makeDailyPlot.R

# rm(list = ls())

# load packages
library(lubridate)
library(tidyverse)
library(ggthemes)
library(formatR)

# Filter festlegen
jahr <- 2020
lastday <- ifelse((year(today()) - jahr) != 0, yday(ymd(paste0(jahr, "1231"))), yday(today()) - 
  5)
beginjahr <- 1954

# Daten einlesen
dailyData <- read.table("data/dailyData.csv", header = TRUE, dec = ".", sep = ";") %>% 
  select(c("Date", "YDay", "Year", "AVGTemp", "NiederschlagMM"))
dailyData <- weatherData %>% select(c("Date", "YDay", "Year", "AVGTemp", "NiederschlagMM"))
colnames(dailyData)[3:5] <- c("Jahr", "Temperatur", "Niederschlag")

# Daten filtern und aggregieren
weatherDat <- dailyData %>% filter(Jahr <= jahr) %>% filter(YDay <= lastday) %>% 
  group_by(Jahr) %>% summarise_at(c("Temperatur", "Niederschlag"), list(mw = mean, 
  summe = sum), na.rm = TRUE) %>% ungroup() %>% mutate(typeYear = (Jahr >= 2000) + 
  (Jahr == jahr)) %>% filter(Jahr >= beginjahr)

# Make the empty plot with the geom_segments Get the borders and round down or up
tempRange <- weatherDat %>% summarise_at(c("Temperatur_mw"), list(min = min, max = max), 
  na.rm = TRUE) %>% mutate(min = floor(min), max = ceiling(max)) %>% unlist()
tempTicks <- tempRange[1]:tempRange[2]
tempsegDat <- data.frame(x = rep(beginjahr, length(tempTicks)), xend = rep(jahr + 
  0.5, length(tempTicks)), y = tempTicks, yend = tempTicks)
dailyTemp <- ggplot(data = weatherDat, aes(x = Jahr, y = Temperatur_mw)) + geom_segment(data = tempsegDat, 
  aes(x = x, y = y, xend = xend, yend = yend), inherit.aes = FALSE, linetype = "dashed", 
  alpha = 0.3, col = "black") + geom_rangeframe(col = "black") + geom_line(alpha = 0.7, 
  col = "red", size = 0.7) + geom_point(size = 1.5, alpha = 0.7, col = "red") + 
  xlab("Jahr") + ylab("Durchschnittstemperatur (°C)") + theme_tufte(base_size = 11) + 
  theme(legend.position = "none") + scale_x_continuous(breaks = c(beginjahr, seq(1950, 
  jahr, by = 10), jahr)) + scale_y_continuous(limits = c(tempRange[1], tempRange[2])) + 
  theme(text = element_text(size = 11, family = "sans-serif"))

#### Plot Precip vs Temp ####

# Round Precip to nearest 50
precipRange <- weatherDat %>% summarise_at(c("Niederschlag_summe"), list(min = min, 
  max = max)) %>% mutate(min = floor(min/50) * 50, max = ceiling(max/50) * 50) %>% 
  unlist()
# Get Average Values
avgValues <- weatherDat %>% summarise_at(c("Temperatur_mw", "Niederschlag_summe"), 
  mean) %>% unlist()
dailyPrecip <- ggplot(data = weatherDat, aes(y = Temperatur_mw, x = Niederschlag_summe, 
  color = factor(typeYear), alpha = factor(typeYear))) + geom_segment(aes(y = avgValues[1], 
  yend = avgValues[1], x = precipRange[1] * 1.1, xend = precipRange[2] * 0.9), 
  inherit.aes = FALSE, linetype = "dashed", alpha = 0.5, col = "black", data = data.frame()) + 
  geom_segment(aes(y = tempRange[1] * ifelse(tempRange[1] > 0, 1.1, 0.9), yend = tempRange[2] * 
    0.9, x = avgValues[2], xend = avgValues[2]), inherit.aes = FALSE, linetype = "dashed", 
    alpha = 0.5, col = "black", data = data.frame()) + geom_point(size = 1.5) + 
  geom_rangeframe(col = "black", sides = "br") + theme_tufte(base_size = 15) + 
  ylab("Durchschnittstemperatur (°C)") + xlab("Niederschlag (mm)") + scale_color_manual(values = c("blue", 
  "black", "red"), breaks = c(0, 1), name = "Jahr", labels = c("1881-1999", paste0("2000-", 
  jahr - 1))) + scale_y_continuous(limits = c(tempRange[1], tempRange[2]), position = "right") + 
  scale_x_continuous(limits = precipRange) + theme(legend.position = c(0, 0), legend.justification = c(0, 
  0), legend.title = element_text(size = 7, face = "bold", hjust = 0.5)) + annotate("text", 
  y = tempRange[1] * ifelse(tempRange[1] > 0, 1.1, 0.9), x = avgValues[2] + 0.01 * 
    avgValues[2], label = "Durchschnittswerte", size = 2, angle = 90) + scale_alpha_manual(values = c(0.4, 
  1, 1), guide = FALSE) + guides(colour = guide_legend(override.aes = list(alpha = c(0.4, 
  0.9)))) + theme(text = element_text(size = 11, family = "sans-serif")) + theme(legend.text = element_text(size = 6))

print(dailyPrecip)