Skip to content

Instantly share code, notes, and snippets.

Embed
What would you like to do?
output$bubble2<-renderGvis({
if (values$starting) return(NULL)
Sys.sleep(0.3)
selected<-input$selected
isStates<-T
isReal <- grepl('real',selected)
if (!grepl('growth',selected)) {
output$titleB<-renderText('Support real or nominal GDP growth only')
return(NULL)}
else {output$titleB<-renderText(paste0('Motion Chart of ',input$selected))}
decisionToken = ''
if (isReal & isStates) decisionToken <- 'rs'
if (isReal & !isStates) decisionToken <- 'ru'
if (!isReal & isStates) decisionToken <- 'ns'
if (!isReal & !isStates) decisionToken <- 'nu'
x<-c('r','n')
names(x)<-x
y<-c('states','regions')
names(y)<-c('s','u')
decisionVec <-strsplit(decisionToken,split='')[[1]]
dataName <- paste0(x[decisionVec[1]],'GDP_sector_',y[decisionVec[2]],'_growth')
motion_data <- get(dataName,inherits=T)
coordX<-sectors[input$coordX]
coordY<-sectors[input$coordY]
X<-motion_data[[coordX]]
Y<-motion_data[[coordY]]
Aloc<-input$stateA
Bloc<-input$stateB
locPair<-c(Aloc,Bloc)
if(length(unique(locPair))!=2) return(NULL)
X<-filter(X,GeoName %in% locPair)
Y<-filter(Y,GeoName %in% locPair)
X1<-melt(X,id.var='GeoName')
Y1<-melt(Y,id.var='GeoName')
names(X1)<-c('GeoName','Year',input$coordX)
names(Y1)<-c('GeoName','Year',input$coordY)
Z<-inner_join(X1,Y1,by=c('GeoName','Year'))
Z$Year<-type.convert(sub("X",'',Z$Year))
Z$Color<-factor(Z$GeoName)
Z$GeoName<-factor(Z$GeoName)
return(gvisMotionChart(Z,idvar='GeoName',timevar='Year', colorvar='Color',
xvar=input$coordX,yvar=input$coordY,options=list(width='760px',height='300px')))
})
library(shiny)
library(shinydashboard)
library(DT)
library(reshape2)
library(googleVis)
library(ggplot2)
if (!require(bubbles)) install.packages('bubbles')
library(bubbles)
if (!require(fields)) install.packages('fields')
library(fields)
if (!require(corrplot)) install.packages('corrplot')
library(corrplot)
library(dplyr)
suppressPackageStartupMessages(library(googleVis))
# convert matrix to dataframe
load('./data.RData')
load('./sectorData.RData')
load('./sectorGrowthData.RData')
load('./byYear.RData')
data = list()
data$'personal income per capita' <- income.perCapita
data$'population' <- population
noRegions<-c('personal income per capita','population')
usRegions<-unique(GDP_regions[['per capita real GDP']]$GeoName)
usRegion2Abbr<-c('US','NE','ME','GL','PL','SE','SW','RM','FW')
names(usRegion2Abbr)<-usRegions
heatXChoices<-c('states','US regions','sectors')
heatYChoices<-c('years','sectors')
heatRelative<-c('absolute','relative')
support_byYear<-c("nominal GDP","real GDP","nominal GDP growth","real GDP growth")
rela_abso<-"Absolute or Relative:"
ts_cs<-"Correlation Type"
US_GDP_Visual<-'US GDP Data Visualization'
noSectors<-c("personal income per capita","population","per capita real GDP growth","per capita real GDP")
sectorsAbbreviations=c("Agriculture", "Mining", "Util", "Construt.",
"Manufac.", "Wholesale", "Retail", "Trans", "IT",
"Finance", "R estate", "Profess.", "Manage", "waste",
"Edu.", "Health care", "entertain.", "food & hotel",
"Other Xgov.", "Govern.")
names(sectorsAbbreviations) <- sectors
names(sectors) <- sectorsAbbreviations
SectorAbbre<-function(x) { return(sectorsAbbreviations[x]) }
SectorAbbre<-Vectorize(SectorAbbre)
choice <- c(c('personal income per capita','population'),names(GDP_states))
for (key in names(GDP_states)) { data[[key]]<-GDP_states[[key]] }
states <- postCodes$US.State.
