1796 search results for "ggplot2"

The Life Scientists at FriendFeed: 2009 summary

December 23, 2009
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The Life Scientists at FriendFeed: 2009 summary

It’s Christmas Eve tomorrow and so I declare the year over. My Christmas gift to you is a summary of activity in 2009 at the FriendFeed Life Scientists group. It’s crafted using R + Ruby, with raw data and some code snippets available. If you want to see the most popular items from the group

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got R? get social science for R!

December 19, 2009
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Drew Conway has a great list of 10 must-have R packages for social scientists. If you’re a social scientist (or really, any kind of scientist) who doesn’t use R, now is a great time to dive in and learn; there are tons of tutorials and guides out there (my favorite is Quick-R, which is incredibly

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Browse R Graphics with the R Graph Gallery and the R Graphical Manual

December 15, 2009
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One of R's biggest strengths is its unparalleled graphing capabilities.  Just see any of our previous posts on ggplot2, visualization, or other posts tagged with R. R has several fundamentally different systems for plotting, including base graphics, lattice, and ggplot2.  Furthermore, many add-on packages come with their own functions for producing problem-domain specific graphics. For example,

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Summarising data using bar charts

December 12, 2009
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Summarising data using bar charts

A bar graph is a frequently used type of display that compares counts, frequencies, totals or other summary measures for a series of categories, e.g. sales in different market sectors or in quarters in a financial year. The bar graph can be laid out with the categories either on the vertical or horizontal axis of

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Must-Have R Packages for Social Scientists

December 11, 2009
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Must-Have R Packages for Social Scientists

After recently having to think critically about the value of various R packages for social science research, I realized that others might find value in a post on “must-have” R packages for social scientists. After the immensely popular post on this topic for Python packages a follow-up seemed appropraite. If you conduct social

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My Five Rules for Data Visualization

December 3, 2009
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My Five Rules for Data Visualization

Tonight the NYC R Meetup will be discussing data visualization in R using ggplot2. As part of tonight’s meeting I will be providing a very brief show and tell, which includes mostly code examples and external resources. This exercise has had me thinking quite a bit about data visualization. In addition, a

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R examine objects tutorial

November 21, 2009
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R examine objects tutorial

This article is quick concrete example of how to use the techniques from Survive R to lower the steepness of The R Project for Statistical Computing‘s learning curve (so an apology to all readers who are not interested in R). What follows is for people who already use R and want to achieve more control Related posts:

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Canabalt

November 12, 2009
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Canabalt

At the office today, I got into a discussion with two of my fellow graduate students about the distribution of scores you can get while playing Canabalt. Because (1) the layout of the levels in the game is fully randomized and (2) the difficulty of certain actions (specifically jumping through windows) is exceptionally high, we

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Graph Examples from Visualizing Data by William Cleveland

November 12, 2009
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Graph Examples from Visualizing Data by William Cleveland

The trellis graphics approach was pioneered by various statistical researchers and the ideas are used extensively in the book “Visualizing Data” by William Cleveland. There are various resources on the website for trellis graphics including S code for creating the majority of the graphs that appear in the book. Inspired by efforts on the Learning

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Split, apply, and combine in R using PLYR

November 4, 2009
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While flirting around with previously mentioned ggplot2 I came across an incredibly useful set of functions in the plyr package, made by Hadley Wickham, the same guy behind ggplot2.  If you've ever used MySQL before, think of "GROUP BY", but here you can arbitrarily apply any R function to splits of the data, or write one yourself. Imagine you have...

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