715 search results for "maps"

UK dialect maps

July 10, 2013
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UK dialect maps

A few weeks ago Joshua Katz published some awesome dialect maps of the United States with the help of a web interface coded with Shiny. Now we propose a similar setup for the UK data based on our rapporter.net "R-as-a-Service" with some further add-ons, like regional comparison and dynamic textual analysis of the differences: You may...

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Interactive Heatmaps (and Dendrograms) – A Shiny App

July 7, 2013
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Interactive Heatmaps (and Dendrograms) – A Shiny App

Heatmaps are a great way to visualize data matrices. Heatmap color and organization can be used to  encode information about the data and metadata to help learn about the data at hand. An example of this could be looking at the raw data  or hierarchically clustering samples and variables based on their similarity or differences.

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R2leaflet (v0.1) – make interactive online maps from R

June 11, 2013
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R2leaflet (v0.1) – make interactive online maps from R

I have been working on a simple R function to take latitude and longitude of points of interest, and text for pop-up labels, and produce an interactive online map. Interactive graphics are incredibly useful in getting people interested in your … Continue reading →

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Working with shapefiles, projections and world maps in ggplot

May 23, 2013
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Working with shapefiles, projections and world maps in ggplot

In this post I show some different examples of how to work with map projections and how to plot the maps using ggplot. Many maps that are using the default projection are shown in the longlat-format, which is far from optimal. Here I show how to use either the Robinson or Winkel Tripel projection. Read more

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heatmaps with p-values (2)… coloured according to odds ratio

May 7, 2013
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heatmaps with p-values (2)… coloured according to odds ratio

I like heatplots with p-values -or frequencies, or whatever-. Not very conclusive, but pretty anyway. And when talking about graphs, pretty will make our neurons to fire in more interesting ways: neurons like “pretty” graphs. Moreover, observing your data can … Sigue leyendo →

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Stamen maps with spplot

Stamen maps with spplot

Several R packages provide an interface to query map services (Google Maps, Stamen Maps or OpenStreetMap) to obtain raster images …Continuar leyendo »

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R – Defining Your Own Color schemes for HeatMaps

March 25, 2013
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R – Defining Your Own Color schemes for HeatMaps

This post is intended at those who are beginners at R, and is inspired by a small post in Martin's bioblog.First, we plot a "correlation heatmap" using the same logic that Martin uses. In our example, let's use the Movies dataset that comes with ggplot...

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Using maps and ggplot2 to visualize college hockey championships

March 13, 2013
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Using maps and ggplot2 to visualize college hockey championships

Short: I plot the frequency of college hockey championships by state using the maps package, and ggplot2 Note: this example is based heavily on the example provided athttp://www.dataincolour.com/2011/07/maps-with-ggplot2/ data reference:http://en.wikipedia.org/wiki/NCAA_Men%27s_Ice_Hockey_Championship Question of interestAs a good Minnesotan, I've believed for quite some time that the colder, Northern states enjoy a competitive advantage when it...

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GPS Basemaps in R Using get_map

February 14, 2013
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GPS Basemaps in R Using get_map

There are many different maps you can use for a background map for your gps or other latitude/longitude data (i.e. any time you're using geom_path, geom_segment, or geom_point.)get_mapHelpfully, there's just one function that will allow you to query Google Maps, OpenStreetMap, Stamen maps, or CloudMade maps: get_map in the ggmap package. You could also use either get_googlemap, get_openstreetmap, get_stamenmap, or get_cloudmademap, but...

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Visualising 2012 NFL Quarterback performance with R heat maps

February 3, 2013
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Visualising 2012 NFL Quarterback performance with R heat maps

With only 24 hours remaining in the 2012 NFL season, this is a good time to review how the league's QBs performed during the regular season using performance data from KFFL and the heat mapping capabilities of R. #scale data to mean=0, sd=1 and convert to matrix QBscaled <- as.matrix(scale(QB2012)) #create heatmap and don't reorder

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