Python and R resources for beginners...

...with special regards to science and data analysis applications, visualization and graphics

This collection has been started by Dávid Fazekas and continued by Dénes Türei. Submissions are welcome.

• Python and R resources for beginners...
  • Setting up a Python environment
  • Where to start?
  • Non Python specific but important resources
  • Interactive Python learning platforms
  • Programming exercises
  • Python tutorials
  • Python resources
  • Online Courses
  • Talks
  • Python environments
  • Python modules for data analysis
  • Python visualization and plotting
  • Graphs (networks)
  • Alternative network visuzalizations
  • Visualization in general
  • Visualization methods
  • Other tools for graphics and typography: post processing figures, designing slides, posters and figures, typesetting reports, papers, theses and books
    • TikZ
    • Pictograms, icons, patterns, reusable graphics
    • Fonts
  • R blogs and tutorials
  • Connecting Python and R
  • Statistics
  • IDEs (integrated development environments)
    • Python IDEs
    • R IDEs
  • Image processing
  • Chemistry
    • Chemical typography
      • LaTeX
  • Books
    • Python beginner and intermediate books
    • Advanced Python
    • R books
  • Lectures
  • Podcasts
  • Miscellanous
  • About programming
  • Coding style
  • Development tools
    • Documentation
  • Performance
    • Profiling
    • Efficient libraries
    • C extensions
    • Rust
  • Regular expression resources
  • awk, sed, grep
  • Unix, Linux and bash
    • Introductory Bash
    • SSH
  • Virtual machines
  • Windows
  • Linux
  • Life in academia

Python and R resources for beginners...

...with special regards to science and data analysis applications, visualization and graphics

Here you find a list of useful resources for learning Python or R, as well as a virtual environment where many tools are readily available for you. You will find this collection helpful either if you are a scientist and you just realized that you need computational tools for processing your data, or you already have done it for a while but looking for new tools and alternatives, or you just would like to start learning Python or R for any other purpose.

Setting up a Python environment

This repo contains a virtual environment created by David Fazekas and set up for easy installation. It is unmaintained for a couple of years already, I can not guarantee it works. The idea of this virtual environment is that you can start using Python without learning how to install Python and modules, which is not always straightforward and might even require some system administration knowledge. Of course later you can learn these, but the virtual environment provides a little help for a quick start. To install this environment to your own computer follow the description.

Alternatively you can install Python and modules the traditional way. See below.

General points

• Python has two major versions available: Python 2 and 3. These are incompatible, although it is possible to write code which runs both in 2 and 3. If you have both of them installed they will reside in their own directories and you install modules for them independently. It is highly recommended to use only Python 3 today. Almost all the important modules have been already ported to Python 3. Python 2 exists only to keep it possible to run old code when it's really necessary. The most important science and data analysis modules like numpy and scipy are about to end Python 2 support.
• On some Linux distributions and Mac OS X still Python 2 is the default. Sometimes the python command in the shell is a link to either python2 or python3. Same stands for pip/pip2/pip3. Check your system and be aware where are your python and pip exacutables and where is the site-packages or dist-packages directory (where the modules install).
• Always know which Python distribution you use and where do you install the modules. If you just call pip install numpy and then you start a Python shell from the Anaconda distribution, don't be surprised if import numpy gives a ModuleNotFoundError. If you are in doubt you can find out by which python and something like ls -l /usr/bin/python and similar ways.

Linux

In Linux distributions you will find up-to-date and well maintained packages for both Python 2 and 3 and also many modules. One issue is that pip and the distibution's package manager don't know about each other but will complain if files already exist (because the other package manager installed them) and won't recognize if a dependency is already installed by the other manager. Most often I just give the force option to overwrite the files.

Mac OS X

OS X comes with a built-in Python 2. Sometimes this is quite old. Anyways probably you will want to install an up-to-date Python 3 distribution. Most convenient is to install a package manager for OS X (most popular is HomeBrew, another one is MacPorts) and you can use these to install Python 3 and many other modules.

Python on Windows

Install Python by the provided installer and don't forget to tick the "include in the path" box. Also you might consider to install cygwin and git (actually git installer already offers also cygwin). This way you will have BASH and git which are essential for development. See more about Windows here.

pip

pip is the most often used package manager for Python. If pip does not come with your installation you can install it by easy_install pip. Or by your operating system's package manager.

Editor

If you are about to start writing code it is important to have a good text editor. What makes a good text editor is the followings:

• Syntax highlighting: it automatically colors the different elements of the language so it will be easier for you to recognize them and read the code
• Autocompletion: it automatically offers suggestions to complete words while you are typing so you don't need to type of long function or variable name more than once
• Automatic indentation and closing parentheses automatically. These features can make writing code even more convenient.
• Nice color scheme and line numbering: it is important to have a color scheme which has appropriate contrast and gentle with your eyes (usually dark background color schemes).
• Line numbering: you must have the lines numbered so you can easily find the line blamed by the error message.
• Search and replace also by regular expressions, go to line key combination.

In Linux usually not a problem as the default ones like gedit can be tuned to be quite good. Personally I use Kate from KDE. For Mac and Windows you need to install one, for example Notepad++ is popular for Windows, TextMate is popular for Mac. Also see the IDEs listed below and you can consider the new JupyterLab which is and IDE in the web browser.

Anaconda

Anaconda is a Python and R distribution and package manager for science and data analysis. They promise standardized packege management which make collaboration and deployment easier. Some people like it, I've never used it. For me pip and the system's package manager have been always easy to use and sufficient.

Where to start?

• https://www.python.org/ - The home of Python
• http://stackoverflow.com/documentation/python/topics - In 2016 StackOverflow started their Documentation project, which aims to be a community developed schoolbook and knowledgebase. This rapidly developed to one of the best Python learning resources on the web.
• https://www.codecademy.com/learn/python - Interactive platform to learn Python (as well as many other languages). Start here if you have never wrote code before.
• https://www.youtube.com/watch?v=HBxCHonP6Ro&list=PL6gx4Cwl9DGAcbMi1sH6oAMk4JHw91mC\_ - Long Python video tutorial for the very beginners
• https://github.com/mshang/python-elevator-challenge - Beginner tutorial with programming an elevator in Python

Non Python specific but important resources

• http://stackoverflow.com/ - This very important resource worths special mention. As you will se if you google for any programming issue, in 90% you will end up on this site. SO is a Q&A (question and answer) site where anybody can ask programming related questions and answer or comment others questions. Users collect reputation points for their contributions which made it a very efficient platform for community building around mutual help. Your question likely will be answered very quickly, but be careful not to ask something already answered by answers for other questions.
• https://bitbucket.org/ - We suggest you to familiarize yourself as soon as possible with version control frameworks. Nowdays the most popular is git. Version control helps you to keep track of changes, keep your project files in order, backup your work often, avoid data loss, to collaborate and to share your code in a standard and convenient way. BitBucket allows you to to create more private repos than the most well known git server, http://github.com/.
• https://maryrosecook.com/blog/post/git-from-the-inside-out - If you write code please start using git. Sooner is better, even your random exercises you can commit to a git repo. Here is an in depth introduction to git starting from the basics from Mary Rose Cook.

Interactive Python learning platforms

• https://www.codecademy.com/learn/python - Interactive platform to learn Python (as well as many other languages)
• http://www.learnpython.org/ - Lots of tutorials from the basics
• https://www.datacamp.com/courses/intro-to-python-for-data-science - Interactive tutorial focusing on Data Science applications
• https://www.datacamp.com/courses/intermediate-python-for-data-science - Interactive tutorial focusing on visualization with matplotlib

Programming exercises

When you write code with the aim of learning it is often difficult to find a task, you want to code, but don't know what to code. In Euler Problems you find hundreds of small mathematics problems, each of them you can solve just in a few lines of code, ideal even if you have only half hour for practicing. As you develop you can return to already solved problems, and find out better and nicer implementations.

