If I am recalling things correctly, and this will certainly be contentious for some folk, but Python is a general purpose programming language while R is a statistical programming language.
I think it is generally the case that with enough hacking you can accomplish nearly any task with any tool, but some tools are more geared towards performing certain tasks. I like to stress to brand new students that the question is never "can" but "should" or "which is easier?" when it comes to this kind of thing. Sometimes even a brutal hack is more efficient or mentally easier to accomplish than fighting to learn something in a new language that you aren't comfortable with. "I know this could be better R, but I need to get the job done. [translation: please don't judge my for loops]" is a completely sane and normal reaction. Programming communities or those offering consultations shouldn't turn into handwriting judges. This doesn't mean we should reject or toss away the importance of language idioms, but turn our attention to respecting that it's the research content that matters and not which plotting library or whatever else they used. I think that SWC does a great job of this balance and showing it in action. I don't believe there is a clear line to be drawn between Python and R, except in the cases when a specific package is needed and does not exist for that other language. The choice between Python or R is never *just* about the task, but also about the skills, experiences, and support networks of the person who will be writing the code for that task. Elizabeth On Thu, Mar 31, 2016 at 2:13 PM, Matthew Gidden <[email protected]> wrote: > Hi everyone, > > On Thu, Mar 31, 2016 at 7:20 AM, Steve Haddock <[email protected]> wrote: > >> I would say I have intermediate to high experience with R, and have >> taught it a few times. Even so, although I feel I could come up with a >> working solution for most random questions, I am still not very confident >> that I will be giving the optimal Hadley-approved R-like solution (probably >> involving plyr !). It is going to take more than reading a book to reach >> that comfort level. >> >> I think the proper mindset for R is *totslly* different than Python or >> most other languages, and I would not lump them together in any way. You >> can't think "how would i do this in Python?" and then try to translate that >> straight into R language. Your Matlab experience is going to be the best >> guide. In R if you are using a for loop you are almost always "doing it >> wrong". >> So using vector operations, Boolean subsetting, and taking advantage of >> the power of dataframes are the keys to getting off on the right foot. >> There are plenty of syntactical differences, but these are things you can >> learn pretty quickly. For example, to grab the 2nd up to the last item of >> a series, you wouldn't use x[2:end] but would use x[-1] (negative indices >> meaning leave those out). >> For teaching, I would also go the sacrilegious route of using = instead >> of <- as the assignment operator. A few laudable books actually use this >> too. The "proper" way is just visually confusing and extra keystrokes. >> >> The power of R and the situations where you would choose it over Python >> (pandas notwithstanding) are pretty clear when you get down to it. >> > I'm hoping not to create too much of a diversion, but is this true? I've > tried to find some definitive source that actually lays out a good case in > this regard and haven't been successful (happy to take tips). The classic > answer as far as I can tell is that R is for statistics and nice-looking > plots, whereas Python is for more varied projects, web apps, numerical > work, etc. > > I find that there is a lot of bleed-over, however. People tend to like > their tools, so if they're used to R and want to build a complex analysis > tool, they do so, even if its painful and includes system calls, file > manipulation, nontrivial computational tasks (they'll write a loop at let > it run for a few hours..) etc. I'm still a diehard matplotlib (and > associated derivative tools) user, even though I'm really impressed with > how nice ggplot looks visually (I use the ggplot style -- still not as > good) and how succinct it is. Finally, GIS work seems to be a tossup. R > supports it, but there are some nice Python libraries too (I'm partial to > the rasterio/fiona/shapely stack myself). > > I'd be interested in hearing what this community thinks is the pretty > clear line between the two. > > Cheers, > Matt > > >> >> Data visualization is a great way to impress people that there are viable >> alternatives to spreadsheets. I had a fun experience of replacing my >> friend's GUI graph generation workflow with about 3 lines of R code to plot >> his dataset and colorize each point by another factor -- stuff he had to >> regenerate each time with a few dozen clicks. You also could start by >> focusing on getting their data into R as effectively as possible, using >> factors and dataframes, along the lines of Hadley's tidy data paper. >> >> -Steve >> >> ----- q•b ----- >> >> >> On Mar 29, 2016, at 23:09, Jason Bell <[email protected]> wrote: >> >> G’day Software Carpentry Instructors >> >> >> >> This being my first post to this list, as I recently become a software >> carpentry instructor (as of last week) and I hope this is the appropriate >> channel to ask a few questions in regards to learning, and then teaching, R >> and python to my local research colleagues. >> >> >> >> I am in the unusual position of providing eResearch support to all of the >> researchers at my University – distributed throughout 20 campuses. I look >> after a number of systems, including