## Table of Contents

- Additional references
  - "Thoughts" on language/software stability
  - Controlling your software environment
  - Preservation/Archiving
  - Workflows
  - Numerical and statistical issues
  - Publication practices
  - Experimentation
- Tracking environment information
  - Getting information about your Git repository
  - Getting information about Python(3) libraries
    - Getting information about your system
    - Getting the list of installed packages and their version
    - How to list imported modules?
    - Saving and restoring an environment with pip
    - Installing a new package or a specific version
  - Getting information about R libraries
    - Getting the list imported modules and their version
    - Getting the list of installed packages and their version
    - Installing a new package or a specific version

## Additional references

### "Thoughts" on language/software stability

As we explained, the programming language used in an analysis has a clear influence on the reproducibility of your analysis. It is not a characteristic of the language itself but rather a consequence of the development philosophy of the underlying community. For example C is a very stable language with a very clear specification designed by a committee (even though some compilers may not respect this norm).

On the other end of the spectrum, Python had a much more organic development based on a readability philosophy and valuing continuous improvement over backwards-compatibility. Furthermore, Python is commonly used as a wrapping language (e.g., to easily use C or FORTRAN libraries) and has its own packaging system. All these design choices tend to make reproducibility often a bit painful with Python, even though the community is slowly taking this into account. The transition from Python 2 to the not fully backwards compatible Python 3 has been a particularly painful process, not least because the two languages are so similar that is it not always easy to figure out if a given script or module is written in Python 2 or Python 3. It isn't even rare to see Python scripts that work under both Python 2 and Python 3, but produce different results due to the change in the behavior of integer division.

R, in comparison is much closer (in terms of developer community) to languages like SAS, which is heavily used in the pharmaceutical industry where statistical procedures need to be standardized and rock solid/stable. R is obviously not immune to evolutions that break old versions and hinder reproducibility/backward compatibility. Here is a relatively recent true story about this and some colleagues who worked on the statistics introductory course with R on FUN reported us several issues with a few functions (`plotmeans` from `gplots`, `survfit` from `survival`, or `hclust`) whose default parameters had changed over the years. It is thus probably good practice to give explicit values for all parameters (which can be cumbersome) instead of relying on default values, and to restrict your dependencies as much as possible.

This being said, the R development community is generally quite careful about stability. We (the authors of this MOOC) believe that open source (which allows to inspect how computation is done and to identify both mistakes and sources of non-reproducibility) is more important than the rock solid stability of SAS, which is proprietary software. Yet, if you really need to stay with SAS (similar solutions probably exist for other languages as well), you should know that SAS can be used within Jupyter using either the Python SASKernel or the Python SASPy package (step by step explanations about this are given here). Using such literate programming approach allied with systematic version and environment control will always help.

### Controlling your software environment

As we mentioned in the video sequences, there are several solutions to control your environment:

- The easy (preserve the mess) ones: CDE or ReproZip
- The more demanding (encourage cleanliness) where you start with a clean environment and install only what's strictly necessary (and document it):
  - The very well known Docker
  - Singularity or Spack, which are more targeted toward the specific needs of high performance computing users
  - Guix, Nix that are very clean (perfect?) solutions to this dependency hell and which we recommend

It may be hard to understand the difference between these different approaches and decide which one is better in your context.

Here is a webinar where some of these tools are demoed in a reproducible research context: Controling your environment (by Michael Mercier and Cristian Ruiz)

You may also want to have a look at the Popper conventions (webinar by Ivo Gimenez through google hangout) or at the presentation of Konrad Hinsen on Active Papers (http://www.activepapers.org/).

### Preservation/Archiving

Ensuring software is properly archived, i.e, is safely stored so that it can be accessed in a perennial way, can be quite tricky. If you have never seen Roberto Di Cosmo presenting the Software Heritage project, this is a must see. https://www.softwareheritage.org/

For regular data, we highly recommend using https://www.zenodo.org/ whenever the data is not sensitive.

### Workflows

In the video sequences, we mentioned workflow managers (original application domain in parenthesis):

- Galaxy (genomics), Kepler (ecology), Taverna (bio-informatics), Pegasus (astronomy), Collective Knowledge (compiling optimization) , VisTrails (image processing)
- Light-weight: dask (python), drake (R), swift (molecular biology), snakemake (like `make` but more expressive and in `python`) …
- Hybrids: SOS-notebook, …

You may want to have a look at this webinar: Reproducible Science in Bio-informatics: Current Status, Solutions and Research Opportunities (by Sarah Cohen Boulakia, Yvan Le Bras and Jérôme Chopard).

