The code for this project can be found on our github R tutorials page. Smoke from Siberia almost makes it to the west coast of the US.Īs always, keep an eye on our blog for new entries in this series on map building, and tips and tricks to improve your R mapping skills. You see dominant wind and weather systems carry the smoke from the western US to the east coast and beyond. file.remove(dir(pattern'Rplot')) py('output.mp4', paste0(old.
Copy raster in r mp4#
#animate the plotĪnd that’s it! This will give you the animated map as shown below (to save space the animated gif was converted to a mp4 movie). With all this specified you can then ask gganimate to render the final plot. It is therefore important to format your input data well and provide it as a consistent data frame (tibble). Note that in the above map we define an graphing element called transition_manual(date) which defines that if rendered the routine should step through all dates of the input data frame. # formulate graphing elementĬaption = "Data source: Copernicus ADS at ") + We can now build the map, only taking care of small changes particular to the gganimate package in order to animate the map over time (or any variable for that matter). With that all the required elements are preformated and can be used within a ggplot routine.
ProjectRaster(., res=50000, crs = robinson) %>%
Clean Code & Getting Help with R 2.1 Get to Know R 2.
Copy raster in r full#
For a full resolution map you can disable resampling. SECTION 2 INTRO TO R & WORK WITH TIME SERIES DATA 2. Note that you can change the resolution of the output by adjusting the resampling value. Using pipes, and in one movement, we convert this reprojected raster file to a dataframe. # formulate a ECMWF API requestįor rapid rendering we reproject and resample the original map. I’ll use data downloaded earlier this season, but you can always use more current data in your own analysis / map. For any given date you can download forecasts up to 120 hours into the future. # load librariesįont_add_google("Prata", regular.wt = 400)ĭownloading the data using our ecmwfr package is easy (see links below). In this example I’m going to use data from the ECMWF Atmosphere Data Store (ADS) service and their forecasts of black carbon transport throughout the atmosphere of the northern hemisphere in response to heavy forest fires in the west of the US and over eastern Siberia.įirst we setup our working environment ! You will need our ecmwfr package for downloading climate data, the tidyverse for processing and plotting, raster to deal with gridded climate data and sf + rnaturalearth for vector features, showtext for fancy fonts, finally both gganimate and gifski to render the final animated gif file.
Copy raster in r how to#
Here, I’m building upon a previous tutorial to illustrate how to animate climatological raster data. Although maps are generally meant to summarize multi-dimensional data in a simple a format as possible animation can provide additional insight into the data at hand.
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