: #FF9900;
}
.skin-blue .main-header .navbar{
background-color: #FF9900;
}
.skin-blue .main-sidebar{
background-color: #FF9900;
}
.skin-blue .main-sidebar{
font-family: Georgia, Times, "Times New Roman", serif;
font-weight: bold;
font-size: 12px;
color:#FFFF1C;
}
.skin-blue .main-sidebar .sidebar .sidebar-menu .active a{
color: #FFFF1C;
}
.skin-blue .main-header .logo:hover{
background-color: #FF9900;
}
')
))
))
server code:
library(ggplot2)
library(plyr)
library(rCharts)
library(shiny)
library(xtable)
library(RColorBrewer)
library(shinythemes)
library(shinydashboard)
library(dplyr)
library(plotly)
PopData <- read.csv("CountyPopLong.csv", header=T)
ChangeData <- read.csv("CountyChangeLong.csv", header=T)
CountyPop <- read.csv("CountyPopWide.csv", header=T)
CountyChange <- read.csv("CountyChangeWide.csv", header=T)
CntyInfo <- read.csv("CntyCensusInfo.csv", header=T)
CityPop <- read.csv("CityData.csv", header=T)
CityPopLong <- read.csv("CityPop.csv", header=T)
CityChange <- read.csv("CityChangeWide.csv", header=T)
CityChangeLong <- read.csv("CityChangeLong.csv", header=T)
CityInfo <- read.csv("CityCensusInfo.csv", header=T)
TwpInfo <- read.csv("TwpCensusInfo.csv", header=T)
TwpPop <- read.csv("TownshipPopWide.csv", header=T)
TwpPopLong <- read.csv("TownshipPopLongWithCounty.csv", header=T)
TwpChange <- read.csv("TwpChangeWide.csv", header=T)
TwpChangeLong <- read.csv("TwpChangeLong.csv", header=T)
MHIdata <- read.csv("CountyMHILong.csv", header=T)
PCIdata <- read.csv("CountyPCILong.csv", header=T)
CityACS <- read.csv("CityACSLong.csv", header=T)
TwpACS <- read.csv("TwpACSLong.csv", header=T)
CountyBGLong <- read.csv("CountyBG2000to2010long.csv", header=T)
CityBGLong <- read.csv("CityBG2000to2010long.csv", header=T)
TwpBGLong <- read.csv("TwpBrainGain2000to2010long.csv", header=T)
CountyBG90Long <- read.csv("CountyBG1990to2000long.csv", header=T)
CityBG90Long <- read.csv("CityBG1990to2000long.csv", header=T)
TwpBG90Long <- read.csv("TwpBrainGain1990to2000long.csv", header=T)
options(RCHART_WIDTH=800, RCHART_HEIGHT=600)
function(input, output) {
output$table <- renderDataTable(iris)
set.seed(122)
histdata <- rnorm(500)
######################## POPULATION OUTPUTS ########################
output$PopTable <- renderDataTable({
data <- CountyPop
if (input$County != "All"){
data <- data[data$County %in% input$County,]
}
data
}, options =list(aoColumnDefs = list(list(sClass="alignCenter",aTargets=c(list("_all")) )), pageLength=10))
output$PopChart <- renderChart({
names(PopData) = gsub("\\.", "", names(PopData))
n1 <- nPlot(Population ~ Year, data = PopData, group = "County", type = 'lineChart')
n1$addParams(dom = 'PopChart')
n1$yAxis(axisLabel = "Population", width = 62)
n1$xAxis(axisLabel = "Year")
n1$chart(forceY = c(0, 1))
n1$xAxis(tickValues=c(1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010))
return(n1)
})
output$countyPopDownload <- downloadHandler(
filename = function() { paste('MNCountyPopData', '.csv', sep='') },
content = function(file) {
write.csv(PopData, file)
})
output$CityPopTable <- renderDataTable({
data <- CityPop
if (input$County2 != "All"){
data <- data[data$County %in% input$County2,]
}
data
}, options =list(aoColumnDefs = list(list(sClass="alignCenter",aTargets=c(list("_all")) )), pageLength=10))
output$CityPopChart <- renderChart({
var <- reactive({input$County3})
DF <- subset(CityPopLong, County %in% var())
names(DF) = gsub("\\.", "", names(DF))
n2 <- nPlot(Population ~ Year, data = DF, group = "Name", type = 'lineChart')
n2$addParams(dom = 'CityPopChart')
n2$yAxis(axisLabel = "Population", width = 62)
n2$xAxis(axisLabel = "Year")
