I used to give a lecture on when to use R and when to use Python. I gave it for many years, but every year both languages would grow closer and closer together. I eventually stopped giving the lecture, because they're so much the same that it doesn't benefit students to talk about it anymore. The only thing that's different anymore, in my opinion, is it depends on how your brain thinks about problems. If you think about and solve problems from a programmer mind set, Python will be easier for you brain to wrap around. If you come from SAS, MPlus, or SPSS, R might be easier for your brain to wrap around. Much like picking skis or snowboards, try them both and go with the one that feels right for the way you work.

Python is unquestionably more straightforward as a language in general. However, it's fundamentally a general-purpose scalar language, not a vector-data language like R or a matrix language like matlab. That fact makes the type of data manipulation and analysis that is meat and potatoes in R less convenient in Python. "Hello world" is easier in Python, but real data analysis is easier in R. I use Python for general programming, but it's just not worth the trouble to force Python to pretend to be R for data analysis, econometrics, or statistics. Python is way, way behind in all forms of data analysis. For example, Python is only now considering basic ideas like "missing" values being different from "not a number" values, which the creators of R thought of and planned for from day 1. If I had to pick one language as "better," I might choose Python, but it's not better for data analysis, which is what's being discussed here.

As a noob with only excel background, I got into R much more easily. One huge advantage of R imo is RStudio. Such a great tool to work with. Also in R the documentation is helpful, even the error messages are useful. I'm starting with python, but for me it's not as sticky and intuitive. I find Spyder as an IDE ok, but imo it's way behind RStudio.

The syntax example for R is way more complicated than it needs to be. You technically don’t even need to load any packages to read in a CSV and calculate the mean.

I've waited long for this video! Right now I'm learning Python and in my company, they use both depending on if we are using classical statistical models or ML. However, I'm also an economist who would like to get more involved in academia and I think R is more used there than Python. Both are excellent choices tbh

I came to R from using C, visual engineering environment (an instrument control language used in metrology), SAS & SQL. Nowadays I make my living with R, automating reporting, text mining, and developing data manipulation tools for an intelligence team. It has to be said that in my industry, I haven't yet come acress a Python user. It might just be that the big players in town are all either R or SAS background.

For a new programer, I'd say learn Python. It's much much easier to get a job with Python, your in the general software engineer camp vs being locked to data scientist roles.

I don't want to worry too much about data types when doing my analysis. The fact that base R supports operations of matrices and data frames makes it much easier to use. R knows when you are subtracting two series (column/vector whichever) to subtract it term by term for example, it's pretty messy with python when you get lists, series, arrays and such going about all with different methods for that one exact operation.

Two minutes in, you're pedalling the standard nonsense that R is a statistical package. I've been using R for twelve years and pretty much never for statistics. Text processing, data cleaning, report writing (markdown) and GIS, GIS, GIS. R is really good for mapping and geospatial data processing (not just spatial statistics).

Power BI vs. Tableau

When we speak about analysis, we speak about mathematics and more precisely statistics... in my point of view, R has more mathematical libraries than python ... and please keep python for web development and other stuffs

Very good, I mostly use R. But when Combining R with python and sql, then you are unstoppable data programing machine. So learn both, it's a lot of fun.

5:56 you can use colMeans(nba[sapply(nba, is.numeric)]) for calculating means of the numeric columns, you don't even have to import any libraries. I understand the python way is still cleaner, however, there are tons of situations where the other way around is true. 7:09 library(tidyverse) and you get every functionality that python pandas can offer, you don't have to remember a lot of things for doing a simple task.

Personally I prefer R when doing hardcore data analysis. Dplyr, ggplot2. and the rest of the tidyverse enable you to do more with dramatically less code compared to Python. For anything outside of hardcore Data analysis I use Python.

I learn both. My conclusion is python is better but i love Rmarkdown and ggplot more than jupyter and matplotlib + seaborn.

filtering with pandas df[df['column'] == x] vs R data.table df[column == x, ] What is easier to read? Become even worse in pandas then you have more complex condition. Not to mention multi index in pandas. It is a hell. And direct comparison with reading of csv in r that you can also do in 2 lines: x

for finding the mean of the column in R, you use mean() function. I dont know why you have shown pipes in the R section of syntax example

I think R is better, as pandas is much slower and less easy to use than dplyr. Data prep takes up most of my time, so this is huge. Both R and python are relatively equivalent to me for machine learning. Alot of these ml packages are just R and Python wrappers to c code. Maybe if you work for a fang and do alot of pure deep learning, python may be better, but I think that situation is rather rare.

I don’t think it’s a big deal, it’s a preferential thing! I’m currently learning how to use Python

It truly depends: once on personal preference, and also on what your work, that is your company, requires you to use. I prefer Python, and I think Python will grow to offer the same amount of features (if not more) as R in the future.

