This is the most compact and easy to understand video about R that I've came across. No over explanation, the video is short yet full of information. Stumble into your channel must be the greatest thing that happened today ❤
Couldn't agree more about the usefulness of the dplyr pipe and verbs. Before adopting them a few years ago, I would often generate multiple dataframes within an analysis, each with some modifications that would allow me to plot or tabulate data in a specific way. Now, I generally produce just one or two dataframes at the outset of the analysis and rely on dplyr verbs to quickly and easily manipulate the data before piping it into ggplot or a table. And because the piped code is written in an intuitive manner, it is easy to understand what is being done to the data each time. Excellent overview of some very useful commands.
Totally! I actually learned R from the tidyverse perspective in the first place. Never learned base R, except of few things. But after working through the first edition or R4DS, I had a feeling I learned how to do basic data science. Especially dplyr and ggplot were just amazingly useful. By the way, I have 4 PhD students, who learned R from this book in a month ... all 4 of them, and all are veterinary scientists. So, statistical programming is not that hard! The dplyr and ggplot became, kind of, a second nature to me, so it was difficult to create such tutorial, because it's soooo obvious. I though that I need to do emmeans, QR and other complex stuff ... which I actually enjoy, but I think now that it might be more useful for more people to make a few more tutorials for the beginners. What do you think?
@@yuzaR-Data-Science I like your videos and I can tell you put considerable thought into them, and great graphics. In terms of subject matter, I think that there are quite a few channels posting videos on the basics such as ggplot, dplyr, etc. I was drawn to your channel because of your coverage of much less known, but incredibly useful topics/packages, such quantile regression, glmulti, bootstrap regression, ggstatsplot/ggbetweenstats (which I now use all the time!), etc. I can appreciate that your audience may be larger targeting more beginner topics, although there is perhaps more competition for these topics as well. Being somewhat selfish, I do hope you still cover some of these less well known topics/packages as they have proved very useful for myself in the past! Thanks!
Thanks for your honest opinion, Zane! Yes you are right. There are way too many beginner videos, but I have a feeling many channels stop there. And with them the audience doesn't go further. There are soo many useful function, where people like "easystats" put soo much effort into. And it's not used, because the beginners stack with a channel with lots of subs and views who makes 10 separate long videos on 10 dplyr commands. I find many of them - waste of my time, so I will never make a 10 Minutes video on only "group_by". That's not good for R language, while R is amazing, exactly because it is functional and has something like ggbetweenstats or glmulti. I can reassure you, that I'll continue to do useful content and will try to keep up or even to improve the quality. I would love to cover the basics, like dplyr and ggplot, to just relearn and revisit the topics myself (the second edition of R4DS is almost there). I will just try to make every minute of my content as useful and as fun as possible. And then, there are so many further useful things I would love to cover, like the whole pallete of tidymodels packages, GAMs, GLMER, shiny etc. By the way, fill free to share what (packages or topics) you would find the most useful!
@@yuzaR-Data-Science The technique that I have found most useful over the past year is by far linear mixed effects models, mostly performed using the lme4 and merTools packages. For 25 years I have been using multivariate linear regression to model the degradation of materials. In some cases, the material responds similarly to environmental factors, but just starts out with different properties. In other cases, the material response to those environmental factors varies with the material’s composition. The introduction of random intercepts and slopes to linear models effectively captures these differences and significantly improves the predictability of the material’s properties.
agree, I use lmer and glmer for my work too, since we have either repeated measurements of animals or from the same farm. I definetly plan to cover mixed models on my channel in the near future. But untill then, I can recommend you to try out the emmeans package, which you already have seen as a video on my channel. emmeans works well with mixed-models. However, I mainly use random intercept. But random slopes suppose to be work fine as well.
just found your page, everything you have has been very helpful, if you have time and is something you want to make, a video on rmarkdown on how to write scientific papers and make them publication ready would certainly be beyond amazing!
Great suggestion! Since I am scientist myself, I might do some videos on that. But the R people have this quarto now, advanced rmakkdown, which suppose to be for academics. I am not convinced yet, but I’ll follow their progress
@@consolkubayi879 unfortunately, there is not enough time for everything. but you can find blog articles, which might be helpful for you. just google my youtube name and you'll find the blog. thanks!
While I was learning to master those commands from your blog, you started by table2...then table1 while explaining about the command "select", what is the difference between table2 and table2?
you mean, the difference between table1 and table2? just write them in the console, press enter and then compare. they are slightly different and are already part of tidyverse. they exist for exercising sake only
This is the most compact and easy to understand video about R that I've came across. No over explanation, the video is short yet full of information. Stumble into your channel must be the greatest thing that happened today ❤
Thank you very much for a nice feedback! 😊 That means a lot to me! Glad you enjoyed. Hope you'll like my other videos too. Cheers
Couldn't agree more about the usefulness of the dplyr pipe and verbs. Before adopting them a few years ago, I would often generate multiple dataframes within an analysis, each with some modifications that would allow me to plot or tabulate data in a specific way. Now, I generally produce just one or two dataframes at the outset of the analysis and rely on dplyr verbs to quickly and easily manipulate the data before piping it into ggplot or a table. And because the piped code is written in an intuitive manner, it is easy to understand what is being done to the data each time. Excellent overview of some very useful commands.
