On Twitter I asked about R packages people are using to conduct spatial analysis. I’m interested in knowing what people are actually using on a day-to-day basis. It goes without saying that this is extraordinarily unscientific but I got 27 replies with a total of 45 packages, many of which I’ve never heard of before […]
Continue reading →purrr functions with the furrr packageR’s purrr package is an amazing set of tools for applying a function to the items in a vector or list. Although the default for this type of operation in purrr is to run each step sequentially the furrr package makes it incredibly easy to improve performance by running the steps concurrently (in parallel). purrr […]
Continue reading →For years we have used ArcGIS’ Network Analyst to compute network distances and route-based service areas. In the past we found open source options to be lacking. We experimented, for example, with pgRouting in 2014 and our rough assessment was that it did not provide an adequate alternative. Details are in our blog post on […]
Continue reading →Below we list the tools and innovations from the RStudio Conference in Austin, Texas that we found the most useful and exciting. As with last year’s conference, the event is well organized and the community of R users is enthusiastic and professionally diverse which makes for a fun and interesting conference. And, similar to last […]
Continue reading →caret packageThe intention of this post is to highlight some of the great core features of caret for machine learning and point out some subtleties and tweaks that can help you take full advantage of the package. In the process of introducing caret we’re also pointing out important misunderstandings about modeling in general and caret specifically […]
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 →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 →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 →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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