We are R-Ladies

@WeAreRLadies

RoCur (Rotating Curator) for the community . This week's curator:

The whole world!
Vrijeme pridruživanja: svibanj 2018.

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  1. prije 3 sata

    This is a great tutorial that goes through vectorization, the "apply" function, & parallel programming using the foreach & doParallel 📦for when our R analyses require > computing power and/or faster programming: and recording here:

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  2. prije 3 sata

    For an overview and tutorial on how to use for natural language processing, including text processing, tokenization, analyzing linguistic features & semantic similarity, check out this talk by and recording here:

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  3. prije 3 sata

    For those who are interested in text analysis, check out the talk by covering text pre-processing, analysis of word frequencies, sentiment analysis, word clouds and topic modelling: (github repo here: )

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  4. prije 4 sata

    Another fab resource for data analysis including introductory slides, a twitter analysis workable example and also DIY challenges:

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  5. prije 4 sata

    If you've ever wanted to analyze Twitter data, this is a fabulous tutorial outlining steps to download data through to visualization. ICYMI, twitter only lets you download data from the last 2 weeks, so plan ahead if you have a specific analysis in mind!

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  6. prije 4 sata

    To take your workflow a step further and learn how to apply to your research (& avoid issues w/ different computing environments or software versions) check out the talk by and recording here:

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  7. prije 4 sata

    You can work through the materials in this github repo by to learn how to write a book or thesis in :

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  8. prije 4 sata

    A useful introduction and tutorial for how to write articles in by can be found here: (bonus if your journal already has an template!)

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  9. prije 4 sata

    For a great overview of the "evolution" of , best practices for , an introduction to and how to work with in R via check out the presentation by here:

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  10. prije 4 sata

    The community really wanted to learn advanced topics, so in this thread I will highlight some of the useful resources and tips from our events incl. , , , , , , 📦, & more

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  11. 3. velj

    If there is anything in particular that would be helpful to the community please comment below! 💜5/5

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  12. 3. velj

    This week I’ll be tweeting about and what we are up to, using for infectious diseases genomic epi research esp , some of my fave 📦s , , , , , , , and whatever else is helpful! 4/n

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  13. 3. velj

    My journey in R has evolved from using one specific function in one specific package, to using R on a daily basis for my entire workflow from data curation, analysis, , reporting and more! Now I’m proudly a co-organizer for 💜 3/n

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  14. 3. velj

    I moved to 🇦🇺 to do my PhD in genetic epidemiology and worked in Ghana 🇬🇭. Now I’m doing my post doc in malaria in the Asia-Pacific. I’m currently tweeting from Madang, Papua New Guinea 🇵🇬 so I’ll try my best to tweet at time-zone friendly times! 2/n

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  15. 3. velj

    Hi community 💜 my name is Shazia and I’m your curator for the week! I’m originally from a beautiful country in South America, Ecuador 🇪🇨 and my father is from the US 🇺🇸. I grew up all over the 🌎🌍 in 5 countries and now I live in Melbourne, Australia 🇦🇺 since 2014 1/n

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  16. 2. velj
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  17. 2. velj

    Thanks, R Ladies Supporters for being a great audience! I enjoyed my week of curation! Best wishes to everyone's R, stats and data science futures.

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  18. 2. velj

    This is the general framework I follow when helping a client conduct an analysis of variance. While this process may take different paths depending on the data - like a generalised linear model - the overall process is the same.

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  19. 2. velj

    One of the most important things not discussed at all is having a discussion with the client about their research objects and what precisely they want to know - this guides my process more than anything. The next thing I do is plot the data prior to running any analyses.

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  20. 2. velj

    What I have not talked about is assessing diagnostics. This is surprisingly easy - call `plot()` on lm, nlme or merMod objects to see some diagnostic plots. The function `bartlett.test()` can be used for testing for heteroscedasticity.

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