# Rstats

## Slicing the onion 3 ways- Toy problems in R, python, and Julia

Between writing up my thesis, applying to jobs hire me! I’m quite good at programming, and the ongoing pandemic, I don’t really have time to write full blogposts. I have however decided to brush up my python skills and dive headfirst into Julia. As such, I like to answer the toy problems posted at fivethirtyeight’s riddler weekly. These will likely be even a few weeks late but it motivates me to tend to the blog and keep up my programming.

## The Riddler - June 26th

#for working with polygons library(sf) library(sfheaders) library(tidyverse) library(gtools) set.seed(22081992) Riddler Express This weeks express deals with an erratic driver: In Riddler City, the city streets follow a grid layout, running north-south and east-west. You’re driving north when you decide to play a little game. Every time you reach an intersection, you randomly turn left or right, each with a 50 percent chance. After driving through 10 intersections, what is the probability that you are still driving north?

## #TidyTuesday - Building Stalk Portfolios with R

Every Tuesday, the R4DataScience community posts a dataset online as part of #TidyTuesday as practice wrangling and modelling data. For the week of 5th May 2020, the dataset concerned the video game Animal Crossing. Intro Radiohead - How I Made My Millions I don’t play Animal Crossing (unfortunately Nintendo Switches sold out as the UK went into lockdown), but it seems that everyone around me does so I’ve become fascinated by how it has created almost a surrogate life for people, performing manual tasks to pay off loans to Tom Nook, the nefarious bankster of the player’s island.

## An Introduction to Modelling Soccer Matches in R (part 2)

I wrote this one pretty quickly compared to part 1 (which goes much deeper into mathematical concepts), and only realized after how much of a similarity it has to many of Ben Torvaney’s posts on the subject. This probably isn’t a coincidence given how much I’ve used his work previously in posts on this blog. Any imitation here is meant as flattery. The purpose of this post is really as a bridge between what I really want to write about- the maths behind the models in part 1, and extensions of these models into other distribution in parts 3-n so it might be a little derivative of stuff written elsewhere.

## Advent Calendar of Football Trivia Analyses

One of the most consistent fonts of posts on this blog is The Guardian’s football trivia page The Knowledge. A particular reason for this is that the small contained questions lend themselves to small blogposts that I can turn around in an hour or two, as opposed to being endlessly redrafted until I lose interest. However, I still sometimes don’t quite get round to finishing some of these posts, or have trouble justifying a blog post on a very small and ‘trivial’ answer to a question.

## R-inforcement Learning Part One- Tic Tac Toe

I’m extremely biased, but to me, one of the real success* stories in neuroscience over the last (just over) two decades has been in studying reward signals. Since the seminal 1997 paper, a lot of work has gone into figuring out how the brain assigns value to outcomes. *ugh, maybe. This isn’t a blog post about that My PhD project looks at novel ways of eliciting valuation behaviour to study these signals, but as a key part of the modelling involved in this work, it’s important to get a handle on reinforcement learning.

## Predicting the Unpredictable- Analysing Rowing in Cambridge pt. 1

In my free time away from PhD and data science work, I (used to) enjoy rowing. Aside from obvious benefits like socialising, providing a (very intense) workout, seeing the outdoors at least a few times a week… there are really two things that I love(d) about rowing: It’s the sport that is closest to a simple engineering problem. Going fast basically boils down to how in time and how hard you can get 1-8 guys to move an oar through the water.

## Scraping Dynamic Websites with PhantomJS

For a recent blogpost, I required data on the ELO ratings of national football teams over time. Such a list exists online at eloratings.net and so in theory this was just a simple task for rvest to read the html pages on that site and then fish out the data I wanted. However, while this works for the static websites which make up the vast majority of sites containing tables of data, it struggles with websites that use JavaScript to dynamically generate pages.

## The Guardian Knowledge June 2019

Most Wednesday’s I enjoy reading The Knowledge blog on the Guardian’s website and reading the football trivia therein. When time (and questions) allow, I like to answer some of the questions posed, example of which are here, here, and here. League of Nations The first question comes from Which player had the nationality with the lowest FIFA World Ranking at the time of him winning the Premier League? — The Tin Boonie (@TheTinBoonie) June 18, 2019 a similar question is also answered in this weeks column:

## Riddler 1st February 2019

Riddler Classic In my spare time I enjoy solving 538’s The Riddler column. This week I had a spare few hours waiting for the Superbowl to start and decided to code up a solution to the latest problem to keep me busy. The question revolves around a card game in which whatever choice a player makes, they are likely to lose to a con artist. Formally this is phrased as: