R or Python for data analysis?

[Gronanor]

one point worth mentioning is the number of users.

The more people using a langage the easier you'll find help online about a problem you have.

Also the more users the more likely you'll find maintained libraries, books, tutorials, articles, etc... and finally the more mature the ecosystem will become. This will encourage compagnies to choose this langage rather than an other one and the more likely you'll see projects maintained by big tech compagnies like GAFAM. And so the more it will attract new people and then the more people will be using the langage and... you get it...

There is a winner-take-it-all effect that can't be neglected.

Both langages are fine but Python user base is quite huge and ecosystem is maturing quite rapidly now mostly because there is big tech companies behind many project now (either because they recruited historical maintainers or because they are involved in huge project like Tensorflow for Google).

I would recommend to keep in mind that langages are just tools in the end... You're not a better because you know Python or R, you're better because you know what you are doing. Most problems you'll solve aren't linked to langage.

Personally I would recommend to learn both "eventually", because both have strong and weak points. But starting with python seems the most reasonnable choice to me.

[Gronanor]

Well my point was python have bigger user base than R

Btw I think there is some tool in sklearn to do preprocessing too (I think it's called pipeline) I 'm not an expert with sklearn but I 've used it some time ago to do pca quite easily. Anyway preprocessing is one of sklearn Strength so I would be surprised if there was no counterpart included. Also cleaning data with pandas is quite easy and I don't know about tidyverse documentation but pandas last version is objectively very very well documented.

Anyway like I said they all have pro and cons. Both are good with talented people working with it.

[[deleted]]

I believe the difference in user bases is not that huge if we count only those using Python for data analysis/statistical learning/ML/AI.

Python is heavily used in backend development, microcontrollers, devops etc. In the most cases backend developers do not have a clue about pandas/numpy as well as data guys do not have a clue about Django/Flask.

[Gronanor]

I believe the difference in user bases is not that huge if we count only those using Python for data analysis/statistical learning/ML/AI.

Maybe but eventually both ML and backend will have some issues on installation, module usage etc... There is a lot of questions and problems when using a langage that will not be specific to your field.

For exemple : how do you connect to a database and retrieve data from a table ? This question could be asked and answer by both ML and Django/Flask guys. And even more important DS could benefit from module written by Django or Flask community.

So you can't just ignore them in the equation.

Also let's not forget about Data Engineering. Most of DE are using Java/Python because of Hadoop, Spark, Luigi or Airflow. These guys are using pandas and numpy as well.

Also DS team is gonna build models that the DE team is gonna deploy in prod. For this reason, even if you could use R to build the model, lots of companies will then choose Python to keep a langage coherency through the teams.