• https://projecteuler.net/archives
• http://www.ling.gu.se/~lager/python_exercises.html - 46 very simple exercises for completely beginners
• http://rosalind.info/ - Learning bioinformatics by problem solving: a structured repository of bioinformatics problems; you can earn badges and reputation by solving these problems

Python tutorials

• https://imgur.com/WRuJV6r - Debugging workflow for beginners
• https://automatetheboringstuff.com/ - Python for beginners, a different way, with many tutorials
• http://www.jessicayung.com/ - Many articles about Python basic topics and machine learning, good style, concise but still in-depth information, by Jessica Young.
• https://realpython.com/ - Good and thorough tutorials both for beginners and more advanced Python users.
• http://python.swaroopch.com/ - Learn Python from the beginning by tutorials
• https://www.codementor.io/python/tutorial - Useful tutorials from experts
• https://github.com/faif/python-patterns, https://python-patterns.guide/ - Programming patterns in Python
• http://www.python-course.eu/ - A full Python course for beginners, covering all important basic topics
• https://maryrosecook.com/blog/post/a-practical-introduction-to-functional-programming - This is a very nice introduction to functional programming in Python from Mary Rose Cook.
• https://chrisalbon.com/ - A massive collection of tutorials from Chris Albon, covering statistics, data manipulation, machine learning, modeling, plotting
• https://nbviewer.jupyter.org/github/twiecki/CythonGSL/blob/master/examples/cython_gsl_ipythonnb.ipynb - Little quick start tutorial from Thomas V. Wiecki about how to boost Python performance with including some C code at critical parts.
• https://rszalski.github.io/magicmethods/ - A comprehensive guide for magic methods from Rafe Kettler. Very helpful when you design your classes.
• https://stackoverflow.com/questions/582336/how-can-you-profile-a-python-script Collection of profiling tools.
• https://www.sharpsightlabs.com/blog/ - This is a company offering courses, but in their blog there are a number of great materials.
• https://www.dataquest.io/blog/ - Same as the previous one: nice free tutorials from an education company.
• https://kanoki.org/tag/python/ - Lots of data science tutorials about pandas, visualization, statistics, machine learning, etc. from a guy called Vinay.
• https://www.datisticsblog.com/ - From R to Python series.

Python resources

Here we list blogs and essays which are not primarily tutorials, but give an introduction or insight into specific topics.

• https://julien.danjou.info/blog/tags/Python - Python related articles from Julien Danjou's blog. You can find excellent pieces here, for example about testing, profiling, exception handling, pep8 standard, etc.
• https://dbader.org/ - Excellent articles about how Python works from Dan Bader.
• https://jakevdp.github.io/ - Interesting insights into specific topics.
• http://seriously.dontusethiscode.com/ - Nice Python tricks from :james.
• http://pyvideo.org/ - Thousands of talks from Python conferences about a wide range of topics.
• http://xahlee.info/ - Webpage of Xah Lee, with a huge collection of tutorials and opinion articles on programming.
• https://www.programcreek.com/python/ - Some very inspiring examples in form of little snippets from the community.

Online Courses

• https://www.coursera.org/specializations/python - A Coursera specialization for learning Python

Talks

• https://www.youtube.com/watch?v=OSGv2VnC0go - Writing good quality code
• https://www.youtube.com/watch?v=sPiWg5jSoZI - Python 3 metaprogramming

Programming languages and paradigms

• https://www.youtube.com/watch?v=UogkQ67d0nY - Great intro into both functional and array programming.
• https://www.youtube.com/watch?v=QyJZzq0v7Z4 - Insights into the history of programming paradigms.
• https://www.youtube.com/watch?v=GZuZgCDql6g - Cool intro for array programming.

Python environments

• http://www.bpython-interpreter.org/ - Nice, colorful command line environment with smart autocompletion and built in help functions.
• https://gist.github.com/lonetwin/5902720 - You can easily customize your Python shell with editing your *~/.pythonrc- file. For example, copy the one in this git repo into yours, and you will have a colored shell with autocompletion.
• https://www.pythonanywhere.com/ - A full Python environment in the cloud with lots of libraries and many Python versions available. You can write Python scripts in the browser, and even deploy your application as a webpage. Free plan is available.
• https://jupyter.org/ - Interactive Python environment in the browser: Python runs in the background on your machine, and you write the code and get the output in the browser, in so called notebooks. Note: formerly known as IPython, they just renamed when it became language agnostic (originally it was only for Python, but now can be used also with other languages, for example R).
• https://blog.jupyter.org/jupyterlab-is-ready-for-users-5a6f039b8906 - A complete IDE developed from Jupyter.
• https://www.continuum.io/why-anaconda - Python environment intended for science and data analysis, with easy availability of relevant modules (at least in theory: eventually installation might be more complicated).

Generic Python modules

• http://krondo.com/in-which-we-begin-at-the-beginning/ - Book about twisted, a module you can easily build web servers and other network resources with.
• https://www.crummy.com/software/BeautifulSoup/ - A module for easy HTML processing.

Python modules for data analysis

• http://www.numpy.org/ - Efficient operations on multidimensional numeric arrays (i.e. matrices of numbers).
• https://www.scipy.org/ - Collection of many stats and science methods, like regression, statistical tests.
• http://pandas.pydata.org/ - Built on top of numpy, pandas provides a more convenient handling of data tables, i.e. here you can have row and column names, methods for convenient rearranging and filtering your data. You can imagine a programmable excel sheet, or something like data frames in R. It's design is not so nice as its R counterpart dplyr, also suffers from performance issues. The API is far from being intuitive, this great cheatsheet might help a bit: https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf
• https://tomaugspurger.github.io/modern-1-intro.html - In pandas the multiple possible solutions for many basic data operations can be confusing, especially that often the one of the solutions have acceptable performance, often a very unintuitive one. And anyways pandas syntax, in my opinion, is quite ugly and convoluted. This excellent guide helps you to find your way across the mess if you have to use pandas.
• https://jupyter.org/ - Interactive Python environment in the browser: Python runs in the background on your machine, and you write the code and get the output in the browser, in so callednotebooks. Note: this is the same as IPython was, they just renamed when it became language agnostic (originally it was only for Python, but now can be used also with other languages).
• https://boltons.readthedocs.org/en/latest/ - Many useful tools for advanced Python programming
• https://www.datisticsblog.com/2018/08/r2py_pandas/ - A really great guide for pandas if you are already familiar with dplyr.
• https://www.shanelynn.ie/summarising-aggregation-and-grouping-data-in-python-pandas/ - Aggregating data in pandas is much less intuitive than in dplyr: here is a really clear tutorial.
• https://sharkcoder.com/data-visualization/pandas-tricks - Nice and long pandas tutorial.

