R vs Python | Which is Better for Data Analysis?

R and Python are the programming language of choice for most data analyst and scientists. Let's take a look at them and see which one is better for you!

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*All opinions or statements in this video are my own and do not reflect the opinion of the company I work for or have ever worked for*

[ALZulas]

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.

[jerchiury]

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.

[lauragonzalez]

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

[bendirval]

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.

[bernardogrivon]

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.

[jamesstonehouse]

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.

[davidreynolds]

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.

[gustavob]

You're right we should try both. I'd say just do not get attached to tools, they always change. Have a good understanding of both and use whatever suits better to the task at hand. The important thing is to 1. understand the problem 2. find out what would solve the damn problem 3. Test it.

[Vivian-veqt]

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.

[keith]

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.

[davidyolchuyev]

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

[datamics]

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.

[tanvird]

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.

[annoyingprecision]

Liked and subscribed! Thank you for the valuable input.

[simonparker]

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).

[mohamedjelassi]

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

[aldorodriguez]

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

[fernando_dominguez]

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!

[TheFootballPlaya]

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?

[solom]

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.