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Giulio Valentino Dalla Riva gvdr

  • School of Mathematics and Statistics, University of Canterbury
  • Christchurch, New Zealand
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gvdr / do_the_mfob
Last active Dec 19, 2015
A Markovian trait based fields of bullets method. Based on dendropy and random, mfob is a small module that permits a fast implementation of a fields of bullets model where the probability of extinction depends on a binary trait evolved from the root as a markov process.
View do_the_mfob
Created on Fri Jun 21 10:52:11 2013
@author: Giulio Valentino Dalla Riva
#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):
gvdr / proc_OSS.jl
Created Apr 22, 2015
Compute the ordinary Procrustes sum of squares of two matrices in Julialang. All credits to procOPA {shapes}
View proc_OSS.jl
#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
#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)
gvdr / matrices_dataframe.R
Created Jul 1, 2016
How to handle a matrix in a dataframe. How I do it?
View matrices_dataframe.R
#' 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]
gvdr / Roberta_Grouping.R
Created Oct 25, 2017
How to group by numeric variables in a dataframe and compute percentiles
View Roberta_Grouping.R
# 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 / install_notes.txt
Created Jul 18, 2018
installation notes for DATA201 and DATA422
View install_notes.txt
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 /
Last active Oct 25, 2018
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)

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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View lottery_ferno.jl
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
View notations_operators.jl
# 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))
View death_rate_variation.R
# let's download the table
# we prefer to work with clean names