R vs Python

Python and R are both common and powerful language for data science tasks. In this video Martin Keen, Master Inventor, provides an overview of R and Python and what questions you need to be asking before you choosing how to proceed on your next project.

[PerformanceTezek]

Thank you Toni Kroos. You you gave people the pleasure of watching and now educate people your're pure diamond.

[friendlyoctopus]

from now on i'm importing numpy as numpty instead of np

[exsldierclud]

I have 10 years working with R and I have never ever had a problem I couldn't solve with it. From ML to basic data analysis and visualization ( I'm a soil scientist). I program in both but I really like R

[guitarislife]

THIS SHOULD BE THE STANDARD. Thank you for actually presenting your findings rather than give an "it depends" answer. Great video.

[davemilnes]

May I just say your ability to mirror-write is breathtaking!

[galatemalate]

In personal experience never used R for statistical analysis haha, only for data gathering and cleansing. I’ve been got data from SQL Server, Csv Files, Text Files, GitHub repo metadata, APIs and Point of Sales. For cleansing libraries such as dplyr, lubridate, anydate, stringr, etc, magrittr helps to keep data transformation really clear. Finally once the data is cleaned up and ready, land it to the warehouse.
My personal opinion is that you can do the same ETL processes in R than in Python even with less code (if you know how to use R properly)

[labtimeRP]

In my opinion, when we talk about Python as a competitor of R, we should refer to it as Python-Pandas. More credit should be given to Wes McKinney to the surge of Python for data science

[robertrichardson]

I use and teach both. In fact I am teaching one class in R right now and one class in Python. I've also helped establish professional organizations with data science frameworks. This video is actually very in line with what I think. I've had a hard time teaching Python to people who have only used Excel, R is easier. If I'm doing a complex analysis requiring advanced probability concepts, I work in R. But I see why professional companies have leaned more towards Python. Its infrastructure makes Python better for security, reproducibility, consistent AI for apps and machinery, working with streaming, graphical, or network data, and text mining.

[sinan_islam]

You will end up using both, because Machine Learning and Statistics cannot be split apart. Python for ML and R for stats.

[lazarbaruch]

For a long time, I haven't seen such an informative presentation. You just made an inventory without explaining how the specific language features make the difference.

[marcello]

I totally agree on his take.. though python is not only OO but also functional

[jrltown]

For data exploration in R, the {dplyr} and {tidyr} packages are excellent for sub-setting and wrangling. I think this approach is superior to pandas in python. In fact the grammar and semantic approach to data wrangling enabled by {dplyr} is a foundational reason to use R over python (i.e. pandas). These two libraries (dplyr, tidyr) along with {ggplot2} make up a core of the Tidyverse approach to using R. This approach is so well thought out that it often makes me chuckle when people suggest or argue that python is better.

Clearly there are cases where python is better, just as there are cases where R is better. But when it comes to analytics and reproducibility I've not yet seen a dispostive argument in favor of python.

[ngocbinhnguyen]

If you use R before Python, R is better. If you use python before R, Python is better. I love all, and use both in my works.

[jaffarbh]

I use both in my workflow. Speed-wise, I think Python is about twice as fast for general purpose operations (like for loops), but in practice the performance depends largely on the packages used.

[auzaluis]

Ok. I'm an R user and when he was explaining Python's pros I was thinking R can do the same. It's unfair mentioning Pandas without mentioning Tidyverse

[lawrencedoliveiro]

2:55 Jupyter notebooks can support a range of languages. It started as IPython, which was Python-centric. And while Python is still the foundation and implements the Notebook API, there is a range of kernels allowing you to run other languages in notebook cells.

[stellaobeleagu]

On point there, R is my best for manipulation and visualization. In all you do, just learn Python and R.

[francisdelacruz]

Specialists usually go for R Economists, Biologists, Epidemiologists et al, those whose domain knowledge required statistics. In economics there is no question you go for R due to the packages for economics. If stat and associated data is a means to an end then it's R. If your a programmer who primarily collates and collects data with basic processing then likely it's Python.

[syhusada]

Starting with R is better, but for a weird reason, that is: "more incentive to learn python and other things".
I suspect, most who started with Python are less likely to learn R because they think Python can handle "everything".

[stilltoomanyhats]

What's the pirate's favourite programming language?
You think it would be R, but their true love be the C.