R and Python require totally different mindsets. Picking the better one of them is like asking "which is a better career, statistician or engineer?". With my mathematical background I find R code much more straightforward, and when I started to use Python, it's not like any single piece of code is unreadable but the entire workflow is unfamiliar: how tasks are broken down, why makes a copy here, and so on. It also took me quite some time to be convinced that Python does not have a library for the Spatio-Temporal Autoregression model (for a few hours I thought I just hadn't searched the right way) since R offers abundent solutions for spatio-temporal data. Eventually I realized that modelling is never at the top of engineers' priority list, and mathematicians/statisticians can focus on the intellectual work only because engineers have got their hands dirty. Also, Python makes it easier to collaborate with other platforms. If I were to communicate with laymen rather than other professionals in my company, deploying a dashboard or web app would be the best explanation. Again, it didn't occur to me because this hadn't been my top priority, so I preferred R. Now the job has changed and I'm using Python more often.

It would be nice to have a video with examples or real world scenarios for both cases.

I am an economist trying to dedicate to data analysis and I still didn't understand the pros and cons of both, so this video is exactly what I needed. Thank you! 😄

When I subscribed to this channel two weeks ago I did it because I wanted to be ready for my data analyst interview. I passed it very well and I think this channel helped at least when it came to learning more about the job and the differences between a data analyst and a data scientist. I will start on the first day of March and I am looking forward to it. I am studying for a master in Big Data at the same time and I am learning R there, whereas I need to learn Python for work. R doesn't look difficult to me but Python kinda looks more familiar for me and those with a background in other general purpose programming languages. I agree about the huge amount of libraries in R and I think that it is really great for visualization. However, since Python is becoming the most popular programming language I would already prefer it for that reason alone not counting anything else.

Honestly, I like both. Since I came from SPSS and statistics background, R suits me better. But when I need to analyze missing values or do some graphics, Python helps me a lot more.

This guy is not using R correctly if that's how he's calculating the mean for a dataset lol

I've personally enjoyed my SQL, Excel, R, PowerBI group I've got set-up. The only thing I really planned for was learning PowerBI but the rest came about oddly naturally. Great video by the way!

You definitely cherry picked to get mean of column you don't need to load packages just the base summary function will give that. summary(data) will give you the mean, median, Q1, Q3, min and max of every numeric column, not to mention the counts of qualitative columns.

I feel this video is a little biased at 5:55, as I don't think anybody will write that code to just get the mean

Great video. But I think you could make a syntax example with R much easier. If you want to know the mean of a variable or of data you only type mean(data) or mean(data$variable), of course, depends on this variable or data is numeric or not. Thanks for your video. Regards.

By the way R can be embedded in web application.

Love your overall points Alex, but saying that R can't be integrated in web apps is plane wrong!

The youtube algorithm brought me here lol. I think an example of data analysis using popular libraries on both for comparison is good. Like the processing time, the amount of codes written etc. For me python is easier since in colleague we used cpp and fortran for learning basics of algorithm and numerical methods. The one time i had to use spss for statistics assignments i got really confused.

Debates of Python VS R are pointless imo. They are good in their own ways. Most importantly, they are better than SAS.

Excellent summary, great balance of conciseness and examples. "R is harder to learn, but has more features"... specifically for analytics, right? My understanding is Python has far more features in general. Never heard of someone building a mobile app in R.

SQL VS NOSQL

It helps to know many programming languages - that much I have learned so far

They’re just tools to get my work done. I use both on daily basis.

Thank you Alex for this video!

I prefer the R syntax. I find it easier to remember “weird” syntax.

Stopped watching at 5:55 because either Alex was biased or he has no idea whatsoever about R, since he did not use the mean( ) function which is even a base R function and you don't need to install and load any package to use it.

I think it depends on ours strenghts in whatever the lenguage is, R is a multi-good to know functions program, if not, it could be difficult, so the same with Py in another order: Having Expp with R in Stats I could say that both have advantajes, and I would use one or another in diff purpruses depending how I started learning with them, the important thing here is know their global utilities

Thank you! I'll try both. Approximately in a month I'll have a course in Coursera about R (from Google Data Analyst Certification), but after that I'm interested to try Python as well.

Loved it! Thank you very much for your content, just started following you. My advice is just express your opinion like you did, makes content far more unique. Cheers!

There must be a reason behind the growing number of R packages. My clients won't care about if I produce results in R or Python. If they ask me to build an app, then I'll reconsider.

Alex, your level of R is beginner, with all due respect. There is a noticeable bias in his speech. In general, because Python is more related to C and programming languages in general, it is easier for programmers. R is easier for people with no prior programming knowledge; they learn to program as long as they learn to use R. They can use R on a day-to-day basis without delving into functional programming, object-oriented programming, and so on. The scalability issue is real. IT teams like Python best for launching products because it's their comfort zone. However, it's easier to build a dashboard or application in R. Give it a try! I agree with the argument that there is no best, but it is necessary to reduce the bias of our point of view to make a comparison.

