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Some thoughts about the R language survey

The only point of this note is to invite you to fill in the R language survey launched by the R Consortium earlier this month. The official announcement of the survey reads: Please take the survey yourself and help us spread the word on social media, by word of mouth, and any other way you can think of. The survey...

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Asking R questions

This note lists a few places where one can ask R-related questions and get an answer, usually in no more than a few hours. Why Anyone who has learnt a programming language has a history of questions that they have asked to: themselves – usually followed by a lot of documentation reading others, offline – hence the benefits of research labs and offices others,...

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More components of the R ecosystem

This note lists a few of the organizations that are pushing the R language forward, as of early 2017. R is happy language right now. Historically, the R Project for Statistical Computing has been supported by the R Foundation since its inception in 2...

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Technologies worth learning for data science

As a complement to my note on R as a data science language, this note lists ten other technologies that you might want to learn to use, or at least monitor, if you are interested in learning data science. Communication Git is a concurrent versioning...

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R as a data science language

R as a data science language

The R language is a ‘DSL’ – a domain-specific language. The domain that it deals with, however, is not well-defined. In this note, I call R a “data science language” and link to a few resources that make the point better than I could. R as a domain-specific language A few years ago, John D. Cook gave a presentation called “The...

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Turning KML into tidy data frames

Turning KML into tidy data frames

This note briefly introduces the tidykml package, which turns basic KML geometries into tidy data frames that can be visualized with ggplot2. Summary The tidykml package provides a quick way to import data from Google My Maps into R, in a format that makes it easy to manipulate the data and visualize it with ggplot2. Below is an example that uses data...

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Scraping Web sources: Two illustrations

Scraping Web sources: Two illustrations

Per request from a couple of students in a course on open data that I contribute to, here’s a short guide to the “why” and “how” questions about (Web) scraping, with links to examples to illustrate the usefulness of the technique. What is scraping? (Web) Scraping consists in writing computer code to automate the download and/or parsing of online data sources....

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Remember to use the RDS format

Note to self – Remember to serialize R objects as RDS files when it makes sense. Importing Stata data into R The European Social Survey recently announced that it had added Round 7 of its survey to its cumulative dataset, which can be downloaded in CSV, SPSS or Stata format. While my instinctive preference for storing data is to use CSV, in the...

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Compiling the ggplot2 book on Mac OS

Compiling the ggplot2 book on Mac OS

This note explains to compile Hadley Wickham’s ggplot2 book on Mac OS. This guide has 8 steps. If you have already installed R and RStudio, you should be able to get through Steps 1-4 very quickly. Similarly, if you use Git, Steps 5-6 should also be very straightforward. The longest steps are Step 7 (package dependencies) and Step 8 (book compilation). 1. Install the Command Line...

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One year of R / Notes

September 20, 2016
By

My collection of R notes is now slightly over one year old. This note reflects on how useful the exercise of blogging about R has been so far, and answers some of the questions that I have received about it. Blogging about R I created my collection of R notes with the intention to keep track of technical notes that I...

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