Hi I have watched one of your video comparing R and Python a few year ago. At that time, I started learning Python. Then following your advices I learned R (that I really love). I would say that R is prefered for people with a math/stat background. Then I learned Julia and Nim for perfromance. Overall your advice helped me a lot, thank you!
Very helpful. As a social scientist (not working in text-as-data), R is straightforwardly more useful. I superficially learned Python first. Then, I learned R and found it more useful for just about everything I need to do. One consideration I'd add (unless you said it and I missed it) is that R users tend to use R Studio as the IDE, which makes things easier while getting started and remains useful (knitr, markdown) as you gain skill. With R Studio, you are able to see all the objects in memory, as well as storage type. You can open a dataframe as a spreadsheet or pop it out as a new window and look at it side-by-side with any section of your code. When I learned Python (I really only use R now, so I'm probably biased), the best we had was Jupyter notebooks. I don't know if people are using something better, but when I learned Python, I found it pretty frustrating to have to constantly print things to check on objects' attributes and contents. My understanding is R Studio runs Python code now, but I don't much evidence of people using it. Have Python's IDE options improved since I learned five years ago?
Good to hear that you learnt R and then created the video. I can understand @6:41 - After learning c , c++, basic , cobol ie having a programming background. R really felt funny and weird because there are multiple ways you can do the samething. But later i fell in love with R . I have heard numpy and pandas are inspired from R datastructures. You have computer engineers backing up development and usage of python whereas bunch of academicians and statisticians for R. R initially looked like hotchpotch but after looking at numpy and pandas with basic python...... i just laugh at my judgements reversing over time. Python seems to be more in line with traditional expectation from OOPS syntax...i can go on ..... but both could have been more streamlined for the workflow of datascience
For me the problem with Python was the comp science knowledge that becomes almost mandatory to move forward. Whereas in R, the quality of tutorials and the IDE make it such that you don't need to know a lot of CS knowledge to get the most out of it. I personally believe as a non CS grad python will get surprisingly difficult as you get further along. Also as for the popularity ratings, Baby shark was the most popular RUclips video doesn't mean everybody should watch it, just meant the kids loved it. So my point is popularity doesn't mean most important. Remember, the world ran just fine before Python came along. The "popularity" could just be fresh grads searching for tutorials and not mission critical infrastructure.
@@RichardOnDataHi Richard, i loved the video, very informative and easy to follow and understand. I have been away from programming for a very long time, about 30 years to be exact and im looking to get back into it. There so many different opinions about if one should or shouldn't learn to code, what do you think about this and if you would direct me to a good python course i can use, you see im over 50 and wish to maximize my time. Thank you again Richard, blessings!
Both. I mainly do financial statistics. My main codebase is in R but I have Python helper files that can be loaded into an R script when needed, and then call those Python functions directly in the R script.
I’m a neurobiologist PhD… I’ve been using R for years and I’m aiming to work outside academia, now I’m learning python and isn’t that straight forward to me perhaps due to school bias… but python seems to be the norm outside academia and that’s why I’m trying to learn.
Yes, have to live in both worlds since I am still in school but I have to say I am definitely doing more in Python now that I have been deep diving into ML and AI. The course I am taking in Natural Language Processing is definitely just Python based because of NLTK. Looking forward to your next videos and just keep them coming. Best to you and yours. The Angry Statistician.
If you're in healthcare or pharma, the other language to know is SAS. I know, it's old hat, but it has a simple syntax, a rich macro language and it is certified for use in FDA-regulated industries.
For me, my preferred language for data analysis, modeling and plotting is definitely R. But when it comes to web scraping, specifically using selenium, Python is much better
True, Selenium in Python works better than RSelenium. In R I would recommand hayalbaz for web browser automation using Chromote (but rvest will soon have that too). In Python, Playwrights is amazing!
Very objective take. As much as I love R I am seeing myself using Python a lot more. Especially for MLOps. Python is such a nice glue language that you have to use it eventually.
the same video you did last year i think R or Python it depends of the jobs will achieve ..R i think stay alive for longer ...python is everywhere and all the experts use python maybe easy or i dont know open source or something else
I'm pretty much an R user but I too would suggest Python to someone new. But the popularity and usage is also a self fulfilling prophecy. R as said is for statisticians while python includes way more people. And ChatGPT will only expand this preference as the training model has more examples with Python. It's way easier to obtain an hallucinations for R. So is R useless ? Hell no, as Richard said it's more entry level and some categories like biostatisticians or doctors will have less issues with R Also when not doing ML, R is way more "reproducible-friendly" with the tidyverse A one-time analysis or exploratory analysis is way faster to write But of course that's from someone using both languages and biased --- Still the message is: For a general user Python is king But some people shouldn't dismiss R It all depends on the work activities
It's also worth adding that R is much better developed in fields of classic statistics and ML as well as econometrics. Thus it very popular (equally or even more than python) in financial fields like f.e. credit risk.
