R-bloggers

Introduction Hello everyone! I’m excited to announce the release of our latest collaborative effort (R package), designed to make complex consumer price and inflation calculations a breeze: emWeightedCPI . Here I will Introduce you to what this package is about. What is emWeightedCPI? Our R package “emWeightedCPI” (hosted on github) ... [Read more...]

I had not planned to blog this (this is an incredibly time-crunched week for me) but CERT/CC and CISA made a big deal out of a non-vulnerability in R, and it’s making the round on socmed, so here we are. A security vendor decided to try to get ... [Read more...]

Our group has started using a new knowledge base system, so I have been writing up and revisiting some of my documentation. Here I am going to share a guide I wrote about citing R packages in academic writing. Which software to cite Let’s make a dist... [Read more...]

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The post Extract values from vector in R: dplyr appeared first on Data Science Tutorials Unravel the Future: Dive Deep into the World of Data Science Today! Data Science Tutorials. Extract values from vector in R, we will delve into extracting specific values from a vector using the nth, first, ... [Read more...]

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Introduction Today, we’re diving into a useful new function from the TidyDensity R package: check_duplicate_rows(). This function is designed to efficiently identify duplicate rows within a data frame, providing a logical vector that flags each ... [Read more...]

Motivation One of the most common comments I hear is that logistic regression (also called Binomial regression) is some kind of “advanced magic”, “machine learning”, “artificial intelligence” or “big data”. This is not true. In this post, I will ... [Read more...]

Introduction Today, we’re diving into a useful new function from the TidyDensity R package: check_duplicate_rows(). This function is designed to efficiently identify duplicate rows within a data frame, providing a logical vector that flags each ... [Read more...]

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Introduction In data analysis, especially when dealing with multiple samples or distributions, ensuring comparability and removing biases is crucial. One powerful technique for achieving this is quantile normalization. This method aligns the dis... [Read more...]

Introduction In data analysis, especially when dealing with multiple samples or distributions, ensuring comparability and removing biases is crucial. One powerful technique for achieving this is quantile normalization. This method aligns the dis... [Read more...]

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The R Consortium’s working group R Submissions Working Group is spearheading an innovative approach to clinical trial data sharing, according to a feature in Nature. This initiative, led by Eric... The post Enhancing Clinical Trial Data Sharing with R Consortium’s R Submissions Working Group appeared first on R ... [Read more...]