https://github.com/dirkschumacher/r-orms
2.Operations Research With R
https://github.com/sfeuerriegel/OperationsResearchWithR
http://www.rblog.uni-freiburg.de/2017/06/25/operations-research-with-r/
Author: Stefan Feuerriegel, ETH Zurich
R is widely taught in business courses and, hence, known by most data scientists with business background. However, when it comes to optimization and Operations Research, many other languages are used. Especially for optimization, solutions range from Microsoft Excel solvers to modeling environments such as Matlab and GAMS. Most of these are non-free and require students to learn yet another language. Because of this, we propose to use R in optimization problems of Operations Research, since R is open source, comes for free and is widely known. Furthermore, R provides a multitude of numerical optimization packages that are readily available. At the same time, R is widely used in industry, making it a suitable and skillful tool to lever the potential of numerical optimization.
The materials starts with a review of numerical and linear algebra basics for optimization. Here, participants learn how to derive a problem statement that is compatible with solving algorithms. This is followed by an overview on problem classessuch as one and multi-dimensional problems. Starting with linear and quadratic algorithms, we also cover convex optimization, followed by non-linear approaches such as gradient based (gradient descent methods), Hessian based (Newton and quasi-Newton methods) and non-gradient based (Nelder-Mead). We finally demonstrate the potent capabilities of R for Operations Research: we show how to solve optimization problems in industry and business, as well as illustrate the use in methods for statistics and data mining (e.g. quantile regression). All examples are supported by appropriate visualizations.
Lectures
Exercise sheets
Book
Work-in-progress
Misc
"Operations Research with R" by Stefan Feuerriegel is licensed under CC BY-SA 4.0. To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/4.0
This course wouldn't have been possible without the intensive help by many of my collaborators, including Julius Gordon and Joscha Märkle-Huß.
3.Operations Research with R — Graphical Method
Exploring the “lpSolve” R package
https://medium.com/swlh/operations-research-with-r-graphical-method-18f4ba34fea6
python很火，但是跟python类似的软件很多，例如R、Julia等,参见编程语言的历史。
就数据科学而言（data science），统计出身的数据科学家偏爱R一点，程序设计出身的数据科学家偏爱python一点；其实多数数据科学家是python、R并用的——发挥各自的优势；很多数据科学还同时使用tableau、Excel、google spreadsheet。
python是通用的编程语言，是开源软件（open source）。简言之python类似安卓系统或苹果iso系统，python的活力在于有很多拓展功能包或功能库（library），类似手机操作系统上的app。所以从这个意义上讲，重点不是python，而是要做哪个方向的研究，选择响应的功能库（library）。例如常见的数据分析库是pandas、numpy，常见的数据可视化用matplotlib，常见的网页数据抓取是BeautifulSoup、scapy。
python的书非常多，通用的、针对特定的领域的，都是如此。
建议通过http://libgen.rs/免费下载英文电子书。
我推荐3本通用性强、有中译本的书（估计不同人的推荐会非常不同）
1.Automate the Boring Stuff with Python
1.1英文版
Sweigart, A. (2020). Automate the Boring Stuff with Python: Practical Programming for Total Beginners (2nd). No Starch Press.
http://libgen.rs/book/index.php?md5=BFE1E2B65DA651477404660AE468D148
https://automatetheboringstuff.com/
https://www.amazon.cn/dp/B07VSXS4NK/
https://www.amazon.com/-/zh/dp/1593279922
1.2中文翻译版（原书第1版）
Sweigart, A.(2016). Python编程快速上手——让繁琐工作自动化 编程快速上手——让繁琐工作自动化 (1st ). 人民邮电出版社. https://www.amazon.cn/dp/B01M68PABD
http://libgen.rs/book/index.php?md5=5EA4E155C46104B393AFF7366772476C
1.3原书第2版对应视频文件
https://www.udemy.com/course/automate/
2.learn python in hard way
Shaw, Z. (2018). Learn More Python 3 the Hard Way: The Next Step for New Python Programmers. Addison-Wesley. http://libgen.rs/book/index.php?md5=5C39D11E8BDEE52F84E0F4A55B55F30D
Shaw, Z. (2018). “笨办法”学Python 3笨办法”学Python 3 (2nd). 人民邮电出版社有限公司. https://www.amazon.cn/dp/B07Y525WFQ
3.python for data analysis
Nature Food volume 2, pages326–329 (2021)
Future foods, such as microalgae, mycoprotein and mealworm, have been suggested as nutritious and sustainable dietary options. Here we consider one of the most profound, yet neglected, benefits of future foods farming systems—their potential to provide essential nutrition in the face of systemic disturbances—and discuss major barriers to realizing this prospect.
https://www.nature.com/articles/s43016-021-00269-x/figures/1
