It doesn’t get much better for me than when I can combine R and weather data in new ways. I’ve got something brewing with my Nest thermostat and needed to get some current wx readings plus forecast data. I could have chosen a number of different sources or API’s but I wanted to play with the data over at forecast.io (if you haven’t loaded their free weather “app” on your phone/tablet you should do that NOW) so I whipped together a small R package to fetch and process the JSON to make it easier to work with in R.

The package contains a singular function and the magic is all in the conversion of the JSON hourly/minutely weather data into R data frames, which is dirt simple to do since RJSONIO and sapply do all the hard work for us:

# take the JSON blob we got from forecast.io and make an R list from it
fio <- fromJSON(fio.json)
 
# extract hourly forecast data  
fio.hourly.df <- data.frame(
  time = ISOdatetime(1960,1,1,0,0,0) + sapply(fio$hourly$data,"[[","time"),
  summary = sapply(fio$hourly$data,"[[","summary"),
  icon = sapply(fio$hourly$data,"[[","icon"),
  precipIntensity = sapply(fio$hourly$data,"[[","precipIntensity"),
  temperature = sapply(fio$hourly$data,"[[","temperature"),
  apparentTemperature = sapply(fio$hourly$data,"[[","apparentTemperature"),
  dewPoint = sapply(fio$hourly$data,"[[","dewPoint"),
  windSpeed = sapply(fio$hourly$data,"[[","windSpeed"),
  windBearing = sapply(fio$hourly$data,"[[","windBearing"),
  cloudCover = sapply(fio$hourly$data,"[[","cloudCover"),
  humidity = sapply(fio$hourly$data,"[[","humidity"),
  pressure = sapply(fio$hourly$data,"[[","pressure"),
  visibility = sapply(fio$hourly$data,"[[","visibility"),
  ozone = sapply(fio$hourly$data,"[[","ozone")
)

You can view the full code over at github and there’s some sample usage below.

library("devtools")
install_github("Rforecastio", "hrbrmstr")
 
library(Rforecastio)
library(ggplot2)
 
# NEVER put credentials or api keys in script bodies or github repos!!
# the "config" file has one thing in it, the api key string on one line
# this is all it takes to read it in
fio.api.key = readLines("~/.forecast.io")
 
my.latitude = "43.2673"
my.longitude = "-70.8618"
 
fio.list <- fio.forecast(fio.api.key, my.latitude, my.longitude)
 
# setup "forecast" highlight plot area
 
forecast.x.min <- ISOdatetime(1960,1,1,0,0,0) + unclass(Sys.time())
forecast.x.max <- max(fio.list$hourly.df$time)
if (forecast.x.min > forecast.x.max) forecast.x.min <- forecast.x.max
fio.forecast.range.df <- data.frame(xmin=forecast.x.min, xmax=forecast.x.max,
                                    ymin=-Inf, ymax=+Inf)
 
# plot the readings
 
fio.gg <- ggplot(data=fio.list$hourly.df,aes(x=time, y=temperature))
fio.gg <- fio.gg + labs(y="Readings", x="Time")
fio.gg <- fio.gg + geom_rect(data=fio.forecast.range.df,
                             aes(xmin=xmin, xmax=xmax,
                                 ymin=ymin, ymax=ymax), 
                             fill="yellow", alpha=(0.15),
                             inherit.aes = FALSE)
fio.gg <- fio.gg + geom_line(aes(y=humidity*100), color="green")
fio.gg <- fio.gg + geom_line(aes(y=temperature), color="red")
fio.gg <- fio.gg + geom_line(aes(y=dewPoint), color="blue")
fio.gg <- fio.gg + theme_bw()
fio.gg

test