@@RichardOnData Isn't it like Julia has some computation problems? I'm on community's discord and sometimes I see posts about problems with the language itself.
So I’m in a MS business analytics degree, and we had the opportunity to learn R programming. So I learned it, still learning it as we speak and becoming really efficient and effective at it as I go on Kaggle and practice with different types of datasets. But so many companies are requiring Python. I just came to the conclusion that im not going to overwhelm myself and just stick with R and SQl and just say it is what it is. I’m not Superman out here, and these companies must stop acting like Gods and show some flexibility
Out of curiosity, why not take time to learn Python? It wouldn’t hurt, knowledge is power, and there are a ton of amazing Pandas and basic Python tutorials on Udemy and YT. It would obviously make you more valuable towards a company and there is a library in Python you can utilize to use R code in Python.
I usually don't subscribe, to avoid unnecessary suggestions. But with current RUclips algorithm. It doesn't really matter whether I am subscribed or not. You should mention this as well. I doesn't hurt in anyway to subscribe to a channel. So hit the subscribe button😂😅 So why not.... I subscribed 😉👍
I can't believe FORTRAN is #12! I programmed my master's thesis project in 1995 in FORTRAN and I thought nobody used it anymore. As a statistician I'm guessing R is the way to go.
For me the greatest difference between the two languages is the mentality. R users are taught basic programming fundamentals and learn that for every solution there is a package they can use. Python users are taught programming first and how the language is used to create packages. So R users learn to use the language at a higher level, and when they go deeper then things get messy. Also in 2024 I wouldn't keep putting labels such as R for statistics and Python general purpose etc. This kind of labels is absolutely nonsense.
![Eminem - Somebody Save Me (feat. Jelly Roll) [Official Music Video]](http://i.ytimg.com/vi/Vwa0HenQMi4/mqdefault.jpg)
Both, but I am biased towards R.
Hi I have watched one of your video comparing R and Python a few year ago. At that time, I started learning Python. Then following your advices I learned R (that I really love). I would say that R is prefered for people with a math/stat background. Then I learned Julia and Nim for perfromance. Overall your advice helped me a lot, thank you!
Very helpful. As a social scientist (not working in text-as-data), R is straightforwardly more useful. I superficially learned Python first. Then, I learned R and found it more useful for just about everything I need to do.
One consideration I'd add (unless you said it and I missed it) is that R users tend to use R Studio as the IDE, which makes things easier while getting started and remains useful (knitr, markdown) as you gain skill. With R Studio, you are able to see all the objects in memory, as well as storage type. You can open a dataframe as a spreadsheet or pop it out as a new window and look at it side-by-side with any section of your code. When I learned Python (I really only use R now, so I'm probably biased), the best we had was Jupyter notebooks. I don't know if people are using something better, but when I learned Python, I found it pretty frustrating to have to constantly print things to check on objects' attributes and contents. My understanding is R Studio runs Python code now, but I don't much evidence of people using it. Have Python's IDE options improved since I learned five years ago?
Good to hear that you learnt R and then created the video. I can understand @6:41 - After learning c , c++, basic , cobol ie having a programming background. R really felt funny and weird because there are multiple ways you can do the samething. But later i fell in love with R . I have heard numpy and pandas are inspired from R datastructures. You have computer engineers backing up development and usage of python whereas bunch of academicians and statisticians for R. R initially looked like hotchpotch but after looking at numpy and pandas with basic python...... i just laugh at my judgements reversing over time. Python seems to be more in line with traditional expectation from OOPS syntax...i can go on ..... but both could have been more streamlined for the workflow of datascience
I'm a newbie of R and I like it. Thanks for the great video.
For me the problem with Python was the comp science knowledge that becomes almost mandatory to move forward. Whereas in R, the quality of tutorials and the IDE make it such that you don't need to know a lot of CS knowledge to get the most out of it. I personally believe as a non CS grad python will get surprisingly difficult as you get further along.
Also as for the popularity ratings, Baby shark was the most popular RUclips video doesn't mean everybody should watch it, just meant the kids loved it. So my point is popularity doesn't mean most important. Remember, the world ran just fine before Python came along.
The "popularity" could just be fresh grads searching for tutorials and not mission critical infrastructure.
Fiats are more common than tractors, but would you use a Fiat instead of a tractor as a farmer just because Fiats are more widely used?
I have friends who are data scientist and a lot of them us R as well as the college phd students etc. python is like for everyone and everything
For everyone and everything is right!
@@RichardOnDataHi Richard, i loved the video, very informative and easy to follow and understand. I have been away from programming for a very long time, about 30 years to be exact and im looking to get back into it. There so many different opinions about if one should or shouldn't learn to code, what do you think about this and if you would direct me to a good python course i can use, you see im over 50 and wish to maximize my time. Thank you again Richard, blessings!
Both. I mainly do financial statistics. My main codebase is in R but I have Python helper files that can be loaded into an R script when needed, and then call those Python functions directly in the R script.