ConvertNum<-function(num) {
if (!is.numeric(num)) {return(num)}
if (abs(num)<=1) {return(paste0(floor(num*1000)/10,'%'))}
else if (abs(num)<1000 & abs(num)>1) {return(as.character(num))}
else if (abs(num) < 1e5) {
x = floor(num/1e2)/1e1
return(paste0(x,'K'))}
else if (abs(num) < 1e8) {
x = floor(num/1e4)/1e2
return(paste0(x,'M'))}
else if (abs(num) < 1e10) {
x = floor(num/1e7)/1e2
return(paste0(x,'B')) }
else {
x = floor(num/1e10)/1e2
return(paste0(x,'T')) }
}
ConvertNum<-Vectorize(ConvertNum)
FindStateAbbreviation<-function(name) {
z<-filter(postCodes, US.State.==name)
if (nrow(z)<1) {return(name)}
else {return(z$Abbreviation.)}
}
FindStateAbbreviation<-Vectorize(FindStateAbbreviation)
FindRegionAbbreviation<-function(name) { usRegion2Abbr[[name]] }
FindRegionAbbreviation<-Vectorize(FindRegionAbbreviation)
NormalizeDFAlongRows<-function(DF) {
keys<-rownames(DF)
names<-names(DF)
is.numeric.col<-sapply(DF,is.numeric)
numericCols<-names[is.numeric.col]
DF2<-DF[,numericCols]
for (key in keys) DF2[key,]<-DF2[key,]/(sum(DF2[key,],na.rm=T)+1e-10)
DF[,numericCols]<-DF2[,numericCols]
return(DF)
}
NormalizeDFAlongColumns<-function(DF) {
names<-names(DF)
is.numeric.col<-sapply(DF,is.numeric)
for (x in names[is.numeric.col]) DF[[x]]<-DF[[x]]/(sum(DF[[x]],na.rm=T)+1e-10)
DF
}
TreatAsMissing<-function(DF,bound=1e16){
names<-names(DF)
is.numeric.col<-sapply(DF,is.numeric)
for (x in names[is.numeric.col]) {
bad <- abs(DF[[x]])>bound
DF[[x]][bad] <- NA
}
return(DF)
}
StackDFs<-function(myList,newColName) {
myNames<-names(myList)
DF<-as.data.frame(matrix(nrow=0,ncol=length(myNames)))
# a list of data frame of the same dim (1,n)
for (key in myNames) {
x <- myList[[key]]
DF<-rbind(DF,x)
}
DF[[1]]<-myNames
S <-names(DF)
S[1] <-newColName
return(DF)
}
output$heat<-renderPlot({
if (values$starting) return(NULL)
Sys.sleep(0.6)
heatX<-input$heatX
if (heatX=='None') {
output$title4<-renderText("Heat Map X variable cannot be None!")
