560 search results for "shiny"

New video course: Campaign Response Testing

April 8, 2015
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New video course: Campaign Response Testing

I am proud to announce a new Win-Vector LLC statistics video course: Campaign Response Testing John Mount, Win-Vector LLC This course works through the very specific statistics problem of trying to estimate the unknown true response rates one or more populations in responding to one or more sales/marketing campaigns or price-points. This is an old … Continue reading...

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Six Ways You Can Make Beautiful Graphs (Like Your Favorite Journalists)

April 8, 2015
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Six Ways You Can Make Beautiful Graphs (Like Your Favorite Journalists)

This post shows how to make graphs like The Economist, New York Times, Vox, 538, Pew, and Quartz. And you can share–embed your beautiful, interactive graphs in apps, blog posts, and web sites. Read on to learn how. If you like interactive graphs and need to securely collaborate with your team, contact us about Plotly Enterprise.

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Exploring San Francisco with choroplethrZip

April 7, 2015
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Exploring San Francisco with choroplethrZip

by Ari Lamstein Introduction Today I will walk through an analysis of San Francisco Zip Code Demographics using my new R package choroplethrZip. This package creates choropleth maps of US Zip Codes and connects to the US Census Bureau. A choropleth is a map that shows boundaries of regions (such as zip codes) and colors those regions according to...

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Design patterns for action buttons

April 7, 2015
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Design patterns for action buttons

Action buttons can be tricky to use in Shiny because they work differently than other widgets. Widgets like sliders and select boxes maintain a value that is easy to use in your code. But the value of an action button is arbitrary. What should you do with it? Did you know that you should almost

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SWMPr 2.0.0 now on CRAN

April 5, 2015
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SWMPr 2.0.0 now on CRAN

I’m pleased to announce that my second R package, SWMPr, has been posted on CRAN. I developed this package to work with water quality time series data from the System Wide Monitoring Program (SWMP) of the National Estuarine Research Reserve System (NERRS). SWMP was established in 1995 to provide continuous environmental data at over 300

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More Airline Crashes via the Hadleyverse

March 31, 2015
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I saw a fly-by #rstats mention of more airplane accident data on — of all places — LinkedIn (email) today which took me to a GitHub repo by @philjette. It seems there’s a web site (run by what seems to be a single human) that tracks plane crashes. Here’s a tweet from @philjette announcing it:

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Improved memory usage and RJSONIO compatibility in jsonlite 0.9.15

March 30, 2015
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Improved memory usage and RJSONIO compatibility in jsonlite 0.9.15

The jsonlite package implements a robust, high performance JSON parser and generator for R, optimized for statistical data and the web. Last week version 0.9.15 appeared on CRAN which improves memory usage and compatibility with other packages. Migrating to jsonlite The upcoming release of shiny will switch from RJSONIO to jsonlite. To...

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Data Visualization cheatsheet, plus Spanish translations

March 30, 2015
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Data Visualization cheatsheet, plus Spanish translations

We’ve added a new cheatsheet to our collection. Data Visualization with ggplot2 describes how to build a plot with ggplot2 and the grammar of graphics. You will find helpful reminders of how to use: geoms stats scales coordinate systems facets position adjustments legends, and themes The cheatsheet also documents tips on zooming. Download the cheatsheet

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R Stats + Digital Analytics: 8 Blogs you should Follow

March 29, 2015
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R Stats + Digital Analytics: 8 Blogs you should Follow

Are you interested in using R for your digital analytics projects? Do you need to perform prediction modelling and visualizations on your digital data and Excel can´t just do the job as you wanted?Or, you simply have no idea how R could help you in your digital analytics problems and you would like to see some real working examples...

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Parallel R with BatchJobs

March 28, 2015
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Parallel R with BatchJobs

Parallelizing R with BatchJobs – An example using k-means Gord Sissons, Feng Li Many simulations in R are long running. Analysis of statistical algorithms can generate workloads that run for hours if not days tying up a single computer. Given the amount of time R programmers can spend waiting for results, getting acquainted parallelism makes

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