2620 search results for "ggplot2"

Getting started with PostgreSQL in R

May 18, 2015
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Getting started with PostgreSQL in R

When dealing with large datasets that potentially exceed the memory of your machine it is nice to have another possibility such as your own server with an SQL/PostgreSQL database on it, where you can query the data in smaller digestible chunks. For example, recently I was facing a financial dataset of 5 GB. Although 5

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The perfect t-test

May 18, 2015
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The perfect t-test

I've created an easy to use R script that will import your data, and performs and writes up a state-of-the-art dependent or independent t-test. The goal of this script is to examine whether more researcher-centered statistical tools (i.e., a one-click analysis script that checks normality assumptions, calculates effect sizes and their confidence intervals, creates good figures,...

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Analyzing R-Bloggers’ posts via Twitter

May 18, 2015
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Analyzing R-Bloggers’ posts via Twitter

For those who don’t know, every time a new blog post gets added to R-Bloggers, it gets a corresponding tweet by @Rbloggers, which gets seen by Rbloggers’ ~20k followers fairly fast. And every time my post gets published, I can’t help but check up on how many people gave that tweet some Twitter love, ie. “favorite”d or...

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Interactive maps for the web in R

May 15, 2015
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Interactive maps for the web in R

Static MapsIn the last post I showed how to download economic data from the World Bank's website and create choropleth maps in R (Global Economic Maps).In this post I want to focus more on how to visualize those maps.Sp PackageProbably the simplest way...

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U.S. Drought Monitoring With Hexbin State Maps in R

May 15, 2015
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U.S. Drought Monitoring With Hexbin State Maps in R

On the news, today, of the early stages of drought hitting the U.S. northeast states I decided to springboard off of yesterday’s post and show a more practical use of hexbin state maps than the built-in (and still purpose unknown to me) “bees” data. The U.S. Drought Monitor site supplies more than just a pretty

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GeoJSON Hexagonal “Statebins” in R

May 14, 2015
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GeoJSON Hexagonal “Statebins” in R

There’s been lots of buzz about “statebin” maps of late. A recent tweet by @andrewxhill referencing work by @dannydb pointed to a nice shapefile that ends up being a really great way to handle statebin maps (and I feel like a fool for not considering it for a more generic solution earlier). Here is the

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A first look at htmlwidgets

May 14, 2015
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by Joseph Rickert A strong case can be made that base R graphics supplemented with either the lattice library or ggplot2 for plotting by subgroups provides everything a statistician might need for both exploratory data analysis and for developing clear, crisp for communicating results. However, it is abundantly clear that web based graphics, driven to a large extent by...

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Bertrand or (The Importance of Defining Problems Properly)

May 13, 2015
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Bertrand or (The Importance of Defining Problems Properly)

We better keep an eye on this one: she is tricky (Michael Banks, talking about Mary Poppins) Professor Bertrand teaches Simulation and someday, ask his students: Given a circumference, what is the probability that a chord chosen at random is longer than a side of the equilateral triangle inscribed in the circle? Since they must reach the … Continue reading...

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Agent Based Modelling with data.table OR how to model urban migration with R

May 12, 2015
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Agent Based Modelling with data.table OR how to model urban migration with R

Introduction Recently I found a good introduction to the Shelling-Segregation Model and to Agent Based Modelling (ABM) for Python (Binpress Article by Adil). The model follows an ABM approach to simulate how urban segregation can be explained. I will concentrate on the R-code, if you want to know more about the Shelling-Segregation Model (which brought

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Data Mining the California Solar Statistics with R: Part III

May 11, 2015
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Data Mining the California Solar Statistics with R: Part III

Data Mining the California Solar Statistics with R: Part III Today I want to combine the California solar statistics with information about the annual solar insolation in each county as well as information about the population and median income. These can then be used as predictors in the models I'll build in the next post.

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