summerschool_simtech_2023/material/3_wed/vis/tasks.qmd

# Setting up Pluto.jl

Pluto is nice as you can prototype pretty fast.

::: callout-important
Pluto.jl has its own dependency management included!

If you want to add packages that are not registered, you have to activate your own environment. For example

```julia
using Pkg
Pkg.activate(mktempdir())
Pkg.add("/path/to/your/package/CoolPackage")
Pkg.add(url="https://github.com/username/MyPackage.jl")

using CoolPackage,MyPackage
```

::: 

To run pluto in the first place use:

``` julia
]add Pluto
Pluto.run()
```


# Task 1: Visualize some statistic properties {#1}

## 1. Data

### Generate 500 normally distributed samples

::: callout-tip
You might want to make your results reproducible by fixing some seeds for the random generators. The two most common random generators used in julia are `Random.MersenneTwister` and `StableRNGs.StableRNG` - For this execrise I would recommend the latter (even though MersenneTwister is much more common to be used), thus run:

``` julia
using StableRNGs
randn(StableRNG(1),100)
```

to get 100 random numbers.
:::

Scale the random numbers to fullfill `std(x) ≈ 10`

### functionize it
Next wrap that code in a function `simulate` which takes two arguments, a random seed and the number of samples

## 2. cumulative mean
Calculate the cumulative mean of a single simulation. save it to a variable

Note that there is no `cummean` function, but clever element-wise division in combination with `cumsum` should lead you there - or you just use a loop :shrug:

::: {.callout-tip collapse="true"}
## click to show solution

 `cumsum(x) ./ (1:length(x))` 
:::

## 3. Plotting!
Now for your first plot. Use a `scatter` plot^[after a `using CairoMakie`] to visualize the cummulative mean output, if you do not generate a `Figure()` + `ax = f[1,1] = Axis(f)` manually, you can get it back by the scatter call. `f,ax,s = scatter()`. This is helpful as we later want to extend the `Axis` and `Figure` with other plot elements

Use `hlines!` to add a horizontal line at your "true" value

## 4. Subplot
### simulate repeatedly
Let's simulate 1000x datasets, each with a  different seed, and take the mean over all simulated values

::: {.callout-tip collapse="true"}
## click to show tip
An easy way to call a function many times is to broadcast it on an array created e.g. via `1:1000` - you could also use `map` to do it, but I don't think it is as clear :)
:::



::: {.callout-tip collapse="true"}
## click to show solution

 `simulate.(1:1000,nmax)`
:::

### Mean it
calculate the mean of each simulation 

::: {.callout-tip collapse="true"}
## click to show solution

 ```julia
using Statistics
mean.(simulate.(1:1000,nmax))

# or 
sum.(...) ./ nmax
 ```
:::

### Add it as a subplot
We want to add a histogram of the 1000 means to the plot.

1. Add a new Axis to `f[1,2]`
2. use it to plot the histogram of the means via `hist!` - don't forget to change the `direction=:x` to flip the histogram
3. link the axes using `linkaxes`

## 5. Prettify it
There are some simple tricks to make a plot look nicer:

- remove the "box" using `hidespines!(ax,:r,:t)
- resize the right sub-plot to be smaller `colsize!` and `Relative(X)`
- hide the x-grid (type `ax.`+ `TAB` to find all possible attributes)
- hide the `xlabels` + `xticks` + `bottomspine` from the right subplot
- add two Labels `(A)` and `(B)` to the plot
- Bonus: use `color` to color the cummulative sum samples according to how many samples went into that sum. `colormap=:Reds` looks good to me!

::: {.callout-tip collapse="true"}
## Bonus: Click for more fancy labels

You can create a slightly fancier label by adding a circle around it :)
```julia
	Label(f[1,2,TopLeft()],"B",padding=[0,0,5,0])
	Label(f[1,2,TopLeft()],"⭕",padding=[0,0,8,0],fontsize=30)
```
:::

# Task 2: Interactivity! {#2}


Using the `Pluto.jl` reactive backend, changing a value in some cell will automatically update all other cells - including plots.

We can use Sliders instead of fixing the parameters of the simulation

A slider is defined like this:
```julia
@bind yourVarName PlutoUI.Slider(from:to) # from:step:to is optional, step by def 1
```

## Adding interactivity via sliders

1. Define a slider that controls the number of samples from 1:500
2. Define a second slider that adds a constant offset to all values of the simulation simulation
3. make sure to fix the x/y-limits to get a nice looking plot :-)

:::{.callout-tip collapse="true"}
## Bonus: Advanced slider management

After understanding the slightly awkward syntax, the following gives a nice collection of Sliders, Checkboxes, Widgets etc. with at the same time being drag-and-dropable and in a sidebar. Neat!

```julia
using PlutoExtras

PlutoExtras.BondTable([
    PlutoExtras.@BondsList "Sliders" let 
        "name A" = @bind(varA,PlutoUI.Slider(1:500))
        "name B" = @bind(varB, PlutoUI.Slider(-5:5))
    end
    ])
```
:::

# 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