Production ready R

• How do you write robust, readable code
  • Programming
    • local, function-level
  • Software engineering
    • global, project-level

how to write analysis that can run again and again

Programming

• Principle -- less outside context needed = clearer code

  baz <- foo(bar, qux)
  

  df2 <- arrange(df, qux)
  

Naming of variables and functions

• programming is both about talking to computers and talking to people
• collaboration with future you/others

Some potential gotchas

  df[, vars]  # type unstable -- you have to know if vars if one or multiple, could get dataframe or vector
  filter(df, x==y) # non-standard -- silent incorrect results
  data.frame(x = "a") # data frame with one column, value depends on some global value

3 classes of surprises

• unstable types, non-standard evalution, hidden arguments

df <- data.frame(
  a = 1L,
  b = 1.S,
  y = Sys.time(),
  z = ordered(1)
)

str(sapply(df[1:4], class)) # vector
str(sapply(df[1:2], class))
str(sapply(df[3:4], class)) # matrix
str(sapply(df[0], class))

functions that always succeed are always the most dangerous use purrr

library(purrr)

type of output should not depend on input

non-standard

because of how r handles scoping?

• functions should fail early if there's a problem
• specify/make it explicit

offenders of hidden arguments

• stringAsFactors - causes weird effects
• system language
• time zone
• default text encoding
• line endings
• na.action

-- warnPartialMatchArgs, warnPartialMatchAttr, warnPartialMatchDollar

dplyr is stricter

"At the heart of R, theres a tension ...trying to be helpful and guess at what you want? or a programming language where it should fail early and make you aware of problems"

Question: dataframe in dplyr dply dataframe overrides print and subsetting, ignores drop=FALSE

Software engineering

"I've never done a data analysis in production...some things I think and some things friends have told me"

"In Production"

• not just a static report
• when you're running it again and again
• kinda like reproducible research
  • timescale is different, more often

Isolation

• different projects depending on different versions of packages
• libraries vs. packages
  • package is one package
  • library is a collection of packages
• isolate dependencies
• packrat
  • saves packages and versions to a text file
  • others can re-install packages easily
• Microsoft checkpoint
  • checkpoint dependent on daily snapshots of CRAN
• Renv for specifying versions of R

Verification

• automate things
• Use knitr/rmarkdown
• use git
  • check diff before checking in
• commit derived files so that you can verify your changes
• tests for fragile analysis paths
  • assertr, etc.

Documentation

• rmarkdown/knitr
  • The spin function is useful
    • Where code is the main focus, and markdown is in comments for code
• rStudio connect beta -- publish and share within company

Other things to consider

• Continuous integration?
• Build automation?
  • make files

Questions

• rmarkdown in oxygen?

  • Pending

• Recommended interactive plot?

  • Shiny
  • What about standalone?
    • Doesn't plan on writing a standalone -- not dependent on a server running R -- charting lib
    • Currently -- rCharts

• What signs to look for whether or not to use a package?

  • look for tests
  • documentation
  • check out source code a little bit

• Out of your packages, which one to contribute to -- good for people trying to get into open source and need a little bit love?

  • look at issues
    • make simplest reproducible examples
    • https://github.com/jennybc/reprex helps you use rmarkdown to post on GitHub and StackOverflow
    • http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example/5965451#5965451
    • examples
      • too complex
      • simpler
  • readr is in need of attention
  • tend to focus in on one R package at a time
    • dplyr is next after finishing book -- draft here
  • tends to cut off the world when he's trying to dig into stuff

• Credentials, where should they go? best practice

  • set things in environment variables
  • .Renviron
  • don't check in the credentials to the web
  • thing to watch: https://github.com/MangoTheCat/keyring

Rapidfire questions

• proudest package

  • ggplot2

• hardest to maintain

  • ggplot2, dependencies, rcommand?

• best hangout in Houston

  • double trouble

• Red Sox or Astros

  • neither

• beach or mountain

  • beach

• why r vs. python?

  • ggplot2, r community, shiny, RStudio

• what would you like to change?

  • nothing within my power to change