480 search results for "boxplot"

Upcoming Tutorial: Analyzing US Census Data in R

May 11, 2015
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Today I am pleased to announce that on May 21 I will run a tutorial titled Analyzing US Census Data in R. While I have spoken at conferences before, this is my first time running a tutorial. My hope is that everyone who participates will learn something interesting about the demographics of the state, county

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choroplethr v3.1.0: Better Summary Demographic Data

May 5, 2015
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choroplethr v3.1.0: Better Summary Demographic Data

Today I am happy to announce that choroplethr v3.1.0 is now on CRAN. You can get it by typing the following from an R console: install.packages("choroplethr") This version adds better support for summary demographic data for each state and county in the US. The data is in two data.frames and two functions. The data.frames are:

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choroplethr v3.1.0: Better Summary Demographic Data

May 5, 2015
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choroplethr v3.1.0: Better Summary Demographic Data

Today I am happy to announce that choroplethr v3.1.0 is now on CRAN. You can get it by typing the following from an R console: install.packages("choroplethr") This version adds better support for summary demographic data for each state and county in the US. The data is in two data.frames and two functions. The data.frames are:

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Data Mining the California Solar Statistics with R: Part II

May 4, 2015
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Data Mining the California Solar Statistics with R: Part II

Data Mining the California Solar Statistics with R: Part II In today's post I'll be working some more with the working data set from California Solar Statistics. Last time I imported the data, cleaned it up a bit, grouped it by county and year, and made some plots to look at how residential solar installations

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Cascading style sheets for R plots (via the Rcssplot package)

April 23, 2015
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Cascading style sheets for R plots (via the Rcssplot package)

This post is contributed by Tomasz Konopka. Comments are [email protected] One of the great features of R is its capable graphics framework. In principle, the framework allows us to customize all aspects of the visual presentation of data. In practice, however, customization is rather tedious. For example, R’s own boxplot function has 17 custom arguments, not counting ...; stripchart has 20. Tweaking the default...

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Time Series Graphs & Eleven Stunning Ways You Can Use Them

April 22, 2015
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Time Series Graphs & Eleven Stunning Ways You Can Use Them

Many graphs use a time series, meaning they measure events over time. William Playfair (1759 - 1823) was a Scottish economist and pioneer of this approach. Playfair invented the line graph. The graph below–one of his most famous–depicts ho...

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Analysing The Rock ‘n’ Roll Madrid Marathon

April 18, 2015
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Analysing The Rock ‘n’ Roll Madrid Marathon

Nobody’s going to win all the time. On the highway of life you can’t always be in the fast lane (Haruki Murakami, What I Talk About When I Talk About Running) I started running two years ago and one if my dreams is to run a marathon someday. One month ago I run my first … Continue reading...

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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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Predicting Mobile Phone Prices

April 6, 2015
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Predicting Mobile Phone Prices

Recently a colleague of mine showed me a nauseating interactive scatterplot that plots mobile phones according to two dimensions of the user’s choice from a list of possible dimensions.  Although the interactive visualization was offensive to my tastes, the JSON … Continue reading →

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Machine Learning in R for beginners

March 25, 2015
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Machine Learning in R for beginners

Introducing: Machine Learning in R Machine learning is a branch in computer science that studies the design of algorithms that can learn. Typical machine learning tasks are concept learning, function learning or “predictive modeling”, clustering and finding predictive patterns. These tasks are learned through available data that were observed through experiences or instructions, for example. The post

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