Background Libraries Open Data Historical Job Postings Job Ontologies Prototyping Munging Extracting the terms Extracting the relevant adverts Extracting related skills Conclusions Creating a database Populating our database Using the database Functions for querying Getting the jobid’s Trying it out dbplyr and SQL syntax Turning it all into an ...

Background Libraries Open Data Historical Job Postings Job Ontologies Prototyping Munging Extracting the terms Extracting the relevant adverts Extracting related skills Conclusions Creating a database Populating our database Using the database Functions for querying Getting the jobid’s Trying it out dbplyr and SQL syntax Turning it all into an ...

Background The Application, the Model and the Data Application Model Data Fitting using nonlinear least squares (NLS) with the nls.multstart package Fitting a single curve Fitting all the cuves Fits NLS Summary Fitting using frequentist multilevel modelling (nlme) Fitting the model to everyone Fits NLME Summary Bayesian multilevel modelling ...

Background The Application, the Model and the Data Application Model Data Fitting using nonlinear least squares (NLS) Fitting a single curve Fitting all the cuves Fits NLS Summary Fitting using frequentist multilevel modelling (nlme) Fitting the model to everyone Fits NLME Summary Bayesian multilevel modelling using MCMC with brms Modelling ...

Background Readability Formulas The Data Set Setup Packages Reading in the data Audience reactions Calculating readability scores Cleaning Again Exploration Views per day Changes over Time Viewership Topics Audience Laughter Engagement Conclusions Background This analysis came about because the dataset was the topic of the Stockholm R User Group (SRUG) ...

Background Readability Formulas The Data Set Setup Packages Reading in the data Audience reactions Calculating readability scores Cleaning Again Exploration Views per day Changes over Time Viewership Topics Audience Laughter Engagement Conclusions Background This analysis came about because the dataset was the topic of the Stockholm R User Group (SRUG) ...

TL;DR Background Getting Data Analysis Sleep Depth Extracting the data Cleaning and Analysis Plotting Conclusions Heart Rate Data Extracting the data During the defence Heart Rate Distributions Waking Heart Rate Distributions Conclusions Sleeping Heart Rate Distributions Conclusions Summary TL;DR I recently defended my PhD thesis, and it was ...

TL;DR Background Getting Data Analysis Sleep Depth Extracting the data Cleaning and Analysis Plotting Conclusions Heart Rate Data Extracting the data During the defence Heart Rate Distributions Waking Heart Rate Distributions Conclusions Sleeping ...