return(NULL)
} else if (heatX=='sectors' & input$heatY!='sectors') {return(NULL)}
heatY<-input$heatY
correlational<-(heatX==heatY)
selected<-input$selected
isReal<-grepl('real',selected)
isGrowth<-grepl('growth',selected)
isTimeSeries<-grepl('time',input$heatR)
if (!(selected %in% support_byYear)) {
output$title4<-renderText("Only support (real or nominal) GDP (.|growth)")
return(NULL)}
if (correlational & !isGrowth) {
output$title4<-renderText("Only support (real or nominal) GDP growth")
return(NULL)
}
# choose the right data to use
T1<-ifelse(isReal,'rGDP','nGDP')
T2<-ifelse(grepl('states',heatX),'_states','_regions')
T3<-ifelse(isGrowth,'_growth','')
T4<-'_byYear'
if (heatY=='sectors' & !correlational) {
varName <- paste0(T1,T2,T3,T4)
DF<-(get(varName)[[getYear()]])
}
else if (input$sector=='all' & !correlational) {
if (heatX=='states') DF <- getDataStates()
else { DF <- getDataRegions() }
} else if (input$sector!='all' & !correlational) {
if (heatX=='states' & grepl('real',selected)) {myList<-getSectorRGDPSectorDataStates()}
else if (heatX=='states' & !isReal) {myList<-getSectorNGDPSectorDataStates()}
else if (heatX=='regions' & isReal) {myList<-getSectorRGDPSectorDataRegions()}
else {myList<-getSectorNGDPSectorDataRegions()}
if (!isGrowth) {DF<-myList[['original']]}
else { DF<-myList[['growth']]}
}
if (correlational) {
if (isTimeSeries) {
if (heatX=='states'){
if (input$sector=='all') { DF<-getDataStates() }
else {if(isReal) {DF<-getSectorRGDPSectorDataStates()[['growth']]}
else {DF<-getSectorNGDPSectorDataStates()[['growth']]}
}
} else if (heatX == 'US regions') {
if (input$sector=='all') { DF<-getDataRegions()
} else {
if (isReal) { DF<-getSectorRGDPSectorDataRegions()[['growth']] }
else { DF<-getSectorNGDPSectorDataRegions()[['growth']] }
DF<-filter(DF,!grepl('United States',GeoName)) }
} else if (heatX=='sectors') {
if (isReal) { X<-rGDP_sector_regions_growth}
else { X<- nGDP_sector_regions_growth }
for (key in names(X)) { X[[key]]<-filter(X[[key]],GeoName=='United States')}
DF<-StackDFs(X,'Sectors')
} else {return(NULL)}
} else {
# stub, not implemented yet for cross sessional correlations
if (heatX=='states'){
} else if (heatX=='regions') {
} else if (heatX=='sectors') {
}
}
if (!exists('DF')) {
return(NULL)}
myScale = 1.0
if (heatX=='states') {
rownames(DF)<-FindStateAbbreviation(DF[,1])
myScale = 0.7
}
else if (heatX =='US regions') {rownames(DF)<-FindRegionAbbreviation((DF[,1]))}
else if (heatX == 'sectors') {rownames(DF)<-sectorsAbbreviations}
DF<-DF[,-1]
DF<-t(DF)
corMatrix<-cov2cor(var(DF,na.rm=T))
return(corrplot(corMatrix,method='circle',tl.cex=myScale,type='lower'))
}
DF<-TreatAsMissing(DF)
if (input$heatR=='relative') {
if (input$heatY=='sectors') DF<-NormalizeDFAlongRows(DF)
else DF<-NormalizeDFAlongColumns(DF)
}
DF<-melt(DF,id.var='GeoName')
if (heatY=='sectors') DF<-transmute(DF,GeoName=factor(GeoName),Sector=factor(variable),value)
else DF<-transmute(DF,GeoName=factor(GeoName),years=factor(variable),value)
if (heatX=='US regions') DF<-filter(DF,GeoName!='United States')
if (heatY=='sectors') g <- ggplot(DF, aes(x=GeoName, y=SectorAbbre(Sector),fill=value))
else g <- ggplot(DF,aes(x=GeoName,y=sub('X','',years),fill=value))
g<- g+ geom_tile() + scale_fill_gradientn(colors=c('black','dark red','red','orange','yellow','white'))
g<- g + xlab(switch(paste0(heatX,'W'),statesW='States',regionsW='US regions',sectorsW='Sectors'))
g<- g + ylab(switch(paste0(heatY,'W'),statesW='States',yearsW='Years',regionsW='US regions',sectorsW='Sectors'))
g<- g+ theme(axis.text.x=element_text(angle = 90, vjust = 0.5))
return(g)
})
# show statistics using infoBox
output$maxBox <- renderInfoBox({
if (values$starting) { Xyear<-'X2007'}
else { Xyear = getYear() }
DF<-getDataFrame()
DF<-TreatAsMissing(DF,bound=ifelse(grepl('growth',input$selected),5,1e16))
outData <- DF[,Xyear]
max_value <- max(outData,na.rm=T)
max_state <- FindStateAbbreviation(DF$GeoName[outData == max_value])
infoBox(max_state, ConvertNum(max_value), icon = icon("chevron-up"),color='blue')
})
output$minBox <- renderInfoBox({
if (values$starting) { Xyear='X2007'}
else { Xyear = getYear() }
DF<-getDataFrame()
DF<-TreatAsMissing(DF,bound=ifelse(grepl('growth',input$selected),5,1e16))
outData <- DF[,Xyear]
min_value <- min(outData,na.rm=T)
min_state <- FindStateAbbreviation(DF$GeoName[outData == min_value])
infoBox(min_state, ConvertNum(min_value), icon = icon("chevron-down"),color='red')
})
output$medBox <- renderInfoBox({
if (values$starting) { Xyear='X2007'}
else { Xyear = getYear() }
DF<-getDataFrame()
DF<-TreatAsMissing(DF,bound=ifelse(grepl('growth',input$selected),5,1e16))
outData <- DF[,Xyear]
infoBox(paste("MEDIAN", input$selected),
ConvertNum(median(outData,na.rm=T)),
icon = icon("calculator"),color='green')
})
values<-reactiveValues(starting=T)
session$onFlushed(function() { values$starting <- F})
# observe session
observe({
if (input$selected %in% noSectors)
updateSelectizeInput(session,'sector',selected='all')
})
observe({
Sys.sleep(0.1)
selected <- input$selected
updateSliderInput(session,'slider1',value=startYear[[selected]],min=startYear[[selected]],max=endYear[[selected]])
})
observe({
nonCorr<-input$heatX != input$heatY
Sys.sleep(0.1)
if (grepl('growth',input$selected) & nonCorr)
updateSelectizeInput(session,inputId='heatR',choices=c('absolute'))
else if (!grepl('growth',input$selected) & nonCorr) {updateSelectizeInput(session,inputId='heatR',choices=heatRelative)}
else {updateSelectizeInput(session,inputId='heatR',choices=c('time series'))}
})
observe({
Sys.sleep(0.1)
isInside <- input$heatY %in% c(heatYChoices,input$heatX)
if (input$heatX!='sectors' & isInside)
{ updateSelectizeInput(session,'heatY',choices=unique(c(heatYChoices,input$heatX)),selected=input$heatY) }
else if (input$heatX != 'sectors' & !isInside) {
updateSelectizeInput(session,'heatY',choices=unique(c(heatYChoices,input$heatX)))}
else { updateSelectizeInput(session,'heatY',choices=c('sectors')) }
})
observe({
Sys.sleep(0.1)
if (input$heatX==input$heatY)
updateSelectizeInput(session,'heatR',label=ts_cs,choices=c('time series'))#,'cross sectional'))
else if (!grepl('growth',input$selected)) {
originalOrElse<-ifelse(input$heatR%in%heatRelative,input$heatR,heatRelative[1])
updateSelectizeInput(session,'heatR',label=rela_abso,choices=heatRelative,selected=originalOrElse)}
})
observe({ updateSelectizeInput(session,'coordY',label='Y coordinate',setdiff(sectorsAbbreviations,input$coordX),selected='Finance') })
observe({ updateSelectizeInput(session,'stateB',label='second state',setdiff(states,input$stateA),selected='New York') })
