This page features most recent and most popular posts on R.
Latest posts on R
- Three R Libraries Every Data Scientist Should Know (Even if You Use Python) - Dec 20, 2021Check out these powerful R libraries built by the world’s biggest tech companies.
- Data Preparation in R using dplyr, with Cheat Sheet! - Oct 20, 2021Leverage the powerful data wrangling tools in R’s dplyr to clean and prepare your data.
- Four Different Pipes for R with magrittr - Oct 6, 2021The magrittr package supplies the pipe operator (%>%), but it turns out that the package actually contains four pipe operators in total. Let's go into them a bit.
- Path to Full Stack Data Science - Sep 27, 2021Start your journey toward mastering all aspects of the field of Data Science with this focused list of in-depth self-learning resources. Curated with the beginner in mind, these recommendations will help you learn efficiently, and can also offer existing professionals useful highlights for review or help filling in any gaps in skills.
- ebook: Learn Data Science with R – free download - Sep 7, 2021Check out this new book for data science beginners with many practical examples that covers statistics, R, graphing, and machine learning. As a source to learn the full breadth of data science foundations, "Learn Data Science with R" starts at the beginner level and gradually progresses into expert content.
Most popular (badge-winning) recent posts on R
- Three R Libraries Every Data Scientist Should Know (Even if You Use Python) [Silver Blog]Check out these powerful R libraries built by the world’s biggest tech companies.
- Path to Full Stack Data Science [Gold Blog]Start your journey toward mastering all aspects of the field of Data Science with this focused list of in-depth self-learning resources. Curated with the beginner in mind, these recommendations will help you learn efficiently, and can also offer existing professionals useful highlights for review or help filling in any gaps in skills.
- A Guide On How To Become A Data Scientist (Step By Step Approach) [Platinum Blog]Becoming a Data Scientists is an exciting path, but you cannot learn data science within one year or six months—instead, it’s a lifetime process that you have to follow with proper dedication and hard work. To guide your journey, the skills outlined here are the first you must acquire to become a data scientist.
- The Most In-Demand Skills for Data Scientists in 2021 [Platinum Blog]If you are preparing to make a career as a Data Scientist or are looking for opportunities to skill-up in your current role, this analysis of in-demand skills for 2021, based on over 15,000 Data Scientist job postings, should offer you a good idea as to which programming languages and software tools are increasing and decreasing in importance.
- 15 Free Data Science, Machine Learning & Statistics eBooks for 2021 [Platinum Blog]We present a curated list of 15 free eBooks compiled in a single location to close out the year.
- R or Python? Why Not Both? [Silver Blog]Do you use both R and Python, either in different projects or in the same? Check out prython, an IDE designed to handle your needs.
- Text Mining with R: The Free eBook [Silver Blog]This freely-available book will show you how to perform text analytics in R, using packages from the tidyverse.
- Data Science Minimum: 10 Essential Skills You Need to Know to Start Doing Data Science [Platinum Blog]Data science is ever-evolving, so mastering its foundational technical and soft skills will help you be successful in a career as a Data Scientist, as well as pursue advance concepts, such as deep learning and artificial intelligence.
- Wrapping Machine Learning Techniques Within AI-JACK Library in R [Silver Blog]The article shows an approach to solving problem of selecting best technique in machine learning. This can be done in R using just one library called AI-JACK and the article shows how to use this tool.
- Python for data analysis… is it really that simple?!? [Silver Blog]The article addresses a simple data analytics problem, comparing a Python and Pandas solution to an R solution (using plyr, dplyr, and data.table), as well as kdb+ and BigQuery solutions. Performance improvement tricks for these solutions are then covered, as are parallel/cluster computing approaches and their limitations.
- Time Series Classification Synthetic vs Real Financial Time Series [Silver Blog]This article discusses distinguishing between real financial time series and synthetic time series using XGBoost.
- Python and R Courses for Data Science [Silver Blog]Since Python and R are a must for today's data scientists, continuous learning is paramount. Online courses are arguably the best and most flexible way to upskill throughout ones career.
- Plotnine: Python Alternative to ggplot2 [Silver Blog]Python's plotting libraries such as matplotlib and seaborn does allow the user to create elegant graphics as well, but lack of a standardized syntax for implementing the grammar of graphics compared to the simple, readable and layering approach of ggplot2 in R makes it more difficult to implement in Python.
- Data Science for Managers: Programming Languages [Silver Blog]In this article, we are going to talk about popular languages for Data Science and briefly describe each of them.
- Data Science Jobs Report 2019: Python Way Up, TensorFlow Growing Rapidly, R Use Double SAS [Gold Blog]Data science jobs continue to grow in 2019, and this report shares the change and spread of jobs by software over recent years.
- What you need to know: The Modern Open-Source Data Science/Machine Learning Ecosystem [Silver Blog]We identify the 6 tools in the modern open-source Data Science ecosystem, examine the Python vs R question, and determine which tools are used the most with Deep Learning and Big Data.
- Python leads the 11 top Data Science, Machine Learning platforms: Trends and Analysis [Gold Blog]Python continues to lead the top Data Science platforms, but R and RapidMiner hold their share; Almost 50% have used Deep Learning tools; SQL is steady; Consolidation continues.
- How to correctly select a sample from a huge dataset in machine learning [Silver Blog]We explain how choosing a small, representative dataset from a large population can improve model training reliability.
- R vs Python for Data Visualization [Gold Blog]This article demonstrates creating similar plots in R and Python using two of the most prominent data visualization packages on the market, namely ggplot2 and Seaborn.
- Who is a typical Data Scientist in 2019? [Gold Blog]We investigate what a typical data scientist looks like and see how this differs from this time last year, looking at skill set, programming languages, industry of employment, country of employment, and more.
- Running R and Python in Jupyter [Silver Blog]The Jupyter Project began in 2014 for interactive and scientific computing. Fast forward 5 years and now Jupyter is one of the most widely adopted Data Science IDE's on the market and gives the user access to Python and R