Σεμινάρια R (κοινότητα aua.R) (R_SEM)
Σύνδεσμοι
Γενικοί σύνδεσμοι |
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http://tryr.codeschool.com | Datacamp R | Swirl |
Κατηγορίες συνδέσμων |
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Ελληνικά βιβλία |
Style Guides |
Google's R Style Guide The goal of the R Programming Style Guide is to make our R code easier to read, share, and verify. The rules below were designed in collaboration with the entire R user community at Google. |
Ξενόγλωσσα βιβλία |
R for Data Science To βιβλιο R for Data Science στην online μορφή του | Geocomputation with R | Modern Dive: An Introduction to Statistical and Data Sciences via R | R Programming for Data Science Ένα από τα κλασσικότερα βιβλία για την R από τους πιο γνωστούς εκπαιδευτές του κλάδου | Advanced R Κλασσικό βιβλίο από τον guru Hadley Wickham | R for Data Science R for Data ScienceGarrett GrolemundHadley WickhamWelcomeThis is the website for “R for Data Science”. This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data. | R for data science | Efficient R programming Colin Gillespie Robin Lovelace | Hands-On Programming with R | The art of data science Roger D. Peng and Elizabeth Matsui | Exploratory data analysis Roger D. Peng |
Online Documentation |
Διάφορα |
Why I use R for Data Science – An Ode to R "Working in Data Science, I often feel like I have to justify using R over Python. And while I do use Python for running scripts in production, I am much more comfortable with the R environment. Basically, whenever I can, I use R for prototyping, testing, visualizing and teaching. But because personal gut-feeling preference isn’t a very good reason to give to (scientifically minded) people, I’ve thought a lot about the pros and cons of using R. This is what I came up with why I still prefer R…" |
R Online Learning |
Datacarpentry R |