There are a number of excellent MOOC courses on learning R. However, I think the quickest path is to pick up a copy of Jared Lander's "R for Everyone: Advanced Analytics and Graphics."
http://www.amazon.com/Everyone-Advanced-Analytics-Graphics-Addison-Wesley/dp/0321888030/ref=cm_cr_arp_d_product_top?ie=UTF8 The first 13-chapters give you basics to intermediate of R with worked examples and data. Lander's explication of data visualization (ggplot2 in particular) is outstanding. Subsequent chapters get into using R for data analysis and statistics. Landers also created a video series to accompany the book. It's ~ $240. http://www.amazon.com/Everyone-Advanced-Analytics-Graphics-Addison-Wesley/dp/0321888030/ref=cm_cr_arp_d_product_top?ie=UTF8 In short, working through Lander's book and video should get you off to a good start. On Wed, Mar 30, 2016 at 2:09 AM, 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. > > > > [image: cid:[email protected]] <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] > > [image: cid:[email protected]] > <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 >
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