In RS-eco/rISIMIP: R Package for processing ISIMIP data in R

knitr::opts_chunk$set(echo = FALSE, fig.width=8, fig.height=8, warning=FALSE, comment=NA, message=FALSE, eval=F)

Data Set-Up

Load rISIMIP package

library(rISIMIP)

Data Pre-processing

GDP data

We read GDP data for 1995, 2050, 2080

# Remove all items except filedir
rm(list=ls())

# Specify path of file directory
filedir <- "/media/matt/Data/Documents/Wissenschaft/Data/"

# Time frames
timeframes <- c("1995", "2050", "2080")

library(raster)
gdp_data <- stack(mapply(FUN=function(x,y){calc(stack(readISIMIP(path=filedir, type="gdp", scenario="rcp26soc", startyear=x, endyear=y)), mean)}, 
                         c(1980, 2036, 2066), c(2009, 2065, 2095)))
names(gdp_data) <- timeframes
gdp_data <- as.data.frame(rasterToPoints(gdp_data))
colnames(gdp_data) <- c("x", "y", "1995", "2050", "2080")

Plot data

Geographic Map

library(dplyr)
# Set 0 values to NA
gdp_data[gdp_data == 0] <- NA

# Load outline data
library(ggplot2)
data(outline, package="ggmap2")

# Turn into long format and plot
gdp_data %>% tidyr::gather(year, gdp, -c(x,y)) %>%
  mutate(year = factor(year, labels=c("1995", "2050", "2080"))) %>%
  tidyr::drop_na() %>%
  mutate(gdp = cut(gdp, c(0, 25, 75, 150, 300, 500, 700, 1500, 10000))) %>%
  ggplot() + geom_tile(aes(x=x, y=y, fill=gdp)) + 
  facet_wrap(~ year, ncol=1) + 
  geom_sf(data=outline, fill="transparent", colour="black") + 
  scale_fill_manual(name="GDP", values=c("#00007F", "blue", "#007FFF", 
                                                  "cyan", "#7FFF7F", "yellow", 
                                                  "#FF7F00", "red", "#7F0000"), 
                       na.value="transparent") + 
  theme_bw() + theme(strip.background= element_blank()) + 
  scale_x_continuous(name=expression(paste("Longitude (",degree,")")), expand=c(0.05,0.05),
                     breaks=c(-180, -90, 0, 90, 180)) + 
  scale_y_continuous(name=expression(paste("Latitude (",degree,")")), expand=c(0.05,0.05),
                     breaks=c(-60, -40, -20, 0, 20, 40, 60,80)) + 
  coord_sf(xlim=c(-180,180), ylim=c(-60,84))

Change map

# Calculate change in population
delta_gdp <- gdp_data %>% 
  mutate_at(vars(`2050`:`2080`), funs(. - `1995`)) %>% 
  dplyr::select(-matches("1995")) %>% 
  tidyr::gather(year, gdp, -c(x,y))

# Set 0 values to NA
delta_gdp[delta_gdp == 0] <- NA

# Define Year label
delta_gdp$year <- factor(delta_gdp$year, labels=c("2050", "2080"))

# Drop NAs
delta_gdp<- tidyr::drop_na(delta_gdp)

# Turn gdp into categories
delta_gdp$gdp <- cut(delta_gdp$gdp, 
                         c(-2000, -1000, -500, 0, 500, 1000, 2000, 5000, 10000))

# Plot change in gdp
ggplot() +
  geom_tile(data=delta_gdp, aes(x=x, y=y, fill=gdp)) + 
  facet_wrap(~ year, ncol=1) + 
  geom_sf(data=outline, fill="transparent", colour="black") + 
  scale_fill_manual(name="GDP change",
                    values=c("#00007F", "blue", "#007FFF", 
                             "cyan", "#7FFF7F", "yellow", 
                             "#FF7F00", "red", "#7F0000"), 
                       na.value="transparent") + 
  theme_bw() + theme(strip.background= element_blank()) + 
  scale_x_continuous(name=expression(paste("Longitude (",degree,")")), expand=c(0.05,0.05),
                     breaks=c(-180, -90, 0, 90, 180)) + 
  scale_y_continuous(name=expression(paste("Latitude (",degree,")")), expand=c(0.05,0.05),
                     breaks=c(-60, -40, -20, 0, 20, 40, 60,80)) + 
  coord_sf(xlim=c(-180,180), ylim=c(-60,84))

GDP time series

# Get data 
gdp_data <- rISIMIP::readISIMIP(path=filedir, type="gdp", scenario="rcp26soc",
                                startyear=2006, endyear=2099)

# Calculate total gdp for every year
gdp_data <- data.frame(gdp=raster::cellStats(gdp_data, stat="sum", na.rm=TRUE),
                       year=c(2006:2099))

# Plot total population over time
ggplot(data = gdp_data, aes(x = year, y = gdp)) + 
  labs(x= "Year", y="GDP") + 
  geom_line() + theme_classic()