sun, 07-apr-2013, 15:50
Cold November

Cold November

Several years ago I showed some R code to make a heatmap showing the rank of the Oakland A’s players for various hitting and pitching statistics.

Last week I used this same style of plot to make a new weather visualization on my web site: a calendar heatmap of the difference between daily average temperature and the “climate normal” daily temperature for all dates in the last ten years. “Climate normals” are generated every ten years and are the averages for a variety of statistics for the previous 30-year period, currently 1981—2010.

A calendar heatmap looks like a normal calendar, except that each date box is colored according to the statistic of interest, in this case the difference in temperature between the temperature on that date and the climate normal temperature for that date. I also created a normalized version based on the standard deviations of temperature on each date.

Here’s the temperature anomaly plot showing all the temperature differences for the last ten years:

It’s a pretty incredible way to look at a lot of data at the same time, and it makes it really easy to pick out anomalous events such as the cold November and December of 2012. One thing you can see in this plot is that the more dramatic temperature differences are always in the winter; summer anomalies are generally smaller. This is because the range of likely temperatures is much larger in winter, and in order to equalize that difference, we need to normalize the anomalies by this range.

One way to do that is to divide the actual temperature difference by the standard deviation of the 30-year climate normal mean temperature. Because of the nature of the distribution standard deviations are based on, approximately 66% of the variation occurrs within -1 and 1 standard deviation, 95% between -2 and 2, and 99% between -3 and 3 standard deviations. That means that deep red or blue dates, those outside of -3 and 3, in the normalized calendar plot are fairly rare occurrances.

Here’s the normalized anomalies for the last twelve months:

The tricky part in generating either of these plots is getting the temperature data into the right format. The plots are faceted by month and year (or YYYYY-MM in the twelve month plot), so each record needs to have month and year. That part is easy. Each individual plot is a single calendar month, and is organized by day of the week along the x-axis, and the inverse of week number along the y-axis (the first week in a month is at the top of the plot, the last at the bottom).

Here’s how to get the data formatted properly:

library(lubridate)
cal <- function(dt) {
    # Reads a date object and returns a tuple (weekrow, daycol)
    # where weekrow starts at 1 and daycol starts at 1 for Sunday
    year <- year(dt)
    month <- month(dt)
    day <- day(dt)
    wday_first <- wday(ymd(paste(year, month, 1, sep = '-'), quiet = TRUE))
    offset <- 7 + (wday_first - 2)
    weekrow <- ((day + offset) %/% 7) - 1
    daycol <- (day + offset) %% 7

    c(weekrow, daycol)
}
weekrow <- function(dt) {
    cal(dt)[1]
}
daycol <- function(dt) {
    cal(dt)[2]
}
vweekrow <- function(dts) {
    sapply(dts, weekrow)
}
vdaycol <- function(dts) {
    sapply(dts, daycol)
}
pafg$temp_anomaly <- pafg$mean_temp - pafg$average_mean_temp
pafg$month <- month(pafg$dt, label = TRUE, abbr = TRUE)
pafg$year <- year(pafg$dt)
pafg$weekrow <- factor(vweekrow(pafg$dt),
   levels = c(5, 4, 3, 2, 1, 0),
   labels = c('6', '5', '4', '3', '2', '1'))
pafg$daycol <- factor(vdaycol(pafg$dt),
   labels = c('u', 'm', 't', 'w', 'r', 'f', 's'))

And the plotting code:

library(ggplot2)
library(scales)
library(grid)
svg('temp_anomaly_heatmap.svg', width = 11, height = 10)
q <- ggplot(data = subset(pafg, year > max(pafg$year) - 11),
            aes(x = daycol, y = weekrow, fill = temp_anomaly)) +
    theme_bw() +
    theme(axis.text.x = element_blank(),
          axis.text.y = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          axis.ticks.x = element_blank(),
          axis.ticks.y = element_blank(),
          axis.title.x = element_blank(),
          axis.title.y = element_blank(),
          legend.position = "bottom",
          legend.key.width = unit(1, "in"),
          legend.margin = unit(0, "in")) +
    geom_tile(colour = "white") +
    facet_grid(year ~ month) +
    scale_fill_gradient2(name = "Temperature anomaly (°F)",
          low = 'blue', mid = 'lightyellow', high = 'red',
          breaks = pretty_breaks(n = 10)) +
    ggtitle("Difference between daily mean temperature\
             and 30-year average mean temperature")
print(q)
dev.off()

You can find the current versions of the temperature and normalized anomaly plots at:

tags: R  weather  temperature 
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