How to use this research compendium

This research compendium comprises all the input and output files as well as the R code needed to reproduce our paper:

Visser, L., Kalmar, J., Linkersdörfer, J., Görgen, R., Rothe, J., Hasselhorn, M., & Schulte-Körne, G. (2020). Comorbidities between specific learning disorders and psychopathology in elementary school children in Germany. Frontiers in Psychiatry. http://doi.org/10.3389/fpsyt.2020.00292

The compendium uses the R package drake to define a reproducible processing pipeline linking inputs to outputs/targets similar to GNU make (for more information on the drake package, see here). This vignette describes how to reproduce the analysis, check the analysis pipeline, and inspect analysis steps and outputs.

Copy the analysis directory structure

After attaching the package, executing the function copy_analysis() copies the analysis directory structure to the current working directory (or an explicitely specified directory). The directory should now contain the following files:

.
└── .drake
    └── [...]
└── analysis
    ├── data
    │   └── data_orig.rds               # raw data
    ├── figures
    │   └── figure_1.tiff               # figures used in the manuscript
    │   └── figure_2.tiff               
    │   └── figure_3.tiff
    ├── manuscript
    │   └── manuscript.docx             # manuscript as submitted
    │   └── supplemental.docx           # supplemental materials as submitted
    └── templates
        └── manuscript_template.docx    # manuscript template
        └── supplemental_template.docx  # supplemental materials template

These files represent all input files required to reproduce the analysis as well as all output files generated in the process. The function further copies a .drake directory to the same directory. This hidden directory stores information about the consistency of the analysis pipeline and all intermediate outputs.

Reproduce the analysis

Reproducing the analysis can be done in a two-step procedure:

  1. Executing the function reproduce_analysis() copies the analysis directory structure (if it isn’t already present) and then runs drake::make(). The output provides evidence of reproducibility without actually re-running the analysis. If the analysis is reproducible, the output should look like this:

  2. To independently re-create the analysis results on ones own computer, one can run reproduce_analysis() with the option re_run = TRUE. This will delete the copied .drake folder and re-run the entire analysis from scratch, re-creating all relevant intermediate steps and outputs.

Explore the analysis pipeline and results

Executing get_plan() returns the underlying drake plan used to run the analysis. The returned data frame lists all intermediate results (targets), the commands executed to create them, and the dependencies between them.

The drake package provides several ways to visualize the analysis pipeline, e.g.

Furthermore, drake provides functions for viewing individual targets (drake::readd()) or load them into the current environment (drake::loadd()).

For example, to view the cleaned data set, use:

readd(data_filtered)
#> # A tibble: 3,014 x 55
#>    gender grade       age land   analyze cft_iq_own_kl cft_impl cody_t_own
#>    <fct>  <fct>     <int> <fct>    <dbl>         <int> <fct>         <int>
#>  1 female 4. Klasse   122 Hessen       1           115 no               59
#>  2 male   3. Klasse   108 Bayern       1            99 no               41
#>  3 female 4. Klasse   121 Bayern       1           107 no               54
#>  4 female 4. Klasse   121 Hessen       1            77 no               46
#>  5 male   3. Klasse   106 Bayern       1            99 no               49
#>  6 female 3. Klasse   111 Bayern       1            96 no               44
#>  7 male   3. Klasse   111 Bayern       1            99 no               48
#>  8 female 4. Klasse   122 Bayern       1           107 no               58
#>  9 female 4. Klasse   126 Hessen       1            95 no               40
#> 10 male   4. Klasse   121 Bayern       1            86 no               44
#> # ... with 3,004 more rows, and 47 more variables: cody_z_own <dbl>,
#> #   cody_impl <fct>, wllp_t_own <int>, wllp_z_own <dbl>, wllp_impl <fct>,
#> #   childs_complete <int>, anamn_1 <fct>, anamn_24 <fct>, sca_e_z_own <dbl>,
#> #   des_z_own <dbl>, adhs_z_own <dbl>, ssv_z_own <dbl>, fbbssv_complete <int>,
#> #   anamn_12_c___15 <fct>, anamn_23_icd___44 <fct>, anamn_23_icd___70 <fct>,
#> #   anamn_23_icd___2 <fct>, anamn_23_icd___3 <fct>, anamn_23_icd___4 <fct>,
#> #   anamn_23_icd___5 <fct>, anamn_23_icd___54 <fct>, anamn_23_icd___55 <fct>,
#> #   anamn_23_icd___9 <fct>, anamn_23_icd___91 <fct>, anamn_23_icd___28 <fct>,
#> #   anamn_23_icd___48 <fct>, anamn_23_icd___80 <fct>, anamn_23_icd___83 <fct>,
#> #   anamn_23_icd___92 <fct>, anamn_23_icd___18 <fct>, wrt_t_own <dbl>,
#> #   wrt_z_own <dbl>, wrt_impl <chr>, nationality <fct>, education_mother <fct>,
#> #   dsm5_cutoff_35 <fct>, dsm5_cutoff_35_read <fct>,
#> #   dsm5_cutoff_35_spell <fct>, dsm5_cutoff_35_math <fct>,
#> #   dsm5_cutoff_35_01 <fct>, dsm5_cutoff_35_n <dbl>, dsm5_cutoff_35_012 <fct>,
#> #   sca_e_z_cat <fct>, adhs_z_cat <fct>, des_z_cat <fct>, ssv_z_cat <fct>,
#> #   psychopaths_n <dbl>

to view the results of the poisson regression model, use:

or, to view one of the figures included in the paper, use:

Cleaning up

Executing delete_analysis() removes the analysis and .drake directories from the working directory (or an explicitely specified directory).