Monthly Archives: September 2019

Why not everyone who smokes develop cancer or who eats a lot develop fatty liver disease? Predicting diseases with machine learning

September 30, 2019
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Why not everyone who smokes develop cancer or who eats a lot develop fatty liver disease? Predicting diseases with machine learning

We are much better at handling diseases than 30 years ago. For example cancer survival rates are much higher now. The significant portion of this increase can be attributed directly to our ability to detect and diagnose cancer earlier. Also, use of insulin and other drugs to control blood glucose in diabetic patients reduced the risk of developing coronary...

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Create Rmarkdown Document with SAS Code and Output – SAS engine

September 30, 2019
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Create Rmarkdown Document with SAS Code and Output – SAS engine

Getting started - SAS Engine for Rmarkdown To get started using SAS as your statistical software/data processing “engine” take a look at the following article: http://www.ssc.wisc.edu/~hemken/SASworkshops/Markdown/SASmarkdown.html. Also read up on the SASmarkdown package https://cran.r-project.org/web/packages/SASmarkdown/. Display the current knitr engine The following Rmarkdown chunk shows the commands to see what are your current knitr engine settings. Be sure to put {r} after the 3 backticks...

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New package: GetQuandlData

September 30, 2019
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New package: GetQuandlData

Introduction Quandl is one of the best platforms for finding and downloading financial and economic time series. The collection of free databases is solid and I’ve used it intensively in my research and class material. But, a couple of things from the native package Quandl always bothered me: Multiple data is always returned in the wide (column oriented) format (why??); No local caching...

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EARL London 2019 Conference Recap

September 30, 2019
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EARL London 2019 Conference Recap

I had an awesome time at the Enterprise Applications of the R Language (EARL) Conference held in London in September, 2019. EARL reminded me that it is good to keep showing up at conferences. I entered and the first thing I heard was organisers at the table welcoming me “Damian is that you? Awesome to Article EARL London 2019...

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Meetup Recap: Survey and Measure Development in R

September 30, 2019
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Meetup Recap: Survey and Measure Development in R

Have you ever taken a survey at the doctor or for a job interview and wondered what exactly that data was used for? There is a long-standing series of methodologies, many coming from psychology, on how to reliably measure “latent” traits, such as depression or loyalty, from self-report survey data.  While measurement is a common method in

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Cleaning Anomalies to Reduce Forecast Error by 9% with anomalize

September 29, 2019
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Cleaning Anomalies to Reduce Forecast Error by 9% with anomalize

In this tutorial, we’ll show how we used clean_anomalies() from the anomalize package to reduce forecast error by 9%. R Packages Covered: anomalize - Time series anomaly detection Cleaning Anomalies to Reduce Forecast Error by 9% We can often...

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More models, more features: what’s new in ‘parameters’ 0.2.0

September 29, 2019
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More models, more features: what’s new in ‘parameters’ 0.2.0

The easystats project continues to grow, expanding its capabilities and features, and the parameters package 0.2.0 update is now on CRAN. The primary goal of this package is to provide utilities for processing the parameters of various statistical ...

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Fall & Winter Workshop Roundup

September 29, 2019
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Fall & Winter Workshop Roundup

Join RStudio at one of our Fall and Winter workshops! We’ll be hosting a few different workshops in a variety of cities across the US and UK. Topics range from building tidy tools, to teaching data science, to mastering machine learning. See below for more details on each workshop and how to register. Building Tidy Tools with Hadley Wickham When:...

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Understanding Bootstrap Confidence Interval Output from the R boot Package

September 29, 2019
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Understanding Bootstrap Confidence Interval Output from the R boot Package

Nuances of Bootstrapping Most applied statisticians and data scientists understand that bootstrapping is a method that mimics repeated sampling by drawing some number of new samples (with replacement) from the original sample in order to perform inference. However, it can be difficult to understand output from the software that carries out the bootstrapping without a more nuanced understanding of how...

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More models, more features: what’s new in ‘parameters’ 0.2.0

September 29, 2019
By
More models, more features: what’s new in ‘parameters’ 0.2.0

The easystats project continues to grow, expanding its capabilities and features, and the parameters package 0.2.0 update is now on CRAN. The primary goal of this package is to provide utilities for processing the parameters of various statistical ...

Read more »

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