### Numerical and statistical issues

We have mentioned these topics in our MOOC but we could by no way cover them properly. We only suggest here a few interesting talks about this.

- In this talk, Pierre Dragicevic provides a nice illustration of the consequences of statistical uncertainty and of how some concepts (e.G. p-values) are commonly badly understood.
- Nathalie Revol, Philippe Langlois and Stef Graillat present the main challenges encountered when trying to achieve numerical reproducibility and present recent research work on this topic.

### Publication practices

You may want to have a look at the following two webinars:

- Enabling open and reproducible research at computer systems’ conferences (by Grigori Fursin). In particular, this talk discusses artifact evaluation that is becoming more and more popular.
- Publication Modes Favoring Reproducible Research (by Konrad Hinsen and Nicolas Rougier). In this talk, the motivation for the ReScience journal initiative are presented.
- Simine Vazire - When Should We be Skeptical of Scientific Claims?, which is discussing publication practices in social sciences and in particular HARKing (Hypothesizing After the Results are Known), p-hacking, etc.

### Experimentation

Experimentation was not covered in this MOOC, although it is an essential part of science. The main reason is that practices and constraints can vary so wildly from one domain to another that it could not be properly covered in a first edition. We would be happy to gather references you consider as interesting in your domain so do not hesitate to provide us with such references by using the forum and we will update this page.

- A recent talk by Lucas Nussbaum on Experimental Testbeds in Computer Science.

## Tracking environment information

### Getting information about your Git repository

When taking notes, it may be difficult to remember which version of the code or of a file was used. This is what version control is useful for. Here are a few useful commands that we typically insert at the top of our notebooks in shell cells

    git log -1

    commit 741b0088af5b40588493c23c46d6bab5d0adeb33
    Author: Arnaud Legrand <arnaud.legrand@imag.fr>
    Date:   Tue Sep 4 12:45:43 2018 +0200

        Fix a few typos and provide information on jupyter-git plugins.

    git status -u

    On branch master
    Your branch is ahead of 'origin/master' by 4 commits.
      (use "git push" to publish your local commits)

    Changes not staged for commit:
      (use "git add <file>..." to update what will be committed)
      (use "git checkout -- <file>..." to discard changes in working directory)

    	modified:   resources.org

    Untracked files:
      (use "git add <file>..." to include in what will be committed)

    	../../module2/ressources/replicable_article/IEEEtran.bst
    	../../module2/ressources/replicable_article/IEEEtran.cls
    	../../module2/ressources/replicable_article/article.bbl
    	../../module2/ressources/replicable_article/article.tex
    	../../module2/ressources/replicable_article/data.csv
    	../../module2/ressources/replicable_article/figure.pdf
    	../../module2/ressources/replicable_article/logo.png
    	.#resources.org

    no changes added to commit (use "git add" and/or "git commit -a")

Note: the -u indicates that git should also display the contents of new directories it did not previously know about.

Then, we often include commands at the end of our notebook indicating how to commit the results (adding the new files, committing with a clear message and pushing). E.g.,

    git add resources.org;
    git commit -m "Completing the section on getting Git information"
    git push

    [master 514fe2c1 ] Completing the section on getting Git information
     1 file changed, 61 insertions(+)
    Counting objects: 25, done.
    Delta compression using up to 4 threads.
    Compressing objects: 100% (20/20), done.
    Writing objects: 100% (25/25), 7.31 KiB | 499.00 KiB/s, done.
    Total 25 (delta 11), reused 0 (delta 0)
    To ssh://app-learninglab.inria.fr:9418/learning-lab/mooc-rr-ressources.git
       6359f8c..1f8a567  master -> master

Obviously, in this case you need to save the notebook before running this cell, hence the output of this final command (with the new git hash) will not be stored in the cell. This is not really a problem and is the price to pay for running git from within the notebook itself.

