Writing about visualization, demographics, dashboards, and spatial data science.

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I use data from the US Census Bureau’s American Community Survey all of the time. I also use R all of the time. Naturally, this means that I often use ACS data in R - which is pertinent given last week’s release of the new 2010-2014 ACS estimates. I wanted easy access to the data to facilitate my on-going research on demographic trends in US metros, and work at the TCU Center for Urban Studies; as such, I wrote a small R package to provide quick access to the data, acs14lite (https://github.

There are three functions available in the tigris package (https://github.com/walkerke/tigris) to fetch road data. primary_roads() loads all interstates for the entire US; primary_secondary_roads() gets you interstates and US/state/county highways, by state; and roads() gets you all road segments for a given county within a state. In this example, we’ll use the primary_secondary_roads() function to get our data for Route 1 in California. library(tigris) library(leaflet) library(rgdal) library(geojsonio) library(widgetframe) ca <- primary_secondary_roads(state = 'California') rt1 <- ca[ca$FULLNAME == 'State Rte 1', ] We can then plot with the leaflet package:

In this document, I will demonstrate how I created an interactive CartoDB state squares map of obesity in the United States for 2013. Credit is due to Bill Dollins for sharing a state squares file in GeoJSON format. You can access the file from his GitHub repository. The data processing is done in R, and the visualization is done with the CartoDB web GUI. The workflow below uses the kwgeo R package to upload data directly to CartoDB.

Web: http://personal.tcu.edu/kylewalker Twitter: https://twitter.com/kyle_e_walker Yesterday, I posted to Twitter an interactive map using the classic John Snow Cholera dataset and tiles made from Snow’s map, which attracted a fair share of interest. Interactive Snow cholera map w/@LeafletJS, #rstats, @rstudio: http://t.co/vKYnx6lSxT Thx @lincolnmullen @abresler pic.twitter.com/1zkq7UiyKr — Kyle Walker (@kyle_e_walker) March 9, 2015 I was inspired to try this by Lincoln Mullen’s tweet that custom historical tiles could be used in an RStudio Leaflet map.

Before coming to TCU, I worked as a data analyst for the Church Pension Group, which manages the retirement funds and provides other financial services for the Episcopal Church. I was part of a small research group that completed both internal and public-facing studies using the company’s data. You can take a look at some of the studies I worked on here. While I was at CPG, I developed an interest in the sociology of religion, as changing rates of religious adherence were of critical importance to CPG’s work, as they impact the overall viability of parishes (and in turn the fiscal health of the Church).

This past week, the good people at RStudio advertised over Twitter the release of htmlwidgets for R, a project in collaboration with rCharts wizards Ramnath Vaidyanathan and Kenton Russell. The packages showcased are incredible; I was particularly intrigued by the dygraphs package, which creates interactive time-series charts. Aside from maps, time series line charts are the most common chart type I use in my teaching, as I often discuss how characteristics of places evolve over time.

I frequently come across criticisms of PowerPoint as a presentation tool, which is interesting to me given the ubiquity of its use across industries. When I worked as a data analyst prior to coming to TCU, I frequently prepared PowerPoints using a company template for my boss’s presentations or for talks of my own. In academia, we have considerable freedom in how we can communicate information; however, PowerPoint is still widely used in the classroom and is everywhere at professional conferences.

When covering Russia and the former republics of the USSR in World Regional Geography, a key part of my material addresses the issues that some of these countries have had in their transitions from centralized to market economies. Some of these countries experienced dramatic demographic shifts after the dissolution of the USSR, including a noticeable decline in life expectancy. I’d been using some static Excel charts to illustrate life expectancy declines in Russia, Belarus, and Ukraine in previous courses.

Please note: some NVD3 charts are performing very slowly in the latest version of Google Chrome at the moment; see this GitHub issue. As such, this post is best viewed in other browsers. I recently came across this really interesting post from Ben Jones that explores the history and future of world population change with Tableau. I haven’t used Tableau much, but I was impressed with the different ways in which Ben used the software to visualize various aspects of global population change.

I find population pyramids to be very effective teaching tools. In short, a population pyramid is a type of chart that shows the population size of different age cohorts on the x-axis, with gender usually displayed back-to-back to create the shape of a “pyramid.” It is used to illustrate a snapshot of the age and sex structure of a population, and can serve as a tool that aids in discussion of many thematic issues such as population growth, aging, and gender imbalance.