gvdr

"""
Created on Fri Jun 21 10:52:11 2013

@author: Giulio Valentino Dalla Riva
@email: gvd16@uclive.ac.nz
"""
#generate a tree list following a birth and death process
#the process is conditioned to N leaves (with success)
#the list is long rang
def generate_trees_bdN(birth=0.57721,death=0.130357,N=49,rang=314):

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#This is just a quick translation of an ordinary Procrustes sum of squares method, as done in the R function procOPA.
#All credits to Dryden, I. L. (2014). shapes package. R Foundation for Statistical Computing, Vienna, Austria. Contributed package. Version 1-1.10. URL http://www.R-project.org

#The function are, for now, as they are. I'll be working on them in the future...

#Compute a matrix with ones on the main diagonal
#and -1/n elsewhere
function scaled_ones(n)
    eye(n) - (1/n) * ones(n,n)
end

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#' 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]

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# 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

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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

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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

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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

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

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# 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

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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