reticulate with arcpy on a Windows machine
We do a decent amount of geocoding here at ZRSA and are always looking for ways to streamline our process. We tend to use R for tidying and standardizing address data prior to geocoding. This makes using the Google Geocoding API a perfect solution for geocoding since we can access it via R using a […]
Continue reading →The purrr package is a functional programming superstar which provides useful tools for iterating through lists and vectors, generalizing code and removing programming redundancies. The purrr tools work in combination with functions, lists and vectors and results in code that is consistent and concise. In this post we focus primarily on the map family of […]
Continue reading →tidycensus and tmap packagesThe tidycensus and tmap R packages make an incredible duo for working with and visualizing US Census data. The tidycensus package, authored by Kyle Walker, streamlines geographic and tabular data downloads while the tmap package, written by Martijn Tennekes, vastly simplifies creating maps with multiple layers, accepts many different spatial object types and makes it easy to add scale […]
Continue reading →Writing reports in R Markdown allows you to skip painful and error-prone copy-paste in favor of dynamically-generated reports written in R and markdown that are easily reproducible and updateable. R Markdown reports that are heavy on graphs and maps, though, can yield large HTML files that are not optimized for web viewing. R Markdown offers […]
Continue reading →The FedData package (created by R. Kyle Bocinsky) is a great new R package that provides easy access to some important federal datasets. The package is well-designed and provides functions to download climate, elevation, hydrography and other data for your area of interest. The following five sources of data are currently available for download with […]
Continue reading →In a popular previous post on raster processing in R we demonstrated how to reclassify, clip and map raster data using sample data from the GIS software QGIS as the example. The post concluded by plotting the raster in R. This is fine if you’re doing analysis and creating static reports but what if you […]
Continue reading →rvest (+ a postscript on interacting with web pages with RSelenium)Copying tables or lists from a website is not only a painful and dull activity but it's error prone and not easily reproducible. Thankfully there are packages in Python and R to automate the process. In a previous post we described using Python's Beautiful Soup to extract information from web pages. In this post we […]
Continue reading →The amount of spatial analysis functionality in R has increased dramatically since the first release of R. In a previous post, for example, we showed that the number of spatial-related packages has increased to 131 since the first R release. This means, of course, that more and more of your spatial-related workflow can be conducted […]
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