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APACo

Overview of the repository

The aim of this repository is to allow the understanding, the reproduction, and finally the evaluation of the analytical pipeline implemented to get the results of the APA&Co project (ClinicalTrials.gov ID: NCT04732923). The APA&Co project was conducted to observe the change in physical condition, physical activity, and motivation for physical activity in coronary artery disease patients at 1 year after having followed a cardiac rehabilitation program. This research project was managed by the hospital center of Cholet (France).

The repository has four branches (master, gh-pages, docker, and pipeline):

  • The master branch hosts the content of the {APACo} package. The content of the package includes datasets that have been built from the data obtained through the measurements implemented in the APA&Co project. These datasets actually are censored versions of the original datasets to limit the access to only the materials required to understand the results of the project. The package also includes the code (the functions) that has been developed to analyse the data of the project. The code is based on R programming language. Of note, the functions of the package have not been written with the aim to make them easily reusable in other projects. That being said, one of the major functions of the package, called analyse_change and that provides both figures and tables describing the differences between two marginal distributions of paired data, has been developped so that it may be reused when working with similar data.

  • The gh-pages branch hosts the files on which stands the {APACo} package website. This website contains a Reference tab where the list of the datasets and functions included in the package is shown. Each item of the list briefly presents the content of the datasets or the arguments of the considered function. If you want to know the computations that the {APACo} package functions implement, you shoud go to the R/ folder of the master branch where each file contains the code of a given function.

  • The docker branch contains the code to build a Docker image and to push that image to a Docker Hub repository. A Docker image is a kind of virtual environment, that can be configured as needed, and that is immutable. In the present case, the Docker image includes R software (v4.3.2) and a version of the renv package. The interest of this Docker image is to provide an environment in which we can be sure that the analytical pipeline of the project will work and will provide exactly the same results, for ever.

  • The pipeline branch contains the code that runs the analytical pipeline of the APA&Co project. This pipeline, that is based on the targets package, is an organized sequence of actions that use the datasets and the functions of the {APACo} package to generate the results embedded in .tiff figures and .html files. The main interest of the pipeline is to automatically (so non-interactively) drive the analyses. This is an important step to ensure the reproducibility of the analyses and the results. The branch also contains R code that uses the renv package to allow the restoration of all the package dependencies relating to the analytical pipeline, which is another step required to ensure the reproducibility of the analytical pipeline and thus the results. Of note, this branch also hosts a GitHub Actions workflow that builds a Docker image from the one hosted in the Docker Hub repository (as described above) with the run of the installation of the package dependencies of the analytical pipeline, and then the run of the analytical pipeline that generates the results of the APA&Co project. Then, the workflow opens a Docker container that allows to move the results embedded in .tiff/.html files to the present GitHub repository. You may access these files by consulting the last dockerized-pipeline workflow run. When you are on the web page of the workflow and click on the link of a workflow run (the last one for example, at the top of the list), you are conducted to a new web page where an Artifacts section contains the created files embedded in a zipped folder. You will can finally download that folder on your machine to consult the results if the worklow has been run since less than 90 days.

How the content of this repository can be used to allow the reproduction of the analyses of the APA&Co project?

We suggest two approaches to reproduce the analyses of the APA&Co project: running the analytical pipeline on a PC with RStudio; or running the analytical pipeline on a PC with Docker. These two approaches have some advantages and limits depending on you are interested in the understanding or the reproducibility of the analyses.

Running the analytical pipeline on a PC with RStudio

This approach may be interesting if you want to explore, after running the analytical pipeline, the content of the created objects and the content of the functions in the same place, that is, for the present case, in an RStudio environment. However, the main downside of this approach is that reproducibility is not fully garanteed. Indeed, the success of an analytical pipeline run is dependent on various factors that cannot be controlled by the content of the present repository, in particular, the current version of R you are using, and the configuration of the system that lives on your machine. This is why, in order to reproduce the analytical pipeline exactly as it was performed to get the results of the project, the Docker-based approach is better, and actually, not so far to be the best. Anyway, if you want to reproduce the analytical pipeline on your machine using an RStudio environment, please follow the steps below.