Totally! I actually learned R from the tidyverse perspective in the first place. Never learned base R, except of few things. But after working through the first edition or R4DS, I had a feeling I learned how to do basic data science. Especially dplyr and ggplot were just amazingly useful. By the way, I have 4 PhD students, who learned R from this book in a month ... all 4 of them, and all are veterinary scientists. So, statistical programming is not that hard! The dplyr and ggplot became, kind of, a second nature to me, so it was difficult to create such tutorial, because it's soooo obvious. I though that I need to do emmeans, QR and other complex stuff ... which I actually enjoy, but I think now that it might be more useful for more people to make a few more tutorials for the beginners. What do you think?
@@yuzaR-Data-Science I like your videos and I can tell you put considerable thought into them, and great graphics. In terms of subject matter, I think that there are quite a few channels posting videos on the basics such as ggplot, dplyr, etc. I was drawn to your channel because of your coverage of much less known, but incredibly useful topics/packages, such quantile regression, glmulti, bootstrap regression, ggstatsplot/ggbetweenstats (which I now use all the time!), etc. I can appreciate that your audience may be larger targeting more beginner topics, although there is perhaps more competition for these topics as well. Being somewhat selfish, I do hope you still cover some of these less well known topics/packages as they have proved very useful for myself in the past! Thanks!
Thanks for your honest opinion, Zane! Yes you are right. There are way too many beginner videos, but I have a feeling many channels stop there. And with them the audience doesn't go further. There are soo many useful function, where people like "easystats" put soo much effort into. And it's not used, because the beginners stack with a channel with lots of subs and views who makes 10 separate long videos on 10 dplyr commands. I find many of them - waste of my time, so I will never make a 10 Minutes video on only "group_by". That's not good for R language, while R is amazing, exactly because it is functional and has something like ggbetweenstats or glmulti. I can reassure you, that I'll continue to do useful content and will try to keep up or even to improve the quality. I would love to cover the basics, like dplyr and ggplot, to just relearn and revisit the topics myself (the second edition of R4DS is almost there). I will just try to make every minute of my content as useful and as fun as possible. And then, there are so many further useful things I would love to cover, like the whole pallete of tidymodels packages, GAMs, GLMER, shiny etc. By the way, fill free to share what (packages or topics) you would find the most useful!
@@yuzaR-Data-Science The technique that I have found most useful over the past year is by far linear mixed effects models, mostly performed using the lme4 and merTools packages. For 25 years I have been using multivariate linear regression to model the degradation of materials. In some cases, the material responds similarly to environmental factors, but just starts out with different properties. In other cases, the material response to those environmental factors varies with the material’s composition. The introduction of random intercepts and slopes to linear models effectively captures these differences and significantly improves the predictability of the material’s properties.
agree, I use lmer and glmer for my work too, since we have either repeated measurements of animals or from the same farm. I definetly plan to cover mixed models on my channel in the near future. But untill then, I can recommend you to try out the emmeans package, which you already have seen as a video on my channel. emmeans works well with mixed-models. However, I mainly use random intercept. But random slopes suppose to be work fine as well.
Many thanks for your effort, a video I will immediately recommend to my students new to R. Very short and precise, I couldnt do it better!
Thanks so much 🙏 I also struggled in the beginning of learning R, so I try to teach the way I would have loved to be teached
man u just won another subscriber. This is one of the best videos about dplyr that i have ever seen :). Congrats dude!
Glad you enjoyed! Hope other videos are helpful too! Thanks for a nice feedback!
just found your page, everything you have has been very helpful, if you have time and is something you want to make, a video on rmarkdown on how to write scientific papers and make them publication ready would certainly be beyond amazing!
Great suggestion! Since I am scientist myself, I might do some videos on that. But the R people have this quarto now, advanced rmakkdown, which suppose to be for academics. I am not convinced yet, but I’ll follow their progress
Oh, it's nice, what a wonderful video I have ever watched about data wrangling. thank you!
Glad you enjoyed it, Getalew! Thanks you for watching!
Thank you so much for your help. You are the best to ever exist in this stats world!
🥰
Happy to help!
@@yuzaR-Data-Science are you available for further assistance and do you offer tutorials to even better my way of stats analysis?
@@consolkubayi879 unfortunately, there is not enough time for everything. but you can find blog articles, which might be helpful for you. just google my youtube name and you'll find the blog. thanks!
It is useful learning these commands. Thanks
You are welcome 🙏
Thank you very much for another superb video.
My pleasure! Thank you for watching!!!
Thanks so much for sharing sir
You are very welcome! Thanks for watching!
YuzaR king of R
Thank you very much! :)
While I was learning to master those commands from your blog, you started by table2...then table1 while explaining about the command "select", what is the difference between table2 and table2?
you mean, the difference between table1 and table2? just write them in the console, press enter and then compare. they are slightly different and are already part of tidyverse. they exist for exercising sake only
All these years of learning R and I never came across "describe" before.
Then you might find more useful function on my channel 😉
@@yuzaR-Data-Science No doubt! Learning never stops...
@@haraldurkarlsson1147 👍
P R O M O S M ❣️
👍
you were speakig very slowly when it was easy stuff, then when it became hard you went turbo mode
Hey 👋 thanks for the feedback! I try to keep the balance between clarity and information density in the future. Thank you for watching
Promo>SM
Thanks. I do that from time to time