Pandas alternatives

• https://siuba.readthedocs.io/en/latest/ - A dplyr inspired data analysis package. The interface is much more intuitive and nicer than pandas. It uses pandas as a backend, so you can always fall back to using pandas methods. Authored by Michael Chow.
• https://learn.siuba.org/intro-data-science/ - An introductory book for data science in Python, using siuba (Python's dplyr equivalent) from Michael Chow.
• https://github.com/h2oai/datatable - A data.table (high performance R data manipulation package) clone in Python, written in C++.
• https://www.linkedin.com/pulse/python-data-analysis-really-simple-ferenc-bodon-ph-d-/ - Performance benchmark of some data manipulation tools, including pandas, dplyr, data.table and kdb+.
• https://h2oai.github.io/db-benchmark/ - Benchmark of even more data frame backends for Python, R and Julia.
• https://www.pola.rs/ - A data frame library written in Rust, with nice Python API. One of the top performant in the benchmark above.
• https://github.com/pwwang/datar - A good looking data frame library with tidyverse-like API. Uses numpy and pandas as backends.

Python visualization and plotting

We have seen a number of efforts emerging in the past years with the aim to provide powerful data visualization in Python, so sciencists and data analysts would not need to be envy of R user colleagues. Perhaps the perfect ggplot2 or lattice equivalent is still to come (although two very fresh libraries, Altair and Plotnine are promising), but each of the frameworks listed below are very good in certain tasks, and of course have its limitations. Thus, it is difficult to chose a plotting library, likely you will try more of them.

• https://python-graph-gallery.com/ - Collection displaying hundreds of charts, always providing the reproducible python code.
• http://matplotlib.org/ - The "grandmother of all Python plotting applications", extremely customizable, but complex plotting module. Note: you can use matplotlib either by its object oriented interface or by the matlab inspired pylab interface, be aware of the differences.
• https://github.com/rougier/matplotlib-cheatsheet - Similar to the Rstudio cheatsheets: great for a quick start with matplotlib and also as a reminder during every day work.
• https://www.pythoncharts.com/matplotlib/rotating-axis-labels/ - Matplotlib is a great library, unlike pandas its API is well designed. Still, looking at a basic everyday task, rotating the tick labels, we see that there are at least 5 different solutions and none of them is perfect.
• http://seaborn.pydata.org/ - Module built on matplotlib, providing simple methods and nicer default styles. And you can still access the low level matplotlib interface, in case you need it.
• http://gureckislab.org/courses/spring20/labincp/chapters/09/00-plots.html - Great matplotlib book chapter from the cognition and perception class of Todd Gureckis.
• https://sharkcoder.com/ - Some great matplotlib, plotly, seaborn and D3.js tutorials: see the Data Visualization menu.
• https://plot.ly/python/ - An easy way to make interactive html plots in IPython notebook. Note: it is not obvious from their webpage, but you don't need to register even for the free plan to use this library locally and generate html plots; you can install by pip and use it just like any other module.
• http://bokeh.pydata.org/en/latest/ - A plot.ly alternative.
• http://lightning-viz.org/ - Similar to the former two. Note, in Python you can run lightning without server, just like the other plotting libraries.
• http://holoviews.org/index.html - Feature rich, data driven plotting framework with matplotlib, seaborn and bokeh backends and connections to mpld3 and plotly via extensions.
• http://pygal.org/en/stable/ - Similar to the 2 above, creates interactive plots.
• https://mpld3.github.io/index.html - Here you create the plots with matplotlib, and transform them to d3.js objects, so they remain interactive in the browser, independently from Python.
• https://github.com/vega/vega/wiki/Documentation - A generic visualization grammar (i.e. a declarative language to describe visualizations).
• https://vega.github.io/vega-lite/ - A simplified version of Vega.
• https://vincent.readthedocs.io/en/latest/ - A tool to define plots in Python and translate them to Vega.
• https://altair-viz.github.io/ - A declarative visualization module for Python using Vega in the background.
• http://vispy.org/gallery.html - This creates and renders dynamic visualizations using the graphic card of your computer.
• http://ggplot.yhathq.com/ - An attempt to make the famous R ggplot visualization library available in Python. Does not provide all the features and exactly the same interface, but still a nice library.
• https://github.com/sirrice/pygg - Provides access to R ggplot2 from Python.
• https://github.com/dgrtwo/gleam - Creates interactive visualizations for browser; Python needs to run in the background.
• https://dansaber.wordpress.com/2016/10/02/a-dramatic-tour-through-pythons-data-visualization-landscape-including-ggplot-and-altair/ - An amusing and detailful comparison of matplotlib, pandas, seaborn, ggplot and altair, with 5 examples and analysis. If you want to make an informed decision about which library to use, don't skip this.
• http://pbpython.com/visualization-tools-1.html - Another overview of tools, but with less details and less examples.
• https://lisacharlotterost.github.io/2016/05/17/one-chart-code/ - A wider but less detailed comparison.
• https://www.pythoncharts.com/ - Many elaborate tutorials about Python visualization, not just the basics, but many fine details, still concise and on point.
• https://matplotlib.org/1.4.3/style_changes.html - Good material about color maps.
• https://plotnine.readthedocs.io/en/stable/index.html - So far the most faithful implementation of ggplot2 in Python. A great declarative plotting library.
• https://e2eml.school/matplotlib_ticks.html - It's often not easy to find answers about matplotlib, but these course materials provide a really complete guide, with many details you don't find at other places. Here I link to one section, but navigating to the course page you can access all the further sections (don't click on the links but change the URL of the section linked here).

Graphs (networks)

• http://igraph.org/ - Powerful graph analysis package with full featured R and Python interface and great visualization module.
• http://www.data-imaginist.com/2017/Announcing-ggraph/ - A really great new module for graph visualization with R ggplot2. It works together with igraph: you can use igraph graphs and layouts seemlessly.
• http://sachaepskamp.com/qgraph - Another super R module for graph analysis from Sacha Epskamp. It extends igraph with additional layouts and methods, and they work together very well.
• https://bitbucket.org/snippets/deeenes/LjL5R/good-fruchterman-reingold-layout - Sometimes it is difficult to plot a graph with nodes evenly spaced and avoiding overlaps. Here are some good parameters for the Fruchterman Reingold layout using qgraph but the resulted layout can be used with igraph (sadly in the recent version of R igraph the fr_layout method does not handle these parameters hence we must calculate the layout with qgraph).
• https://datastorm-open.github.io/visNetwork/ - Interactive, HTML and javascript based network visualization from R.
• https://graph-tool.skewed.de/ - A high-performance graph library with Python interface.
• https://networkx.github.io/ - A network analysis module written purely in Python, recently with more features and better visualization.
• https://pygraphviz.github.io/ - Python interface for graphviz, the graph visualization library known from its great layout algorithms.
• http://kateto.net/network-visualization - In depth R network visualization tutorial including dynamic, interactive figures and animations, from Katya Ognyanova. Is worth to check out other nice materials on the webpage.
• https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007244 - 10 simple rules guide for network figures.

Alternative network visualizations

• http://mkweb.bcgsc.ca/linnet/, http://www.hiveplot.net/ - Hive plots from Martin Krzywinski: a great alternative to make visible structures in large and complex networks
• http://www.biofabric.org/index.html - Another tool to make visualization of complex networks more clear and informative. By William Longabaugh.
• http://circos.ca/ - A fancy visualization tool from Martin Krzywinski to present a connectogram and many other variables in a complex figure.
• http://circos.ca/documentation/course/ - On the Circos webpage you can find a nice course with many complex visualization examples
• https://jokergoo.github.io/circlize_book/book/ - Fantastic book which serves as a manual for Circlize, the R implementation of Circos, from Zuguang Gu.
• Arc diagrams from Gaston Sanchez.
• http://hangingtogether.org/?p=3053 - Sankey diagrams.

Visualization in general

• https://www.data-to-viz.com/ - Helps to decide how to visualize your data based on the types of the variables and gives an example for each visualization methods.
• https://datavizcatalogue.com/search.html - Similar to the previous one, an overview of visualization types with examples.
• https://graphicsprinciples.github.io/ - General principles of visualization in form of a concise cheatsheet.
• https://helenajambor.wordpress.com/ - Rich blog about scientific visualization by Helena Jambor.
• https://github.com/cxli233/FriendsDontLetFriends - Examples of good and bad visualization practices, by Chenxin Li.
• http://circos.ca/ - A fancy visualization tool from Martin Krzywinski to present a connectogram and many other variables in a complex figure
• http://mkweb.bcgsc.ca/brewer/ - Many blog posts, tutorials and examples about data visualization from Martin Krzywinski.
• http://colororacle.org/ - Tool for checking color blind readability of figure colors
• https://personal.sron.nl/~pault/ - Guide for color blind safe palettes from Paul Tol. Especially see the tech notes
• http://colorbrewer2.org/ - Color palettes from Cynthia Brewer. You can access these from R using the RColorBrewer package.
• http://junkcharts.typepad.com/junk_charts/ - Many visualization critics by Kaiser Fung: he takes a wrong data visualization and shows how it could be done better
• http://serialmentor.com/dataviz/ - Excellent free book about basic questions of visualization from Claus O Wilke.
• https://www.data-imaginist.com/ - Blog with nice visualization tutorials from Thomas Lin Pedersen.
• https://tuowang.rbind.io/post/2021-03-25-ggplot2-extensions/ - Great ggplot theming examples from Tuo Wang.

About particular visualization methods

• https://wellcomeopenresearch.org/articles/4-63 - Raincloud plot: boxplot and violinplot alternative.
• https://lvdmaaten.github.io/tsne/ - Dimensionality reduction method for 2D/3D visualization.
• https://distill.pub/2016/misread-tsne/ - Interactive insigths into parameter sensitivity and artefacts in the t-SNE dimensionality reduction method.

Other tools for graphics and typography: post processing figures, designing slides, posters and figures, typesetting reports, papers, theses and books

• https://www.typewolf.com/cheatsheet - Orient yourself quickly regarding the correct use of various quotation marks, hyphens, dashes and other special characters. Did you know that 5 different single quote marks and 5 different dashes exist?
• https://practicaltypography.com/ - A more detailed, but still quite concise guide for basic typography, by Matthew Butterick. This link will first redirect you to the donation page; please consider donating to the author, though you can access the the book without any donation, simply by navigating to the main page.
• https://github.com/gztchan/awesome-design - A curated collection of graphic design resources from Tony Chan.
• https://css-doodle.com/ - A library for drawing static or animated vector graphics with CSS within HTML files.
• http://inkscape.org/ - Inkscape is a professional vector graphics editor and a free alternative of Adobe Illustrator. Its default format is standard SVG, while you can import and export many other formats, for example, of course PDF. Of note, you really should not pay $240 per year for an application to a company only to design vector graphics. Nor should you make your institute pay it for you. This way you only chain yourself to Adobe and the more time and energy you invest in learning to use this sophisticated application it just constrain you to keep using it and keep paying for it. What's the point in this when an excellent free and open source alternative is available? Inkscape is powerful and what you learn and achieve using Inkscape remains yours forever.
• http://gimp.org/ - GIMP stands for GNU Image Manipulation Program. Is a professional bitmap graphics editor and a free alternative of Adobe Photoshop. What I wrote about Inkscape and Illustrator as alternatives completely stands also for GIMP and Photoshop.
• https://www.latex-project.org/ - LaTeX is the best tool and the state of the art standard for scientific typesetting and publishing. Created in the 80s by Leslie Lamport, and developed by the scientific community with the aim of having a tool which completely fits their needs. LaTeX is a free and open source software built on top of TeX which has been created by Donald Knuth also addressing the needs of scientific typography. The difference is that TeX does very basic elementary things in the background, like how to fill a line of text with symbols, while LaTeX provides macros for more complex tasks, like how to size and position items of a list or titles on a page in order to make it look good. I can not say LaTeX is an alternative of Adobe InDesign, but for scientific publishing it is definitely superior. You can of course use it for typesetting fiction books or entertaining journals, but maybe you will have more difficulties and if you just want an open source alternative for this, there is Scribus. If you need help with LaTeX don't go to StackOverflow but to its sister site dedicated for LaTeX. One more important thing: you can not simply download and install LaTeX, it comes packaged in many different distributions, including different fonts, typesetting engines and macro packages. First you should look up which of these are available for your operating system. And many templates and styles are also available, for example journals used to have their own article style, universities their own presentation and thesis styles.
• https://www.wikiwand.com/en/Beamer\_%28LaTeX%29 - Beamer is a LaTeX package for creating presentations. In my opinion for scientific presentations it is much better than PowerPoint and Keynote which are the most awful applications I have ever seen and I am really happy I could completely avoid them in the last 15 years. Also Beamer is a free and open source software. Most of the default themes look not very nice and old-style, but you can easily modify them to have something better looking. See examples here, here, here, here, here, here, here or here. If you work at EMBL or in the Saez-Rodriguez Group at RWTH Aachen University you can find my Beamer theme, slightly modified from PaloAlto, in my git repos there: @EMBL or @Aachen. Other notes: the final format of your slides will be PDF which is perfectly cross-platform. You should check or ask the tech support for the aspect ratio of the projector in your lecture room. Prepare wide-screen (16:9 or 16:10) slides if those fit as you can have more space this way. Also check if your connection can transmit this resolution and have an HDMI cable and adapter with you if necessary. VGA cables are sometimes limited to 4:3 aspect ratio and 1024x768 resolution which is quite poor.
• http://www.texstudio.org/ - TeXstudio is a great editor for LaTeX. It comes with autocompletion, built in help, embedded compilation tool, PDF viewer and many other handy tools.
• http://gpick.org/ - Gpick is a nice little color picker and palette editor application for Linux by Albertas Vyšniauskas. I use it with great satisfaction to create palettes what I use later in R, Python, Inkscape, GIMP or whereever else. I don't know about alternatives for Mac or Windows but definitely there are.
• https://www.zint.org.uk/manual/chapter/3 - Zint is a powerful and well designed QR code generator application. It has CLI, GUI (Qt) and web interfaces. QR codes are straightforward, the world is full of solutions to generate them, but most of these are not free or very limited in features. Why not to simply use one of the best and totally free solutions?

TikZ

• http://www.texample.net/tikz/examples/ - PGF/TikZ is a LaTeX package for creating high quality scientific graphics authored by Till Tantau. To find out what kind of graphics, see the examples in the galery. If your figure involves lots of maths, algebra or needs many alignements and positioning TikZ might be useful for you. It is worth to take a look on the 1161 pages user manual of TikZ, it is really amazing! Or you can start with the short introduction.
• https://sites.google.com/site/kochiuyu/Tikz - A versatile collection of TikZ examples from Chiu Yu Ko.
• https://wiki.physik.uzh.ch/cms/latex:tikz - A few TikZ examples from the wiki of the Institute of Physics in University of Zürich.
• https://awesomeopensource.com/project/walmes/Tikz - A wonderful, huge collection of TikZ examples from Prof. Walmes M. Zeviani.
• http://www.kaarebmikkelsen.dk/?p=113 - How to add fancy labels to your equations.
• https://latexdraw.com/ - A fantastic resource of TikZ illustration tutorials.
• http://perso.ens-lyon.fr/nemo.fournier/latex.html - Some nice TikZ examples from Nemo Fournier.

Pictograms, icons, patterns, reusable graphics

Creating graphics, it's really time consuming to draw everything from scratch. Instead it's convenient to use re-usable elements, for example a pictogram symbolising a mouse, a dotted pattern, a Twitter logo, etc. There are plenty of resources where you can find these things for free.