### Getting information about Python(3) libraries

#### Getting information about your system

This topic is discussed on StackOverflow.

    import platform
    print(platform.uname())

    uname_result(system='Linux', node='icarus', release='4.15.0-2-amd64', version='#1 SMP Debian 4.15.11-1 (2018-03-20)', machine='x86_64', processor='')

#### Getting the list of installed packages and their version

This topic is discussed on StackOverflow. When using `pip` (the Python package installer) within a shell command, it is easy to query the version of all installed packages (note that on your system, you may have to use either `pip` or `pip3` depending on how it is named and which versions of Python are available on your machine

Here is for example how I get this information on my machine:

    pip3 freeze

    asn1crypto==0.24.0
    attrs==17.4.0
    bcrypt==3.1.4
    beautifulsoup4==4.6.0
    bleach==2.1.3
    ...
    pandas==0.22.0
    pandocfilters==1.4.2
    paramiko==2.4.0
    patsy==0.5.0
    pexpect==4.2.1
    ...
    traitlets==4.3.2
    tzlocal==1.5.1
    urllib3==1.22
    wcwidth==0.1.7
    webencodings==0.5

In a Jupyter notebook, this can easily be done by using the `%%sh` magic. Here is for example what you could do and get on the Jupyter notebooks we deployed for the MOOC (note that here, you should simply use the `pip` command):

    %%sh
    pip freeze

    alembic==0.9.9
    asn1crypto==0.24.0
    attrs==18.1.0
    Automat==0.0.0
    ...
    numpy==1.13.3
    olefile==0.45.1
    packaging==17.1
    pamela==0.3.0
    pandas==0.22.0
    ...
    webencodings==0.5
    widgetsnbextension==3.2.1
    xlrd==1.1.0
    zope.interface==4.5.0

In the rest of this document, I will assume the correct command is `pip` and I will not systematically insert the `%%sh` magic.

Once you know which packages are installed, you can easily get additional information about a given package and in particular check whether it was installed "locally" through pip or whether it is installed system-wide. Again, in a shell command:

    pip show pandas
    echo "            "
    pip show statsmodels

    Name: pandas
    Version: 0.22.0
    Summary: Powerful data structures for data analysis, time series,and statistics
    Home-page: http://pandas.pydata.org
    Author: None
    Author-email: None
    License: BSD
    Location: /usr/lib/python3/dist-packages
    Requires:

    Name: statsmodels
    Version: 0.9.0
    Summary: Statistical computations and models for Python
    Home-page: http://www.statsmodels.org/
    Author: None
    Author-email: None
    License: BSD License
    Location: /home/alegrand/.local/lib/python3.6/site-packages
    Requires: patsy, pandas

#### How to list imported modules?

Without resorting to pip (that will list all available packages), you may want to know which modules are loaded in a Python session as well as their version. Inspired by StackOverflow, here is a simple function that lists loaded package (that have a `__version__` attribute, which is unfortunately not completely standard).

    def print_imported_modules():
        import sys
        for name, val in sorted(sys.modules.items()):
            if(hasattr(val, '__version__')):
                print(val.__name__, val.__version__)
            else:
                print(val.__name__, "(unknown version)")

    print("**** Package list in the beginning ****");
    print_imported_modules()
    print("**** Package list after loading pandas ****");
    import pandas
    print_imported_modules()

    **** Package list in the beginning ****
    **** Package list after loading pandas ****
    _csv 1.0
    _ctypes 1.1.0
    decimal 1.70
    argparse 1.1
    csv 1.0
    ctypes 1.1.0
    cycler 0.10.0
    dateutil 2.7.3
    decimal 1.70
    distutils 3.6.5rc1
    ipaddress 1.0
    json 2.0.9
    logging 0.5.1.2
    matplotlib 2.1.1
    numpy 1.14.5
    numpy.core 1.14.5
    numpy.core.multiarray 3.1
    numpy.core.umath b'0.4.0'
    numpy.lib 1.14.5
    numpy.linalg._umath_linalg b'0.1.5'
    pandas 0.22.0
    _libjson 1.33
    platform 1.0.8
    pyparsing 2.2.0
    pytz 2018.5
    re 2.2.1
    six 1.11.0
    urllib.request 3.6
    zlib 1.0

#### Saving and restoring an environment with pip

The easiest way to go is as follows:

    pip3 freeze > requirements.txt # to obtain the list of packages with their version
    pip3 install -r requirements.txt # to install the previous list of packages, possibly on an other machine

If you want to have several installed Python environments, you may want to use Pipenv. I doubt it allows to track correctly FORTRAN or C dynamic libraries that are wrapped by Python though.

