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+# Recording dependencies
+
+```{questions}
+- How can we communicate different versions of software dependencies?
+```
+
+```{instructor-note}
+- 10 min teaching
+- 20 min exercises
+```
+Our codes often depend on other codes that in turn depend on other codes ...
+
+- **Reproducibility**: We can version-control our code with Git but how should we version-control dependencies?
+  How can we capture and communicate dependencies?
+- **Dependency hell**: Different codes on the same environment can have conflicting dependencies.
+
+```{figure} img/python_environment.png
+:alt: An image showing a mess of dependecies in a Python environment
+:width: 60%
+
+From [xkcd](https://xkcd.com/).
+```
+
+````{discussion} Kitchen analogy
+- Software <-> recipe
+- Data <-> ingredients
+- Libraries <-> cooking books/blogs
+
+```{figure} img/kitchen/recipe.png
+:alt: Cooking recipe in an unfamiliar language
+:width: 50%
+
+Cooking recipe in an unfamiliar language [Midjourney, CC-BY-NC 4.0]
+```
+
+```{figure} img/kitchen/libraries.png
+:alt: Kitchen with few open cooking books
+:width: 50%
+
+When we create recipes, we often use existing recipes written by others (libraries) [Midjourney, CC-BY-NC 4.0]
+```
+````
+
+---
+
+## Tools and what problems they try to solve
+
+**Conda, Anaconda, pip, virtualenv, Pipenv, pyenv, Poetry, requirements.txt,
+environment.yml, renv**, ..., these tools try to solve the following problems:
+- **Defining a specific set of dependencies**, possibly with well defined versions
+- **Installing those dependencies** mostly automatically
+- **Recording the versions** for all dependencies
+- **Isolate environments**
+  - On your computer for projects so they can use different software
+  - Isolate environments on computers with many users (and allow self-installations)
+- Using **different Python/R versions** per project
+- Provide tools and services to **share packages**
+
+Isolated environments are also useful because they help you make sure
+that you know your dependencies!
+
+**If things go wrong, you can delete and re-create** - much better
+than debugging.  The more often you re-create your environment, the
+more reproducible it is.
+
+---
+
+## Exercises
+
+``````{challenge} (optional) Dependencies-1: Time-capsule of dependencies
+Situation: 5 students (A, B, C, D, E) wrote a code that depends on a couple of libraries.
+They uploaded their projects to GitHub. We now travel 3 years into the future
+and find their GitHub repositories and try to re-run their code before adapting
+it.
+
+Answer in the collaborative document:
+
+- Which version do you expect to be easiest to re-run? Why?
+- What problems do you anticipate in each solution?
+
+  `````{tabs}
+    ````{group-tab} Conda
+      **A**:
+      You find a couple of library imports across the code but that's it.
+
+      **B**:
+      The README file lists which libraries were used but does not mention
+      any versions.
+
+      **C**:
+      You find a `environment.yml` file with:
+      ```
+      name: student-project
+      channels:
+        - conda-forge
+      dependencies:
+        - scipy
+        - numpy
+        - sympy
+        - click
+        - python
+        - pip
+        - pip:
+          - git+https://github.com/someuser/someproject.git@master
+          - git+https://github.com/anotheruser/anotherproject.git@master
+      ```
+
+      **D**:
+      You find a `environment.yml` file with:
+      ```
+      name: student-project
+      channels:
+        - conda-forge
+      dependencies:
+        - scipy=1.3.1
+        - numpy=1.16.4
+        - sympy=1.4
+        - click=7.0
+        - python=3.8
+        - pip
+        - pip:
+          - git+https://github.com/someuser/someproject.git@d7b2c7e
+          - git+https://github.com/anotheruser/anotherproject.git@sometag
+      ```
+
+      **E**:
+      You find a `environment.yml` file with:
+      ```
+      name: student-project
+      channels:
+        - conda-forge
+      dependencies:
+        - scipy=1.3.1
+        - numpy=1.16.4
+        - sympy=1.4
+        - click=7.0
+        - python=3.8
+        - someproject=1.2.3
+        - anotherproject=2.3.4
+      ```
+    ````
+
+    ````{group-tab} Python virtualenv
+      **A**:
+      You find a couple of library imports across the code but that's it.
+
+      **B**:
+      The README file lists which libraries were used but does not mention
+      any versions.
