Today we’ll learn how to run an ANOVA. We also use the packages tidyr and ez to modify a data frame’s format and run ANOVAs of different types, and as always expanded our knowledge of dplyr and ggplot2 calls.
Recently, we (that is Page and Christina) successfully launched the Parisian installation of R-Ladies Global. It’s a meetup group and at the same time a non-profit coding club for all R proficiency levels, whether you’re a new or aspiring R user, or an experienced R programmer interested in mentoring, networking, and maybe picking up some new skills. We are a community designed to encourage, support and ultimately drive the development of our own R skills through a range of events, including meetups where members tackle hands-on tutorials and exercises to learn specific functionalities, informal gatherings, talks about latest trends, and debates. Our goal is to promote access to STEM (Science, Technology, Engineering, Mathematics) careers and tools for women (trans and cis) and gender-variant people. Men are welcome, too, by the way. We just need a member to bring them to the next meetup. In other words, we try to be a harassment-free zone. Sadly, that’s easier to do when men are screened beforehand.
Our department has recently started a series on academic skills, where grad students and postdocs at Penn can ask panelists about various experiences pertaining to writing a grant, or giving a job talk – things that are often not communicated in a formal way. This month’s session was about “What I would have liked to know before starting grad school”, where advanced grad students and fresh postdocs reflected on things they would have found useful to know or to have reflected on in advance. I thought I’d share some of the excellent points that were made by the panel and audience.
Grad school can be a great thing, but it comes with its own challenges. For me, the flexibility that often comes with being a grad student (unless you work on a project with a very fixed outline where your tasks are clearly defined from the start) is something that makes it especially awesome, but also especially challenging. You can work on what you really care about while managing your work schedule yourself. But you also have a lot of responsibility and need to be intrinsically motivated and reasonably disciplined to pull through. With that in mind, here come five thoughts and pieces of advice that can help you make your way through grad school.
Today we’ll learn how to take an old statistics test (logistic regression) but expand it to when you have two variables (multiple regression). The package purrr is introduced and, as always, we’ll expand our knowledge of dplyr and ggplot2.
For full materials, see the course website for Lesson 4.
Meet Anne Scheel, PhD candidate at LMU Munich. She stood up and asked the author of the opinion piece on “methodological terrorism” for a statement after her keynote at the DGPS conference. Since tough questions in front of big audiences by young women are still a rare thing to encounter at conferences (and elsewhere), we were curious to know how this went down for her. And of course, we also took the opportunity to discuss the content of the piece in question. Continue reading An interview with a next generation methodological freedom fighter
Criticism, and how to (not) do it has been a hotly discussed topic. For example, there is a very useful three-point guide by Uri Simonsohn how to handle criticism in a civil way. If you do science, you will be criticized at some point and you will have to criticize others. After all, our whole peer review system hinges on picking out all that might be wrong. Not everyone knows how to give and handle feedback, actually, and it’s really very hard sometimes (and this is for example an integral part of science woman’s origin story). Some people might spend their whole scientific career never learning anything about being constructive, be it as recipient or criticizer. Continue reading Critical culture
Following up linear regression, in this lesson we’ll learn the math of logistic regression, and run a logistic regression in R. As always, we’ll expand our knowledge of dplyr and ggplot2.
For full materials, see the course website for Lesson 3.
I started thinking about writing this blog entry when I first read about the speakyourstory initiative in this insightful Nature column a few months ago. This initiative raises awareness of microagression in the form of subtle sexism in the world of research. Subtle sexism is often less obvious both to its initiators and recipients than overt sexism, but can nevertheless be quite harmful – or I should say could be harmful, since we know amazingly little about its real effects. Microagressive comments are, of course, not restricted to sexism, but to an abundance of topics people can be – often unintentionally – biased about (read more here or here).
Guest post by Page Piccinini
With some basics under our belt from Lesson 1, in this lesson we’ll continue working with dplyr and ggplot2, while also learning about the math behind linear regression and how to implement it in R. Plus you get to finish with a report about how the popularity of your name changes over time.
For full materials, see the course website for Lesson 2.
You might have noticed that this blog was very quiet during the summer months. That is, in part, thanks to the fact that we actually went on holidays! (Plus, while preparing for those vacations there might have been a finish-everything-madness, but we admit to nothing).
Why is this newsworthy? Continue reading Summer is over, and that’s ok