#### Installing a new package or a specific version

The Jupyter environment we deployed on our servers for the MOOC is based on the version 4.5.4 of Miniconda and Python 3.6. In this environment you should simply use the `pip` command (remember on your machine, you may have to use `pip3`).

If I query the current version of `statsmodels` in a shell command, here is what I will get.

    pip show statsmodels

    Name: statsmodels
    Version: 0.8.0
    Summary: Statistical computations and models for Python
    Home-page: http://www.statsmodels.org/
    Author: Skipper Seabold, Josef Perktold
    Author-email: pystatsmodels@googlegroups.com
    License: BSD License
    Location: /opt/conda/lib/python3.6/site-packages
    Requires: scipy, patsy, pandas

I can then easily upgrade `statsmodels`:

    pip install --upgrade statsmodels

Then the new version should then be:

    pip show statsmodels

    Name: statsmodels
    Version: 0.9.0
    Summary: Statistical computations and models for Python
    Home-page: http://www.statsmodels.org/
    Author: Skipper Seabold, Josef Perktold
    Author-email: pystatsmodels@googlegroups.com
    License: BSD License
    Location: /opt/conda/lib/python3.6/site-packages
    Requires: scipy, patsy, pandas

It is even possible to install a specific (possibly much older) version, e.g.,:

    pip install statsmodels==0.6.1

### Getting information about R libraries

#### Getting the list imported modules and their version

The best way seems to be to rely on the `devtools` package (if this package is not installed, you should install it first by running in `R` the command `install.packages("devtools")`).

    sessionInfo()
    devtools::session_info()

    R version 3.5.1 (2018-07-02)
    Platform: x86_64-pc-linux-gnu (64-bit)
    Running under: Debian GNU/Linux buster/sid

    Matrix products: default
    BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.8.0
    LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.8.0

    locale:
     [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C
     [3] LC_TIME=fr_FR.UTF-8        LC_COLLATE=fr_FR.UTF-8
     [5] LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8
     [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C
     [9] LC_ADDRESS=C               LC_TELEPHONE=C
    [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C

    attached base packages:
    [1] stats     graphics  grDevices utils     datasets  methods   base

    loaded via a namespace (and not attached):
    [1] compiler_3.5.1
    Session info ------------------------------------------------------------------
     setting  value
     version  R version 3.5.1 (2018-07-02)
     system   x86_64, linux-gnu
     ui       X11
     language (EN)
     collate  fr_FR.UTF-8
     tz       Europe/Paris
     date     2018-08-01

    Packages ----------------------------------------------------------------------
     package   * version date       source
     base      * 3.5.1   2018-07-02 local
     compiler    3.5.1   2018-07-02 local
     datasets  * 3.5.1   2018-07-02 local
     devtools    1.13.6  2018-06-27 CRAN (R 3.5.1)
     digest      0.6.15  2018-01-28 CRAN (R 3.5.0)
     graphics  * 3.5.1   2018-07-02 local
     grDevices * 3.5.1   2018-07-02 local
     memoise     1.1.0   2017-04-21 CRAN (R 3.5.1)
     methods   * 3.5.1   2018-07-02 local
     stats     * 3.5.1   2018-07-02 local
     utils     * 3.5.1   2018-07-02 local
     withr       2.1.2   2018-03-15 CRAN (R 3.5.0)

Some actually advocate that writing a reproducible research compendium is best done by writing an R package. Those of you willing to have a clean R dependency management should thus have a look at Packrat.

