## Table of Contents

- Getting information about your Git repository
- Getting information about Python(3) libraries
  - Getting the list of installed packages and their version
  - How to list imported modules?
  - Setting up 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
    - Installing a pre-compiled version
    - Using devtools
    - Alternatively, you may want to install an older package from source
    - Potential issues

## 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, I often include commands at the end of my 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 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 for example how I get these 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. Inspiring from 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__)

    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

### Setting up 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 can be 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 on 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")

#### Alternatively, you may want to install an older package from source

If you devtools fails or if you do not want to depend on it, you can install it from source via `install.packages()` directed to 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.