removed unnused data

material/3_wed/docs/tasks.ipynb

@@ -0,0 +1,84 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Task 1: Docstrings {#1}\n",
+        "1. Add Docstrings to some of your functions.\n",
+        "2. Load the package, and check you can see the docstrings using e.g. `?rse_mean`\n",
+        "\n",
+        "-----\n",
+        "\n",
+        "# Task 2: Documenter.jl {#1}\n",
+        "### Folderstructure\n",
+        "1. create folders/files:\n",
+        "```\n",
+        "docs/\n",
+        "├── src/\n",
+        "├── src/mydocs.jl\n",
+        "└── make.jl\n",
+        "```\n",
+        "\n",
+        "### add some docs\n",
+        "2. with mydocs containing\n",
+        "\n",
+        "\n",
+        "````{verbatim}\n",
+        "```@docs \n",
+        "func(x)\n",
+        "```\n",
+        "\n",
+        "````\n",
+        "\n",
+        "and\n",
+        "\n",
+        "`make.jl` containing\n",
+        "```julia\n",
+        "using Documenter, Example\n",
+        "\n",
+        "makedocs(sitename=\"My Documentation\")\n",
+        "```\n",
+        "\n",
+        "### 3. Generate\n",
+        "Generate the docs using `include(\"make.jl\")` after activating the `./docs/Project.toml`\n",
+        "\n",
+        ":::callout\n",
+        "## Bonus-Task\n",
+        " Use [`LiveServer.jl`](https://github.com/tlienart/LiveServer.jl) to automatically update a local preview of your documentation (follow [this tutorial](https://github.com/tlienart/LiveServer.jl#serve-docs) )\n",
+        ":::\n",
+        "\n",
+        "### 4. Add a tutorial\n",
+        "Now add a tutorial `./docs/src/tutorial.md` which should contain a brief example simulating some data (using `rand`) and calculating mean, tstat and std on them.\n",
+        "\n",
+        "Use the `makedocs(...page=)` keywordargument.\n",
+        "\n",
+        "----\n",
+        "\n",
+        "# Task 3: PkgTemplate.jl {#3}\n",
+        "Generate a package MySecondStatsPackage using PkgTemplate. \n",
+        "\n",
+        "- Add github-actions for:\n",
+        "    - coverage\n",
+        "    - unittests\n",
+        "    - docs\n",
+        "- MIT license\n",
+        "- README.md\n",
+        "\n",
+        "::: callout-tipp\n",
+        "   Don't forget to activate the github-page in the github settings!\n",
+        ":::"
+      ],
+      "id": "7ec915e0"
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "name": "python3",
+      "language": "python",
+      "display_name": "Python 3 (ipykernel)"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

material/3_wed/linalg/slides.qmd

@@ -1,7 +1,11 @@
+---
+
+---
 ## Slides
 
 The slides are available [in pptx format here](Julia_Matrices_Optimization_JuMP_Stuttgart2023.pptx). Note that there are a few extra slides in case you are motivated to learn more!
 
 ## Exercise
 
-The exercise is available [as a jupyter-notebook here](Julia_Matrices_Optimization_JuMP_Stuttgart2023.ipynb). 
+The exercise is rendered [as html here](Julia_Matrices_Optimization_JuMP_Stuttgart2023.ipynb) but can also be downloaded {{< downloadthis Julia_Matrices_Optimization_JuMP_Stuttgart2023.ipynb label="Download as ipynb" >}}
+ 

material/3_wed/vis/handout.qmd

@@ -229,7 +229,18 @@ data(penguins) * mapping(:bill_length_mm, :bill_depth_mm, color=:species) * (lin
 data(penguins) * mapping(:bill_length_mm, :bill_depth_mm, color=:species) * (smooth() + visual(Scatter)) |> draw
 ```
 
+### Advanced
+
+```julia
+h = data(penguins) * mapping(:bill_length_mm, :bill_depth_mm, color=:species) * (smooth() + visual(Scatter)) |> draw
+h.grid
+ax = h.grid[1,1].axis
+ax + tab -> ax.xticks
+h
+```
 
 ## Task 3
 
-[Click here for the next task](tasks.qmd#3)
+[Click here for the next task](tasks.qmd#3)
+
+df = CSV.read(download("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-04-11/egg-production.csv"),DataFrame)

material/3_wed/vis/tasks.qmd

@@ -158,4 +158,40 @@ PlutoExtras.BondTable([
 ```
 :::
 
-# Task 3: AlgebraOfGraphics
+# Task 3: AlgebraOfGraphics
+
+For this task we need a dataset, and I choose the US EGG dataset for it's simplicity for you.
+
+to load the data, use the following code
+```julia
+using DataFrames, HTTP, CSV
+# dataset via https://github.com/rfordatascience/tidytuesday/tree/master
+df = CSV.read(download("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-04-11/egg-production.csv"),DataFrame)
+```
+
+:::callout-tip
+## If you dislike Pluto.jl
+If you dont like to use Pluto.jl, you can of course switch back to VSCode. Then you have to create a new environment and add the packages you use before.
+:::
+
+
+## 🥚 vs. 🗓
+Visualize the number of eggs against the year
+
+:::callout-tip
+To get a first overview, `first(df)` , `describe(df)` and `names(df)` are typically helpful
+:::
+
+## Split them up
+Next split them up, choose `color` and `col` and choose reasonable columns from the dataset
+
+## Rotate the labels
+Use the trick from the handout to modify a plot after it was generated: Rotate the x-label ticks by some 30°
+
+:::callout-tip
+instead of rotating each axis manually, you can also replace the `draw` command in your pipeline with an anonymous function. This allows you to specify additional arguments e.g. to the axis, for all "sub"-plots
+```julia
+... |> x-> draw(x;axis=(;xlims = (-3,2))) # <1>
+```
+1. Note the `;` before xlims, this enforces that a `NamedTuple` is created
+