Introduction from the R core team

Swirl teaches you R programming and data science interactively, at your own pace, and right in the R console

Follow the tutorials taught in Data Carpentry to learn R.

"Shiny is a framework for creating web applications using R code. It is designed primarily with data scientists in mind, and to that end, you can create pretty complicated Shiny apps with no knowledge of HTML, CSS, or JavaScript. "

"a list of typical errors and warnings. When clicking on the bullet points of the list, you are headed to detailed instructions on how to deal with the corresponding error or warning message."

From the introduction: "R makes it easy to work with and learn from data. It also happens to be a complete programmming language, but if you’re reading this guide then that might not be of interest to you. That’s OK — the goal here is not to teach you how to program in R. The goal is to teach you just enough R to be confident to explore your data. In this guide, we use R in the same way we use any other statistics software: To check and visualise data, run statistical analyses, and share our results with others. To do that it’s worth learning the absolute basics of the R language and key recent extensions to it. "

Designed around psychology data with two sections: "Absolute Beginners' Guide to R" and "Putting R to Work" with more detailed case studies.

"This lesson is an introduction to programming in Python for people with little or no previous programming experience. It uses plotting as its motivating example [...] This lesson references JupyterLab, but can be taught using a regular Python interpreter as well. Please note that this lesson uses Python 3 rather than Python 2."

Exercise-based guide to coding in Python.

"Full Stack Python is an open book that explains concepts in plain language. "

"Achieve mastery through challenge. Improve your skills by training with others on real code challenges." Suitable for those with some Python experience.

"The Python Package Index (PyPI) is a repository of software for the Python programming language." The documentation for packages is often quite thorough and community forums can be very helpful.

Introductory statistics in Python using Scipy.

"implements a probabilistic programming language in Python."

"This methodology will not find every bug in every program, but it is highly effective for the sort of short programs that beginner programmers are assigned as homework. These techniques then scale up to finding bugs in non-trivial programs."

"The United States Indigenous Data Sovereignty Network (USIDSN) helps ensure that data for and about Indigenous nations and peoples in the US (American Indians, Alaska Natives, and Native Hawaiians) are utilized to advance Indigenous aspirations for collective and individual wellbeing. USIDSN’s primary function is to provide research information and policy advocacy to safeguard the rights and promote the interests of Indigenous nations and peoples in relation to data."

OpenRefine (formerly Google Refine) is a powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data.

"an open source textbook aimed at introducing undergraduate students to data science. [...] In this book, we define data science as the study and development of reproducible, auditable processes to obtain value (i.e., insight) from data." Uses R's tidyverse packages and Jupyter notebooks.

Exploratory Desktop provides an advanced, interactive, and reproducible data wrangling and analysis experience powered by R and visualization.

Well-curated directory with robust searching and filtering capabilities

List of tools for working with datasets

Bioconductor provides tools (in R) for the analysis and comprehension of high-throughput genomic data.

"Qiita (canonically pronounced cheetah) is an entirely open-source microbial study management platform. It allows users to keep track of multiple studies with multiple ‘omics data. Additionally, Qiita is capable of supporting multiple analytical pipelines through a 3rd-party plugin system, allowing the user to have a single entry point for all their analyses. Qiita’s main site provides database and compute resources to the global community, alleviating the technical burdens, such as familiarity with the command line or access to compute power, that are typically limiting for researchers studying microbial ecology. Qiita’s platform allows for quick reanalysis of the datasets that have been deposited using the latests analytical technologies. This means that Qiita’s internal datasets are living data that is periodically re-annotated according to current best practices. "