RaukR 2018 Notes - Day 2: Object oriented programming, packages and scripts

0.1 Object oriented programming

A programming paradigm
Work with objects
Easy to model things
The object is a class, and contains data and methods
OOP systems in R: S3, S4, RC and R6.
- An S3 class object is one of R base types with class attribute set, for example a vector c(“a”,“b”,“c”) with factor variables and type integer.
- To create an S3 object, simply give a name to a data structure:
- qf <- structure(list(), class = "genomic_features")
- Easy to change class of objects - be careful!
To create objects of your class, use a constructor, then use a validator to validate inside a helper function
To seal S4 class: setClass("class_name", sealed = T)
The variables inside an S4 class are stored in slots
- To retrieve data, use my.gene@name or my.gene[1]
R6 implemented to be fast, similar to Java or C++
The class itself contains functions needed for changing parameters in the class, like hair colour or age

0.1.1 Tasks

0.1.1.1 1 S3 classes

What is the class of the object returned by the lm() function?

x <- lm(speed ~ dist, cars)
class(x)

## [1] "lm"

What basic data type is it built upon?

typeof(x)

## [1] "list"

What attributes does the object of the lm class have?

attributes(x)

## $names
##  [1] "coefficients"  "residuals"     "effects"       "rank"         
##  [5] "fitted.values" "assign"        "qr"            "df.residual"  
##  [9] "xlevels"       "call"          "terms"         "model"        
## 
## $class
## [1] "lm"

What is the structure of the lm object?

str(x)

## List of 12
##  $ coefficients : Named num [1:2] 8.284 0.166
##   ..- attr(*, "names")= chr [1:2] "(Intercept)" "dist"
##  $ residuals    : Named num [1:50] -4.62 -5.94 -1.95 -4.93 -2.93 ...
##   ..- attr(*, "names")= chr [1:50] "1" "2" "3" "4" ...
##  $ effects      : Named num [1:50] -108.894 29.866 -0.501 -3.945 -1.797 ...
##   ..- attr(*, "names")= chr [1:50] "(Intercept)" "dist" "" "" ...
##  $ rank         : int 2
##  $ fitted.values: Named num [1:50] 8.62 9.94 8.95 11.93 10.93 ...
##   ..- attr(*, "names")= chr [1:50] "1" "2" "3" "4" ...
##  $ assign       : int [1:2] 0 1
##  $ qr           :List of 5
##   ..$ qr   : num [1:50, 1:2] -7.071 0.141 0.141 0.141 0.141 ...
##   .. ..- attr(*, "dimnames")=List of 2
##   .. .. ..$ : chr [1:50] "1" "2" "3" "4" ...
##   .. .. ..$ : chr [1:2] "(Intercept)" "dist"
##   .. ..- attr(*, "assign")= int [1:2] 0 1
##   ..$ qraux: num [1:2] 1.14 1.15
##   ..$ pivot: int [1:2] 1 2
##   ..$ tol  : num 1e-07
##   ..$ rank : int 2
##   ..- attr(*, "class")= chr "qr"
##  $ df.residual  : int 48
##  $ xlevels      : Named list()
##  $ call         : language lm(formula = speed ~ dist, data = cars)
##  $ terms        :Classes 'terms', 'formula'  language speed ~ dist
##   .. ..- attr(*, "variables")= language list(speed, dist)
##   .. ..- attr(*, "factors")= int [1:2, 1] 0 1
##   .. .. ..- attr(*, "dimnames")=List of 2
##   .. .. .. ..$ : chr [1:2] "speed" "dist"
##   .. .. .. ..$ : chr "dist"
##   .. ..- attr(*, "term.labels")= chr "dist"
##   .. ..- attr(*, "order")= int 1
##   .. ..- attr(*, "intercept")= int 1
##   .. ..- attr(*, "response")= int 1
##   .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
##   .. ..- attr(*, "predvars")= language list(speed, dist)
##   .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
##   .. .. ..- attr(*, "names")= chr [1:2] "speed" "dist"
##  $ model        :'data.frame':   50 obs. of  2 variables:
##   ..$ speed: num [1:50] 4 4 7 7 8 9 10 10 10 11 ...
##   ..$ dist : num [1:50] 2 10 4 22 16 10 18 26 34 17 ...
##   ..- attr(*, "terms")=Classes 'terms', 'formula'  language speed ~ dist
##   .. .. ..- attr(*, "variables")= language list(speed, dist)
##   .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
##   .. .. .. ..- attr(*, "dimnames")=List of 2
##   .. .. .. .. ..$ : chr [1:2] "speed" "dist"
##   .. .. .. .. ..$ : chr "dist"
##   .. .. ..- attr(*, "term.labels")= chr "dist"
##   .. .. ..- attr(*, "order")= int 1
##   .. .. ..- attr(*, "intercept")= int 1
##   .. .. ..- attr(*, "response")= int 1
##   .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
##   .. .. ..- attr(*, "predvars")= language list(speed, dist)
##   .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
##   .. .. .. ..- attr(*, "names")= chr [1:2] "speed" "dist"
##  - attr(*, "class")= chr "lm"

