In IBAHCM/RPiR: Reproducible Programming in R

Things to remember

• Any programming language can solve any problem
• But some things are much more natural in some languages than others
• And some languages also have some amazing libraries to help!

Programming styles

• Imperative vs. Declarative
• Structured, Functional
• Event-driven
• Object-oriented
• Concurrent
• Generic, Procedural, Reflective, Logical, ...

Language types

• High and low level
• Compiled, JIT and interpreted/scripting
• Domain specific and general purpose
• Static and dynamic typing
• Strict and lazy evaluation

What do you need to think about?

• What you want to do

• What the easiest way of doing it is

• What languages make that easy

So...

What do you want to do?

• Access data
• Analyse it
• Build models
• Test hypotheses
• Report the results
• Share the methods and data

What's good about R?

• Approachable scripting language
• Interactive interpreter
• Dynamic typing, imperative, structured
• Great statistical and graphical libraries
• Powerful development environment in RStudio
• Making it easy to develop your own packages

What's bad about R?

• Interpreted languages are slow
• So new libraries are rarely written in R!
• Complex handwritten models are slow
• Graphics capabilities are fixed and complicated, though extensive...
• Lack of type checking means you’ll often struggle to know what went wrong
• Not all libraries are well written

What's different about R?

• Formulae and factors
• Data frames, and ease of manipulating data
• Plotting data
• Massive database of user-supplied packages
• Easy(-ish!) to create your own packages

A note of packages you use {.smaller}

• When you use a package to carry out an analysis, you should always say so!
• You have two commands to tell you what to do:
• library(help="packagename")
  • This shows the DESCRIPTION file, in particular the version you are using
• citation("packagename")
  • This tell you how to reference it in your paper

{R, comment=""} citation("deSolve")

What does this tell us?

R is designed for:

• reading data
• processing data
• building models
• analysing results
• displaying results
• sharing new functionality

So what have you learned?

• Write code
• Write documentation
• Identify and isolate reusable code
• Generate documented results
• Keep track of code changes
• Share code with others reliably
• Read, understand and test other people's code
• Collaborate with one another to improve your code

Conclusions

• R has arguably the best libraries to make it easy to process, analyse and report data
• And it’s also reasonably general purpose
• So it’s a good place to start
• But don’t get too hung up on one language
• There are lots of problems with R!

Languages

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Writing Fragile Code

Writing fragile code...

• When we’re using Word we want it to never crash!
• Partly this is because we can’t fix it even if we know there’s a problem
• Partly because we might lose something important
• When we write our own code, we do want it to crash... why?
• We don’t want it to carry blithely on when something doesn’t make sense
• Otherwise we might get the wrong answer at the end without ever knowing there was a problem!
• Writing fragile code that crashes or stops easily is a good thing in scientific programming, at least when you’re writing your own code

IBAHCM/RPiR documentation built on Jan. 12, 2023, 7:41 p.m.