#### Getting the list of installed packages and their version

Finally, it is good to know that there is a built-in R command (`installed.packages`) allowing to retrieve and list the details of all packages installed.

    head(installed.packages())

Package | LibPath | Version | Priority | Depends | Imports | LinkingTo | Suggests | Enhances | License | LicenseisFOSS | Licenserestrictsuse | OStype | MD5sum | NeedsCompilation | Built |
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
BH | /home/alegrand/R/x8664-pc-linux-gnu-library/3.5 | 1.66.0-1 | nil | nil | nil | nil | nil | nil | BSL-1.0 | nil | nil | nil | nil | no | 3.5.1 |
Formula | /home/alegrand/R/x8664-pc-linux-gnu-library/3.5 | 1.2-3 | nil | R (>= 2.0.0), stats | nil | nil | nil | nil | GPL-2 | GPL-3 | nil | nil | nil | nil | no | 3.5.1
Hmisc | /home/alegrand/R/x8664-pc-linux-gnu-library/3.5 | 4.1-1 | nil | lattice, survival (>= 2.40-1), Formula, ggplot2 (>= 2.2) | methods, latticeExtra, cluster, rpart, nnet, acepack, foreign, |  |  |  |  |  |  |  |  |  |  |
gtable, grid, gridExtra, data.table, htmlTable (>= 1.11.0), |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
viridis, htmltools, base64enc | nil | chron, rms, mice, tables, knitr, ff, ffbase, plotly (>= |  |  |  |  |  |  |  |  |  |  |  |  |  |
4.5.6) | nil | GPL (>= 2) | nil | nil | nil | nil | yes | 3.5.1 |  |  |  |  |  |  |  |
Matrix | /home/alegrand/R/x8664-pc-linux-gnu-library/3.5 | 1.2-14 | recommended | R (>= 3.2.0) | methods, graphics, grid, stats, utils, lattice | nil | expm, MASS | MatrixModels, graph, SparseM, sfsmisc | GPL (>= 2) | file LICENCE | nil | nil | nil | nil | yes | 3.5.1
StanHeaders | /home/alegrand/R/x8664-pc-linux-gnu-library/3.5 | 2.17.2 | nil | nil | nil | nil | RcppEigen, BH | nil | BSD3clause + file LICENSE | nil | nil | nil | nil | yes | 3.5.1 |
acepack | /home/alegrand/R/x8664-pc-linux-gnu-library/3.5 | 1.4.1 | nil | nil | nil | nil | testthat | nil | MIT + file LICENSE | nil | nil | nil | nil | yes | 3.5.1 |

#### Installing a new package or a specific version

This section is mostly a cut and paste from the recent post by Ian Pylvainen on this topic. It comprises a very clear explanation of how to proceed.

- Installing a pre-compiled version

  If you're on a Debian or a Ubuntu system, it may be difficult to access a specific version without breaking your system. So unless you are moving to the latest version available in your Linux distribution, we strongly recommend you to build from source. In this case, you'll need to make sure you have the necessary toolchain to build packages from source (e.g., gcc, FORTRAN, etc.). On Windows, this may require you to install Rtools.

  If you're on Windows or OS X and looking for a package for an older version of R (R 2.1 or below), you can check the CRAN binary archive. Once you have the URL, you can install it using a command similar to the example below:

      packageurl <- "https://cran-archive.r-project.org/bin/windows/contrib/2.13/BBmisc_1.0-58.zip"
      install.packages(packageurl, repos=NULL, type="binary")

- Using devtools

  The simplest method to install the version you need is to use the `install_version()` function of the `devtools` package (obviously, you need to install `devtools` first, which can be done by running in `R` the command `install.packages("devtools")`). For instance:

      require(devtools)
      install_version("ggplot2", version = "0.9.1", repos = "http://cran.us.r-project.org")

- Installing from source code

  Alternatively, you may want to install an older package from source If devtools fails or if you do not want to depend on it, you can install it from source via `install.packages()` directed using the right URL. This URL can be obtained by browsing the CRAN Package Archive.

  Once you have the URL, you can install it using a command similar to the example below:

      packageurl <- "http://cran.r-project.org/src/contrib/Archive/ggplot2/ggplot2_0.9.1.tar.gz"
      install.packages(packageurl, repos=NULL, type="source")

  If you know the URL, you can also install from source via the command line outside of R. For instance (in bash):

      wget http://cran.r-project.org/src/contrib/Archive/ggplot2/ggplot2_0.9.1.tar.gz
      R CMD INSTALL ggplot2_0.9.1.tar.gz

- Potential issues

  There are a few potential issues that may arise with installing older versions of packages:

  - You may be losing functionality or bug fixes that are only present in the newer versions of the packages.
  - The older package version needed may not be compatible with the version of R you have installed. In this case, you will either need to downgrade R to a compatible version or update your R code to work with a newer version of the package.