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Reproducible programming

“He who gives up [code] safety for [code] speed deserves neither.” (via)

General rules

  1. the scripts should be written in a way, that either you, or someone else will be able to run it anytime.

  2. Clean structure, according to the best programming practices

  • Variable names according the convention (use _. not " " or -, don’t use keywords)

  • <- instead of =

  • spaces before and after

  • Indentation

  • curly brackets

  • don’t store unnecessary objects

  • avoid repetition

  • comment your codes in detail

  • Use sessionInfo() to know your package versions

  • Use as generalized code as possible - use names and regular expressions instead of indices.

  • if possible, define your paths at the beginning and use paste() or file.path() later. Or use relative paths

  • try to do everything from R, because changes in e.g. excel are undocumented.

Use version control

  • git is an easy tool to do version control.

Rmarkdown, R notebooks

  • Rmarkdown document, with code chuncks
  • Markdown language is easy to learn, see the cheatsheat
  • many templates available - flexible themes
  • Description, codes and figures at the same place.
  • Figures are saved separately as well - or use links
  • Appearance of the code depends on the chunk options.

Show code and output

plot(cars)

Show only output

Show code only, don’t run

plot(cars)
  • Suppress warnings and messages, if needed.
  • Use cheatsheets
  • easy to add pictures, links, etc.
  • easy to add nice tables
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
data("mtcars")
knitr::kable(mtcars, format = "html")
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
print(xtable::xtable(mtcars), type = 'html')
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.00 6.00 160.00 110.00 3.90 2.62 16.46 0.00 1.00 4.00 4.00
Mazda RX4 Wag 21.00 6.00 160.00 110.00 3.90 2.88 17.02 0.00 1.00 4.00 4.00
Datsun 710 22.80 4.00 108.00 93.00 3.85 2.32 18.61 1.00 1.00 4.00 1.00
Hornet 4 Drive 21.40 6.00 258.00 110.00 3.08 3.21 19.44 1.00 0.00 3.00 1.00
Hornet Sportabout 18.70 8.00 360.00 175.00 3.15 3.44 17.02 0.00 0.00 3.00 2.00
Valiant 18.10 6.00 225.00 105.00 2.76 3.46 20.22 1.00 0.00 3.00 1.00
Duster 360 14.30 8.00 360.00 245.00 3.21 3.57 15.84 0.00 0.00 3.00 4.00
Merc 240D 24.40 4.00 146.70 62.00 3.69 3.19 20.00 1.00 0.00 4.00 2.00
Merc 230 22.80 4.00 140.80 95.00 3.92 3.15 22.90 1.00 0.00 4.00 2.00
Merc 280 19.20 6.00 167.60 123.00 3.92 3.44 18.30 1.00 0.00 4.00 4.00
Merc 280C 17.80 6.00 167.60 123.00 3.92 3.44 18.90 1.00 0.00 4.00 4.00
Merc 450SE 16.40 8.00 275.80 180.00 3.07 4.07 17.40 0.00 0.00 3.00 3.00
Merc 450SL 17.30 8.00 275.80 180.00 3.07 3.73 17.60 0.00 0.00 3.00 3.00
Merc 450SLC 15.20 8.00 275.80 180.00 3.07 3.78 18.00 0.00 0.00 3.00 3.00
Cadillac Fleetwood 10.40 8.00 472.00 205.00 2.93 5.25 17.98 0.00 0.00 3.00 4.00
Lincoln Continental 10.40 8.00 460.00 215.00 3.00 5.42 17.82 0.00 0.00 3.00 4.00
Chrysler Imperial 14.70 8.00 440.00 230.00 3.23 5.34 17.42 0.00 0.00 3.00 4.00
Fiat 128 32.40 4.00 78.70 66.00 4.08 2.20 19.47 1.00 1.00 4.00 1.00
Honda Civic 30.40 4.00 75.70 52.00 4.93 1.61 18.52 1.00 1.00 4.00 2.00
Toyota Corolla 33.90 4.00 71.10 65.00 4.22 1.83 19.90 1.00 1.00 4.00 1.00
Toyota Corona 21.50 4.00 120.10 97.00 3.70 2.46 20.01 1.00 0.00 3.00 1.00
Dodge Challenger 15.50 8.00 318.00 150.00 2.76 3.52 16.87 0.00 0.00 3.00 2.00
AMC Javelin 15.20 8.00 304.00 150.00 3.15 3.44 17.30 0.00 0.00 3.00 2.00
Camaro Z28 13.30 8.00 350.00 245.00 3.73 3.84 15.41 0.00 0.00 3.00 4.00
Pontiac Firebird 19.20 8.00 400.00 175.00 3.08 3.85 17.05 0.00 0.00 3.00 2.00
Fiat X1-9 27.30 4.00 79.00 66.00 4.08 1.94 18.90 1.00 1.00 4.00 1.00
Porsche 914-2 26.00 4.00 120.30 91.00 4.43 2.14 16.70 0.00 1.00 5.00 2.00
Lotus Europa 30.40 4.00 95.10 113.00 3.77 1.51 16.90 1.00 1.00 5.00 2.00
Ford Pantera L 15.80 8.00 351.00 264.00 4.22 3.17 14.50 0.00 1.00 5.00 4.00
Ferrari Dino 19.70 6.00 145.00 175.00 3.62 2.77 15.50 0.00 1.00 5.00 6.00
Maserati Bora 15.00 8.00 301.00 335.00 3.54 3.57 14.60 0.00 1.00 5.00 8.00
Volvo 142E 21.40 4.00 121.00 109.00 4.11 2.78 18.60 1.00 1.00 4.00 2.00
 mtcars %>%
    DT::datatable(filter = 'top', 
          options = list(scrollX = TRUE, keys = TRUE, pageLength = 5),  
          rownames = FALSE)
  • easy to share with collaborators

workflowr package

  • does the above steps automatically
  • keeps a nice folder structure
  • easy to use git

Exercises:

  • set up git with Rstudio

  • set up your github account with workflowr

  • create a new project using wflow_start()

  • create a new markdown file in the analysis folder, my_first_project

  • load luad file from extdata

  • delete empty columns using a for cycle and if statements.

  • modify days since birth that it appears in years. google the exact number of days in a year.

  • Create tables for Diagnosis.Age, Sex, Race.Category and American.Joint.Committee.on.Cancer.Tumor.Stage.Code

  • do chi-sq test for American.Joint.Committee.on.Cancer.Tumor.Stage.Code and Sex Plot the data (e.g. barplot, counts by gender)

  • do linear regression for the Mutation.Count and Aneuploidy.Score, gender and age. show the results in table. Plot the data.