# 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 e.g. `fun.(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