• https://fontawesome.com/ - A collection of 14k+ nicely designed pictograms (icons). Packaged as a font, which makes it's use even easier in almost any medium: HTML, SVG, LaTeX.
• https://freesvg.org/ - More than 150k free SVG drawings, most of them are low quality, but still many of them are useful, e.g. patterns, or single color pictograms.
• https://svgsilh.com/ - Similar to the previous one, with better quality on average.
• https://www.svgrepo.com/ - Another free SVG repo with 300k+ items.

Fonts

A good choice of fonts can really make a piece of graphics stand out. The main difficulty is to find the right font in the right time: browsing through thousands of fonts is very slow and unefficient, while good public tagging or classification systems are not really available so far.

• https://www.fontsmith.com/blog/2019/06/24/a-guide-to-type-styles - A short introduction about font styles: quickest way to get the basic terminology (especially the PDF available at the bottom of the page).
• https://www.fontsquirrel.com/ - A great collection of quality free fonts.
• https://fonts.google.com/ - More than 1.5k free font families. Thanks to the API, it's easy to embed the fonts into webpages and they are readily available in some font manager apps. The web interface is convenient for browsing the fonts and taking them to a test drive.
• https://www.myfonts.com/ - Huge database of commercial fonts. A few of them are free, but typically the less valuable ones or a few specimens from larger families.
• https://fonts.ilovetypography.com/ - Another large commercial font store and a nice typography blog.
• No link here, but you can find many font sharing forums in the Russian facebook, vk.com. Just search it by google.
• https://fontba.se/ - This program has a $30/y subscription fee, but it offers crowd sourced and AI enhanced tagging. It's also faster than the following two.
• https://github.com/FontManager/font-manager - A free alternative, capable of everything that fontba.se, except that you can tag only manually.
• https://github.com/fontmatrix/fontmatrix - Another free font manager, great for organizing of your font collection.
• https://fontforge.org/ - A font editor full of featues, fast and easy to use. Comes handy if you want to add some missing accent, modify a symbol, etc.
• https://birdfont.org/ - Great FOS font editor, looks pretty, but feels less responsive than FontForge.

R blogs and tutorials

• https://www.rstudio.com/resources/cheatsheets/ - Very helpful and concise cheatsheets covering ggplot2, dplyr and some other topics. Have them on your desk printed! Similar for Python matplotlib: https://github.com/rougier/matplotlib-cheatsheet
• https://www.bigbookofr.com/ - An enormous curated collection of R resources and books, similar to the present document, but with longer recommendations.
• https://monashbioinformaticsplatform.github.io/r-more/ - Introduction to best practices, among others tidyverse
• https://trinkerrstuff.wordpress.com/
• https://www.r-bloggers.com/
• https://kevinushey.github.io/blog/ - In depth articles on many R topics including Rcpp from Kevin Ushley, an RStudio developer.
• https://rpubs.com/bradleyboehmke/data_wrangling - It is not easy to understand how tidyr's gather works. Here Brad Boehmke explains it with visual help.
• https://www.zevross.com/blog/ - Technical blog from Zev Ross, a professional GIS data analyst, many fantastic tutorials not only GIS related.
• https://sebastiansauer.github.io/
• https://blog.exploratory.io/
• http://adolfoalvarez.cl/the-hitchhikers-guide-to-the-hadleyverse/ - A brief overview of all R packages authored by Hadley Wickham.
• https://www.tidyverse.org/ - Not only the very nice and comprehensive documentation of ggplot2, dplyr and other related packages from Hadley Wickham, but many articles and other resources.
• http://adv-r.had.co.nz/ - If you want to go really pro with R read this fantastic free online book with many examples from Hadley Wickham.
• Viridis color palettes for R
• http://www.win-vector.com/blog/ - Interesting insights into many topics in R data analysis from Nina Zumel and John Mount. They also have a nice book, see below at R books.
• http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html - Selva Prabhakaran
• http://stat545.com/ - Webpage of the University of British Columbia Statistics course. Very good materials most of them with R.
• https://rpubs.com/ - A page designed for publishing R tutorials. You often find here very nice ones by google, but I could not find any index or catalogue on the webpage itself, you see the recent ones by default which are not guaranteed to be the best ones.
• https://sebastiansauer.github.io/ - Dozens of great R tutorials, posts about statistics and tidyverse from Sebastian Sauer.
• http://www.listendata.com/ - Tutorials and learning materials for R, Python, data analysis and statistics from Deepanshu Bhalla
• https://martinsbioblogg.wordpress.com/ - Many posts with good R examples in Martin Johnsson's bioinformatics blog
• http://mqasim.me/
• http://www.sthda.com/english/ - Statistical Tools for High-throughput Data Analysis from Alboukadel Kassambara. Many excellent materials mostly about R.
• http://www.brodrigues.co/ - Great posts about statistics and various R packages from Bruno Rodrigues.
• http://colinfay.me/ - Colin Fay's webpage with fantastic R tutorials, many insights in numerous topics.
• https://edwinth.github.io/ - Some really nice tutorials from Edwin Thoen, among others about NSE and tidyeval.
• http://www.gastonsanchez.com/ - A generic introduction to data analysis from Gaston Sanchez, not only R but bash, git, makefile, etc.
• https://nsaunders.wordpress.com/blog/ - Data analysis and bioinformatics blog from Neil Saunders: mostly visualization and R.
• http://cfss.uchicago.edu/syllabus.html - Computing for the Social Sciences course University of Chicago. Materials for many topics in R.
• https://tjmahr.github.io/year-archive/ - Tristan Mahr's professional blog with many R related posts.
• http://cyclismo.org/tutorial/R/ - A comprehensive R tutorial from Kelly Black, University of Georgia. You may find also a Matlab tutorial if you are interested.
• https://deanattali.com/ - About shiny from Dean Attali.
• https://urbaninstitute.github.io/urban_R_theme/ - Nice and clear ggplot theme.
• https://www.sharpsightlabs.com/blog/ - This is a company offering courses, but in their blog there are a number of great materials.
• https://www.dataquest.io/blog/ - Same as the previous one: nice free tutorials from an education company.
• https://wilkelab.org/SDS375/slides/redundant-coding.html - Examples for better figure labeling, from Claus O. Wilke.
• https://wilkelab.org/SDS375/schedule.html - Claus O. Wilke's data visualization course at University of Texas at Austin.
• https://wiki.archlinux.org/title/R - Some good tips and tricks for using R, not all of them are Arch Linux specific.
• https://r-universe.dev/organizations/ - R package repositories, beyond CRAN and Bioconductor. Also a great place to distribute your package.
• https://easystats.github.io/easystats/ - A package collection focused on statistics, reporting, visual styles and minimal dependencies.
• https://rstats.wtf/ - Beginner and intermediate R book for data analysts, about best practices in data analysis workflows and projects.
• https://www.brodrigues.co/ - Many great R tutorials and posts about data science and visualization from Bruno Rodrigues.
• https://www.rebeccabarter.com/ - Blog of Rebecca Barter, with many data analysis R tutorials, especially around tidyverse.

Connecting Python and R

• https://github.com/randy3k/rchitect - Run R code from Python, share objects seamlessly.
• https://github.com/rpy2/rpy2 - Does the same as rchitect: provides an interface to R from Python.
• https://rstudio.github.io/reticulate/ - Use Python from R.

Statistics

These are not Python related but generic.

