Cookiecutter research project

This is an example, or 'cookiecutter', repository for a research project. git clone the project and use it as a skeleton for your own research project. A full explanation may be found in this accompanying blog post. The idea is that this project structure should be as simple as possible, but no simpler.

While the example happens to be in Python, the structure of the project should be applicable to any language. The structure is sketched out below:

The project here is very much a toy example and not intended to be accurate or say anything at all about the real world!

Example Paper and slides

Within the example project, there are latex templates for both slides and a working paper. These are based on Paul Goldsmith-Pinkham's excellent templates, the originals of which you can find here for slides and here for the working paper. I've modified both, for example to ensure that the latex paper appendix uses within-section labels for figures and tables.

I've also added guidance on paper writing directly to the latex template. It's taken from various posts on the subject.

Example Code

The code is in Python 3 and uses the ONS API to download some macroeconomic time series, process them into tidy data, and then use them within a dynamic factor model† inspired by Chad Fulton's tutorials/notebooks which you can find here and here.

Note that paths, the series to download, and visualisation settings are all defined in config.yaml. The data processing steps are separated: raw data to file first, then cleaned data into tidy format in a csv, and finally the time series are used in a statistical model. The plots are similarly just examples.

More on the example dynamic factor model found in analysis.py in the blog post.

Re-running the example project

To re-run the analysis part of the project with conda, first install the required packages in a new conda environment:

conda env create -f ccenv.yml

You then need to activate the environment to use in your IDE. The command for this is conda activate ccenv. Note that, to try to ensure compatibility with all operating systems, the environment file is simplified. It only specifies the version of Python, conda is trusted to resolve dependencies and pick the right versions of other required packages.

Then run the files in this order to create the final PDF outputs:

• download_raw_data.py
• create_clean_data.py
• analysis.py
• both Latex files*

There are a couple of ways to run the python files. The first is to run them from the command line, from the root directory. For example: python -m src.download_raw_data To run all of them at once, use python -m src.master Or, from within an IDE, run the contents of src/master.py (but run it as if from the root folder).

* You may find that because the .eps files used for figures are not in a sub-directory of the main .tex folder, you must add a flag to the Latex compiler. In TexShop, the steps are:

• Go to Preferences
• Go to Tab "Engine"
• Go to the field "pdfTeX"
• In the LaTeX Input Field add --shell-escape at the end so that it changes from pdflatex --file-line-error --synctex=1 to pdflatex --file-line-error --synctex=1 --shell-escape