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R-Ladies

R-Ladies is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand R-Ladies rather than just read about it. In short: R-Ladies is an organization that promotes gender diversity in the community of users of the R statistical programming language. It is made up of local chapters affiliated with the worldwide coordinating organization R-Ladies Global.

R-Ladies — main illustration
R-Ladies — illustration

Key takeaways

  • R-Ladies belongs to computer science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect R-Ladies to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of R-Ladies from memory before moving on to harder problems.

Reference excerpt

R-Ladies is an organization that promotes gender diversity in the community of users of the R statistical programming language. It is made up of local chapters affiliated with the worldwide coordinating organization R-Ladies Global.

History On October 1, 2012, Gabriela de Queiroz, a data scientist, founded R-Ladies in San Francisco (United States) after participating in similar free initiatives through Meetup. In the following four years, three other groups started: Taipei in 2014, Minneapolis (called “Twin Cities”) in 2015, and London in 2016. The chapters were independent until the 2016 useR! Conference, where it was agreed to create a central coordinating organization. In that year, Gabriela de Queiroz and Erin LeDell of R-Ladies San Francisco; Chiin-Rui Tan, Alice Daish, Hannah Frick, Rachel Kirkham and Claudia Vitolo of R-Ladies London; as well as Heather Turner joined to apply for a grant from the R Consortium, with which they asked for support for the global expansion of the organization. In September 2016, with this scholarship, R-Ladies Global was founded and in 2018 it was declared as a high-level project by the R Consortium. The current leadership team consists of Averi Giudicessi, Athanasia Monika Mowinckel, Shannon Pileggi, Riva Quiroga and Yanina Bellini Saibene. The leadership team has an overarching role in steering and directing the organization. It works closely together with more than 20 volunteers who take care of various areas such as campaigns, the directory, mentoring, public communications and social media, or the community Slack. As of 2024, the R-Ladies Global community consists of 219 groups in 63 countries.

Organization R-Ladies meetings are organized around workshops and talks, led by people that identify as female or as gender minorities (including but not limited to cis/trans women, trans men, non-binary, genderqueer, agender, pangender, two-spirt, gender-fluid, neutrois). The organization is coordinated by the Global Team. The chapters, however, operate decentralized and new chapters can be founded by anyone using the publicly available "starter-kit". R-Ladies groups aim to promote a culture of inclusion within their events and community. In addition, they promote gender equality and diversity in conferences, in the workplace, collaboration among gender minorities, and analysis of data about women. The Directory is a public directory listing 1,267 profiles of R-Ladies (in 2024). R-Ladies Global also showcases blogs written and maintained by their members. R-Ladies also collaborates with other projects, such as NASA Datanauts or PyLadies.

Gabriela de Queiroz

Gabriela de Queiroz is the Director of AI at Microsoft and the founder of first R-Ladies chapter, AI Inclusive and co-founder of R-Ladies Global. Before, she also worked at IBM as a chief data scientist.

Early life and education She was raised in Brazil and received her bachelor's degree in statistics from Rio de Janeiro State University. She has a master’s in epidemiology at Oswaldo Cruz Foundation and another one in statistics at California State University, East Bay. de Queiroz moved to the United States in 2012 to begin her master's degree in statistics at California State University, East Bay.

Achievements Interested in creating an inclusive space for women learning the programming language R, she began a Meetup group in the San Francisco Bay area. Since then, the R-Ladies organization has grown to 219 groups in 63 countries. In addition to her work with R-Ladies, de Queiroz is an expert in machine learning and led IBM's AI Strategy and Innovations team. Her team contributed to projects such as TensorFlow. de Queiroz was a finalist of the Women in Open Source Award by Red Hat in 2019, named 40 under 40 by CSUEB as well as listed among the 100 Brilliant Women in AI Ethics™ in 2023.

Notable members Julia Stewart Lowndes – Marine ecologist

References

Illustrations

R-Ladies illustration

Worked examples

Example 1 — a first encounter with R-Ladies

Start with the simplest possible case. Write down what R-Ladies claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to R-Ladies before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about R-Ladies ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of R-Ladies

In research
R-Ladies appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses R-Ladies in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
R-Ladies is common in secondary-school and first-year university syllabi. It links to neighbouring topics R (programming language), Women in computing, so understanding it makes those chapters shorter.
In everyday life
Look for R-Ladies outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study R-Ladies in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what R-Ladies means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain R-Ladies out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is R-Ladies in simple terms?

R-Ladies is an organization that promotes gender diversity in the community of users of the R statistical programming language. It is made up of local chapters affiliated with the worldwide coordinating organization R-Ladies Global.

Why does R-Ladies matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study R-Ladies?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on R-Ladies.

Tags

  • R (programming language)
  • Women in computing

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