Putting everything together

It is time to try to set up a project from scratch and use the different tools that we have covered during the course together! This exercise is very open-ended and you have free hands to try out a bit of what you want. But you should aim to use what you've learned to do the following:

  1. Create a new git repository for the project (either on BitBucket or GitHub)

  2. Add a README file which should contain the required information on how to run the project

  3. Create a Conda environment.yml file with the required dependencies

  4. Create a R Markdown or Jupyter notebook to run your code

  5. Alternatively, create a Snakefile to run your code as a workflow and use a config.yml file to add settings to the workflow

  6. Use git to continuously commit changes to the repository

  7. Possibly make a Docker or Singularity image for your project

This is not a small task and may seem overwhelming! Don't worry if you feel lost or if the task seems daunting. To get the most out of the exercise, take one step at a time and go back to the previous tutorials for help and inspiration. The goal is not necessarily for you to finish the whole exercise, but to really think about each step and how it all fits together in practice.


We recommend to start with git, Conda and a notebook, as we would see these as the core tools to make a research project reproducible. We suggest to keep the analysis for this exercise short so that you have time to try out the different tools together while you have the opportunity to ask for help.

Your own project

This is a great opportunity for you to try to implement these methods on one of your current research projects. It is of course up to you which tools to include in making your research project reproducible, but we suggest to aim for at least git and Conda.


If your analysis project contains computationally intense steps it may be good to scale them down for the sake of the exercise. You might, for example, subset your raw data to only contain a minuscule part of its original size. You can then test your implementation on the subset and only run it on the whole dataset once everything works to your satisfaction.

Alternative: student experience project

If you don't want to use a project you're currently working on we have a suggestion for a small-scale project for you. The idea is to analyze students' experiences at this Reproducible Research course. For this you will use responses from students to the registration form for the course. Below you'll find links to files in *.csv format with answers from 3 course instances:


The goal here is to create a Snakemake workflow, which contains the following:

  1. Has a rule that downloads the csv files (making use of a config.yml file to pass the URLs and file names)

  2. Has a rule that cleans the files (making use of wildcards so that the same rule can be run on each file)

  3. The final step is to plot the student experience in some way

Remember to

  • Keep everything versioned controlled with git
  • Add information to the README file so others know how to re-run the project
  • Add required software to the Conda environment.yml file

Inspiration and tips for the student experience workflow

The first two steps should be part of the Snakemake workflow. If you need some help with the cleaning step, see below for a Python script that you can save to a file and run in the second Snakemake rule.

Click to show a script for cleaning column names

The script (e.g. clean_csv.py):

#!/usr/bin/env python
import pandas as pd
from argparse import ArgumentParser

def main(args):
    df = pd.read_csv(args.input, header=0)
    df.rename(columns=lambda x: x.split("[")[-1].rstrip("]"), inplace=True)
    df.rename(columns={'R Markdown': 'RMarkdown'}, inplace=True)
    df.to_csv(args.output, index=False)

if __name__ == '__main__':
    parser = ArgumentParser()
    parser.add_argument("input", type=str,
                        help="Input csv file")
    parser.add_argument("output", type=str,
                        help="Output csv file cleaned")
    args = parser.parse_args()

Command to execute the script:

python clean_csv.py input_file.csv output_file.csv

The third step is really up to you how to implement. You could:

  • Include the plotting in the workflow using an RMarkdown document that gets rendered into a report
  • Have a script that produces separate figures (e.g. png files)
  • Create a jupyter notebook that reads the cleaned output from the workflow and generates some plot or does other additional analyses

If you need some help/inspiration with plotting the results, click below to see an example Python script that you can save to file and run with the cleaned files as input.

Click to show a script for plotting the student experience

The script (e.g. plot.py):

#!/usr/bin/env python
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
from argparse import ArgumentParser

def read_files(files):
    """Reads experience counts and concatenates into one dataframe"""
    df = pd.DataFrame()
    for i, f in enumerate(files):
        # Extract date
        d = f.split(".")[0]
        _df = pd.read_csv(f, sep=",", header=0)
        # Assign date
        _df = _df.assign(Date=pd.Series([d]*len(_df), index=_df.index))
        if i==0:
            df = _df.copy()
            df = pd.concat([df,_df], sort=True)
    return df.reset_index().drop("index",axis=1).fillna(0)

def count_experience(df, normalize=False):
    """Generates long format dataframe of counts"""
    df_l = pd.DataFrame()
    for software in df.columns:
        if software=="Date":
        # Groupby software and count
        _df = df.groupby(["Date",software]).count().iloc[:,0].reset_index()
        _df.columns = ["Date","Experience","Count"]
        _df = _df.assign(Software=pd.Series([software]*len(_df),
        if normalize:
            _df = pd.merge(_df.groupby("Date").sum().rename(columns={'Count':'Tot'}),_df, left_index=True, right_on="Date")
            _df.Count = _df.Count.div(_df.Tot)*100
            _df.rename(columns={'Count': '%'}, inplace=True)
        df_l = pd.concat([df_l, _df], sort=True)
    df_l.loc[df_l.Experience==0,"Experience"] = np.nan
    return df_l

def plot_catplot(df, outdir, figname, y, palette="Blues"):
    """Plot barplots of user experience per software"""
    ax = sns.catplot(data=df, x="Date", col="Software", col_wrap=3, y=y,
        hue="Experience", height=2.8,
                     hue_order=["Never heard of it",
                                "Heard of it but haven't used it",
                                "Tried it once or twice", "Use it"],
                     col_order=["Conda", "Git", "Snakemake", "Jupyter",
                                "RMarkdown", "Docker", "Singularity"],
    plt.savefig("{}/{}".format(outdir, figname), bbox_to_inches="tight",

def plot_barplot(df, outdir, figname, x):
    """Plot a barplot summarizing user experience over all software"""
    ax = sns.barplot(data=df, hue="Date", y="Experience", x=x, errwidth=.5,
                order=["Never heard of it",
                       "Heard of it but haven't used it",
                       "Tried it once or twice", "Use it"])
    plt.savefig("{}/{}".format(outdir, figname), bbox_inches="tight",

def main(args):
    # Read all csv files
    df = read_files(args.files)
    # Count experience
    df_l = count_experience(df)
    # Count and normalize experience
    df_lp = count_experience(df, normalize=True)
    # Plot catplot of student experience
    plot_catplot(df_l, args.outdir, "exp_counts.png", y="Count")
    # Plot catplot of student experience in %
    plot_catplot(df_lp, args.outdir, "exp_percent.png", y="%",
    # Plot barplot of experience
    plot_barplot(df_lp, args.outdir, "exp_barplot.png", x="%")

if __name__ == '__main__':
    parser = ArgumentParser()
    parser.add_argument("files", nargs="+",
        help="CSV files with student experience to produce plots for")
    parser.add_argument("--outdir", type=str, default=".",
        help="Output directory for plots (defaults to current directory)")
    args = parser.parse_args()

Command to execute the script:

python plot.py file1.csv file2.csv file3.csv --outdir results/