Contents

R or Python

Reference from zhihu and CSDN.

  1. R focuses on better user friendly data analysis, statistics and graphical models, while Python emphasizes productivity and code readability.
  2. R is uncomplicated to apply complex formulas for all kinds of statistical tests and models are readily available and easily used, while Python is flexible for doing something novel like building websites.
  3. R has a steep learning curve at start, you can easily learn advanced stuff once understand the basics. While Python pays more attention to readability and simplicity, which makes its learning curve relatively low and gradual.
  4. R and Python are comparable in terms of packages, the former has comprehensive archive network called CRAN, while the latter has package index called PyPi.

The closer you are to statistics, research and data science, the more you might prefer R; The closer you are to working in an engineering environment, the more you might prefer Python. Therefore, if you have enough time, you can learn both R and Python, but for different focuses. That is, use R to conduct statistical tests, graph data and inspect large data, use Python to write algorithm and deploy services. Moreover, we can do lots of interesting projects efficiently by integrating Python and R with rpython and rpy2,

After all, the set of programming languages is perfect, which means it has no isolated points.

Learning Resources

Comprehensive Resources

  1. Archived webinars, videos and learning roadmap from RStudio and its GitHub.

Basis

  1. Advanced R and its GitHub.
  2. Cheat sheets.
  3. Books recommended by zhihu.

RStudio

  1. RStudio documentation and its GitHub.
  2. R Markdown.
  3. RStudio Shiny.
  4. Frequently Asked Questions.

Data Science

  1. R for Data Science.