I recommend R.

a) a relatively steep learning curve. If you have learnt some other programming language, it will help.

b) easy to use. usually if you have the right data format, models are only a few lines. There are many active forums for R users.

c) very abundant libraries. this is the most appealing part of R. Many statisticians use R for academic or corporate data analysis. There are many libraries for economics or social sciences.

d) free of cost. But you need to invest your time and effort.

Python is good too if you come from a programmer background.

I advise to go for R, as it (still) offers the greater set of ready-made procedures. With few lines of code, you can do a lot. But Phython is a strong second.

As mentioned by others, yes, R is easy for informatics beginner. In Rstudio, everything is clear and comprehensible. Including shiny, it is easy build interactive web applications with R. I think interactive images are very useful for you.

Python, yes, also powerful one, but for me, 95% of my work can do in R. Some large data analysis maybe only possible in Python. In this case, you need use your tools [always written by Python ] in a powerful server or local PC [always Linux system].

Some times, for me, just little tools written in Python I used. In this case, download the Bioconda to setup your Python environment.

In brief, how is your data, and which tool you would like to use handling your data, that's the point, which language you should choose to learn.

I think that the stated criteria are 1) too subjective and 2) are tailored to an N of one. Time is a variable that should be considered when comparing the programs, as it can be measured, perhaps by a utility of the operating system. For example, time between download and first effective use as one criteria; and the time required to compete one or more well defined tasks as another criteria. The last point may require considering set up time to create and/or modify a data set for appropriate use in each program.

Hi David Acosta-Rosero

In my own opinion, one should consider some things in order of doing a good choice when analyzing tools for any kind of statistic/econometric analysis, so apologize me by starting your answer with another question:

Are you using a Windows PC? If so, you should consider:

The size of the dataset. In cases where the dataset you are working with is less than 300 MB, R can be a great choice. The software will handle large datasets and Bid Data applications, but over this amount of data, you'll be required to have a more powerful computer. If you are working with data science applications, or big data, Python will be better. Or even other choices, like Julia Computing (another open, free language, but quicker and more powerful than R, Python, Scala, Mathlab, SPSS, Stata and pretty much all other main options available), or Microsoft SPSS (paid software), because of the fact that this software have the option for you to work with Microsoft's virtual drives, what can save you some RAM (specially if you don't have a great computer available in the moment).

Turning back to your question (sorry again for the prolixity), let's evaluate:

a) Easy to learn

For all three options, you can find online courses for free, or even websites and apps (like Code Academy, for instance), that can help you to learn the language you want. Even so, because R is an open-source code language, and achieved a major popularity before the tremendous growth of Python in Data Science, I guess it is easier for you to learn it. To help, I'll recommend some course options on Coursera that you can take a look. All of these courses are paid, but you can ask for financial aid and do the course for free with a verified certificate:

Johns Hopkins - R Programming (Beginner)

https://www.coursera.org/learn/r-programming

Johns Hopkins - Advanced R Programming (Intermediate)

https://www.coursera.org/learn/advanced-r?specialization=r

Johns Hopkins - Building R Packages (Advanced)

https://www.coursera.org/learn/r-packages?specialization=r

Johns Hopkins - Building Data Visualization Tools (Intermediate)

https://www.coursera.org/learn/r-data-visualization?specialization=r

Imperial College of London - Linear Regression for Public Health (Beginner)

https://www.coursera.org/learn/linear-regression-r-public-health

Duke University - Statistics with R Specialization Program (Intermediate)

https://www.coursera.org/specializations/statistics

UC San Diego - Designing, Running, and Analyzing Experiments (Intermediate)

https://www.coursera.org/learn/designexperiments

Even so, if you want/need to learn Python, there are also great courses over there too.

b) How easy is it to use?

I'll stick with our colleagues here, R have a nice environment, and if you use some alternative programming interface like RStudio, for instance, you'll benefit from a very organized and smooth experience.

Python is also a very natural, user-friendly language, but I'm still learning it, so perhaps I won't be the best person to give you an opinion on this yet (although I learned R quicker, in fact).

Stata have the benefit of providing you many predefined actions, so this can save you time.

Comparing my experience with the three, I'll be with R one more time.

c) Tools (repositories, etc.)

Again, because R and Python are free choices, and open-source projects, you will benefit from many options of packages and repositories. In your case, if you don't work with massive amounts of data or complex algorithms, R can be a better choice.

d) Cost

Both R and Python are free, so I'll again vote in it :)

Summary

Considering all the options, I would personally choose R. If you, or any of our fellow colleagues here need anything related to ideas and recommendations of courses or other sources of education in any language, please feel free to send me a message, or email me at [email protected]. I'll be glad to help.

Best regards

I strongly recommend R.

The latest version of R has just been released few days back which contains python plugin.

So in case you need to use pytho in the future , you can get job done easily

Python gets my vote! The pandas package handles data excellently and plotly makes it look great.

Why not booth?

Take a look https://towardsdatascience.com/from-r-vs-python-to-r-and-python-aa25db33ce17

The R-package ploty is a powerful option to visualize the Social Economics data.