One of the first things a traveller notices when going to Costa Rica is that almost every town or city is named after a Saint. Growing up in Costa Rica, I just assumed this was something true of all Latin American countries, a legacy of Spanish colonial times. California has San Jose, San Fransisco, etc. I thought it was like that from California to Argentina.
After some travelling in Latin America I realised this was simply not true.
Graphs to accompany my poster presentation.
The data set The generated data is plotted below: Metropolis Hastings Algorithm This visual explanation of the Metropolis Hastings algorithm shows the proposal and the true density along with the Metropolis Hastings step. It was inspired and adapted from code by Balazs Torok.
Diagnostic Plots for Beta 2 via Gibbs Sampler Autocorrelation Plot The autocorrelations are negligable, suggesting good mixing.
Running Means Plot The running means have settled.
Three chain trace plot All three chains seem to be in the same place.
This week I had an interesting conversation (as interesting as these conversations can get) about the use of spreadsheets in business. I am still a little bit surprised by how often they are used for things they are not really good for. Now, I don’t want to write about how there are better alternatives to spreadsheets. Instead, I’d like to illustrate how powerful non-spreadsheet tools are by describing a task I completed this week using R.
It is a truism that we live in the information age, yet on a day to day basis we engage remarkably little with insights on the personal information we create. Sure, Netflix shows you films you want to see, Amazon offers books you want to buy and Facebook shows you pictures of cats with boobs or whatever it is you tend to click on, but explicit purpose of that is to get your money. What about using all that data to gain insights on who you are, who your friends are, what you tend to talk about?
This year has been an adventure. I've been on three continents, countless countries and dozens of cities. I thought it would be an interesting project to quantify it all by visualising on a map the places where I slept.
A few friends and I are involved in the Budapest public bicycle data science competition. As of right now we are in the top ten, although in all honesty, it hasn’t really started yet. We signed a non-disclosure agreement, so I cannot really share any cool details. What I can share however, is the function I wrote in R to get calculate the distances and duration of bike trips using Google Maps’ API.
#distance calucations #### This script uses RCurl and RJSONIO to download data from Google's API library(RCurl) library(RJSONIO) #define api key as: api<-"YOURAPI" #the function assumes address input in following dataframe: #lat1,lng1,lat2,lng2 DistDur <- function(address,time = as.
English orthography is a tricky topic. Most people learning English (and a great number of native speakers) are puzzled by a written language with an utter disregard for phonemic representation. It is therefore very worrying that individuals intuitively assume making orthographic mistakes is a sign of low intelligence or unprofessionalism. In this post we are going to show how we looked into the relationship of IQ and orthography using R. We are going to need two things: