ANLY 512 - Lab 2

Identify what information interests you about climate change.

I am interested in researching the global climate change through years and foreseeing the future trends.

Find and collect the data necessary to create your data exploration plan. Organize and summarize the collected data.

Data is retrieved from World Bank dataset using “rWBclimate” package.

Design and create the most appropriate visualizations to explore the data and present that information.

Line charts comparing the average temperature among different scenarios throughout the year, as well as map charts using color scale showing the temperature trend, are developed.

Finally organize the layout of those visualizations in a way that shows your path of data exploration.

Four mapped charts showing the average temerature through year 2020, 2040, 2060, and 2080 are produced to showcase the trend of global warming in Europe, and serve as the final dashboard of this lab.

Develop three questions or ideas about climate change from your visualizations.

The regional difference/tolerance to global warming. Some countries/regions will experience tougher challenge.
Significant differences among the scenarios of past, a2, and b1.
To forecast the economic influence by climate change based on historical examples, and how people can best prepare for it.

Dashboard

A caption

Package Preparation

library(rWBclimate)
library(ggplot2)
library(devtools)
library(shiny)
library(shinydashboard)

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Data Anaysis

gbr.dat.t <- get_ensemble_temp("GBR", "annualavg", 1900, 2100)

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## Loading required package: rjson
### Subset to just the median percentile
gbr.dat.t <- subset(gbr.dat.t, gbr.dat.t$percentile == 50)
gbr.dat.t$fromYear=as.numeric(gbr.dat.t$fromYear)
gbr.dat.t$toYear=as.numeric(gbr.dat.t$toYear)
gbr.dat.t$data=as.numeric(gbr.dat.t$data)
## Plot and note the past is the same for each scenario
ggplot(gbr.dat.t, aes(x=fromYear,y=data,group=scenario,colour=scenario)) +
 geom_point() +
 geom_path() +
 theme_bw() +
 xlab("Year") +
 ylab("Annual Average Temperature in 20 year increments")

gbr.dat.p <- get_ensemble_precip("GBR", "annualavg", 1900, 2100)

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gbr.dat.p <- subset(gbr.dat.p, gbr.dat.p$percentile == 50)
gbr.dat.p$fromYear=as.numeric(gbr.dat.p$fromYear)
gbr.dat.p$toYear=as.numeric(gbr.dat.p$toYear)
gbr.dat.p$data=as.numeric(gbr.dat.p$data)
ggplot(gbr.dat.p, aes(x = fromYear, y = data, group = scenario, colour = scenario)) +
    geom_point() + geom_path() + theme_bw() + xlab("Year") + ylab("Annual Average precipitation in mm")

### Let's compare historical data with our backcasted data

gbr.modelpast <- subset(gbr.dat.t, gbr.dat.t$scenario == "past")
gbr.historical <- get_historical_temp("GBR", "year")

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### Plot create historical plot
hist.plot <- ggplot(gbr.historical, aes(x = year, y = data)) + geom_point() +
    geom_path()

### Create a centroid for the past
gbr.modelpast$centroid <- round((gbr.modelpast$fromYear + gbr.modelpast$toYear)/2)

### Create averages based the same windows used in the model output for
### comparison
win_avg <- function(from, to, df) {
    win <- subset(df, df$year >= from & df$year <= to)

    return(c(mean(win$data), round(mean(c(from, to)))))
}
hist.avg <- matrix(0, ncol = 2, nrow = 0)
for (i in 1:dim(gbr.modelpast)[1]) {
    hist.avg <- rbind(hist.avg, win_avg(gbr.modelpast$fromYear[i], gbr.modelpast$toYear[i],
        gbr.historical))
}
colnames(hist.avg) <- c("data", "centroid")

### Create new dataframe of historical averages and model averages
hist.comp <- rbind(hist.avg, cbind(gbr.modelpast$data, gbr.modelpast$centroid))
hist.comp <- as.data.frame(hist.comp)
hist.comp$Output <- c(rep("Historical", 4), rep("Model", 4))

### overlay the averages with the original raw data plot
hist.plot <- hist.plot + geom_point(data = hist.comp, aes(x = centroid, y = data,
    colour = Output, group = Output, size = 3)) + geom_path(data = hist.comp,
    aes(x = centroid, y = data, colour = Output, group = Output)) + guides(size = FALSE)

hist.plot + xlab("Year") + ylab("Annual average temperature in deg C") + theme_bw()