n2$xAxis(tickValues=c(1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010))
n2$chart(forceY = c(0, 1))
return(n2)
})
output$cityPopDownload <- downloadHandler(
filename = function() { paste('MNCityPopData', '.csv', sep='') },
content = function(file) {
write.csv(CityPop, file)
})
output$TwpPopTable <- renderDataTable({
data <- TwpPop
if (input$County4 != "All"){
data <- data[data$County %in% input$County4,]
}
data
}, options =list(aoColumnDefs = list(list( sClass="alignCenter",aTargets=c(list("_all")) )), pageLength=10))
renderChart_pct <- function(expr, env = parent.frame(), quoted = FALSE) {
func <- shiny::exprToFunction(expr, env, quoted)
function() {
rChart_ <- func()
cht_style <- sprintf("<style>.rChart {width: %s; height: %s} </style>",
#### change these here to desired %
"100%", "100%")
cht <- paste(capture.output(rChart_$print()), collapse = '\n')
HTML(paste(c(cht_style, cht), collapse = '\n'))
}
}
output$TwpPopChart <- renderChart({
var <- reactive({input$County5})
DF <- subset(TwpPopLong, County %in% var())
names(DF) = sub("\\.", "", names(DF))
n2 <- nPlot(Population ~ Year, data = DF, group = "Name", type = 'lineChart')
n2$addParams(dom = 'TwpPopChart')
n2$yAxis(axisLabel = "Population", width = 62)
n2$xAxis(axisLabel = "Year")
n2$xAxis(tickValues=c(1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010))
n2$chart(forceY = c(0, 1))
n2$chart(height=500)
n2$set(width=500)
return(n2)
})
output$twpPopDownload <- downloadHandler(
filename = function() { paste('MNTownshipPopData', '.csv', sep='') },
content = function(file) {
write.csv(TwpPop, file)
})
######################## POPULATION CHANGE OUTPUTS ########################
output$ChangeTable <- renderDataTable({
data <- CountyChange
if (input$County6 != "All"){
data <- data[data$County %in% input$County6,]
}
data
}, options =list(aoColumnDefs = list(list(sClass="alignCenter",aTargets=c(list("_all")) )), pageLength=10))
output$ChangeChart <- renderChart({
names(ChangeData) = gsub("\\.", "", names(ChangeData))
n3 <- nPlot(PercentChange ~ Year, data = ChangeData, group = "County", type = 'lineChart')
n3$addParams(dom = 'ChangeChart')
# n3$yAxis(tickValues=function(d) {return(d + "%")})
## ^infinite recursion
# n3$yAxis(tickFormat="#!function(d) {return d3.format('%Y'));}!#" )
## ^all axis values gone, chart turns static
# n3$yAxis(labelType='percent')
## ^ only for pie charts
# n3$yAxis(tickFormat=function(d) { return(parseInt(d, 10) + "%")})
## ^ "could not find function 'd3.format'"
n3$yAxis(axisLabel = "Percent Change from Previous Decade", width = 62)
n3$xAxis(axisLabel = "Year")
n3$xAxis(tickValues=c(1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010))
return(n3)
})
output$countyChangeDownload <- downloadHandler(
filename = function() { paste('MNCountyPopChangeData', '.csv', sep='') },
content = function(file) {
write.csv(ChangeData, file)
})
output$cityChangeTable <- renderDataTable({
data <- CityChange
if (input$County7 != "All"){
data <- data[data$County %in% input$County7,]
}
data
}, options =list(aoColumnDefs = list(list(sClass="alignCenter",aTargets=c(list("_all")) )), pageLength=10))
output$cityChangeChart <- renderChart({
var <- reactive({input$County8})
DF <- subset(CityChangeLong, County %in% var())
names(DF) = gsub("\\.", "", names(DF))
n4 <- nPlot(PercentChange ~ Year, data = DF, group = "Name", type = 'lineChart')
n4$addParams(dom = 'cityChangeChart')
n4$yAxis(axisLabel = "Percent Change from Previous Decade", width = 62)
n4$xAxis(axisLabel = "Year")
n4$xAxis(tickValues=c(1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010))
# n3$chart(forceY = c(-100, 100))
return(n4)
})
