Important Base R Functions | R Tutorial (2020)
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- Опубликовано: 10 фев 2025
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GitHub: github.com/Ric...
The base R cheat sheet can be found here: rstudio.com/re...
R for Data Science (Amazon link): amzn.to/2HEoj5Z
In this video I go through the functions in the cheat sheet for Base R available on RStudio's website. Topics covered include:
1) str() / summary() / names()
2) ifelse()
3) Functions
4) rm(list = ls())
5) Data frames
6) Lists
7) cbind() / rbind()
8) cut()
9) lm() / glm()
10) rnorm(), dnorm(), pnorm(), qnorm()
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I went through some of your tidyverse videos and what can I say ! It's an absolute perfect job, you cover so much content in so little time, allowing learners to take more time on some particular steps thanks to your available .Rmd files... thank YOU !
Wow, thank you! Yes, one of the absolute most difficult things on my end for these videos is keeping them tight, short, and not excessively long (especially this particular video). I'm glad that you find them to cover a good amount of material in reasonable time!
Hi Richard, thank you for making this video. It's absolutely fantastic. Cheers!
My pleasure!
Hello Richard, incredible tutorial, thanks!! Regards from Costa Rica :)
Thanks for watching!
Where can I download this presentation?
Hello Richard, please do you have any materials related to input-output analysis using R (specially multi-regional input-output analysis).
Hey Richard, thank you for sharing your knowledge with us, you have great content, keep going.
I have a request and a question for you:
1/Can you do overviews about some online courses (365 datascience, datacamp, dataquest) we need point of vue from an expert.
2/ which field are you working in?
Thank you so much!
1) This is a great idea, although I've personally never done any of them myself. I can look at them as far as what others think and how I feel about the broader, high-level curriculum. This is a great idea.
2) For just over 3 years I was involved in the healthcare industry. That was until last year when I made a career shift into the pharmaceutical industry. For ~18 months I was with one of the largest pharma companies in a contracting capacity; I currently work full-time internally at a clinical research organization.
@@RichardOnData hope you doing well, love your content.
I've taken all of the R-related courses from Datacamp and LinkedInLearning. The Datacamp courses are difficult and took me much longer to complete than their estimated completion time suggested. As a complete novice with R, I really was starting from the very beginning. I like how Datacamp is based on active practicing of the concepts to reinforce the knowledge, however there were many times that I was completely stuck and could not solve a problem. They need to put more work into providing better detail in their "help" functionality. LinkedIn Learning has AMAZING video instruction, but you are not required to complete the practice modules. This has its advantages if you want to become familiar with a new concept without needing to spend hours to practice it right away.
Hi Richard, thanks for making great videos on Data Science. I have a request if you could cover some topics in your videos one by one or all together -
1) User Defined Functions from tidyeval. Examples of Functions that covers quo/enquo, !!, rlang::exp() (Reference: adv-r.hadley.nz/expressions.html)
Seems like lot of things are way different in R from other languages specially Python and there are not good videos on youtube on this topic.
2) Which is a better package for ML in R. Their Pros & Cons. Is it caret or some other ?
In Python scikitlearn is like the global choice for ML work but in R its all over the place. Every different course, youtube videos uses something else and it makes it really confusing for somebody who is starting in R on its own.
3) Differences in ML done in real Vs mostly shown in RUclips videos or courses -
3.i) Writing Modular codes in functions in different scripts and making function calls or just writing a ML code as show in most RUclips videos
3.ii) Should one need to build knowledge on other things like Big data, hadoop, spark, cloud- (aws, cloudera etc) or usually there are different resources for such skills in the companies? For example if we are creating a Credit card fraud detection then it will work on live data, so that needs to be deployed on huge live data with amazing response time.
Thanks !!
Every single one of these are excellent topics and I'll make sure to cover each of them before the year's end! Interestingly you are not the first to request tidyeval (quo/enquo, etc.), and that's very understandable. I'll try to do a caret vs. tidymodels comparison soon as well after I cover some more fundamental items in my R tutorial series.
@@RichardOnData thanks Richard !!