• http://simplystatistics.org/ - Nice blog from statistics teachers of John Hopkins University and Harvard University
• http://varianceexplained.org/ - Another nice blog from data sciencist David Robinson
• http://www.data-imaginist.com/ - Webpage of data analyst Thomas Lin Pedersen, author of ggraph.
• http://sachaepskamp.com/ - Webpage of Sacha Epskamp, author of qgraph. Some good tutorials and a nice dissertation.
• http://www.traag.net/ - Webpage of Vincent A Traag, who made available louvain community detection for Python igraph, plus some other tutorials.
• http://www.statsblogs.com/ - Blog about statistics, not only R, but still many R examples and also visualizations.
• http://biostat.mc.vanderbilt.edu/wiki/Main/ClinStat - Big collection of excellent resources from Frank Herrel, professor of biostatistics at Vanderbilt University.
• https://www.datascience.com/blog/introduction-to-bayesian-inference-learn-data-science-tutorials - Simple Bayesian inference example in Python.
• https://statisticsbyjim.com/anova/post-hoc-tests-anova/ - A very clear explanation of ANOVA by Jim Frost.
• https://academic.oup.com/biostatistics/article/17/1/29/1744261 - Caveats about batch effect corrections.

P-values

• https://amstat.tandfonline.com/doi/pdf/10.1080/00031305.2016.1154108?needAccess=true

Debate in Nature Methods

• http://moscow.sci-hub.tw/e0e621b86fe721cd05bcb08dee2be99a/halsey2015.pdf#view=FitH The fickle p-value generates irreproducible results
• http://twin.sci-hub.tw/6071c3e7dc16d3f300e65737a2b8092d/huber2016.pdf#view=FitH - A clash of cultures in discussions of the p-value
• http://twin.sci-hub.tw/63eed7b3a3c7a2c498bc0d634a94c65a/10.1038@nmeth.3932.pdf#view=FitH - Confidence intervals are no salvation from the alleged fickleness of the p-value
• http://twin.sci-hub.tw/e7c2de4af02893f042d4ef126e38f107/halsey2016.pdf#view=FitH - Response to: Confidence intervals are no salvation from the alleged fickleness of the p-value

Others

• https://www.tandfonline.com/doi/pdf/10.1080/01621459.2017.1289846?needAccess=true
• http://fivethirtyeight.com/features/statisticians-found-one-thing-they-can-agree-on-its-time-to-stop-misusing-p-values/
• https://www.youtube.com/watch?v=ez4DgdurRPg - Simulation of p-values accross replicates of the same experiment.
• https://www.nature.com/news/one-size-fits-all-threshold-for-p-values-under-fire-1.22625
• https://www.nature.com/news/scientific-method-statistical-errors-1.14700
• https://www.nature.com/news/psychology-journal-bans-p-values-1.17001
• https://www.nature.com/news/statistics-p-values-are-just-the-tip-of-the-iceberg-1.17412
• http://shiny-eio.upc.edu/bne/efectos2/
• https://repository.upenn.edu/cgi/viewcontent.cgi?article=1054&context=marketing_papers

Suggestions for new p-value

• http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0188299&type=printable
• https://www.vox.com/science-and-health/2017/7/31/16021654/p-values-statistical-significance-redefine-0005
• https://www.r-bloggers.com/why-the-ban-on-p-values-and-what-now/
• http://journals.sagepub.com/doi/pdf/10.1177/0956797613504966

IDEs (integrated development environments)

IDEs help you to keep track of files in your project, their history, dependencies, testing, outputs, etc.

Python IDEs

• https://www.pythonanywhere.com/ - A full Python environment in the cloud with lots of libraries and many Python versions available. You can write Python scripts in the browser, and even deploy your application as a webpage. Free plan is available.
• https://www.yhat.com/products/rodeo - Python IDE similar to RStudio, focusing on data analysis.
• https://www.jetbrains.com/pycharm/ - One of the best IDEs for python, with many tools and complex interface
• https://www.continuum.io/why-anaconda - Python environment intended for science and data analysis, with easy availability of relevant modules

R IDEs

• http://rstudio.com/ - Very popular and powerful IDE for R. Built in editor, file browser, shell, plot viewer, manual and many other helpful tools.
• https://github.com/randy3k/radian - An R console with autocompletion and syntax highlighting.
• https://github.com/jalvesaq/colorout/ - High performance output coloring for R consoles in Unix terminals, written in C, unfortunately not maintained, but still works.

Image processing

In biology computational analysis of images is often unavoidable. With high-throughput microscopy you can acquire hundreds of images just in one hour of time and obviously you can not do all the adjustments and measurements by hand. Luckily there are a number of easy to use tools around. You can identify structures and make measurements and finally come to quantitative data from images. ImageJ is very popular and can be programmed by its own macro language or many other languages including Python. But if you want to use Python maybe better to go for Python image processing modules like scikit-image, OpenCV or ITK.

• A comprehensive Introduction from Justin Bois; check out also the following 2 lessons
• Segmentation tutorial 1
• Segmentation tutorial 2
• http://scikit-image.org/docs/dev/auto_examples/ - Comprehensive scikit-image tutorial.
• https://opencv.org/ - Open Computer Vision: a great library for image and video processing. You can use it in Python but also in C++ or Java.

Chemistry

• http://ctr.wikia.com/wiki/Chemistry_Toolkit_Rosetta_Wiki - Chemistry software toolkits compared by examples shown side-by-side.
• http://openbabel.org/wiki/Main_Page - A great chemistry toolkit with C++ core library and Python interface.
• http://www.rdkit.org/docs/Overview.html - Also with C++ core, Python bindings and integration with many other tools.
• https://lifescience.opensource.epam.com/indigo/ - A great chemistry toolkit, similar to the two above, with C++ core and Python bindings.
• https://lifescience.opensource.epam.com/index.html - It is worth very much to check out other open source tools from EPAM: they have quite some interesting stuff, such as Ketcher, an online molecule drawing app, or Bingo, a chemical structure search engine for DBMS (e.g. MySQL), among others.
• http://users.abo.fi/mivainio/balloon/ - Nice standalone application for 2D -> 3D structure prediction. (You can do this with any big toolkit, but if you need only this functionality I can recommend.)
• http://www.openms.de/ - OpenMS is a feature rich high performance mass spectrometry and proteomics data analysis toolkit. It comes with Python bindings.
• https://github.com/lmmentel/awesome-python-chemistry - A curated list of Python chemistry software
• https://pythoninchemistry.org/ - Python course for chemistry students
• https://www.macinchem.org/ - A great collection of chemistry software focusing on Macintosh but for sure many of them available also for other systems.

Chemical typography

Here we list a couple of tools for high quality drawing of chemical formulae and schema. A popular proprietary tool for this purpose is ChemDraw, however it costs 600 (1 year) to 6,500 (unlimited) USD. There are a number of similar design GUI free software available but I found them especially difficult to use and lacking of essential features. One proprietary but free solution is Marvin Sketch (see below). Some further software are for 3D drawing, among these vimol is a nice piece (see below). However, as always in LaTeX you can find high quality, free and full featured solutions. In addition, you can draw structures from with OpenBabel or RDKit from any language they support, including Python for this you need to create the structure or download from a database.

• https://github.com/ilyak/vimol - 3D chemical structure viewer and editor with vim-like interface from Ilya Kaliman. I really like this one, it's a shame it's not maintained or developed any more.
• https://chemaxon.com/products/marvin - A free but proprietary 2D chemical structure editor from the ChemAxon company.
• https://www.macinchem.org/reviews/python/pythonchem.php - Introduction to drawing with OpenBabel from Python.
• https://openbabel.org/docs/dev/FileFormats/SVG_2D_depiction.html - How to use the OpenBabel command line tool to generate SVG graphics from chemical structure files.
• https://www.slideshare.net/NextMoveSoftware/rdkit-ugm-2016-higher-quality-chemical-depictions - Comparison of RDKit, OpenBabel and some other chemical structure rendering engines.

