Practicing Python and R at the same time

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Discussion Overview

The discussion revolves around the simultaneous learning of Python and R, particularly in the context of their applications in environmental consulting and data analysis. Participants explore the relevance of each language to the user's job, the potential benefits of mastering both, and the implications of prioritizing one over the other.

Discussion Character

  • Exploratory
  • Debate/contested
  • Technical explanation

Main Points Raised

  • One participant notes their background in Python through a course and their current focus on R due to its immediate applicability at work, despite acknowledging that learning both simultaneously is generally not recommended.
  • Another participant emphasizes the long-term benefits of Python for automation and productivity, suggesting that it may be worth learning alongside R.
  • Some participants argue that Python is gaining popularity and has a broader range of tools for data analysis and visualization compared to R, which they suggest may be more beneficial for big data analysis.
  • Several contributions highlight the importance of Python for GIS applications, particularly with ArcGIS, and recommend prioritizing Python to leverage its capabilities in geospatial data analysis.
  • One participant reflects on their initial choice to learn Python first, later switching to R, and expresses a belief that mastering Python could be more advantageous given the context of their work environment.
  • Another participant mentions the potential need to work with existing R programs in the company, indicating that R is well-respected in statistical applications.

Areas of Agreement / Disagreement

Participants express differing opinions on whether to prioritize Python or R, with some advocating for a focus on Python due to its broader applications and others emphasizing the immediate relevance of R for the user's current job. The discussion remains unresolved regarding the best approach to learning both languages simultaneously.

Contextual Notes

Participants acknowledge the varying levels of familiarity with both languages and the specific needs of their work, which may influence their learning paths. There is also an indication of differing opinions on the popularity and utility of Python versus R in the context of environmental data analysis.

geologist
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Hello,

I've learned some of the basics of python through the sololearn Python 3 course (I also started the first week of the MIT Intro to computer science and programming on edx.org, but I found it beyond beginner level and decided to go through the sololearn Python 3 first).

My company (environmental consulting/environmental engineering) is interested in how R can be used for some of the basic statistics we perform (groundwater/soil analytical data). Since a co-worker already knew some R (used it in graduate research), I prioritized R since at this time it was more directly applicable to my job. I've gone through the data camp Intro and intermediate R tutorials.

I know it's not generally recommended to start learning both at the same time, but I'm already familiar with the syntax of both and I intend to learn both regardless (python mainly for open source GIS, e.g. GRASS & QGIS, and ArcGIS). Would it be counter productive to practice and continue building on both at the same time (~15 hours/week).
 
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It sounds like R is of immediate benefit to your company, so that part is answered. Python is a very powerful language that will allow you to automate many processes that would otherwise be very tedious. Its benefit on your job may not be immediate, but you may eventually be able to automate things at work that will greatly increase your productivity. If you enjoy scripting processes, you may want to learn Python anyway on your own time.

PS. I tend to believe that Python is a little over-sold and prefer Perl for scripting processes. But there are very good programmers (certainly better than me) who love Python.
 
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Well obviously R is more important to you, however R is not having a large dynamic [compared to python] as can be seen in the table here: https://usersnap.com/blog/programming-languages-2018/
where python appears with a very strong increasing trend (according to PYPL ranking). The TIOBE ranking also shows an increasing trend for Python: https://www.tiobe.com/tiobe-index/ (compared to R), ranking 4th from 5th it was last year, overpassing C#. Python had an increase of about ~2.4% while R had a decrease of ~0.3%
Since we try to compare R with python, I guess your main target is big data analysis. Python has to offer extremely useful tools both for the data analysis and visualization. So in general I would definitely recommend python to R, given that this won't be bad for your current work [this should always be taken into account]. Afterall there is work for C++ programmers too :biggrin: not that many, but they exist. But I won't be too hard on C++, it has shown some great improvements in terms of usability since C++11.
 
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FactChecker said:
It sounds like R is of immediate benefit to your company, so that part is answered. Python is a very powerful language that will allow you to automate many processes that would otherwise be very tedious. Its benefit on your job may not be immediate, but you may eventually be able to automate things at work that will greatly increase your productivity. If you enjoy scripting processes, you may want to learn Python anyway on your own time.

PS. I tend to believe that Python is a little over-sold and prefer Perl for scripting processes. But there are very good programmers (certainly better than me) who love Python.

Part of the reason for learning python is because GIS software (namely ArcGIS, QGIS, and GRASS GIS) utilize python for scripting. I'm focusing on R and python this year, but they won't necessarily be the only languages I learn.
 
Given the fact you work for a "dirt burning" company :wink: that potentially uses ArcGIS I'd start right there with python and the arcpy libs. With the ArcGIS support and examples available for python scripting, geospatial data analysis and visualization, in my opinion your path to developing competence in scripting to support your things like kriging and plume modeling would be a much easier learning curve with python utilizing the ArcGIS toolbox. Take up R later once you've mastered python.

Plus that other guy uses R, let him to the R and you can do the python. You will program loops around him with python and arcpy.
 
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Kyle Gonterwitz said:
Given the fact you work for a "dirt burning" company :wink: that potentially uses ArcGIS I'd start right there with python and the arcpy libs. With the ArcGIS support and examples available for python scripting, geospatial data analysis and visualization, in my opinion your path to developing competence in scripting to support your things like kriging and plume modeling would be a much easier learning curve with python utilizing the ArcGIS toolbox. Take up R later once you've mastered python.

Plus that other guy uses R, let him to the R and you can do the python. You will program loops around him with python and arcpy.

My company deals with mainly with environmental contamination assessments (https://en.wikipedia.org/wiki/Environmental_consulting), I assume "dirt burning" implies petroleum?. I was actually learning python first, I only start learning R when I saw my co-worker using it for something, which in hindsight I probably should have got good at python first. We haven't had as heavy a use of GIS as would be expected for an environmental company, but that probably will change.
 
geologist said:
My company deals with mainly with environmental contamination assessments (https://en.wikipedia.org/wiki/Environmental_consulting), I assume "dirt burning" implies petroleum?. I was actually learning python first, I only start learning R when I saw my co-worker using it for something, which in hindsight I probably should have got good at python first. We haven't had as heavy a use of GIS as would be expected for an environmental company, but that probably will change.
When you need to work with others at a company, you may have to work in the language that they have done a lot with. There may be a great many R programs there. If so, you should be able to deal with it. R is very well respected and a well established statistical language.
 

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