Does the lm() class implement own str()?

str(unclass(x))

## List of 12
##  $ coefficients : Named num [1:2] 8.284 0.166
##   ..- attr(*, "names")= chr [1:2] "(Intercept)" "dist"
##  $ residuals    : Named num [1:50] -4.62 -5.94 -1.95 -4.93 -2.93 ...
##   ..- attr(*, "names")= chr [1:50] "1" "2" "3" "4" ...
##  $ effects      : Named num [1:50] -108.894 29.866 -0.501 -3.945 -1.797 ...
##   ..- attr(*, "names")= chr [1:50] "(Intercept)" "dist" "" "" ...
##  $ rank         : int 2
##  $ fitted.values: Named num [1:50] 8.62 9.94 8.95 11.93 10.93 ...
##   ..- attr(*, "names")= chr [1:50] "1" "2" "3" "4" ...
##  $ assign       : int [1:2] 0 1
##  $ qr           :List of 5
##   ..$ qr   : num [1:50, 1:2] -7.071 0.141 0.141 0.141 0.141 ...
##   .. ..- attr(*, "dimnames")=List of 2
##   .. .. ..$ : chr [1:50] "1" "2" "3" "4" ...
##   .. .. ..$ : chr [1:2] "(Intercept)" "dist"
##   .. ..- attr(*, "assign")= int [1:2] 0 1
##   ..$ qraux: num [1:2] 1.14 1.15
##   ..$ pivot: int [1:2] 1 2
##   ..$ tol  : num 1e-07
##   ..$ rank : int 2
##   ..- attr(*, "class")= chr "qr"
##  $ df.residual  : int 48
##  $ xlevels      : Named list()
##  $ call         : language lm(formula = speed ~ dist, data = cars)
##  $ terms        :Classes 'terms', 'formula'  language speed ~ dist
##   .. ..- attr(*, "variables")= language list(speed, dist)
##   .. ..- attr(*, "factors")= int [1:2, 1] 0 1
##   .. .. ..- attr(*, "dimnames")=List of 2
##   .. .. .. ..$ : chr [1:2] "speed" "dist"
##   .. .. .. ..$ : chr "dist"
##   .. ..- attr(*, "term.labels")= chr "dist"
##   .. ..- attr(*, "order")= int 1
##   .. ..- attr(*, "intercept")= int 1
##   .. ..- attr(*, "response")= int 1
##   .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
##   .. ..- attr(*, "predvars")= language list(speed, dist)
##   .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
##   .. .. ..- attr(*, "names")= chr [1:2] "speed" "dist"
##  $ model        :'data.frame':   50 obs. of  2 variables:
##   ..$ speed: num [1:50] 4 4 7 7 8 9 10 10 10 11 ...
##   ..$ dist : num [1:50] 2 10 4 22 16 10 18 26 34 17 ...
##   ..- attr(*, "terms")=Classes 'terms', 'formula'  language speed ~ dist
##   .. .. ..- attr(*, "variables")= language list(speed, dist)
##   .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
##   .. .. .. ..- attr(*, "dimnames")=List of 2
##   .. .. .. .. ..$ : chr [1:2] "speed" "dist"
##   .. .. .. .. ..$ : chr "dist"
##   .. .. ..- attr(*, "term.labels")= chr "dist"
##   .. .. ..- attr(*, "order")= int 1
##   .. .. ..- attr(*, "intercept")= int 1
##   .. .. ..- attr(*, "response")= int 1
##   .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
##   .. .. ..- attr(*, "predvars")= language list(speed, dist)
##   .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
##   .. .. .. ..- attr(*, "names")= chr [1:2] "speed" "dist"

# No

What is the class of a tibble?

library(tidyverse)

## -- Attaching packages --------------------------------------------------------------------------------- tidyverse 1.2.1 --

## v ggplot2 2.2.1     v purrr   0.2.5
## v tibble  1.4.2     v dplyr   0.7.5
## v tidyr   0.8.1     v stringr 1.3.1
## v readr   1.1.1     v forcats 0.3.0

## -- Conflicts ------------------------------------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

df <- as_tibble(mtcars)
class(df)

## [1] "tbl_df"     "tbl"        "data.frame"

What is the underlying data type?

typeof(df)

## [1] "list"

Is the str function used by tibbles the standard str function?

str(df)

## Classes 'tbl_df', 'tbl' and 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

str(unclass(df))

## List of 11
##  $ mpg : num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num [1:32] 6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num [1:32] 160 160 108 258 360 ...
##  $ hp  : num [1:32] 110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num [1:32] 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num [1:32] 2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num [1:32] 16.5 17 18.6 19.4 17 ...
##  $ vs  : num [1:32] 0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num [1:32] 1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num [1:32] 4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num [1:32] 4 4 1 1 2 1 4 2 2 4 ...
##  - attr(*, "row.names")= chr [1:32] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...

# No

0.2 R-Scripts

In rstudio: source scriptfile.R
In the terminal: Rscript scriptfile.R
As executable: path/scriptfile.R if:
- Script is executable: chmod +x scriptfile.R
- First line in script is a hashbang: #! etc.
Can provide arguments to an R script
- ./scriptfile.R inputfile outputfile
- Packages are available that support long and short flags (–o, -o)
- Use commandArgs() to access the arguments pased to R at launch
  - Use trailingonly = TRUE to suppress printing of arguments
- packages: getopt, optparse, etc
To pipe text streams from one script to the next, define an open connection, read one line, and close it
- To write results to stream, used stdout produced by the code (print, cat etc) which can be piped to a new process or written to a file

0.2.1 Tasks

0.2.1.1 Executing R Scripts

Make an R script and source it here

source("norm_test.R")

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -3.306946 -0.725706  0.007249  0.001318  0.681208  2.998722

0.2.1.2 Passing and parsing arguments

0.3 R Packages

0.3.1 What is an R package?

A bunch of files and folders, in a specific structure
- Description
- Namespace
- R
- typicalr.Rproj
R packages makes it easy to share code
Improves quality and rigor
Reproducibility
Package name must start with a letter, can’t start or end with a period
- Make it googleable!

0.3.2 Possible package states

Package states:
- Source: Development version of your package
- Bundled: Compressed tar.gz file (.Rbuildignore files are ignored)
- Binary: Bundle that is built for a certain architechure, parsed format
- Installed: a binary package decompressed into a package library
- In-memory: after loading the package with library(package)
Better to call functions directly from package rather than load multiple packages which opens up a lot of functions that aren’t used, like: packagename::function()
Require checks if the package is already installed, if it is, it does nothing. Library reloads the package if it is already loaded.

0.3.3 Package structure

Large functions are to be written in their own R files
Utility functions in their own file
Description file:
- Title, version, authors, description, dependencies, license, encoding, lazydata, url, bugreports, etc.
Version number:
- 4 numbers (0.0.0.0, major.minor.patch.dev), three numbers after release (removes dev)
- Major changes: Large changes, not always backwards compatible
- Minor: Bug fixes and new features
- Patches: Small bugfixes etc.
- Dev: Always start with 9000 (0.0.0.9000)
Manual directory (man):
- Contains documentation on the package (.Rd file)
- Created automatically by Roxygen
Vignette is a more complete guide to your package
- Usually done in Rmarkdown
- devtools::use_vignette("package")
- Vignette titles should match
Namespace makes sure that the correct function from the correct package is used, regardless of which library is loaded (in case over overlapping function names). Removes the need for ::.
Data folder contains functions and/or datasets etc.
Source folder contains compiled code
Install packages from github: `devtools::install_github()

0.3.4 Tasks

0.3.4.1 See separate files

To create a new package, click on file, new project, new directory, R package
Use the Roxygen2 package to create basic documentation etc.
Use devtools::load_all() to reload the package after every change

0.4 Knitr: Some lesser known features (Yihui Xie)

Book: Dynamic Documents with R and knitr
Markdown also compatible with python and other languages
Access variables from a python code chunk in an R code chunk (py$x)
knitr::spin() compiles an rscript to a report
- Use #+ as chunks
Use include = FALSE to exclude everything from a code chunk
To live preview HTML documents, use xaringan::inf_mr('your.rmd')
knitr::knit_watch() will watch an input file continuously, and knit it when it is updated
Animations in Rplots:
- FFmpeg
Change style in rmarkdown: Docco creates a changeable look on markdown documents (Docco classic style)
knitr::imgur_upload() uploads an image to imgur.com and returns the url
- The reprex package use this function to automatically embed plots in the output of reproducible examples *knitr::knit_expand() is used to combine values and text, like this:
- knit_expand(test = "the value of pi is {{pi}}.")
- This will create a line with the actual value of pi
To show plots later in the document, use knitr::fig_chunk()
- ![a figure moved here](r knitr::fig_chunk('cars_plot', 'png')) *knitr::write_bib("knitr") creates a bibliography database for the knitr package

RaukR 2018 Notes - Day 2: Object oriented programming, packages and scripts

Håkon Kaspersen

12 June 2018

0.1 Object oriented programming

0.1.1 Tasks

0.1.1.1 1 S3 classes

0.2 R-Scripts

0.2.1 Tasks

0.2.1.1 Executing R Scripts

0.2.1.2 Passing and parsing arguments

0.3 R Packages

0.3.1 What is an R package?

0.3.2 Possible package states

0.3.3 Package structure

0.3.4 Tasks

0.3.4.1 See separate files

0.4 Knitr: Some lesser known features (Yihui Xie)