observe({
Sys.sleep(0.1)
if (input$heatX==input$heatY)
{output$title4<-renderText(paste(input$heatX,'vs',input$heatY, "Correlation"))}
else if (!('years' %in% c(input$heatX,input$heatY)) & input$heatR!='time series')
{ output$title4<-renderText(paste0("Now it is year ",input$slider1)) }
else { output$title4<-renderText(paste(input$heatX,'vs Years 2D HEAT MAP'))}
})
getYear<-reactive({
if (values$starting) return('X2015')
year<-as.numeric(input$slider1)
if (startYear[[input$selected]]>year) {year<-startYear[[input$selected]]}
if (endYear[[input$selected]]<year) {year<-endYear[[input$selected]]}
return(paste0('X',year))}
)
getDataStates<-reactive({
if (!(input$selected %in% names(data))) return(NULL)
return(data[[input$selected]])
})
getDataRegions<-reactive({
if (!(input$selected %in% names(GDP_regions))) return(NULL)
return(GDP_regions[[input$selected]])
})
getSectorRGDPSectorDataStates<-reactive({
if (!(input$sector %in% names(rGDP_sector_states))) return(NULL)
return(list(original=rGDP_sector_states[[input$sector]],
growth=rGDP_sector_states_growth[[input$sector]]))
})
getSectorNGDPSectorDataStates<-reactive({
if (!(input$sector %in% names(nGDP_sector_states))) return(NULL)
return(list(original=nGDP_sector_states[[input$sector]],
growth=nGDP_sector_states_growth[[input$sector]]))
})
getSectorRGDPSectorDataRegions<-reactive({
if (!(input$sector %in% names(rGDP_sector_regions))) return(NULL)
return(list(original=rGDP_sector_regions[[input$sector]],
growth=rGDP_sector_regions_growth[[input$sector]]))
})
getSectorNGDPSectorDataRegions<-reactive({
if (!(input$sector %in% names(nGDP_sector_regions))) return(NULL)
return(list(original=nGDP_sector_regions[[input$sector]],
growth=nGDP_sector_regions_growth[[input$sector]]))
})
getDataFrame<-reactive({
if (input$selected %in% noRegions | input$selected %in% noSectors | input$sector=='all')
DF<-getDataStates()
else { if (grepl('nominal',input$selected))
myList<-getSectorNGDPSectorDataStates()
else if (grepl('real',input$selected))
myList<-getSectorRGDPSectorDataStates()
if (is.null(myList)) return(NULL)
if (input$sector!='all' & grepl('growth',input$selected))
DF<-myList[['growth']]
else DF<-myList[['original']] }
DF
})
output$title1<-renderText(US_GDP_Visual)
output$title2<-renderText('US GDP States/Regions Time Series Visualization')
output$projection<-renderText("<font size = '4' color = #FFFFFF>Which sectors to project to:</font>")
output$whichStates<-renderText("<font size = '4' color= #FFFFFF>Which States to use?</font>")
output$linePlot <- renderGvis({
if (values$starting) return(NULL)
Sys.sleep(0.3)
output$warning <- renderText(paste('No data to display for',input$selected,'with the current state/region choices'))
selected <- input$selected
if (input$state1=='None' & input$region1 == 'None') return(NULL)
myStates = c()
myRegions = c()
selected <- input$selected
if (input$sector=='all') {
if (input$state1=='None') {myData<-getDataRegions()}
else {myData<-getDataStates()}
} else
{
if (input$state1=='None') {
if (input$region1=='None') return(NULL)
if (grepl('real',selected)) myList<-getSectorRGDPSectorDataRegions()
else myList<-getSectorNGDPSectorDataRegions() }
else {
if (grepl('real',selected)) myList<-getSectorRGDPSectorDataStates()
else myList<-getSectorNGDPSectorDataStates()
