Monthly Archives: December 2016

Why you should master R (even if it might eventually become obsolete)

December 27, 2016
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In last week’s blog post I asked How much data science do you actually remember? It’s a critical question. If you study data science, but forget everything that you learn, you’ll be in big trouble when you go in for an interview. Or, you’ll be in big trouble if you actually get a data science The post Why you...

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Spatial analysis pipelines with simple features in R

December 27, 2016
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In November, the new simple features package for R sf hit CRAN. The package is like rgdal, sp, and rgeos rolled into one, is much faster, and allows for data processing with dplyr verbs! Also, as sf objects are represented in a much simpler way than sp objects, it allows for spatial analysis in R within magrittr pipelines. This post...

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Spatial analysis pipelines with simple features in R

December 27, 2016
By

In November, the new simple features package for R sf hit CRAN. The package is like rgdal, sp, and rgeos rolled into one, is much faster, and allows for data processing with dplyr verbs! Also, as sf objects are represented in a much simpler way than sp objects, it allows for spatial analysis in R within magrittr pipelines. This post...

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Start with wordcloud

Start with wordcloud

I followed my good resolutions on practising data analysis in my previous post and started to play with the French drug database. After importing the data, I started classically with data visualisation. In this database, there is a lot of text data. To visualise this, some wordcloud is always welcome. They are maybe not accurate at all but are from...

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Unchained…

December 26, 2016
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A personal journey in actuarial forecasting... Continue reading →

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Data Preparation, Long Form and tl;dr Form

December 26, 2016
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Data Preparation, Long Form and tl;dr Form

Data preparation and cleaning are some of the most important steps of predictive analytic and data science tasks. They are laborious, where most of the errors are made, your last line of defense against a wild data, and hold the biggest opportunities for outcome improvement. No matter how much time you spend on then, they … Continue reading Data...

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The Basics of Bayesian Statistics

December 26, 2016
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Bayesian Inference is a way of combining information from data with things we think we already know. For example, if we wanted to get an estimate of the mean height of people, we could use our prior knowledge that people are generally between 5 and 6 feet tall to inform the results from the data we collect. If our...

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Descriptive Analytics-Part 5: Data Visualisation (Spatial data)

December 25, 2016
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Descriptive Analytics-Part 5: Data Visualisation (Spatial data)

Descriptive Analytics is the examination of data or content, usually manually performed, to answer the question “What happened?”. In order to be able to solve this set of exercises you should have solved the part 0, part 1, part 2,part 3, and part 4 of this series but also you should run this script which

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Building Shiny App exercises part 3

December 25, 2016
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Building Shiny App exercises part 3

ADD CONTROL WIDGETS Welcome to the third part of our series. In this part you will learn how to build and place inside your app the rest of the widgets which were mentioned in part 2. More specifically we will analyze: 1) helptext, 2) numericInput, 3) radioButtons, 4) selectInput, 5) sliderInput and 6) textInput. As

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Googly: An interactive app for analyzing IPL players, matches and teams using R package yorkr

December 25, 2016
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Googly: An interactive app for analyzing IPL players, matches and teams using R package yorkr

Presenting ‘Googly’, a cool Shiny app that I developed over the last couple of days. This interactive Shiny app was on my mind for quite some time, and I finally got down to implementing it. The Googly Shiny app is based on my R package ‘yorkr’ which is now available in CRAN. The R package … Continue reading Googly:...

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