Thanks to a comment suggestion, the Rforecastio package is now up to version 1.3.0 and has a new parameter which lets you specify which time conversion function you want to use. Details are up on [github](https://github.com/hrbrmstr/Rforecastio).
Category Archives: Weather
Not even going to put an `R` category on this since I don’t want to pollute R-bloggers with this tiny post, but I had to provide the option to let folks specify `ssl.verifypeer=FALSE` (so I made it a generic option to pass in any CURL parameters) and I had a couple gaping bugs that I missed due to not clearing out my environment before building & testing.
I’ve bumped up the version number of `Rforecastio` ([github](https://github.com/hrbrmstr/Rforecastio)) to `1.1.0`. The new
features are:
– removing the SSL certificate bypass check (it doesn’t need it
anymore)
– using `plyr` for easier conversion of JSON-\>data frame
– adding in a new `daily` forecast data frame
– roxygen2 inline documentation
library(Rforecastio) library(ggplot2) library(plyr) # NEVER put API keys in revision control systems or source code! 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) fio.gg <- ggplot(data=fio.list$hourly.df, aes(x=time, y=temperature)) fio.gg <- fio.gg + labs(y="Readings", x="Time", title="Houry Readings") fio.gg <- fio.gg + geom_line(aes(y=humidity*100), color="green") fio.gg <- fio.gg + geom_line(aes(y=temperature), color="red") fio.gg <- fio.gg + geom_line(aes(y=dewPoint), color="blue") fio.gg <- fio.gg + theme_bw() fio.gg
fio.gg <- ggplot(data=fio.list$daily.df, aes(x=time, y=temperature)) fio.gg <- fio.gg + labs(y="Readings", x="Time", title="Daily Readings") fio.gg <- fio.gg + geom_line(aes(y=humidity*100), color="green") fio.gg <- fio.gg + geom_line(aes(y=temperatureMax), color="red") fio.gg <- fio.gg + geom_line(aes(y=temperatureMin), color="red", linetype=2) fio.gg <- fio.gg + geom_line(aes(y=dewPoint), color="blue") fio.gg <- fio.gg + theme_bw() fio.gg
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
Yesterday, I took a very short video capture from [wind map](http://hint.fm/wind/) of the massive wind flows buffeting the northeast. I did the same this morning and stitched them together to see what a difference a day makes.
Nothing earth-shattering here, but it is amazing to see how quickly the patterns can change (they changed intra-day yesterday as well, but I didn’t have time to do a series of videos this morning. the major change was that the very clear northerly pattern seen earlier in the day was replaced with a very clear easterly flow, with much more power than seen in today’s capture).
