For a recent project we needed to significantly reduce the file sizes of some of the GeoJSON files were were using so we turned to Mike Bostock’s amazing TopoJSON APIs. As described on the wiki page TopoJSON is an extension of GeoJSON that significantly reduces file sizes by reducing redundancy. For example, the border between […]
Continue reading →leafletR packageIn a recent post (which you can find here) we identified the first publish date for all spatial packages listed in the Analysis of Spatial Data Task View on the R website. The most recent of these, published in March 2014, is the leafletR package by Christian Graul. We were surprised and impressed that, if […]
Continue reading →It is not uncommon for us to need to extract text from a PDF. For small PDFs with minimal data or text it's fairly straightforward to extract the data manually by using 'save as' or simply copying and pasting the data you need. For a recent project, however, we were asked to extract detailed address […]
Continue reading →rChartsData Driven Documents, or D3 for short, is an incredible JavaScript library for creating interactive data visualization on the web. Earlier this year, for example, we illustrated the power of D3 by interactively linking maps and charts in this visualization. D3, however, can be challenging to work with, especially if you don't have experience with […]
Continue reading →In mid-March, 2014 Google announced that it added support for GeoJSON to the Google Maps API (v3). Although other mapping APIs, namely Leaflet, have supported GeoJSON data for quite some time, easy access to GeoJSON in Google Maps will simplify the coding lives of many developers who often use the Google Maps API. In the […]
Continue reading →dplyrThe R package dplyr, written by Hadley Wickham is only a few months old but has already become an important part of our data analysis/manipulation workflow, replacing functions that we have used for years. There are several reasons why dplyr is such a valuable tool but most important from my perspective are the following: Speed. […]
Continue reading →We use PostgreSQL/PostGIS to manage a lot of our tabular and geographic data from the US Census. In terms of workflow we will either download a shapefile manually from ftp://ftp.census.gov/geo/tiger/ or, if we’re dealing with more than one file (block groups or blocks for example), we will do this from within R (using the download.file() […]
Continue reading →ggmap PackageOne of the great pieces of the new ggmap package is the geocoding functionality. Other R functions can be used to geocode but they fail to provide detailed output like geocode accuracy which is often critical. You need to know if the lat/long in the output refers to a rooftop location or a city center, […]
Continue reading → data.tableFor a recent project we have been working with a relatively large database of all historical air pollution data for New York City going all way back to the 1950s. Including both daily and hourly measurements the database includes about 10 million records and 20 variables. Using traditional R functions working with a database of […]
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