}
growToken <- ifelse(grepl('growth',selected),'growth','original')
myData <- myList[[growToken]]
}
if (is.null(myData)) return(NULL)
year_e <- endYear[[selected]]
Xyear <- getYear()
year_s <- max(c(strtoi(sub('X','',Xyear)),startYear[[selected]]))
DF<-myData[,c('GeoName',paste0('X',seq(year_s,year_e)))]
if (input$state1 != 'None' & !is.null(input$state2)) {myStates=unique(c(input$state1,input$state2))}
else if (input$state1 != 'None') {myStates=input$state1}
else if (input$region1 != 'None' & !is.null(input$region2)) {myRegions=unique(c(input$region1,input$region2))}
else if (input$region1 != 'None') {myRegions=input$region1}
else return(NULL)
if(length(myStates)>0){myColumns=myStates}
else {myColumns=myRegions}
P<-DF %>% filter(GeoName %in% myColumns)
myColumns <- P[,'GeoName']
Y<-data.frame(t(P[,-1]))
if (length(myStates)>0) names(Y) <- FindStateAbbreviation(myColumns)
else names(Y)<-FindRegionAbbreviation(myColumns)
Y['year']<-as.integer(seq(year_s,year_e))
output$warning<-renderText(paste(myColumns,collapge='',input$selected,'Time Series:'))
yTitle <- ifelse(grepl('growth',selected),paste(selected,'percentage'),selected)
if (grepl('growth',selected)) myOption <- list(vAxis="{format:'#,###%'}")
else myOption <- list()
gvisLineChart(Y,xvar='year',yvar=names(Y[-ncol(Y)]),options=myOption)
})
output$bubble <- renderBubbles({
if (!(input$selected %in% names(data))) return(NULL)
Xyear <- getYear()
DF<-getDataFrame()
DF <-DF[,c('GeoName',Xyear)]
if (!is.numeric(DF[[Xyear]])) DF[[Xyear]]<-type.convert(DF[[Xyear]])
DF[is.na(DF)]<-0.0
DF<-DF[order(DF[[Xyear]],decreasing=T),]
DF<-head(DF,input$slider2)
minValue <- min(DF[[Xyear]],na.rm=T)
DF[is.na(DF)]<-minValue-0.1
if (minValue<0) DF[[Xyear]] <- DF[[Xyear]] - 1.2*min(DF[[Xyear]])
colors1<-two.colors(input$slider2,start='#0B2161',end='#F5A9F2',middle='#01A9DB',alpha=1.0)
output$title3<-renderText(paste("Now it is year ",sub("X",'',Xyear)))
bubbles(sqrt(DF[[Xyear]]), DF$GeoName, key = DF$GeoName, color=colors1,textColor='#FFFFFF')
})
shinyUI(dashboardPage(
dashboardHeader(title = "US GDP Data App"),
dashboardSidebar(
sidebarMenu(
menuItem("US GDP Map", tabName = "map1", icon = icon("globe"),badgeLabel='Cross Sectional',badgeColor='light-blue'),
menuItem("US GDP Plot", tabName = "plot1", icon = icon("globe"),badgeLabel='Time Series',badgeColor='blue'),
menuItem("Bubbles", tabName = 'bubble', icon = icon('cloud'),badgeLabel='Top!',badgeColor='green'),
menuItem("Bubble Chart", tabName = 'bubble2', icon = icon('cloud'),badgeLabel='Fun!',badgeColor='orange'),
menuItem("2D Heatmap", tabName = 'heat', icon = icon('fire'),badgeLabel='Hot!!',badgeColor='red')
),
selectizeInput("selected",
"Select Data to Display",
choice,selected="real GDP growth"),
conditionalPanel(
condition = "input.selected!=\"personal income per capita\" && input.selected!=\"population\" && input.selected!=\"per capita real GDP growth\" && input.selected!=\"per capita real GDP\"",
selectizeInput("sector", "Select Sector to Display", c('all', sectors))),
sliderInput("slider1", "Slider input Year:", min=2000, max=2015, value=2000,animate=T)
),
dashboardBody(
tabItems(
tabItem(tabName = "map1",
fluidRow(box(htmlOutput("title1"),width=12,background='light-blue')),
fluidRow(infoBoxOutput("maxBox"),
infoBoxOutput("medBox"),
