Conferences like userR & EARL are the R events to attend every year and personally, and as a company, I can’t imagine skipping one. It’s an important place to be if you want to be up-to-date with the R technology and build up your presence in the community. Our ...

A vector is the most elementary way to store and structure data in R. For now, think of it as a list of numbers, which can be as short as a single number, or as long as about 2 billion(!) numbers. Perhaps you were used to working with lists of numbers ...

I recently came across a paper that explained the equality between the parameters of signal detection theory (SDT) and the parameters of logistic regression in which the state (“absent”/“present”) is used to predict the response (“yes”/“no”, but also applicable in scale-rating designs) (DeCarlo, 1998). Here is a short simulation-proof ...

I recently came across a paper that explained the equality between the parameters of signal detection theory (SDT) and the parameters of logistic regression in which the state (“absent”/“present”) is used to predict the response (“yes”/“no”, but also applicable in scale-rating designs) (DeCarlo, 1998). Here is a short simulation-proof ...

This week’s post follows directly from last week’s investigation of data from the 2016 US Census Bureau’s American Community Survey (ACS) Public Use Microdata Sample (PUMS). We explored mean differences in income across several different types of employment status (self-employed, private sector, government, etc.). We found, using bayesian ...

In this post we generate a PowerPoint report from a Shiny app. This is Part 2 from the 3-post series about generating reports with Shiny. Part 1 covered Excel Report Generation with Shiny. See part 1 for a quick intro and description of the Shiny app that can generate Excel, PowerPoint, and PDF ...

I recently came across a paper that explained the equality between the parameters of signal detection theory (SDT) and the parameters of logistic regression in which the state (“absent”/“present”) is used to predict the response (“yes”/“no”, but also applicable in scale-rating designs) (DeCarlo, 1998; DOI: 10.1037/1082-989X.3.2.186). Here is ...

In the last blog I outlined creating a model which predicts the fastest time for each driver in F1 qualifying. theparttimeanalyst.com/2019/07/10/predicting-f1-qualifying/ Today I am going to be dissecting the model to understands its strengths and weaknesses and to look if their is any bias within the model. ...

Leonardo Bonacci, better known as Fibonacci, has influenced our lives profoundly. At the beginning of the $13^{th}$ century, he introduced the Hindu-Arabic numeral system to Europe. Instead of the Roman numbers, where I stands for one, V for five, X for ten, and so on, the Hindu-Arabic numeral system uses ...

K-Means clustering is unsupervised machine learning because there is not a target variable. Clustering can be used to create a target variable, or simply group data by certain characteristics. Here’s a great and simple way to use R to find clusters, visualize and then tie back to the data ...

I was inspired by this visualisation, showing the optimal routes (by car) from the geographic centre of the USA to all counties. The proverb “All Roads Lead to Rome” immediately came to mind and I set out to hack together something along t...

I was inspired by this visualisation, showing the optimal routes (by car) from the geographic centre of the USA to all counties. The proverb “All Roads Lead to Rome” immediately came to mind and I set out to hack together something along that theme. This is what was required: Find ...

I was inspired by this visualisation, showing the optimal routes (by car) from the geographic centre of the USA to all counties. The proverb “All Roads Lead to Rome” immediately came to mind and I set out to hack together something along that theme. This is what was required: Find ...