Toolbox and Skillset of Reproducible Research

Andrey Koval
October 14, 2014

The Laboratory for Integrative Lifespan Research
Department of Psychology
University of Victoria

Traditional research workflow

traditional flow
academic paper

Reproducible research workflow

reproducible flow
as compared to traditional:
traditional flow

Advantages of RR

reproducible flow

  • Less prone to human error
  • Removes tedious jobs
  • Recordable workflow
  • Correcting mistakes later
  • Removes cognitive strain to synchronize analysis and discussion

TOOLBOX and toolset of RR

reproducible flow

  • R
  • RStudio
  • GitHub
  • git

toolbox and TOOLSET of RR

reproducible flow

  • Data Manipulation
  • Graph Production
  • Statistical Modeling
  • Dynamic Reporting

Goals of RR

reproducible flow
Ultimate : Answering a research question
Practical : Publishing a paper, producing a manuscript
Technical : Producing a dynamic document

RR uses code to

reproducible flow
In the process of achieving these goals we encounter the need to perform particular tasks on a computer:

  1. Load and inspect datasets
  2. Depict data in statistical graphics
  3. Fit statistical models
  4. Compare observed and modeled data
  5. Produce reports discussing the steps above

1. Load and inspect datasets

ds <- mtcars
head(ds, 10)

                   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

2) Depict data as statistical graphics

require(ggplot2)
p <- ggplot(mtcars, aes(x=wt, y=mpg)) 
p <- p + geom_point()
p

plot of chunk block2

3) Fit statistical models

lm(mpg ~ wt, mtcars)


Call:
lm(formula = mpg ~ wt, data = mtcars)

Coefficients:
(Intercept)           wt  
      37.29        -5.34

4) Compare observed and modeled data

require(ggplot2)
ds <- mtcars
ds$mpg_modeled <- predict(lm(mpg ~ wt, mtcars))
p <- ggplot(ds, aes(x=wt, y=mpg)) 
# p <- p + geom_point() 
p <- p + geom_point(aes(y=mpg_modeled),color="red")
p

plot of chunk block4

4) Compare observed and modeled data

require(ggplot2)
ds <- mtcars
ds$mpg_modeled <- predict(lm(mpg ~ wt, mtcars))
p <- ggplot(ds, aes(x=wt, y=mpg)) 
p <- p + geom_point() 
p <- p + geom_line(aes(y=mpg_modeled),color="red")
p

plot of chunk block4a

5) Produce reports

that discuss this comparision and verbalize the conclusions that can be drawn from the analysis

We just did it!