π Today was a great example of how the #rstats community can help getting learning resources! Thank you for all the amazing material about #deeplearning & #reproducibility with R π This feels like a good preamble to the remaining poll results about learning strategies π
With all these materials out there I am now wondering when will I have time to read it all, same with practicing code and the new skills I will learn after going through them!
68% of replies claimed to practice when they have time, but I think this could have been more accurate π
In any case I think practicing is the best way to get better at whatever we aim to learn πͺThis brings me to the next question, where 59% said the best way to learn is to apply code to their own data...
I find this actually a great way of practicing and I do it often. In my job, I will usually have some interesting taks and data, and my logical thought is to test any new package, programming language, method on it and check if it is promising or not π
Applying new skills to our known workflowws can actually help understand better what we are doing. You already know what the data is and what your goal is, not how do you get there with code!
And with this I say bye for today! I have a lot of material to go through, and maybe some of you too found some useful links, blogs and even conference recordings! Thank you again!
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Anyone wants to get started or has used #rspatial and learned from tutorials/courses using packages such as {rgdal} {rgeos} {sp} {raster}? Are you getting used to them already? You might want to reconsider π€
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For the past few months the R Spatial community of developers and active users has been dealing with the news of the retirement of {rgdal} & {rgeos}
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For {sp} is a similar story, although still getting maintenance, a migration to {sf} should be mostly preferred for any new developments and implementations
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Hi all π today I would like to talk about #CV tips. Did you know you can create your CV in #rstats? There are so many cool π¦ out there!
I personally use {vitae} with a good range of eye-catching templates to choose from, I highly recommend it!
What I like the most about building my CV in R is that I can organize everything in an R-project, I push to GitHub which gives me track changes and I can use the great advantages of #rmarkdown and #latex. Here is the repo of my #vitae CV github.com/loreabad6/R-CV
I had to modify a bit the .csl file to adapt certain details to my taste, and I included a πΊοΈ of my journey which has had some nice feedback, as it serves as a visual presentation card π
Morning! Let's continue with the polling results, today and get some #learningtips. Here is what people voted for regarding learnings strategies! Not surprisingly, the big majority likes some hands-on exercises, although a mix between theory and practice is also welcome π
π Very few people voted for learning from books about coding, but have you already seen all the resources out there? #rstats has a big pile, and the best way to browse them all is with the Big Book of R #rmarkdown#bookdown bigbookofr.com/index.html
Most of the people like blogs and written content. Well this is the perfect opportunity to point you to the #RLadies blogs collected by @PipingHotData & @DrMowinckels
π’ Results for yesterday's poll are in everyone! Thank you all for your participation! πThere is so much to talk about, so today, let's start discussing the first question: why have you not tried programming yet? π§
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Most often (43%), the reason to not learn coding is lack of time. Uff! Having no time is always an issue, trust me! There will never be enough time to do all the things we want
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But I do think that if there is something that you are interested in, not necessarily learning how to code, there will always be some time here and there to spare
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