R is useful for quite a range of applications, but not everything. I recommend
planning on learning multiple programming languages eventually, because each
type of problem has its own set of "useful phrases". An example of
this in R is comparing the base, lattice, and ggplot models of graph
generation... each has its own perspective that is valuable in different
contexts. Another example might be in iterative algorithms... these are often
implemented in C or C++ or Fortran and called from R. It is common to build
packages in R to create convenient groups of functions that are useful for
specific problem types, but other languages sometimes have features that make
these packages look clumsy. Knowing about how other languages do things can make
it easier to see better solutions in R, or even avoid struggling with
poorly-suited functions.
In the vein of communicating appropriately, be sure follow the instructions in
the footer of this or any other post, which among other things asks you to not
post in HTML on this list.
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Sent from my phone. Please excuse my brevity.
On September 4, 2014 8:41:28 AM PDT, Basilius Sapientia <basiliussap at
gmail.com> wrote:> Dear community.
>
>I am studying chemistry and physics. We don'te get an intro to
>mathematic
>programms or programming. We shall just find something and use it. So I
>have choosen R. But was that a good choice?
>
>Do you think I could get threw my study with R as my only programming
>language (combined with C++) and as my only mathematic
"calculator". Is
>it
>an alternative to MatLab? Or is R just for statistics?
>
>Hopefully anyone can answer this question? Kind regards!
>
> [[alternative HTML version deleted]]
>
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>and provide commented, minimal, self-contained, reproducible code.