  • Step 1: Install R (recommended versions: >= 4.1.0 and <= 4.3.3), RStudio, and the corresponding version of Rtools (if your are a Windows user) on your machine. To retrieve previous releases of R, you can go here for Windows, and here for Mac.
  • Step 2: Go to the pipeline branch of the APACo repository and click on the green button called Code. Then click on Download ZIP.
  • Step 3: Unzip the zipped folder on your machine, open it, and double-click on the APACo.Rproj file to open the project in RStudio.
  • Step 4: Restore the package dependencies of the project with renv using in the Console the command line shown below and then following the instructions proposed in the Console.
renv::restore()

This may take several minutes so that all the required packages are downloaded from the web and then installed in the project directory.

  • Step 5: Run the analytical pipeline with targets using the following command line in the Console:
targets::tar_make()

This last step will produce all the objects relating to the analytical process. It also will generate the materials (a .html report with the main results, .tiff figures, and .html file-based supplementary materials) that are deemed to be used for a scientific publication. Some materials will take time to be built because they include several high resolution figures.

The list of the objects created during the analytical process is shown in the Console after each ‘target’ expression. Once the analytical pipeline ended, you will can read any created object running targets::tar_read(OBJECT_NAME) in the Console. You also will can load any object in the global environment running targets::tar_load(OBJECT_NAME) in the Console. You can load all the created objects in one go running targets::tar_load_everything() in the Console. All the .tiff figures and .html files generated during the run of the analytical pipeline will appear in the pipeline_out/ folder created at the root of the project folder during the processing phase. To have more information about what are the computations actually performed behind the scene, you can open the _targets.R file placed at the root of the project. This file essentially includes a list of targets::tar_target() functions, with the name of the object created as first argument, and the operations performed to get that object as second argument. A quick way of looking at the code could be to copy the name of a function of interest and paste it in the Console after writing APACo::. For example, for the analyse_change() function of the {APACo} package, you could run in the Console the following command: APACo::analyse_change. Another way, which will provide you a more confortable view of the code, is to go to the Github repository of the {APACo} package. The files placed in the R/ folder contain the code of each of the functions developped for the project.

Running the analytical pipeline on a PC with Docker

Using a Docker image is a robust approach to reproducibly run an analytical pipeline. Indeed, a Docker image is a kind of virtual environment where all the settings can be defined as wanted so that all the analyses can be performed as when developping the analytical pipeline. Please follow the steps below to use this approach with your machine:

  • Step 1: Install Docker Desktop on your machine.
  • Step 2: Open Docker Desktop on your machine. If you are a Windows user, you could have a warning message indicating that Docker Desktop requires a newer WSL kernel version. As suggested in the message, run the command line wsl --update in the Command Prompt app (you know, this software that shows you only a window with a black background; do not be afraid of it). When the installation is done, try again to open Docker Desktop.
  • Step 3: Go to the pipeline branch of the APACo repository and click on the green button called Code. Then click on Download ZIP.
  • Step 4: Unzip the zipped folder on your machine and open it.
  • Step 5: Open, in the unzipped folder, the Command Prompt (Terminal on Mac) app. To do this, if you are a Windows user, when you are inside the unzipped folder (at its root), you can simply write cmd in the address bar displayed at the top of your screen and press Enter. After that, you should see in the Command Prompt window a line beginning by something like that:
C:\Users\YOUR_USER_NAME\Downloads\APACo-pipeline>

In the example above (appropriate for Windows users), C:\Users\YOUR_USER_NAME\ stands for the current user session, and Downloads\APACo-pipeline stands for the remaining part of the path to the unzipped folder named APACo-pipeline. You may have several folder names to use in the path between YOUR_USER_NAME and APACo-pipeline depending on where you have placed the unzipped folder on your machine.

If you are a Mac user and thus want to open the Terminal app in the unzipped folder, you may try the following recommendation: ‘The easiest way to open Terminal in your current folder on Mac is to use Finder to navigate to the location and right-click and open Terminal from the context menu. You can also use the Finder menu or drag and drop folders on top of the Terminal application in your dock.’ (source here).

If you want to manually indicate to the Command Prompt (or Terminal) app that you want to work in the unzipped folder, you can open Command Prompt (or Terminal), and run the following command line by replacing YOUR_USER_NAME by your actual user name and Downloads/APACo-pipeline by the path to the unzipped folder (below, C:/Users/ stands for an example for Windows users):

cd C:/Users/YOUR_USER_NAME/Downloads/APACo-pipeline
  • Step 6: Ensure you have an internet connexion, and build the Docker image with the installation of the required package dependencies and the run of the analytical pipeline that generates the results, this by running the following command line in the Command Prompt (Terminal) app (do not forget the point at the end of the line):
docker build -t apaco_image .

The whole process may take several minutes as it consists in downloading the base Docker image (with R v4.3.2 and a version of the renv package), building a new Docker image on the top of it by downloading and installing the R package dependencies, and finally running the analytical pipeline in R.

  • Step 7: Create a Docker container to move the files embedding the results from a Docker volume to your machine, and more precisely to the folder named shared_folder that is inside the unzipped folder named APACo-pipeline. To do this, run the following command line in the Command Prompt (or Terminal) app:
docker run --rm --name apaco_container -v C:/Users/YOUR_USER_NAME/Downloads/APACo-pipeline/shared_folder:/project/shared_folder:rw apaco_image

In the example above (appropriate for Windows users), C:/Users/YOUR_USER_NAME/Downloads/APACo-pipeline/shared_folder is the absolute path to the folder named shared_folder at the root of the unzipped folder named APACo-pipeline. As previoulsy described, you should replace C:/Users/YOUR_USER_NAME/Downloads/ by the path that fits your needs. This is the only part of the code you should modify (assuming the unzipped folder is still named APACo-pipeline).

  • Step 8: Finally, go to the shared_folder folder at the root of the unzipped folder (i.e., inside the APACo-pipeline folder). The results embedded in .tiff figures and .html files should be there. You can now close the Command Prompt (or Terminal) app.

While the presented approach is in principle robust to reproduce the analytical pipeline, the downside of this approach is that you will not can easily interact with R to explore the content of the functions and of the intermediate objects created to get the results (it is possible using additional command lines). However, if you have sufficient knowledge about R programming language, you can infere the content of the objects from the code shown in the master branch of the APACo repository.

How to be sure of the {APACo} package version that is used when running the analytical pipeline?

The answer to this question is essential if you want to know exactly what pieces of code have been used to get the final results of the project. To have the answer, you have to look at the renv.lock file placed in the pipeline branch. This file contains all the package dependencies needed to run the analytical pipeline. Moreover, for each package, beyond the version that could be shown, you must look at the RemoteSha field of the package section in the renv.lock file to get the commit hash. This commit hash is a number that allows to identify a particular commit and then the state of the package when the commit was performed. Once you get that hash, you can add it to the following incomplete URL: https://github.com/pydemull/APACo/tree/. You will thus get the final URL where is hosted the package as it was when the commit was performed. For example, at the moment of writing theses lines, the hash of the commit corresponding to the version of the {APACo} package used by the analytical pipeline is f088468b46b94d80ace755c89ba2c6ce7effafa5. Thus, the exact code that is used in the analytical pipeline corresponding to this commit can be viewed in the R/ folder at the following adress: https://github.com/pydemull/APACo/tree/f088468b46b94d80ace755c89ba2c6ce7effafa5.

Is the Docker-based approach presented here so robust to allow the reproduction of the analytical pipeline?

Actually, there may be a vulnerability. Indeed, the process implemented in the Docker-based approach starts from a Docker image that has an R version compatible with the analytical pipeline and a working version of the renv package only. This means that the initial Docker image has not yet the package dependencies of the analytical pipeline installed. Therefore, the package dependencies have to be installed during the workflow. Unfortunately, among the package dependencies, there are two non-CRAN packages to be installed: rogme and {APACo}. This is important because if the two repositories that host these packages disappear from the web, it may not be possible to install them during the workflow, and the run of the analytical pipeline will fail. As the deletion of these two repositories is very unlikely, we did not perform extra work to build a Docker image that would contain all the required materials but that would have an interest only for persons who have the time and the skills to directly work with this image.

Licenses

Code

The code of the {APACo} package is provided under GNU General Public License Version 3.0 (please see LICENSE.md file).

Datasets

Licence Creative Commons
The datasets included in the {APACo} package are provided under License Creative Commons Attribution NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).

Acknowledgements

The Dockerfiles and GitHub Actions workflows that allow an exact reproduction of the analytical pipeline of the APA&Co project are versions updated from Bruno Rodrigues’ work. More precisely: