950 search results for "maps"

choroplethrZip v1.3.0: easier demographics, national maps

April 28, 2015
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choroplethrZip v1.3.0: easier demographics, national maps

Introduction choroplethr v3.0 is now available on github. You can get it by typing # install.packages("devtools") library(devtools) install_github('arilamstein/[email protected]') Version 1.3.0 has two new features: Data frame df_zip_demographics contains eight demographic statistics about each ZIP Code Tabulated Area (ZCTA) in the US. Data comes from the 2013 5-year American Community Survey (ACS). Function ?get_zip_demographics will return

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Visualisation with R and Google Maps

For those of you who are interested in using R alongside Google Maps by using the packages geonames (www.geonames.org), RgoogleMaps, ggmap, loa and plotKML. Enjoy the slides of our presentation on this topic during the last RBelgium meetup. &nbs...

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R User Group Recap: Heatmaps and Using the caret Package

April 10, 2015
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R User Group Recap: Heatmaps and Using the caret Package

At our most recent R user group meeting we were delighted to have presentations from Mark Lawson and Steve Hoang, both bioinformaticians at Hemoshear. All of the code used in both demos is in our Meetup’s GitHub repo.Making heatmaps in RSteve started with an overview of making heatmaps in R. Using the iris dataset, Steve demonstrated...

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Recreating the vaccination heatmaps in R

April 9, 2015
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Recreating the vaccination heatmaps in R

In February the WSJ graphics team put together a series of interactive visualisations on the impact of vaccination that blew up on twitter and facebook, and were roundly lauded as great-looking and effective dataviz. Some of these had enough data available to look … Continue reading →

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R & Google Maps and R & Robotics (ROS)

R & Google Maps and R & Robotics (ROS)

The next RBelgium meetup will be about R & Google Maps and R & Robotics (ROS). BNOSAC will be hosting the event this time. This is the schedule: • 17h30-18h: open questions • 18h-19h: R and Google Maps • 19h-20h: R and...

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Interactive Maps for John Snow’s Cholera Data

March 28, 2015
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Interactive Maps for John Snow’s Cholera Data

This week, in Istanbul, for the second training on data science, we’ve been discussing classification and regression models, but also visualisation. Including maps. And we did have a brief introduction to the  leaflet package, devtools::install_github("rstudio/leaflet") require(leaflet) To see what can be done with that package, we will use one more time the John Snow’s cholera dataset, discussed in previous...

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Simple Lower US 48 Albers Maps & Local (no-API) City/State Geocoding in R

March 15, 2015
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Simple Lower US 48 Albers Maps & Local (no-API) City/State Geocoding in R

I’ve been seeing an uptick in static US “lower 48″ maps with “meh” projections this year, possibly caused by a flood of new folks resolving to learn R but using pretty old documentation or tutorials. I’ve also been seeing an uptick in folks needing to geocode US city/state to lat/lon. I thought I’d tackle both

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John Snow, and Google Maps

February 27, 2015
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John Snow, and Google Maps

In my previous post, I discussed how to use OpenStreetMaps (and standard plotting functions of R) to visualize John Snow’s dataset. But it is also possible to use Google Maps (and ggplot2 types of graphs). library(ggmap) get_london <- get_map(c(-.137,51.513), zoom=17) london <- ggmap(get_london) Again, the tricky part comes from the fact that the coordinate representation system, here, is not...

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Making Maps in R with Ryan Peek and Michele Tobias

February 20, 2015
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Today, Ryan Peek and Michele Tobias gave an introduction to making maps in R. Here’s the webcast: (Pardon the little scuffle at the beginning and as we switched computers halfway through. Still getting the hang of hangouts.) Resources: Download all of Ryan’s code and HTML files here. See Michele’s slides on Slideshare here. Code for Michele’s example maps...

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Rediscovering Formula One Race Battlemaps

January 31, 2015
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Rediscovering Formula One Race Battlemaps

A couple of days ago, I posted a recipe on the F1DataJunkie blog that described how to calculate track position from laptime data. Using that information, as well as additional derived columns such as the identity of, and time to, the cars immediately ahead of and behind a particular selected driver, both in terms of

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