output$cityChangeDownload <- downloadHandler(
filename = function() { paste('MNCityPopChangeData', '.csv', sep='') },
content = function(file) {
write.csv(CityChange, file)
})
output$twpChangeTable <- renderDataTable({
data <- TwpChange
if (input$County9 != "All"){
data <- data[data$County %in% input$County9,]
}
data
}, options =list(aoColumnDefs = list(list(sClass="alignCenter",aTargets=c(list("_all")) )), pageLength=10))
output$twpChangeChart <- renderChart({
var <- reactive({input$County10})
DF <- subset(TwpChangeLong, County %in% var())
names(DF) = gsub("\\.", "", names(DF))
n5 <- nPlot(PercentChange ~ Year, data = DF, group = "Name", type = 'lineChart')
n5$addParams(dom = 'twpChangeChart')
n5$yAxis(axisLabel = "Percent Change from Previous Decade", width = 62)
n5$xAxis(axisLabel = "Year")
n5$xAxis(tickValues=c(1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010))
# n3$chart(forceY = c(-100, 100))
return(n5)
})
output$twpChangeDownload <- downloadHandler(
filename = function() { paste('MNTownshipPopChangeData', '.csv', sep='') },
content = function(file) {
write.csv(TwpChange, file)
})
data(movies, package = "ggplot2")
minx <- min(movies$rating)
maxx <- max(movies$rating)
output$trendPlot <- renderPlotly({
# size of the bins depend on the input 'bins'
size <- (maxx - minx) / input$bins
# a simple histogram of movie ratings
p <- plot_ly(movies, x = rating, autobinx = F, type = "histogram",
xbins = list(start = minx, end = maxx, size = size))
# style the xaxis
layout(p, xaxis = list(title = "Ratings", range = c(minx, maxx), autorange = F,
autotick = F, tick0 = minx, dtick = size))
})
}
There is a lot of unused plots in the ui because I've yet to assemble the dashboard in its entirety, so I apologize for the length of all of this.
I also apologize for any faux pas within this post- I don't use github all that much nor am I computer scientist, so this is a learning experience for me.
Thanks for the help,
Ricky.
Hey guys,
I have stumbled upon a chart that should be pretty straightforward, but apparently isn´t. I am attempting to create a bar chart that is stacked, in both positive and negative direction.
Below is a ggplot chart that kind of works.
dat <- read.table(text = " Division Year OperatingIncome
1 A 2012 11460
2 B 2012 7431
3 C 2012 -8121
4 D 2012 15719
5 E 2012 364
6 A 2011 12211
7 B 2011 6290
8 C 2011 -2657
9 D 2011 14657
10 E 2011 1257
11 A 2010 12895
12 B 2010 5381
13 C 2010 -2408
14 D 2010 11849
15 E 2010 517",header = TRUE,sep = "",row.names = 1)
dat1 <- subset(dat,OperatingIncome >= 0)
dat2 <- subset(dat,OperatingIncome < 0)
p<-ggplot() +
geom_bar(data = dat1, aes(x=Year, y=OperatingIncome, fill=Division),stat = "identity") +
geom_bar(data = dat2, aes(x=Year, y=OperatingIncome, fill=Division),stat = "identity")
pThe ggplotly routine screws up the layers...
ggplotly(p)...is there any idea on how to either convert the ggplot chart via ggplotly or even better how to create it from scratch via plot_ly() ??
Thanks a lot in advance.
The sheer fact of including plotly graphs in the R notebook creates a bug when downloading an .html version of the notebook. The downloaded html file cannot be scrolled. It only displays the very top of the notebook.
I have tried with various functions, including ggplotly.
Is there any way to set a spacing between axis labels and axis ticks labels other than changing margins?
Example plot:
plotly::plot_ly(mtcars, x = ~ wt, y = ~ paste0(hp, '0000'))
As in the example plot above the title of the y axis is overlapping tick labels of this axis. Is there a way to set a spacing between them?
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