In RS-eco/bdc: Bavarian Data Cube

knitr::opts_chunk$set(
  collapse = T, comment = NA, warning=F, message=F, eval=T, echo=T, error=F, 
  comment = "#>", fig.path="../figures/"
)

Create time series plot

# Load bdc & ggplot2 package
library(bdc); library(dplyr); library(ggplot2); library(patchwork)

# Load climate data from BDC package
data(dwd_annual_ts_bav, package="bdc")
head(dwd_annual_ts_bav)

# Cold-temperature related variables
p1 <- dwd_annual_ts_bav %>% filter(Jahr >= 1980) %>%
  ggplot(aes(x=Jahr)) + geom_line(aes(y=air_temperature_mean))
p2 <- dwd_annual_ts_bav %>% filter(Jahr >= 1980) %>%
  ggplot(aes(x=Jahr)) + geom_bar(aes(y=frost_days), stat="identity")
p3 <- dwd_annual_ts_bav %>% filter(Jahr >= 1980) %>%
  ggplot(aes(x=Jahr)) + geom_bar(aes(y=ice_days), stat="identity")
p1 + p2 + p3 + plot_layout(ncol=1) & 
  theme_bw() & scale_x_continuous(limits=c(1979.25,2020.75), expand=c(0,0),
                                  breaks=c(1980, 1990,2000,2010,2020))

# Warm-temperature related variables
p4 <- dwd_annual_ts_bav %>% filter(Jahr >= 1980) %>%
  ggplot(aes(x=Jahr)) + geom_bar(aes(y=hot_days), stat="identity")
p5 <- dwd_annual_ts_bav %>% filter(Jahr >= 1980) %>%
  ggplot(aes(x=Jahr)) + geom_line(aes(y=tropical_nights_tminGE20))
p6 <- dwd_annual_ts_bav %>% filter(Jahr >= 1980) %>%
  ggplot() + geom_bar(aes(x=Jahr, y=summer_days), stat="identity")
p7 <- dwd_annual_ts_bav %>% filter(Jahr >= 1980) %>%
  ggplot() + geom_line(aes(x=Jahr, y=sunshine_duration))
p1 + p4 + p5 + p6 + p7 + plot_layout(ncol=1) & 
  theme_bw() & scale_x_continuous(limits=c(1979.25,2020.75), expand=c(0,0),
                                  breaks=c(1980, 1990,2000,2010,2020))

# Precipitation related variables
p8 <- dwd_annual_ts_bav %>% filter(Jahr >= 1980) %>%
  ggplot() + geom_line(aes(x=Jahr, y=precipGE10mm_days))
p9 <- dwd_annual_ts_bav %>% filter(Jahr >= 1980) %>%
  ggplot() + geom_line(aes(x=Jahr, y=precipGE20mm_days))
p10 <- dwd_annual_ts_bav %>% filter(Jahr >= 1980) %>%
  ggplot() + geom_line(aes(x=Jahr, y=precipitation))
p8 + p9 + p10 + plot_layout(ncol=1) & 
  theme_bw() & scale_x_continuous(limits=c(1979.25,2020.75), expand=c(0,0),
                                  breaks=c(1980, 1990,2000,2010,2020))

Create correlation matrix

# GGally, to assess the distribution and correlation of variables 
library(GGally)

# Check correlations (as scatterplots), distribution and print correlation coefficient 
ggpairs(dwd_annual_ts_bav %>% filter(Jahr >= 1980) %>% select(-Jahr)) 

# Check correlation between variables
#cor(dwd_annual_ts_bav %>% filter(Jahr >= 1980) %>% select(-Jahr)) 

# Nice visualization of correlations
ggcorr(dwd_annual_ts_bav %>% filter(Jahr >= 1980) %>% select(-Jahr), 
       nbreaks = 9,method = c("everything", "pearson"), label=T)
rm(list=ls()); invisible(gc())