Miss you Alex!! i worked with both and i've one little thing to say, in R you can write mean(nba) or you can use summary(nba)

I guess you could say that the messages Alex received in regard to his “controversial” post included some R-Rated content ;)

I would like to see a second part video comparison focused on comparing R and Python languages from a business standpoint rather than their more general-purpose, programming capabilities for building applications, and heavy used of sophisticated statistics that do not apply to the average business world. For instance, many of us in business are hoping to learn which language is better for business analysis which, after all, is the trend in using either of these languages. What we learn from the video is that R is being highlighted as useful in purely statistical analysis, while the comparison with Python does not provide any insights into Python's capabilities for statistical analysis. R is being highlighted as great for statistical analysis, however, advanced statistics is mostly used by the scientific and academic community mostly as well as sophisticated business environments whereby most of these advanced statistics are not needed in the general business world. I would like to see the view from a business analyst/business intelligence professional who has truly used both R and Python for exactly the same purpose, using these languages for business analysis. It would be great to move away from the general-purpose and application development and get more into the business uses for each language and on what statistical and data analysis truly serves the vast majority of business users, business intelligence and data analysts analyzing business related data. Looking forward to this second video. Thanks Alex!

Thanks, Alex. Great video. The right tool for the right job.

Your comments are vey biased towards python. Anyone claiming you can’t easily maintain scalable code for big data applications in R simply hasn’t taken the time to educate themselves in the tool.

i actually gave up on R as I moved to a more strategic role and away from hardcore data analysis, i found it harder and harder just to recollect syntax across different libraries. Plus I see Industry is tilting more and more towards Python and learning Python is kind of "future proofing" your time spent on it.

I mostly do data analysis on survey data and in my experience R is more robust in this regard. For instance, there are several packages that will create survey weights for you, but I have yet to find one Python package that actuially works. I do agree that Python syntax is somewhat easier to pick up, but once you understand vectorized operations in R it becomes easier to use.

Use the language your team uses. The guess work can be taken out based on the company you work for or the company you want to work for in the future. If they use R, use R. If they use Python, use Python. If it’s only up to you, flip a coin.

R start vector indexing from 1, not 0. It really did a great job of making programmers ANGRY

In my opinion the biggest advantages of R are its IDE Rstudio and the capacity to execute only the mouse selected portion of code (no, notebooks are not as convenient). Web deployment is possible through Shiny but it seems much more of a hassle than on Python.

Dear Alex, thanks for your review. However no offense but referring to your arguments, you know veeeery little of R. R objects and visuals in particular can be easily embeded to web apps of any kind. Plotly also exists in R and can be combined with ggplot which a heck of a boost in flexibility. Next up mean of a column in R is as easy as mean(data$column), no need for pipes dude. Therefore with all due respect if you spent more time on Python does not mean that its better and vice versa of course. If R was so lame as u say. Python would not adapt ggplot and more remarkably caret for ML and call it pycaret (like seriously).

Google chose Python for its ML/AI coding. So if you are looking at ML or AI, python is the way to go.

I like functional programming. I love R for data science. Anything else I’ll just write out some C or JS

The R code for your example is: x

Python is a compuer scientist designed language, R is a statistician designed one. Python uses = for assignment, while R use -> as assignment symbol. Python's function is more flexible than R. Deep learning packages is written for Python, but not R. So R is a statistics-lise language, and Python is a data science language.

I’m new to programming and I chose Python as a starting point... easier for a noob like me.

Just calling attention to librarian::shelf(tidyverse) You don't need to write 10 lines of library(dplyr), you can write all your package name in a single line code and it will automatically install if needed and load it.

You said Python is better than R, then said its just my opinion. You should have clarified that in the post that its just my opinion. But you wrote a single statement that- "Python is better than R". Any person reading that would think that you are stating this as a fact. This is why it was controversial. Adding the words- "its just my opinion" or "its more suitable for my job" would have taken the controversy out of it. Perhaps you just wanted to stir up unnecessary controversy for attention.

Why not mean(variable) in R's example, it can be short as Python. R is more data wrangling and not application building. Your review lacks that perspective. I suspect you have no experience with data analysis, and if you're an R user you should know that there is no equivalent of dplyr on the Python side.

It's insane how many times you had to ask ahead for forgiveness to avoid potentially offending anyone. We are all different and haver different opinions - get over it people! Very cool video mate. As a statistician I'm very in love with R, but I'm trying to learn Python as I am very aware of it's coverage and power. Cheers

Alex, its been 4 days getting in the Data analyst Game. you are my GOTO guy and the way you started this business story is real. I am following your classes because, they are simple and easy to read. the classes that are taking in LOOOONG and complicating stuff. Please let me know if you have Instagram, its easy to communicate there.Please Explain to your wife why you could to Instagram account.

