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Figure repo for Engemann et al 2019 multimodal stacing paper.
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.gitignore
Makefile
README.md
config.r
figure_behavior.r
figure_concept_elements.r
figure_concept_meg_demo_camcan.py
figure_concept_surfer_demo.py
figure_meg_performance.r
figure_mri_fmri_meg.r
figure_opp_learn.r
utils.r

README.md

paper-multimodal-stacking-figures

Figure repo for Engemann et al 2019 multimodal stacking paper. See https://github.com/OlehKSS/camcan_analysis for the computation the analysis here is building upon.

Dependencies

Making the figures requires a:

  1. the (non-committed) ./input_data directory (obtained from the computation-repository linked above)
  2. a recent Python install including (>=3.5), ideally Anaconda, with Pandas.
  3. ideally, a recent R Version (>= 3.0).

R dependencies

I did my best to keep the R dependencies flat, avoiding the tidyverse and other meta-packages. All logic and control flow is written in conservative base R and avoids pipe operators and other high-level syntax. The following visualization packages are needed to run the code in a pure R console:

To build the Markdown and HTML, you will also need:

The dependencies themselves have rather flat dependencies. Ggplot, may depend on a few elements of the tidyverse. However, running the code here should be possible with a rather minimalistic R setup.

Running the code

The figure elements are created through R scripts, which at the same time implement elements of literate programming through RMarkdown directives.

The prinicipal R scripts begin follow the figure_*.r pattern and can be run in the R console or can be compiled through Rmarkdown into HTML outputs. In both cases, the figure elements are created and written to ./figures.

To build the figures together with the HTML, please consider the Makefile. You can build a single figure:

make fig2

Or all figures:

make all
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