+
+      **C**:
+      You find a `requirements.txt` file with:
+      ```
+      scipy
+      numpy
+      sympy
+      click
+      python
+      git+https://github.com/someuser/someproject.git@master
+      git+https://github.com/anotheruser/anotherproject.git@master
+      ```
+
+      **D**:
+      You find a `requirements.txt` file with:
+      ```
+      scipy==1.3.1
+      numpy==1.16.4
+      sympy==1.4
+      click==7.0
+      python==3.8
+      git+https://github.com/someuser/someproject.git@d7b2c7e
+      git+https://github.com/anotheruser/anotherproject.git@sometag
+      ```
+
+      **E**:
+      You find a `requirements.txt` file with:
+      ```
+      scipy==1.3.1
+      numpy==1.16.4
+      sympy==1.4
+      click==7.0
+      python==3.8
+      someproject==1.2.3
+      anotherproject==2.3.4
+      ```
+    ````
+
+    ````{group-tab} R
+      **A**:
+      You find a couple of `library()` or `require()` calls across the code but that's it.
+
+      **B**:
+      The README file lists which libraries were used but does not mention
+      any versions.
+
+      **C**:
+      You find a [DESCRIPTION file](https://r-pkgs.org/description.html) which contains:
+      ```
+      Imports:
+          dplyr,
+          tidyr
+      ```
+      In addition you find these:
+      ```r
+      remotes::install_github("someuser/someproject@master")
+      remotes::install_github("anotheruser/anotherproject@master")
+      ```
+
+      **D**:
+      You find a [DESCRIPTION file](https://r-pkgs.org/description.html) which contains:
+      ```
+      Imports:
+          dplyr (== 1.0.0),
+          tidyr (== 1.1.0)
+      ```
+      In addition you find these:
+      ```r
+      remotes::install_github("someuser/someproject@d7b2c7e")
+      remotes::install_github("anotheruser/anotherproject@sometag")
+      ```
+
+      **E**:
+      You find a [DESCRIPTION file](https://r-pkgs.org/description.html) which contains:
+      ```
+      Imports:
+          dplyr (== 1.0.0),
+          tidyr (== 1.1.0),
+          someproject (== 1.2.3),
+          anotherproject (== 2.3.4)
+      ```
+    ````
+
+    ````{group-tab} Matlab
+    Can you please contribute an example?
+    ````
+  `````
+
+  `````{solution}
+  **A**: It will be tedious to collect the dependencies one by one. And after
+  the tedious process you will still not know which versions they have used.
+
+  **B**: If there is no standard file to look for and look at and it might
+  become very difficult for to create the software environment required to
+  run the software. But at least we know the list of libraries. But we don't
+  know the versions.
+
+  **C**: Having a standard file listing dependencies is definitely better
+  than nothing. However, if the versions are not specified, you or someone
+  else might run into problems with dependencies, deprecated features,
+  changes in package APIs, etc.
+
+  **D** and **E**: In both these cases exact versions of all dependencies are
+  specified and one can recreate the software environment required for the
+  project. One problem with the dependencies that come from GitHub is that
+  they might have disappeared (what if their authors deleted these
+  repositories?).
+
+  **E** is slightly preferable because version numbers are easier to understand than Git
+  commit hashes or Git tags.
+  `````
+``````
+
+``````{challenge} (optional) Dependencies-2: Create a time-capsule for the future
+Now it is time to create your own time-capsule and share it with the future
+world. If we asked you now which dependencies your project is using, what would
+you answer? How would you find out? And how would you communicate this
+information?
+
+  `````{tabs}
+    ````{group-tab} Conda
+      Try this either with your own project or inside the "coderefinery" conda
+      environment:
+      ```console
+      $ conda env export > environment.yml
+      ```
+
+      Have a look at the generated file and discuss what you see.
+
+      In future you can re-create this environment with:
+      ```console
+      $ conda env create -f environment.yml
+      ```
+
+      More information: <https://docs.conda.io/en/latest/>
+
+      See also: <https://github.com/mamba-org/mamba>
+    ````
+
+    ````{group-tab} Python virtualenv
+      Try this in your own project:
+      ```console
+      $ pip freeze > requirements.txt
+      ```
+
+      Have a look at the generated file and discuss what you see.
+
+      In future you can re-create this environment with:
+      ```console
+      $ pip install -r requirements.txt
+      ```
+
+      More information: <https://docs.python.org/3/tutorial/venv.html>
+    ````
+
+    ````{group-tab} R
+      This example uses renv.
+
+      Try to "save" and "load" the state of your project library using
+      `renv::snapshot()` and `renv::restore()`.
+      See also: <https://rstudio.github.io/renv/articles/renv.html#reproducibility>
+
+      More information: <https://rstudio.github.io/renv/articles/renv.html>
+    ````
+
+    ````{group-tab} Matlab
+      Can you please contribute an example?
+    ````
+  `````
+``````