gvdr

# This file is machine-generated - editing it directly is not advised

julia_version = "1.9.4"
manifest_format = "2.0"
project_hash = "854267dae88bd3bbd6557a02d62085af68e79521"

[[deps.AbstractFFTs]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "d92ad398961a3ed262d8bf04a1a2b8340f915fef"
uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"

gvdr

### A Pluto.jl notebook ###
# v0.12.4

using Markdown
using InteractiveUtils

# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
    quote
        local el = $(esc(element))

gvdr

library(tidyverse)
library(readxl)
library(janitor)
library(ggridges)

# let's download the table
download.file("https://www.istat.it/it/files//2020/03/Tavola-sintetica-decessi.xlsx",
              "Tavola-sintetica-decessi.xlsx")

# we prefer to work with clean names

gvdr

# I'd live to use the usual mathematical notations to do summatories and productories
# turns out, in Julia it's pretty easy as long as we keep things simple
# as we can just embellish the already defined sum() and prod() function
# only tweak is choosing a negative step if we want to sum from i = N to M with N>M

# for the summatory:
function ∑(f::T;from::Int, to::Int) where T <: Function
    step_sum = from > to ? 1 : -1
    sum(safer(f), range(from, stop = to, step = step_sum))
end

gvdr

using StatsBase, Random, Distributions
using Plots


# parameters for the simulations
blocks = 70000; # Number of ticket blocks
tickets = 7000000; # Number of tickets
draws = 200; # Number of prizes
K = 100000; # Number of lotteries to simulate at each replication
R = 100; # Number of replications

gvdr

Keybase proof

I hereby claim:

• I am gvdr on github.
• I am gvdr (https://keybase.io/gvdr) on keybase.
• I have a public key ASBW8zEL_gVtBeSjRwk9_S8_4Xi1x1vlzFwdUc0JDHZDSwo

To claim this, I am signing this object:

gvdr

Diversity in Birth Canal across the world

A quick exploratory analysis of the dataset collected by BEtti and Manica (Betti L, Manica A (2018) Human variation in the shape of the birth canal is significant and geographically structured. Proceedings of the Royal Society B 285(1889): 20181807.)

We perform classic multidimensional scaling and contrast it with the aggregate means by Region and Population.

Let's load the tidyverse framework to wrangle data and plot it

gvdr

Data Wrangling Stack
--------------------

In this course we will use:

- R as default programming language
  -  Tidyverse as the R dialect of choice

- The shell commands (bash or zsh), through the terminal

gvdr

# install.packages("tidyverse") # If not yet installed, run this
library(tidyverse) # Everything will be don in a tidyverse fashion

# This is the kind of dataframe I think Roberta is dealing with.
# Vitd is an integer
# Age is a numeric
# We first need to cut the numeric age into a factor.
roberta_df <- tibble(
  Age = as.integer(runif(100,10,100)), # Age, as an integer
  Vitd = as.integer(runif(100,80,140)) # Vitd, as an integer

gvdr

#' let's have a matrix

n <- 2
m <- 15
my_mat <- matrix(runif(n),m,n)

#' let's name those rows, they will be our observations

row.names(my_mat) <- letters[1:m]