To get historical climate data for a specific date.

climate_data_today.R

library(weatherData)
library(plyr)
library(dplyr)
library(ggplot2)
library(tidyr)
years = seq(1948, 2015, 1)
days = "-03-08"
alldays = paste0(years, days)

dat_climate = list(length = length(alldays))
for(x in alldays){
  dat_climate[[which(x == alldays)]] = 
    getWeatherForDate("KMSN", x, opt_all_columns = T)
}
for(i in 1:length(dat_climate)){
  dat_climate[[i]]$Date = as.Date(dat_climate[[i]]$Date)
}

dat_climate = ldply(dat_climate)
str(dat_climate)
dat_climate_df = select(dat_climate, Date, Max_TemperatureF, Mean_TemperatureF,
                     Min_TemperatureF, Max_Wind_SpeedMPH, Mean_Wind_SpeedMPH,
                     PrecipitationIn) %>% 
  mutate(year = years) %>% 
  bind_rows(data.frame(Date = as.Date("2016-03-08"), Max_TemperatureF = 64,
                          Mean_TemperatureF = 58, Min_TemperatureF = 52,
           Max_Wind_SpeedMPH = NA, Mean_Wind_SpeedMPH = NA, 
           PrecipitationIn = 0, year = 2016)) ## predicted temp...
arrange(dat_climate_df, desc(Mean_TemperatureF))

dat_climate_df_long = gather(dat_climate_df, "envi", "value", 
                             Max_TemperatureF:PrecipitationIn)
dat_2016 = data.frame(x = t(dat_climate_df[69, 2:4]), 
                      envi = c("Max_TemperatureF", "Mean_TemperatureF", 
                               "Min_TemperatureF"))

filter(dat_climate_df_long, envi %in% c("Max_TemperatureF", "Mean_TemperatureF",
                     "Min_TemperatureF")) %>% 
  mutate(y2016 = year == 2016) %>% 
ggplot(aes(x = year, y = value)) +
  geom_point(aes(color = y2016, size = y2016 + 1)) + 
  # geom_smooth(method = "lm", alpha = 0.3) +
  geom_hline(aes(yintercept = x), dat_2016) +
  scale_x_continuous(breaks = seq(1950, 2015, 10)) +
  facet_wrap(~envi, ncol = 3) +
  scale_color_manual(values = viridis::viridis(6)[c(4,1)]) +
  theme_bw(base_size = 20) +
  theme(legend.position = "null")
ggsave("figs_4_slides/climate_0308.png", height = 4, width = 12)

filter(dat_climate_df_long, envi %in% c("Max_TemperatureF", "Mean_TemperatureF",
                                        "Min_TemperatureF")) %>%
  group_by(envi) %>% 
  do(broom::tidy(lm(value ~ seq_along(year), data = .)))

select(dat_climate_df, Date, contains("Temperature"))[, 2:4] %>% colMeans()
hist(dat_climate_df$Mean_TemperatureF)