LaTeX

• https://tex.stackexchange.com/questions/52722/can-you-make-chemical-structure-diagrams-in-latex - For a general guidance please read both answers to this question. They give an overview of the alternatives with minimal examples and opinions.
• http://xymtex.com/fujitas3/xymtex/indexe.html - XyMTeX is a software for drawing chemical formulae and schemata. Developed by an old professor in Japan, Shinsaku Fujita, it seems to be the ultimate "if something you can't do with this probably there is no way to do it" solution. XyMTeX is huge, it's manual is huge (780 pages!), it's features are countless and it's output is the highest quality. Of course as always, using such a great tool requires learning, and there are some smaller and simpler solutions available.
• https://ctan.org/pkg/chemfig - Another great tool with good manual, actively maintained and maybe easier to ues than XyMTeX but maybe not so perfect drawings. By Christian Tellechea.
• https://github.com/aminophen/chemobabel - Generates graphics within LaTeX directly from SMILES or ChemDraw files. Uses OpenBabel for rendering.

Books

Python beginner and intermediate books

• https://automatetheboringstuff.com/ - Python for beginners, a different way, with many tutorials
• http://docs.quantifiedcode.com/python-anti-patterns/ - How NOT to use Python. Advanced level
• https://thehackerguidetopython.com/ - A very nice book both for beginners and advanced Python coders. Hint: subscribe for the free chapter on the webpage, and you will get weekly mail with interesting and very useful blog posts from Julien.
• http://file.allitebooks.com/20160830/Python%203%20Object-Oriented%20Programming,%20Second%20Edition.pdf - In depth material for object oriented programming in Python.
• https://jakevdp.github.io/PythonDataScienceHandbook/ - Data science book freely available as IPython notebooks. From Jake VanderPlas.
• https://py-pkgs.org/ - A fantastic resource about Python packages, covering all important topics such as testing, docs, develoment tools, continuous integration, automation, and most importantly, how all these work together.

Advanced Python

• http://www.effectivepython.com/
• http://shop.oreilly.com/product/0636920032519.do
• http://learnpythonthehardway.org/book/
• http://book.pythontips.com/en/latest/
• http://www.obeythetestinggoat.com/ - Python testing
• https://stackoverflow.com/questions/101268/hidden-features-of-python - Nice collection of small Python tricks
• https://docs.quantifiedcode.com/python-anti-patterns/index.html - List of bad practices in Python

R books

• http://r4ds.had.co.nz/introduction.html - Great book from Garret Gloremund and Hadley Wickham covering many aspects of data analysis workflows.
• http://pdf.th7.cn/down/files/1502/Practical%20Data%20Science%20with%20R.pdf - Practical Data Science with R by Nina Zumel and John Mount

Lectures

• https://www.youtube.com/watch?v=HTLu2DFOdTg
• https://www.youtube.com/watch?v=S_ipdVNSFlo
• https://www.youtube.com/watch?v=sPiWg5jSoZI
• https://www.youtube.com/watch?v=2NSbuKFYyvc

Podcasts

• https://talkpython.fm/
• http://podcastinit.com/
• http://frompythonimportpodcast.com/

Miscellanous

• https://awesomeopensource.com/ - A huge catalogue of open source projects, sometimes only short recommendations like here, sometimes great tutorials and examples.

About programming

Here we collect blogs, assays and other resources about programming, coding, software development in general: design patterns, attitudes, trends, history, etc.

• https://two-wrongs.com/ - Blog from Chris, a software developer in Stockholm.
• https://www.joelonsoftware.com - Blog of Joel Spolsky, among many things about Hungarian notation.

Coding style

• http://pythontips.com/2016/02/27/learning-python-for-data-science/
• http://pep8.org/ - Standard coding style guide. You will be expected to follow these simple rules in any serious project, so better to familiarize yourself from the beginning. Tools are available to autocheck and in limited cases autocorrect pep8 violations: https://flake8.pycqa.org/en/latest/, https://fangpenlin.com/posts/2014/02/05/auto-post-commit-pep8-correction/

Development tools

Documentation

R package documentation

• https://pkgdown.r-lib.org/index.html - With pkgdown you can include in a webpage the reference manual and the vignettes of your package, along with any further custom pages. Don't forget to use bootstrap 5, choose a nice theme and adjust the design to your needs: https://pkgdown.r-lib.org/articles/customise.html - see also the points below.
• https://bootswatch.com/ - Bootstrap 5 themes: these you can use for your pkgdown pages.
• https://rstudio.github.io/bslib/articles/bs5-variables.html - Styling pkgdown pages is possible by Bootstrap 5 paramaters, in this page you find a list of them. These you can put in _pkgdown.yml under the bslib key.
• See here how to automate pkgdown page build and deployment with GitHub Actions. You can create such an action manually, or by use_pkgdown_github_pages from the usethis package. Check out also _pokgdown.yml and extra.css in the above repo for more good examples.

R analysis documentation

• https://workflowr.github.io/workflowr/ - workflowr is a pkgdown wrapper for analysis projects. Its main advantage is the nice presentation: the analysis is presented as a good looking web page. It also offers entry points to organize the project and re-run specific sections of the pipeline. It presents git history on the analysis webpage, and notifies if some parts of the presented results are outdated. A disadvantage is that it doesn't follow the R package layout, making difficult the use of many other tools.

Reproducibility

R

• https://rstudio.github.io/renv/articles/renv.html - renv is a dependency management tool for R. In your scripts use the package::function notation or use library() calls. When you're about to submit your analysis, run renv::init (later for updating use renv::snapshot). Include the renv.lock file in your repo, so anyone who wants to reproduce your analysis will be able to use all the required packages with the exact same versions that worked for you.
• https://workflowr.github.io/workflowr/ - workflowr is also related to reproducibility, see details above in the documentation section.

Performance

Most of the times scripting languages like R and Python are provide sufficient performance to run our tasks on our laptop, or and if that's not enough, we can just go to the big computer cluster of the institute. The performance intensive methods are often contained in extensions written in C or Fortran and the scripting language is only the glue holding together our current particular arrangement, adding easy access and flexibility to the efficient core methods. However, you might find yourself in a situation when your code much runs slower than ideal. Especially if you need a new, computationally intensive method, not implemented yet in any package with bindings to your scripting language. So what to do with performance issues?

Profiling

The first thing you should do is profiling. In Python the cProfile module is fantastic for this purpose. At the end you will get a hierarchic tree of the method calls, with the number of calls and time spent for each method. Based on this you can go back to the code and try to improve the methods which mean a bottleneck, those which consume most of the time or called the most often.

• https://julien.danjou.info/guide-to-python-profiling-cprofile-concrete-case-carbonara/ - A guide about cProfile with a complete case study.

Use efficient libraries

In the profiling you might have found out where are the slow parts in your code. One of the most efficient ways to improve efficiency is to outsorce intensive parts to extensions written in C or other high performance language. Make sure especially that vectorizable operations are done by numpy. Similarly, for graph computations igraph is an efficient C extension, and for mass spec methods you can use OpenMS. And these are just two examples, you should search for similar extensions for your method.

Writing C extensions

If the sufficient performance can't be achieved due to limitations of the scripting language, and no libraries are available, you can write the computationally intensive parts in C++ and create the sufficient bindings. It's especially easy to mix Python and C code with Cython or R and C++ with Rcpp.