I feel everyone should do their own research before accepting the opinons expressed here. I mean no offense to the poser of this video, but the examples here are not representative enough. This video is mainly to play on feelings to get more views. Of course I have bought in as well, but I want to see R fairly represented rather than resented.

Liked and subscribed! Thank you for the valuable input.

Hard to go wrong either way. If your job leans more towards data engineering and ETL then probably Python is a good choice to start with first, IMO. Thanks Alex!

I use R, but want to learn python eventually! Thx for this video

Underrated skill that's complementary to these is Excel PowerQuery... Poor man's PowerBI and the only thing that makes Microsoft's Office suite irreplaceable by even the best of clones.

If you are a non programmer R is easier to learn compared to python where you have to begin with oops and env etc... Which are way harder to grasp And the in built data types of R support data.frame and vector calculations thus making it easier to reason about... And i have deployed like dozens of code into production including web apps so almost all of your arguement are biased for what you use...

I learned R and Python and I can say R is much easier to learn but Python is way more robust. I replaced a VBA code that creates MS Excel workbooks from a template, and it took like 3 seconds to complete. Using R took like 45 seconds.. After I saw the benefits and speed of Python, I put R aside and focus on Python.

R requires running in a Linux environment to ensure stability & reliability- even for simple flat. Windows creates crap. The disaster will accelerate in the upcoming Windows 12- Microsoft developers can't explain what's the problem. The instability can't be replicated in python, sas, C, Java code. What BS.

I think Python is better too, but I do like Hadley Wickham's TidyVerse for R.

Python is a general programming language. R is a statistical programming language. Obviously Python cannot compete with R when it comes to statistics and R cannot compete with Python when it comes to programming in general

Learn sql for data analysis from your playlist it’s enough or need to continue with another course

Amazing video!! Thanks!!

Once again, thanks for sharing your thoughts with us Alex. Can always count on your fair unbiased opinions.

tipical python fanboys thinking their language is good for everything

my whole knowledge in programming is with c++, I even made my calculations for my M.Sc in Statistics and Operations research in c++ . Now I’m not sure what to begin with Python or R. Most my work are hover around numerical analysis.

If all you want to do is read a CSV file and see the mean, you could use RStudio and not program anything in either R or Python. Use the right tool for the right job.

Without viewing, the answer is ... "the one you know". Software is a tool. It is not the artist.

R has the stronger scientific community. Python copied r. Better package and better workflow integration. Objektif orientation inferior to Python.

General rule of thumb for me. 1. parsing? data wrangling? -> python 2. statistics? single cell? -> R

I think the best thing about R is RMarkdown. Being able to hit one button, run my statistical analysis, and output a word document with all the right numbers and figures in it is amazing for reproducible reporting. I'm switching to Python soon. Do you have any recommendations for a similar functionality?

I've been watching your videos as statisticians from Turkey. I'm sure there are people from other countries like me. Please put English subtitles to your videos for a clear understanding

Why not both

I dont unterstand the last point: R is for statistics and Python for machine learning. I thought, machine learning is nothing else than statistic?

Hey Alex do you mind doing a video on the impact of automation on the future of the data analyst career? It would be really helpful to those who are on the fence about starting/changing their careers.

Thanks a lot, you gave us a lot of information about both languages. The conclusion was very good, thank you so much.

Both are needed once things get a little advanced

question: if you had to choose one background to have to work as a data analyst, business or statistics, which one would you choose and why?

I know absolutely nothing about Python, but your example at ~6:30 is a major giveaway that you are not experienced enough with R programming to form a reliable comparison - the example could be done in base R with two lines of simple code. I've never seen such an overcomplicated way to find the mean as you described..

Thank you Alex!

For me, it's clear. Python is better for most of people, but, if one have strong math and stats background, R is probably the best. It's so much easier to collect data, clean it, put everything to work in python. But R is just THE WAY TO GO for statistical analysis. You get so many stuff out of the box. So many statistics, it is amazing. Tl dr: learn both, R for statistical and ML modeling. Python for anything else. If learning both is not an option, probably go the python route.

Simple fact is that a good programmer in either language can handle 95% of data analytics tasks easily.

Like Python more as a language. R really suffers from weak data typing, and programming feels more clunky, but %>% is great, plus access to shiny

Thank you for this video Alex. I have always been intrigued by data analysis and want to learn more of the programming side. I was not familiar with either of these programs but your breakdown of the two help. If I wanted to learn both of these, would it be easier to learn Python first, then try to learn R or should I try to learn about R first, then Python?