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 →Shiny enables you to write powerful interactive web applications entirely in R. Using R you create a user interface and server and Shiny compiles your code into the HTML, CSS and JavaScript needed to display your application on the web. What makes a Shiny app particularly powerful is that it can execute R code on […]
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 →rgdalR has become a go-to tool for spatial analysis in many settings. You can read and edit spatial data, conduct geoprocessing and spatial analysis and create static and interactive maps. Of course, the first step in spatial analysis with R is often reading in your spatial data and this step can be confusing and frustrating. […]
Continue reading →acs, tigris and leaflet packagesCensus data the hard way Plotting census data with ggplot2 Interactive plotting with the leaflet package Options 1: Convert the data.frame back to a SpatialPolygonsDataFrame Options 2: Make use of the existing SpatialPolygonsDataFrame Census data the easy(er) way 1) Set up the packages 2) Get the spatial data (tigris) 3) Get the tabular data (acs) […]
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 →sqldf and data.tableIn a perfect data world the tables you need to join have common IDs. In this setting, in R, you might use the merge function from the base package or the speedy and useful join functions from the dplyr package. But sometimes your join is a little more complicated. Say, for example, that you have […]
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 […]
Continue reading →I used to spend considerably more time begging and, sometimes, badgering government agencies for data. Thanks to the push for more open and transparent data I’m more and more likely to find data I need posted in a nice manageable format on a government website. This was true on a recent project where I needed […]
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