• https://nbviewer.jupyter.org/github/twiecki/CythonGSL/blob/master/examples/cython_gsl_ipythonnb.ipynb - A minimal Cython example.
• https://adv-r.hadley.nz/rcpp.html - Rcpp guide.

Using Rust

Writing C or C++ code is difficult, and actually there are more convenient and more modern alternatives available. One of them is Rust, a new languege which became highly popular among programmers and gaining popularity also in science.

• https://www.nature.com/articles/d41586-020-03382-2 - Why scientists are turning to Rust: an overview in Nature.
• https://jeroen.github.io/erum2018/ - How to add Rust code to R packages: a lecture from Jeroen Ooms.
• https://developers.redhat.com/blog/2017/11/16/speed-python-using-rust/ - How to use Rust from Python: a tutorial from Bruno Rocha.

Regular expression resources

In data analysis we process tremendous amount of data which is sometimes noisy and we need to extract information from messy patterns. Regular expressions sooner or later will be your essential tools no matter which field and language do you work with. Here are a few excellent resources to learn these small tricky things called regex:

• http://rexegg.com/ - great, detailful tutorials from beginner to advanced, often with interactive demo.
• http://regular-expressions.info/ - maybe the most well known regex page, clear and concise.
• http://lucumr.pocoo.org/2015/11/18/pythons-hidden-re-gems/ - about Python's regex module.

sed, awk, grep

• http://www.catonmat.net/series/sed-one-liners-explained - Good resource for learning sed (the stream editor, a swiss army knife of the Unix shell). From Peter Krumin.
• http://www.catonmat.net/blog/awk-one-liners-explained-part-one/ - awk one liners explained: awk is another poweful "swiss army knife" of the Unix shell. This guide is as good as the one about sed, also from Peter Krumin. awk got its name from the initials of its original authors: Alfred Aho, Peter Weinberg and Brian Kernighan.

Collections, cheatsheets

• https://devhints.io/ - Nicely designed cheatsheets for many tools, mostly web related, but quite some of them also used in data science. For example, xpath is used in a large number of XML processing tools, and on the cheatsheet you can quickly find the syntax elements you need: https://devhints.io/xpath.

Unix, Linux and Bash

• https://www.putorius.net - A page full of small tips, tutorials and education materials about Linux system administration and Bash, by Steven Vona.
• https://www.youtube.com/watch?v=LKCVKw9CzFo - Linux 101: Maybe the best intro to Linux I can imagine in 12 minutes. Concise, informative, good style.

Introductory Bash

Basic bash is essential whatever operating system you use. Linux and Mac anyways has Bash as its default command line. It is extremely versatile and convenient. You can have it also in Windows if you want, see the section below. If you have access to any computing cluster or file storage server at your institute or university, you most likely have the easiest (and often the only) access to them by Bash.

There are so many Bash tutorials for beginners. I think most of them are quite dry and you will get bored soon or forget soon what you read. I think aim to learn the most essential 10-20 commands first, and keep using it actively for many weeks. Then according to your needs you can learn more useful things and what you read will be more digestable for you. Among the 3 materials below maybe the first one is the best in style:

• https://ryanstutorials.net/linuxtutorial/
• https://www.cs.sfu.ca/~ggbaker/reference/unix/
• https://swcarpentry.github.io/shell-novice/

If you prefer to learn from podcasts and videos you can find a ton of them on youtube, for example this channel covers many topics including Linux, Bash, SSH, vim, etc:

• https://www.youtube.com/user/nixiedoeslinux/videos

SSH

SSH stands for secure shell and it gives you a bash session on a remote computer (e.g. computing cluster of your institute), also capable to copy files between your computer and the remote one (scp) and to encrypt any communication between any software on different computers. It is convenient to set up a key based authentication for the server so you don't need to type your password any more:

• https://kb.iu.edu/d/aews

Virtual machines

If you use Unix-like system you most likely want to have a Windows virtual machine (VM) and conversely, if you use Windows you most likely need a Linux VM for the very few tasks which can not be performed but only with one of these operating systems. Really you should not expect to have such tasks often, most probably you will start your VM only once a month.

The easiest way to create virtual machines is VirtualBox which has a free and open source edition, but the not OS edition is also free. It integrates seemlessly with your main operating system (especially if you install also the guest additions): you can share your directories, USB devices, network, etc. If you switch the VM to full screen you will have exactly the same experience as it was your main system.

• https://www.virtualbox.org/wiki/Downloads

Windows

In my opinion by using Windows you make most of the things more difficult for yourself because it is designed to restrict your insight and control over the technology. This way you do many things blindly without understanding what is happening in the background, and if you encounter a problem you will have less information available for thinking about a solution. Still many people use Windows because they feel they already invested significant time into "learning" it in the school and they don't want to learn something new.

If you use Windows as your main operating system, you might want to have some Unix compatible tools to make it more convenient to use. Alternatively you might create a virtual machine with Linux. But in this case I think you need a strong reason why you don't install Linux as your main system.

• https://www.putty.org/ - A little SSH and Telnet client for Windows. The easiest way to log in to Unix servers (usually the computing clusters of your institute or university). Note: for an SSH login you need at least 3 information: the address of the server, your user name and your password.
• https://mingw-w64.org/ - GCC for Windows. You need this if you want to compile software using GCC.
• https://www.cygwin.com/ - A POSIX compatible environment for Windows. You need this to have all the very helpful tools and environment you have by default on Linux. And also to run some software which need a POSIX environment.
• https://gitforwindows.org/ - Git for Windows. I think an easy quick solution is to install only this, and it will offer for you to install also Cygwin and Bash, and set up your paths. At the end you will have a nice environment.

Alternatively you can use the "Windows Subsystem for Linux" or virtual machines to use Linux from within Windows. But in this case why not to run Linux and keep a Windows virtual machine for the rare cases when you might need somthing from Windows.

• https://docs.microsoft.com/en-us/windows/wsl/about - Windows Subsystem for Linux is a compatibility layer from Microsoft to run Linux binaries in Windows.
• https://www.virtualbox.org/ - With VirtualBox you can create virtual computers which are almost completely like real computers and you can install and run any operating system on them. They can be made full screen and integrate seemlessly to the desktop of your main (host) operating system. VirtualBox is very popular because is easy to use, has great features and is open source.

Linux

• https://pat-s.me/post/arch-install-guide-for-r/ - How to set up an Arch Linux system for data science purpuse? A guide from Patrick Schratz. Arch Linux is a highly customizable and efficient Linux distribution which requires advanced computer skills to configure and manage. But exactly because of this reason it's an excellent environment for learning more about Linux and computers in general.

Life in academia

• https://www.oacommunity.org/ - Open Academics: advices about life in academia (imposter syndrom, difficulties in PhD, presentations, etc) and templates for writing (cover letters, applications, CVs, grants).
• https://academia.stackexchange.com/ - Q and A site where you can ask and answer questions about difficult situations in academia, and learn from thousands of already answered questions.
• https://karpathy.github.io/2016/09/07/phd/ - A concise but comprehensive guide for doing a PhD - in other words, an intro to life in academia.
• http://phdcomics.com/ - Highly popular comics about life in science. The movie is also great: https://phdmovie.com/
• https://xkcd.com/ - Comics about math and science culture and society.
• https://www.youtube.com/playlist?list=PLkOCBuVPhgedW0QJc_3S7J6puRaBX8rpJ - A collection of home made (or lab made) movies, songs, parodies and other artworks from science subculture.