### Set local path
options(kmlpath = "C:/Users/Yvonne/Desktop/HRU/MS Analytics/ANLY 512-90 Data Visualization/Lab2/Map KML")

# create dataframe with mapping data to plot
eu_basin <- create_map_df(Eur_basin)

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### Get some data
eu_basin_dat <- get_ensemble_temp(Eur_basin, "annualanom", 2080, 2100)

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## Subset data to just one scenario, and one percentile so we have 1 piece
## of information per spatial unit
eu_basin_dat <- subset(eu_basin_dat, eu_basin_dat$scenario == "a2" & eu_basin_dat$percentile ==
    50)

# link map dataframe to climate data

eu_map <- climate_map(eu_basin, eu_basin_dat, return_map = T)
eu_map + scale_fill_continuous("Temperature \n anomaly by 2080", low = "yellow",
    high = "red")

# get all the future time periods
eu_basin_dat <- get_ensemble_temp(Eur_basin, "annualanom", 2000, 2100)

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# subset the data to one observation per spatial unit (basin)
eu_basin_dat <- subset(eu_basin_dat, eu_basin_dat$scenario == "a2" & eu_basin_dat$percentile ==
    50)
### Next we need to create a dataframe that binds each year of data to the
### map dataframe Bind it all together and create master dataframe with an
### identifier for each year.

# get unique years
years <- unique(eu_basin_dat$fromYear)

# create an initial dataframe to join other years to.

master_map <- climate_map(eu_basin, eu_basin_dat[eu_basin_dat$fromYear == years[1],
    ], return_map = F)
master_map$year <- rep(years[1], dim(master_map)[1])

for (i in 2:length(years)) {
    ## Note that return map is False meaning a dataframe is returned not a
    ## ggplot2 map

    tmp_map <- climate_map(eu_basin, eu_basin_dat[eu_basin_dat$fromYear == years[i],
        ], return_map = F)
    tmp_map$year <- rep(years[i], dim(tmp_map)[1])
    master_map <- rbind(master_map, tmp_map)
}

ggplot(master_map, aes(x = long, y = lat, group = group, fill = data)) + geom_polygon() +
    facet_wrap(~year) + scale_fill_continuous("Temperature \n anomaly", low = "yellow",
    high = "red") + theme_bw() + ggtitle(expression(atop("Global Warming in Europe: Temperature anomaly over control period (1961-2009)")))

Build UI / Server structure

ui <- dashboardPage(
  dashboardHeader(),
  dashboardSidebar(),
  dashboardBody()
)
server <- function(input, output) { }


#Dashboard header carrying the title of the dashboard
header <- dashboardHeader(title = "ANLY 512 Lab 2")
#Sidebar content of the dashboard
sidebar <- dashboardSidebar(
  sidebarMenu(
    menuItem("Dashboard", tabName = "dashboard", icon = icon("dashboard")),
    menuItem("Visit-us", icon = icon("send",lib='glyphicon'), 
             href = "https://www.salesforce.com"),
    menuItem("Designed by: Yiwen Zhao")
  )
)

Set up charts structure

frow1 <- fluidRow(
    title = "Global warming in Europe" 
    ,status = "primary"
    ,solidHeader = TRUE 
    ,collapsible = TRUE 
    ,plotOutput("GlobalWarming", height = "600px")
  
)

# combine the two fluid rows to make the body
body <- dashboardBody(frow1)

#completing the ui part with dashboardPage
ui <- dashboardPage(title = 'Global Climate Change', header, sidebar, body, skin='red')

Create the Server functions

# create the server functions for the dashboard  
server <- function(input, output) { 

#creating the plotOutput content
  output$GlobalWarming <- renderPlot({
    ggplot(master_map, aes(x = long, y = lat, group = group, fill = data)) + geom_polygon() +
    facet_wrap(~year) + scale_fill_continuous("Temperature \n anomaly", low = "yellow",
    high = "red") + theme_bw() + ggtitle(expression(atop("Global Warming in Europe: Temperature anomaly over control period (1961-2009)")))
  })

}

Run Dashboard

shinyApp(ui, server)

Shiny applications not supported in static R Markdown documents