powerBI-plotWeather.R

inPowerBI <- exists("dataset")
if (inPowerBI) {
    weatherHistory <- dataset
    weatherHistory$Date <- as.Date(paste(dataset$Year, dataset$Month, dataset$Day, sep = "/"), format = "%Y/%b/%d")
    #warning(paste(dataset$Year, dataset$Month, dataset$Day, sep = "/")[1])
    #warning(weatherHistory$Date[1])
}

library(checkpoint)
checkpoint("2016-04-22")

## based on original code by Alex Bresler
## https://gist.github.com/abresler/46c36c1a88c849b94b07

library(dplyr)
library(tidyr)
library(magrittr)
library(ggplot2)

## use get-weatherData.R to create weatherHistory data frame
## convert to "data" format for plotting

dt <- as.POSIXlt(weatherHistory$Date)
data <- data.frame(
                   day = dt$mday,
                   month = dt$mon+1,
                   year = dt$year + 1900,
                   temp = weatherHistory$Mean_TemperatureF)
## add "newday": days since Jan 1 of year
## Feb is skipped in non-leap years (newday goes from 59 to 61)
dom <- c(31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31)
cumdom <- cumsum(c(0, dom[1:11]))
data$newday <- cumdom[data$month]+data$day

yearStart <- min(data$year)
yearEnd <- max(data$year)
city <- attr(weatherHistory, "CityName")
cityLongName <- weatherHistory$City[1]

## 366 days of historical daily minima, maxima, and average temps
past <- data %>%
    filter(year!=yearEnd) %>%
    group_by(newday) %>%
    summarise(count = n(),  
          lower = min(temp),
          upper = max(temp),
          avg = mean(temp),
          se = sd(temp) / sqrt(length(temp))) %>% 
          mutate(avg_upper = avg + (2.101 * se), # calculate 95% CI for mean
                 avg_lower = avg - (2.101 * se)) # calculate 95% CI for mean          

## current year data
data %>%
     filter(year == yearEnd) -> present # filter out missing data & select current year data

dgr_fmt <- function(x, ...) {
    parse(text = paste(x, "*degree", sep = ""))
}

# create y-axis variable
a <- dgr_fmt(seq(-10, 100, by = 10))

## construct plot object p
p <- ggplot(past, aes(newday, avg)) +
    theme(plot.background = element_blank(),
                panel.grid.minor = element_blank(),
                panel.grid.major = element_blank(),
                panel.border = element_blank(),
                panel.background = element_blank(),
                axis.ticks = element_blank(),
                axis.title = element_blank()) +
    geom_linerange(past, mapping = aes(x = newday, ymin = lower, ymax = upper), colour = "wheat2")

#p
#Next, we can add the data that represents the 95% confidence interval around the daily mean temperatures for 1975-2013.
p <- p +
    geom_linerange(past, mapping = aes(x = newday, ymin = avg_lower, ymax = avg_upper), colour = "wheat4")
#p

p <- p +
    geom_line(present, mapping = aes(x = newday, y = temp, group = 1)) +
    geom_vline(xintercept = 0, colour = "wheat3", linetype = 1, size = 1)

#p

# white horizontal gridlines
p <- p + geom_hline(yintercept = -10, colour = "white", linetype = 1) +
    geom_hline(yintercept = 0, colour = "white", linetype = 1) +
    geom_hline(yintercept = 10, colour = "white", linetype = 1) +
    geom_hline(yintercept = 20, colour = "white", linetype = 1) +
    geom_hline(yintercept = 30, colour = "white", linetype = 1) +
    geom_hline(yintercept = 40, colour = "white", linetype = 1) +
    geom_hline(yintercept = 50, colour = "white", linetype = 1) +
    geom_hline(yintercept = 60, colour = "white", linetype = 1) +
    geom_hline(yintercept = 70, colour = "white", linetype = 1) +
    geom_hline(yintercept = 80, colour = "white", linetype = 1) +
    geom_hline(yintercept = 90, colour = "white", linetype = 1) +
    geom_hline(yintercept = 100, colour = "white", linetype = 1)

# monthly vertical gridlines
p <- p +
    geom_vline(xintercept = 31, colour = "wheat3", linetype = 3, size = .5) +
    geom_vline(xintercept = 59, colour = "wheat3", linetype = 3, size = .5) +
    geom_vline(xintercept = 90, colour = "wheat3", linetype = 3, size = .5) +
    geom_vline(xintercept = 120, colour = "wheat3", linetype = 3, size = .5) +
    geom_vline(xintercept = 151, colour = "wheat3", linetype = 3, size = .5) +
    geom_vline(xintercept = 181, colour = "wheat3", linetype = 3, size = .5) +
    geom_vline(xintercept = 212, colour = "wheat3", linetype = 3, size = .5) +
    geom_vline(xintercept = 243, colour = "wheat3", linetype = 3, size = .5) +
    geom_vline(xintercept = 273, colour = "wheat3", linetype = 3, size = .5) +
    geom_vline(xintercept = 304, colour = "wheat3", linetype = 3, size = .5) +
    geom_vline(xintercept = 334, colour = "wheat3", linetype = 3, size = .5) +
    geom_vline(xintercept = 365, colour = "wheat3", linetype = 3, size = .5)

# month labels
p <- p +
    coord_cartesian(ylim = c(-10, 100)) +
    scale_y_continuous(breaks = seq(-10, 100, by = 10), labels = a) +
    scale_x_continuous(expand = c(0, 0),
                                         breaks = c(15, 45, 75, 105, 135, 165, 195, 228, 258, 288, 320, 350),
                                         labels = c("January", "February", "March", "April",
                                                         "May", "June", "July", "August", "September",
                                                         "October", "November", "December"))

p <- p +
    ggtitle(paste(cityLongName,"'s Weather in ", yearEnd," (degrees Fahrenheit)",sep="")) +
    theme(plot.title = element_text(face = "bold", hjust = .012, vjust = .8, colour = "#3C3C3C", size = 20)) 


present %>% filter(newday %in% c(180:185)) %>% select(x = newday, y = temp) %>% data.frame -> legend_data
legend_data$y <- legend_data$y - mean(legend_data$y) + 15 
normalLabel <- paste(yearEnd, "TEMPERATURE")
maxlabel <- paste("RECORD HIGH SINCE", yearStart)
minlabel <- paste("RECORD LOW SINCE", yearStart)

p <- p +
    annotate("segment", x = 182, xend = 182, y = 5, yend = 25, colour = "wheat2", size = 3) +
    annotate("segment", x = 182, xend = 182, y = 12, yend = 18, colour = "wheat4", size = 3) +
    geom_line(data = legend_data, aes(x = x, y = y)) +
    annotate("segment", x = 184, xend = 186, y = 17.7, yend = 17.7, colour = "wheat4", size = .5) +
    annotate("segment", x = 184, xend = 186, y = 12.2, yend = 12.2, colour = "wheat4", size = .5) +
    annotate("segment", x = 185, xend = 185, y = 12.2, yend = 17.7, colour = "wheat4", size = .5) +
    annotate("text", x = 190, y = 14.75, hjust=0, label = "NORMAL RANGE", size = 2, colour = "gray30") +
    annotate("text", x = 175, y = 14.75, hjust=1, label = normalLabel, size = 2, colour = "gray30") +
    annotate("text", x = 190, y = 25, hjust = 0, label = maxlabel, size = 2, colour = "gray30") +
    annotate("text", x = 190, y = 5, hjust = 0, label = minlabel, size = 2, colour = "gray30")

print(p)