I’m a neurobiologist PhD… I’ve been using R for years and I’m aiming to work outside academia, now I’m learning python and isn’t that straight forward to me perhaps due to school bias… but python seems to be the norm outside academia and that’s why I’m trying to learn.
Yes, have to live in both worlds since I am still in school but I have to say I am definitely doing more in Python now that I have been deep diving into ML and AI. The course I am taking in Natural Language Processing is definitely just Python based because of NLTK. Looking forward to your next videos and just keep them coming. Best to you and yours.
The Angry Statistician.
If you're in healthcare or pharma, the other language to know is SAS. I know, it's old hat, but it has a simple syntax, a rich macro language and it is certified for use in FDA-regulated industries.
Or SPSS which is similar but cheaper and the the user interface is much nicer
For me, my preferred language for data analysis, modeling and plotting is definitely R. But when it comes to web scraping, specifically using selenium, Python is much better
True, Selenium in Python works better than RSelenium. In R I would recommand hayalbaz for web browser automation using Chromote (but rvest will soon have that too). In Python, Playwrights is amazing!
R is the best
The language is pure liquid shit but unfortunately is a necessity in data science.
Very objective take. As much as I love R I am seeing myself using Python a lot more. Especially for MLOps. Python is such a nice glue language that you have to use it eventually.
You really do. To me, not being strong in Python is unsustainable in a data science career long term.
the same video you did last year i think R or Python it depends of the jobs will achieve ..R i think stay alive for longer ...python is everywhere and all the experts use python maybe easy or i dont know open source or something else
Yeah R has a passionate development community and isn’t going anywhere, soon at least.
I'm pretty much an R user but I too would suggest Python to someone new.
But the popularity and usage is also a self fulfilling prophecy. R as said is for statisticians while python includes way more people.
And ChatGPT will only expand this preference as the training model has more examples with Python. It's way easier to obtain an hallucinations for R.
So is R useless ? Hell no, as Richard said it's more entry level and some categories like biostatisticians or doctors will have less issues with R
Also when not doing ML, R is way more "reproducible-friendly" with the tidyverse
A one-time analysis or exploratory analysis is way faster to write
But of course that's from someone using both languages and biased
---
Still the message is:
For a general user Python is king
But some people shouldn't dismiss R
It all depends on the work activities
It's also worth adding that R is much better developed in fields of classic statistics and ML as well as econometrics. Thus it very popular (equally or even more than python) in financial fields like f.e. credit risk.
Greta comparison. I'm a pythonista considering jumping to a second language!. Which-one should I choose, R or Julia?
I like Julia but unless you’re specifically doing scientific computing where speed is of the utmost importance, I’d learn R as your second language.
@@RichardOnData Isn't it like Julia has some computation problems? I'm on community's discord and sometimes I see posts about problems with the language itself.
asking for comments could boost your visabilty too.
i am not 100% certain, but a lot of youtubers ask for them and so it migth be a booster as well
So I’m in a MS business analytics degree, and we had the opportunity to learn R programming. So I learned it, still learning it as we speak and becoming really efficient and effective at it as I go on Kaggle and practice with different types of datasets.
But so many companies are requiring Python. I just came to the conclusion that im not going to overwhelm myself and just stick with R and SQl and just say it is what it is. I’m not Superman out here, and these companies must stop acting like Gods and show some flexibility
Out of curiosity, why not take time to learn Python? It wouldn’t hurt, knowledge is power, and there are a ton of amazing Pandas and basic Python tutorials on Udemy and YT. It would obviously make you more valuable towards a company and there is a library in Python you can utilize to use R code in Python.
I usually don't subscribe, to avoid unnecessary suggestions. But with current RUclips algorithm. It doesn't really matter whether I am subscribed or not.
You should mention this as well. I doesn't hurt in anyway to subscribe to a channel. So hit the subscribe button😂😅
So why not.... I subscribed 😉👍
Thanks!
It remains vague. What exactly can you do with R that is not possible with Python?
You mean to say python along with packages numpy , pandas scikit learn etc....
Julia is the light and the way
I can't believe FORTRAN is #12! I programmed my master's thesis project in 1995 in FORTRAN and I thought nobody used it anymore. As a statistician I'm guessing R is the way to go.
My university economics program uses R. I learned both for obvious reasons
R is more capable of doing amazing things better than python
i like R
For me the greatest difference between the two languages is the mentality. R users are taught basic programming fundamentals and learn that for every solution there is a package they can use. Python users are taught programming first and how the language is used to create packages. So R users learn to use the language at a higher level, and when they go deeper then things get messy. Also in 2024 I wouldn't keep putting labels such as R for statistics and Python general purpose etc. This kind of labels is absolutely nonsense.
lol Rust 2%
11:10 there is a great implementation of pytorch in R, called torch
r is for data scientists while python is for general like games etc.mugo and data data and mugo
R
Python syntax feels very clunky for getting the job done. R just makes sense, but I have an econometrics background, not cs
I wish perl, the regex 👑 king, was still in the race. But, Python is definitely the bigger bang for the kind buck now!
You're evil