Reproducibility with R
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This document discusses ways to make R code more reproducible through cleaner coding practices, functional programming, and collaboration tools. It recommends: 1. Writing cleaner code through descriptive names, spacing, and documentation. 2. Functionalizing code by defining reusable functions to avoid repetition and improve flexibility. 3. Using pipes to chain together functions and solve complex problems through simple pieces. 4. Outsourcing functions to external files and version controlling code with Git and GitHub to enable collaboration.
![model.fit <- lm(age ~ circumference, data = Orange)
# This function is more flexible, can be further customized
# applied in other situations
rmse(model.fit)
## [1] 1041.809
rmse(model.fit, Orange$Tree)
## 3 1 5 2 4
## 602.4244 688.8896 929.9055 1319.1573 1408.7033](https://image.slidesharecdn.com/reproducibility-150325044245-conversion-gate01/85/Reproducibility-with-R-12-320.jpg)
![library(dplyr)
model.rmse <- Orange %>%
lm(age ~ circumference, data=.) %>%
rmse(., Orange$Tree) %>%
barplot
OR like this (Correlation within Iris dataset)
iris %>% group_by(Species) %>%
summarize(count = n(), pear_r = cor(Sepal.Length, Petal.L
arrange(desc(pear_r))
## Source: local data frame [3 x 3]
##
## Species count pear_r
## 1 virginica 50 0.8642247
## 2 versicolor 50 0.7540490
## 3 setosa 50 0.2671758](https://image.slidesharecdn.com/reproducibility-150325044245-conversion-gate01/85/Reproducibility-with-R-14-320.jpg)
Reproducibility with R
- 1. Reproducible R coding CMEC R-Group Martin Jung 12.02.2015
- 2. Goals of reproducible programming? Make your code readible by you and others Group your code and functionalize Embrace collaboration, version control and automation
- 3. First step - readibility 1. Writing cleaner code
- 4. Writing cleaner R code | Names Keep new filenames descriptive and meaningful "helper-functions.R" # or for sequences of processing work "01_Download.R" "02_Preprocessing.R" #... Use CamelCase or Snake_case for variables "spatial_data" "ModelFit" "regression.results" Avoid predetermined names like c or plot
- 5. Writing cleaner R code | Spacing Use Spacing just as in the english language # Good model.fit <- lm(age ~ circumference, data = Orange) # Bad f1=lm(Orange$age~Orange$circumference) Don’t be afraid of using new lines model.results <- data.frame(Type = sample(letters, 10), Data = NA, SampleSize = 10 ) # Same goes for loops # And don't forget good documentation
- 6. More on writing clean code Google R Style Guide Hadley Wickhams Style Guide RopenSci Guide And there even is a r-package to clean up your code: formatR
- 7. Further ways to improve reproduciability Ideally attach your code + data to publications Open-access hoster (DataDryad, Figshare, Zenodo) Restructuring of workflow with RMarkdown / LaTeX / HTML
- 8. Functionalize! Many R users are tempted to write their code very specialized and non-reusable Number 1 rule for clear coding : DRY - Don't repeat yourself! Simple example: We want to fit a linear model to test if in an orange orchard the circumference (mm) increases with age (age of trees). If so we want to quantify and display the Root-Mean-Square-Error (RMSE) of this fit for each individual orange tree in the dataset (N = 5).
- 9. Normal way: # Linear model model.fit <- lm(age ~ circumference, data = Orange) model.resid <- residuals( model.fit ) model.fitted <- fitted( model.fit ) rmse <- sqrt( mean( (model.resid - model.fitted)^2 )) tapply(model.resid - model.fitted, Orange$Tree, function(x) sqrt( mean( (x)^2 )))
- 10. 3 1 5 2 4 0200400600800100012001400
- 11. Defining your functions Essentially most r-packages are just a compilation of useful functions that users have written. # We want to get the RMSE of a linear model rmse <- function(fit, groups = NULL, ...) { f.resid <- residuals(fit);f.fitted <- fitted(fit) if(! is.null( groups )) { tapply((f.resid-f.fitted), groups, function(x) sqrt(mea } else { sqrt(mean((f.resid-f.fitted)^2, ...)) } }
- 12. model.fit <- lm(age ~ circumference, data = Orange) # This function is more flexible, can be further customized # applied in other situations rmse(model.fit) ## [1] 1041.809 rmse(model.fit, Orange$Tree) ## 3 1 5 2 4 ## 602.4244 688.8896 929.9055 1319.1573 1408.7033
- 13. (very) short intro into pipes Pipes (|) are a common tool in the linux / programming world that can be used to chain inputs and outputs of functions together. In R there are two packages, namely dplyr and magrittr that enable general piping between all functions Goal: Solve complex problems by combining simple pieces (Hadley Wickham)
- 14. library(dplyr) model.rmse <- Orange %>% lm(age ~ circumference, data=.) %>% rmse(., Orange$Tree) %>% barplot OR like this (Correlation within Iris dataset) iris %>% group_by(Species) %>% summarize(count = n(), pear_r = cor(Sepal.Length, Petal.L arrange(desc(pear_r)) ## Source: local data frame [3 x 3] ## ## Species count pear_r ## 1 virginica 50 0.8642247 ## 2 versicolor 50 0.7540490 ## 3 setosa 50 0.2671758
- 15. Outsource your functions # Put your function into an extra files # At the beginning of your main processing script # you simply load them via source source("outsourced.rmse.R")
- 16. Easy package writing Open RStudio Install the devtools and roxygen2 package Create a new package project and use the existing function as basis Create the documentation for it Update the package metadata and build your package library(roxygen2) library(devtools) # Build your package with two simple commands # Has to be within your package project document() # Update the namespace install() # Install.package
- 17. However package development has multiple facets and options. More detailed info on Package development with RStudio. Higher acceptance for method papers and analysis code. Make it citable with a DOI
- 18. Software management and collaboration with Github Git is one of the most commonly used revision control systems Originally developed for the Linux kernel by Linus Torvalds
- 20. Github is web-based software repository service offering distributed revision control Californian Startup, now the largest code hoster in the world Offers public repositories for free, private for money and a nice snippet exchange service called gists
- 21. How to Git with rstudio (do it later) 1. Setup an account with a git repository hoster like Github 2. Install RStudio and git for your platform (http://www. rstudio.com/ide/docs/version_control/overview) 3. Link to the git executable within the RStudio options 4. Create a new repository on Github and a new project in RStudio -> Version Control git 5. Clone your empty project (pull), add new files/changes to it (commit) and (push)
- 22. Idea for CMEC R Users: Create a Github organization (like a repository basecamp)
- 23. Further developments There are now packages to push gists and normal git updates directly from within R. In order to use them you need a github api key (instructions on the websites below) rgithub To detailed to show here, but have a look at the gistr package: gistr