our dedicated research storage >> infrastructure (https://my.cqu.edu.au/web/eresearch/data-tools) and also >> our High Performance Computing facility (www.cqu.edu.au/hpc), amongst >> many things. Recently I have been getting a number of researchers who have >> been approaching me requesting help in getting their research data >> completed more quickly. I have been surprised how many different research >> domains are now using R, in which the need for scientific computing skill >> is starting to explode. As an example, I have assisted researchers to run >> their code on our HPC System, in which the results would have taken them >> months to complete on their local machine, to having a full set of data >> results in just a few hours by running many programs on our HPC system at >> once. >> >> >> >> One of the reasons why I am keen to learn and teach R and python, is so I >> can help even more of my colleagues to produce their research data more >> effectively and efficiently. Unfortunately at my local institution their >> isn’t any local training that my colleagues can attend – this I hope >> software carpentry can help to fill this large gap in scientific computing >> training. >> >> >> >> Over the years I have learnt many programming languages (I have been >> quite interested in reading some of the recent emails to this list about >> programming languages), which stated with “BASIC” at high school, to Pascal >> as the first language I learnt at University, to C/C++, ADA, Java, Visual >> Basic, Lego robotics programming, Perl, Bash scripts, Matlab, PHP and HTML >> (did someone mention TeX), using middleware libraries such MPI, P4 and even >> did some python training quite a few years ago and contributed to the open >> source software project “Access Grid” Software. I believe I have an >> acceptable understanding of programming principles in general and therefore >> would like to ask the following questions >> >> >> >> · What is the best (the quickest) way to get up to speed in R >> (and python a little further down the track). As you can appreciate my >> time is extremely limited (like most of us these days) and thus am chasing >> the most efficient method for learning R and python, so I can begin >> providing lessons in the very near future. >> >> · Do you think “instructors” should know more than just the >> teaching material for the “subjects” they plan on teaching. For example, I >> recently ran a local “UNIX Shell” locally and given I have been using bash >> for over 15 years, I was extremely comfortable with the teaching material >> (even though I did pick up a few tips and tricks), there were no unexpected >> questions that I could not answer. I doubt this would be the case with R >> or python, as I don’t use it regularly enough to feel competent to answer >> left field questions. Now, I appreciate that you cannot know everything, >> but having a greater knowledge than just the 3-4 hour lesson material would >> like highly desirable – thus would welcome any suggestions in resources, >> training material that could help me to get up to speed ASAP. >> >> >> >> · I see there are a few “R” lessons within software and data >> carpentry, so I wonder if there are any recommended lessons that are >> designed as an overview and not so much research domain specific? >> >> >> >> · I am also be interested in some visualisation aspects of R as >> well, as a lot of my users are still trying to use “excel” to graph data. >> >> >> >> o I have taught myself how to pass command line arguments in R, as this >> allows you to write a script to submit hundreds or thousands of separate >> jobs to solve on a HPC system. Is this sort of thing covered anywhere? >> >> >> >> Some other “general” questions in regards to what our research colleagues >> should be learning >> >> >> >> · Is there still a place for researchers to learn programming >> languages such as C/C++ - from a program “execution” speed, C is pretty >> hard to compete again, especially when looking to HPC types of programs. >> >> · A colleague has suggested that the “go” programming language ( >> https://golang.org/) is becoming quite popular these days, is anyone >> else seeing this? >> >> Anyway – I hope all of these questions are acceptable to ask here and >> would appreciate any advice and comments you might have. >> >> >> >> Many thanks for your time, >> >> Jason. >> >> >> >> <image001.png> <https://www.cqu.edu.au/> >> >> *Jason Bell* >> >> Senior Research Technologies Officer | Information and Technology >> Directorate >> >> CQUniversity eResearch Analyst | Queensland Cyber Infrastructure >> Foundation (QCIF) >> CQUniversity Australia, Building 19 Room 1.07, Bruce Highway, Rockhampton >> QLD 4702 >> *P* +61 7 4930 9229 *| X* 59229 *| M* 0409 630 897 |* E * >> [email protected] >> >> <image002.jpg> <https://www.cqu.edu.au/social-media> >> >> This communication may contain privileged or confidential information. If >> you have received this in error, >> >> please return to sender and delete. CRICOS: 00219C | RTO Code 40939 >> >> >> >> >> >> >> >> _______________________________________________ >> Discuss mailing list >> [email protected] >> >> http://lists.software-carpentry.org/mailman/listinfo/discuss_lists.software-carpentry.org >> >> >> _______________________________________________ >> Discuss mailing list >> [email protected] >> >> http://lists.software-carpentry.org/mailman/listinfo/discuss_lists.software-carpentry.org >> >> > > _______________________________________________ > Discuss mailing list > [email protected] > > http://lists.software-carpentry.org/mailman/listinfo/discuss_lists.software-carpentry.org >
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