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Webscraping Tutorial

Webscrape /*! * * Twitter Bootstrap * */ /*! * Bootstrap v3.3.7 (http://getbootstrap.com) * Copyright 2011-2016 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) */ /*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */ html { font-family: sans-serif; -ms-text-size-adjust: 100%; -webkit-text-size-adjust: 100%; } body { margin: 0; } article, aside, details, figcaption, figure, footer, header, hgroup, main, menu, nav, section, summary { display: block; } audio, canvas, progress, video { display: inline-block; vertical-align: baseline; } audio:not([controls]) { display: none; height: 0; } [hidden], template { display: none; } a { background-color: transparent; } a:active, a:hover { outline: 0; } abbr[title] { border-bottom: 1px dotted; } b, strong { font-weight: bold; } dfn { font-style: italic; } h1 { font-size: 2em; margin: 0.

Shiny app with Keras backend

In this post I will build a web aspp that allows a company to predict a car’s auction price using a simple deep learning model. I will do this by using a Keras/Tensorflow backend and an Rshiny Front end. You can find the app here ! Set-up Environment library(tidyverse) library(tensorflow) library(keras) install_keras(tensorflow = "1.12") Clean Data First, we clean data and tokenize the auction data in order to be able to process the string data.

Why are French Trains Late?

I have not been able to write for a while as the semester just started, but quite frankly that is a not an issue since no one other than me reads these posts :). Anyways, I wanted to do this week’s tidy tuesday as it was about French train delays which I got to get accustomated to while living in France. library(tidyverse) library(dynlm) library(gganimate) library(maptools) library(maps) library(lettercase) library(magrittr) library(ggfortify) library(pander) library(patchwork) library(kableExtra) trains_raw <- readr::read_csv("https://raw.

Tidy Tuesday Week 1

I will try to participate in Tidytuesday every week to practice my Tidyverse skills, so I will be posting the graphs here as well to track my experience! I tried to use pipes as much as I can this time around, since I am trying to become more of a functional programmer! If you are curious about Tidy Tuesday check-out the github repo! Let me know if you have any questions or suggestions!

Marriage and Divorce in Turkey - Divorce

This second part was a lot more fun! More data was available and I also got to experiment with the gganimate package which is extremely cool. Make sure to not scroll fast over all the graph since there are some gifs! As I said in the previous blog, this post is more focused on divorces and there was a little bit more data available (although I must admit the data is kind of noisy).

Marriage and Divorce in Turkey - Marriage

I was initially going to make this only one post but I realized that there is actually a lot of material to analyze and visualize so I decided to divide this post into two parts. Just like any marriage, part 1 of this series will focus on marriage and part 2 on divorce. I will not write much as the plots speak for themselves, I tried to include some more statistical analysis.

Poll Accuracy in Turkish Elections

I criticized Turkish election polls a lot in the past for multiple reasons, so it is not a big surprise that my first blog post is about the performance of pollsters. (On a side note, Turkey is a country, where all major news outlets shared an election poll of samplesize ~100 based only on one village in Turkey because that village had historicaly voted close to the national vote result…) Election polls in Turkey tend to be very opaque since they generally are ordered by private parties who then share only the final results of the polls without any details on the methodolgies.