Title	Author
README	David Budzynski

Inspired by cookiecutter data science repo

Directory structure

.
├── data
│  └── raw                     <- The original, immutable data dump.
├── Dockerfile                 <- Dockerfile for building the docker image
├── .editorconfig              <- Configuration for your editor
├── LICENSE                    <- License for this project
├── Makefile                   <- Makefile with commands like `make data` or `make train
├── output
│  ├── data                    <- Processed data
│  ├── plots                   <- Generated graphics and figures to be used in reporting
│  └── reports                 <- Generated analysis as HTML, PDF, LaTeX, etc.
├── README.md                  <- The top-level README for developers using this project.
├── references                 <- Data dictionaries, manuals, and all other explanatory materials.
├── rstudio-project-file.Rproj <- RStudio project file
└── src                        <- Source code for use in this project.

Usage

Initializing the project

To initialize the project, simply run make init. This will rename the rstudio-project-file.Rproj to the name of the directory you are currently in.

Development and analysis

After initializing the project, you can start developing your analysis, by adding any code to the src directory. You can also add any data to the data/raw directory.

GNU Make and the Makefile

By using a Makefile, you can define targets that will run certain commands. For example, you can define a target that will run your analysis, or clean the project. To be able to use it, you need to have GNU Make installed on your system. Learn more about GNU Make here To read more about the existing Makefile, see the Makefile in the root of the project.

Most likely the most common targets you will use are:

1. lint
2. analysis
3. clean
4. all (lint and analysis)

To run the analysis target, you need to provide the commands that will run the analysis.

Additionally, the Makefile contain docker-specific targets, that will allow to build image and run the analysis in the container.

Docker

The project contains a Dockerfile that will allow you to build a docker image. This image uses the Rocker project as base image, and installs all the necessary packages and dependencies. You need to pay attention to the Dockerfile as it contains important information about the image, like the R version, and the packages that are installed. Make sure to update the Dockerfile with the correct information. For example, if you want to use a different version of R, you can change that, as well as the packages that are installed. In order to make sure your entire project is reproducible, you need to add run the analysis as part of the docker image build process. This can be done by adding the analysis target to the Dockerfile.

RUN make analysis

Once you have built the image, you can run the analysis in the container. This will allow you to add volume mounts, and run the analysis in a controlled environment and be able to see the results of your analysis executed in the container. To run the image, you can use the following command:

make docker-run

Docker is a very powerful tool, and it is a complex topic. To learn more about it, specifically about using docker with R, you can read Building reproducible analytical pipelines with R.

Project specific information

This is the place where you can add any information that is specific to your project. For example, you can add information about the data, the analysis, or any other information that is relevant to the project that will help other developers understand it better.