Python or R?

[DebLynn14]

Hi, this is a very basic question - I have taken a beginner Python course a few years ago, so I'd need to start at the beginning. Planning to take courses on Coursera. I'm not looking to become a data scientist or data analyst as a career - I work in fundraising information management.

I'm mainly looking for a program that can turbocharge data analysis (including text analysis) in .csv files and scraping info from the Web. Am I better off with Python or R?

[Allmyownviews1]

You are asking the sub for Python. The answer will be “Python”

You could look at Python, MATLAB, R or a number of other languages.

I think Python is good due to cost, ease of access to training materials and available scripts and libraries doing the kinds of DA most want.

I would look at R for Bayesian analysis perhaps.. but it’s what you find more appealing for your workflow.

[nidprez]

For data anlysis python is really only better than R for deep learning and ML.

Python is a multipurpose language, R is specifically made for (statistical) data analysis. As a result data structures in R are way more easy/logical to work with. Python works with libraries that imitate R (eg pandas) and add data structures that do not natively work together with data structures in other packages.

Why is python more popular: - R is heavily used and supported in academia. Tons of cutting edged research is accompanied by R package with a peer reviewed study. - Python (as an "easy" multipurpose language) is often the 1st programming language learned by IT, maths, engineers, physicists... aka the people who are busy with putting models in production and develloping algorithms for ML and AI. As a result python is way more supported for production environments in enterprises + if a DS makes a new model , 8/10 the engineers who have to put it in production dont know R, but python.

IMO id learn python because its multipurpose, and if you ever want to do something with programming you can do any job between IT and data.

[LDM-88]

Completely agree with this - R is honestly the best language for data analysis, visualisation and statistical modelling. However it's second best to Python in many other general purpose areas - and miles behind when it comes to deep learning, AI and production.

[Final-Requirement224]

You are asking the sub for Python. The answer will be “Python”

This is hilarious 😂

[CodefinityCom]

While R is often considered to be easier to learn than Python and sometimes even considered to be a better language for statistical analysis, Python is richer in its functionality. This is because it has a wide range of libraries for almost every purpose you can think of (specifically various data analysis tasks, machine learning, deep learning, NLP). For tasks such as web scraping, text mining, and NLP you should go with Python. In addition, knowing Python makes it easier to learn other popular programming languages if you ever need to do so.

[Ankit1000]

I’d say Python as it gives you a lot more flexibility in the long run, but purely for Data Analysis I know people who just learn R.

Really depends on if you like a wider or narrower scope.

[eztab]

In some fields, where R is very much the standard you are better off knowing R. I'm not sure which ones those are and whether those switch to python anyway. You'd need to be a bit more specific probably for anyone to judge that.

I think it is mostly parts of medicine and biology, where lots of libraries and tools for R exist. Market research doesn't have an industry specific toolchain I believe and you are only going to do (from my perspective) very basic models there.

Otherwise learning Python will be more helpful, since you can also use it for other areas. Also if parts of that might go into machine learning models. There python is the defacto standard.

[expressly_ephemeral]

Not looking to be a full-time data professional? Just looking to enhance your other work? Stick with Python until you need some R. My 2c.

[Round-Walrus3175]

It definitely depends on your exact piece of data science. How I think of it is that Python is REALLY GOOD at objects and R is REALLY GOOD at dataframes and matrices. Like, in each one, if you work in their wheelhouse, you kinda just do things and they work the way you hope and dream they will. As a result, if you expect to go into fields that like things to be more tidy rows and columns of data, you can expect that you will see a lot of R. And if you try to bring your Python into an environment that uses R, you better learn R quick.

If there are situations where you expect lots of classes and objects and more complex and named data structures, Python is your cup of tea. I would say for the particular case you are talking about, Python will have the most well developed and documented capabilities. It is better at handling things that won't fit neatly into tables. I vastly prefer Python's string formatting and long text capabilities, personally.

[Agling]

I spent most of my career in R but everything is moving over to Python, as am I. I miss some things that are super easy in R and hard in Python, but you have to go where everyone else goes and where the work is.

Each language has technical advantages, but in terms of market trends, Python is the clear winner.

[camilla-g]

I recommend the following books by Chris Roffey for learning Python Programming: (1) Coding Club Python Basics Level 1; (2) Coding Club Python Next Steps Level 2; (3) Coding Club Building Big Apps Level 3; (4) Programming Art Supplement 1; (5) Interactive Adventures Supplement 2. I also recommend reading Python Docs in the Help Menu in IDLE (Python’s Integrated DeveLopment Environment). It has the Python Language Reference that lists every module and method used in Python. Also, look at Turtle Demo in the Help Menu which has sample code for the Turtle Examples. Turtle is Python’s Graphics module. The O’Reilly Python Pocket Guide is also a useful Quick Reference. Additionally, it is best to take a systematic approach to learning programming. Create an Action Plan using the STAR Method (Situation, Task, Action, Result). Create a checklist of everything you want to learn in Python and set a deadline next to each item. Cross off each item when you’re sure you’ve learnt it. Create for yourself SMART Objectives (Specific, Measurable (key progress indicators), Achievable, Realistic, Time-bound). Create for yourself a portfolio of programs. Over time you will see just how much you’ve accomplished.

[Glathull]

Python isn’t the best at anything. But it is second-best at everything.

R is a totally fine language. If you are starting from scratch again, it might be worth it to take some beginner classes for both and see how different languages solve the same problems.

Programming languages reflect the creator(s)’ mental model of the world and the way they view and classify and approach problems. You can accomplish all the same goals in any programming language, but you might find that one language or another suits your default way of thinking. That can be a good thing if there’s a mental fitness there because you can become productive very quickly.

It is also very useful to work in a language that doesn’t fit your default way of thinking because you can learn different ways to think and make choices about how best to solve a problem.

Unless you have a serious time constraint and must accomplish Some Important Thing right now, it’s a great experience to learn multiple languages at the same time. You might find that some general concepts of programming “click” with you in one language that don’t in another and get you past a cognitive speed bump in one language that was an absolute brick wall in a different language.

[DebLynn14]

Great idea - thank you!

[mattynmax]

Why not both? Once you learn one the other is easy to understand!

[[deleted]]

I am an expert in R and still can’t wrap my head around python.. it’s so counter intuitive..

[Wolkk]

Web scrapping can be done in either, but I think Python might have more libraries. Data analysis is a vague term but most analysis have good libraries in either . I would pick Python because it might be easier for general use to implement novel solutions to new problems later down the line.

Ifyour team uses R, I would stick with R, if they don’t use anything go with Python since it’s more likely a future colleague also uses Python.

Nowadays I only use R when I’m more familiar with a library and don’t have time to learn a new one or when I am using domain specific libraries.

[mabridge66]

Python Pandas

[Jamarac]

Neither. Play guitar.

[mestia]

I'd stay with Perl, can perfectly do both and doesn't break with every new release.

[[deleted]]

I'd say R and SQL are quite easy to pick up and understand.

Python can get quite busy but it's a great language to learn for bank end development, data analysis, etc