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District Data Labs - How to Transition from Excel to #R | #datascience

District Data Labs - How to Transition from Excel to #R | #datascience | e-Xploration | Scoop.it
How to Transition from Excel to R - An Intro to R for Microsoft Excel Users
luiy's insight:

In today's increasingly data-driven world, business people are constantly talking about how they want more powerful and flexible analytical tools, but are usually intimidated by the programming knowledge these tools require and the learning curve they must overcome just to be able to reproduce what they already know how to do in the programs they've become accustomed to using. For most business people, the go-to tool for doing anything analytical is Microsoft Excel.

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Cluster your Twitter Data with #R and #k-means | #datascience

Cluster your Twitter Data with #R and #k-means | #datascience | e-Xploration | Scoop.it

Hello everbody! Today  I want to show you how you can get deeper insights into your Twitter followers with the help of R


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Paul Butler – Visualizing Facebook Friends: #EyeCandy in #R I #dataviz

Paul Butler – Visualizing Facebook Friends: #EyeCandy in #R I #dataviz | e-Xploration | Scoop.it
luiy's insight:

I’ve received a lot comments about the image, many asking for more details on how I created it. When I tell people I used R, the reaction I get is roughly what I would expect if I told them I made it with Microsoft Paintand a bottle of Jägermeister. Some people even questioned whether it was actually done in R. The truth is, aside from the addition of the logo and date text, the image was produced entirely with about 150 lines of R code with no external dependencies. In the process I learned a few things about creating nice-looking graphs in R.

 

Transparency and Faking It

My first attempt at plotting the data involved plotting very transparent lines. Unfortunately there was just too much data to get a meaningful plot — even at very low opacity, there were enough lines to make the entire image just a bright blob. When I increased the transparency more, the opacity was rounded down to zero by my graphics device and the result was that nothing was drawn.

The solution was to manipulate the drawing order of the lines. I used a simple loop over my data to draw the lines, so it was easy to control which lines are drawn first using order(). I created an ordering based on the length of the lines, so that longer lines were drawn “behind” the shorter, more local lines. Then I used colorRampPalette() to generate a color palette from black to blue to white, and colored the lines according to order they were drawn.

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Introduction to R Shiny: Building web apps in R Shiny for learning and visualization I #datascience #dataviz

Slides: http://files.meetup.com/1685538/IntroductionRShiny.pptx R Shiny, from the people behind R Studio, allows you to quickly and easily build basic web ap...

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Introducing the #streamgraph htmlwidget #R Package | #datascience

Introducing the #streamgraph htmlwidget #R Package | #datascience | e-Xploration | Scoop.it
We were looking for a different type of visualization for a project at work this past week and my thoughts immediately gravitated towards streamgraphs. The TLDR on streamgraphs is they they are generalized versions of stacked area graphs with free baselines across the x axis. They are somewhat controversial but have a “draw you in” […]
luiy's insight:

Streamgraphs require a continuous variable for the x axis, and thestreamgraph widget/package works with years or dates (support for xtsobjects and POSIXct types coming soon). Since they display categorical values in the area regions, the data in R needs to be in long format which is easy to do with dplyr & tidyr.

The package recognizes when years are being used and does all the necessary conversions for you. It also uses a technique similar to expand.grid to ensure all categories are represented at every observation (not doing so makesd3.stack unhappy).

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#SNA applications with #R: Statnet, ergm, igraph, RSiena, networksis, latentnet | #datascience

#SNA applications with #R: Statnet, ergm, igraph, RSiena, networksis, latentnet | #datascience | e-Xploration | Scoop.it
luiy's insight:

1. ergm: Fit, Simulate and Diagnose Exponential-Family Models for Networks.

An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGM). "ergm" is a part of the "statnet" suite of packages for network analysis.

 

http://cran.r-project.org/web/packages/ergm/index.html

 

 

2. igraph: Network analysis and visualization.

Routines for simple graphs and network analysis. igraph can handle large graphs very well and provides functions for generating random and regular graphs, graph visualization, centrality indices and much more.

 

http://cran.r-project.org/web/packages/igraph/index.html

 

 

3. RSiena: Siena - Simulation Investigation for Empirical Network Analysis.

Fits models to longitudinal network data.

 

http://cran.r-project.org/web/packages/RSiena/index.html

 

 

4. networksis: Simulate bipartite graphs with fixed marginals through sequential importance sampling

Tools to simulate bipartite networks/graphs with the degrees of the nodes fixed and specified. 'networksis' is part of the 'statnet' suite of packages for network analysis.

 

http://cran.r-project.org/web/packages/networksis/index.html

 

 

5. latentnet: Latent position and cluster models for statistical networks.

A package to fit and simulate latent position and cluster models for statistical networks.

 

http://cran.r-project.org/web/packages/latentnet/index.html

 

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An R Introduction to Statistics | R #Tutorial I #datascience #openaccess

An R Introduction to Statistics | R #Tutorial I #datascience #openaccess | e-Xploration | Scoop.it
An R introduction to statistics that explains basic R concepts and illustrates with statistics textbook homework exercises.
luiy's insight:

Welcome to R Tutorial. We provide an introduction to the R programming language, and illustrate its use by solving elementary statistics textbook exercises. Beyond the basics, we also cover topics of GPU computing in R. Hope you find the material helpful, and this site serves the intended purpose of being a stepping stone for further study.

 

Thank you for visiting us. Please bookmark us and come back often.

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