infoBoxOutput("minBox")),
fluidRow(htmlOutput("map"), title='US GDP Colored Map')),
tabItem(tabName = "plot1",
fluidRow(box(htmlOutput("title2"),width=12,background='orange')),
fluidRow(box(htmlOutput("warning"),width=12,background='red')),
fluidRow(box(htmlOutput('linePlot'), height=225,width = 12,background='teal')),
fluidRow(box(conditionalPanel(
condition = "1 == 1",
selectizeInput("state1","Select a state name",c('None',states)),height=50,width=6),background='teal'),
box(conditionalPanel(
condition = "input.state1!=\"None\"",
selectizeInput("state2", "Select Another state name", states),height=50,width=6),background='teal')),
fluidRow(box(conditionalPanel(
condition = "input.state1=='None'",
selectizeInput("region1","Select a US region",c('None',usRegions)),height=50,width=6),background='teal'),
box(conditionalPanel(
condition = "input.region1!=\"None\"&& input.state1=='None'",
selectizeInput("region2", "Select Another US region", usRegions), height=50,width=6),background='teal'))),
tabItem(tabName = "bubble",
fluidRow(box(htmlOutput("title3"),width=12,background='navy')),
fluidRow(box(bubblesOutput("bubble",height='550px',width = '490px'), height=550,width = 12,background='olive')),
fluidRow(box(sliderInput("slider2", "Slider input Top n:", min=3, max=15, value=10)))),
tabItem(tabName = "bubble2",
fluidRow(box(htmlOutput("titleB"),width=12,background='navy')),
fluidRow(box(htmlOutput("projection"),height=80,width=4,background='green'),
box(selectizeInput("coordX", "X coordinate", choices=as.vector(sectorsAbbreviations),selected='IT'), height=80,width=4,background='green'),
box(selectizeInput("coordY", "Y coordinate", choices=as.vector(sectorsAbbreviations),selected='Mining'), height=80,width=4,background='green')),
fluidRow(box(htmlOutput("whichStates"),height=80,width=4,background='light-blue'),
box(selectizeInput("stateA", "first state", choices=states,selected='California'), height=80,width=4,background='light-blue'),
box(selectizeInput("stateB", "second state", choices=states,selected='New York'), height=80,width=4,background='light-blue')),
fluidRow(box(htmlOutput("bubble2"), height=325,width = 8,background='blue'))),
tabItem(tabName = "heat",
fluidRow(box(htmlOutput("title4"),width=12,background='red')),
fluidRow(box(plotOutput("heat"),height=420,width = 12,background='navy')),
fluidRow(box(selectizeInput('heatX',"Heat map X variable:",heatXChoices),height=100,width=4,background='navy'),
box(selectizeInput('heatY',"Heat map Y variable:",heatYChoices),height=100,width=4,background='navy'),
box(selectizeInput('heatR',rela_abso,heatRelative),height=100,width=4,background='navy'))
)
))
))
output$map <- renderGvis({
if (values$starting) return(NULL)
Sys.sleep(0.3)
DF<-getDataFrame()
DF1<-DF[,getYear()]
DF1<-TreatAsMissing(DF1,bound=ifelse(grepl('growth',input$selected),5,1e16))
maxV<-max(DF1)
minV<-min(DF1)
medV<-median(DF1)
colorStr=paste("{values:[",minV,",",medV,",",maxV,"],")
gvisGeoChart(DF, "GeoName", getYear(),
options=list(region="US", displayMode="regions",
resolution="provinces", width='1200px',height='700px',
backgroundColor='#F6E3CE',colorAxis=paste0(colorStr,"colors:['red','#FBEFEF','#0404B4']}")))
})
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
You can’t perform that action at this time.