Tux Machines
Posted by Roy Schestowitz on Dec 05, 2022
=> Security and Proprietary Leftovers | today's howtos
Last month, Christian Graul re-published "bReeze: Functions for Wind Resource Assessment" on CRAN. This is wonderful news: it gives R users a chance at the functionality which Python has in windpowerlib. I admit I am a bit late to the party--the original version was published way back in 2018, but there's no better time than the present to write a new blog post to look at how R users can better understand wind potential tied to specific locations.
=> ↺ rOpenSci | Our First Peer-Reviewed Statistical R Packages!
rOpenSci is very excited to announce our first peer-reviewed statistical R packages!
One of rOpenSci’s core programs is software peer-review, where we use best practices from software engineering and academic peer-review to improve scientific software. Through this, we aim to make scientific software more robust, usable, and trustworthy, and build a supportive community of practitioners.
Historically, we have focused on R packages that manage the research data life cycle. Now, thanks to work over the past two years supported by the Sloan Foundation we also facilitate peer-review of packages that implement statistical algorithms.
=> ↺ How to make a plot with two different y-axis in R with ggplot2? (a secret ggplot2 hack)
I can’t tell you how painful it is to be better at something in Excel than in R. And one of the gripes I still have (10 years after making the switch from Excel to R) is that it’s still tough to make dual-axis plots in R.
=> ↺ How to make your own #RStats Wrapped! | Nicola Rennie
Forget about Spotify Wrapped and make your own #RStats Wrapped instead! This blog post will show you how to find your most used functions and make a graphic with {ggplot2}!
=> ↺ Comparison of Partition Around Medoid R programming Implementations
Back in September 2016 I implemented the ClusterR package. One of the algorithms included in ClusterR was the ‘Partition Around Medoids’ (Cluster_Medoids) algorithm which was based on the paper “Anja Struyf, Mia Hubert, Peter J. Rousseeuw, (Feb. 1997), Clustering in an Object-Oriented Environment, Journal of Statistical Software, Vol 1, Issue 4” (at that time I didn’t have access to the book of Kaufman and Rousseeuw, Finding Groups in Data (1990) where the exact algorithm was described), thus I implemented the code and compared my results with the output of the cluster::pam() function, which was available at that time. Thus, my method was not an exact but an approximate one. Recently, a user of the ClusterR package opened an issue mentioning that the results were not optimal compared to the cluster::pam() function and this allowed me to go through my code once again and also to compare my results to new R packages that were not existent at that time. Most of these R packages include a new version of the ‘Partition Around Medoids’ algorithm, “Erich Schubert, Peter J. Rousseeuw,”Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms” 2019, ”.
=> ↺ Episode 539: Adam Dymitruk on Event Modeling : Software Engineering Radio
Adam Dymitruk, CEO and founder of Adaptech Group, joins host Jeff Doolittle for an exploration of the event modeling approach to discovering requirements and designing software systems. Adam explains how the structured approach eliminates the specifics of implementation details and technology decisions, enabling clearer communication for all stakeholders while keeping conversations focused on the business opportunity. Using concrete examples of event modeling in practice, they examine event modeling in the context of other related approaches and methodologies, including event sourcing, event storming, CQRS, and domain-driven design.
=> ↺ How to perform TBATS Model in R
=> ↺ I Perl, Therefore I am | ology [blogs.perl.org]
And js/node/ts, python, etc., and even prolog! But perl is the best. :D
=> ↺ Day 5: Malware and Raku - Raku Advent Calendar
While Raku regex and tokens are meant to work on data structures (such as parsing and validating file types), they can help us to better understand malware. Malware, as any other legit binary, have some signatures within. Some “file signatures” are widely used to blacklist those specific samples (the hashes), but the problem is that blacklisting hashes is not safe enough. Sometimes, the very same kind of malware could be slightly different in small details, and have many different samples related. In this case, apart from relying on dynamic detection (monitoring devices and alerting the user when something seems to be acting suspiciously), genes are also investigated.
Malware genes are pieces of the reversed code (such as strings) that are commonly seen in most or all the samples of a malware family. This sort of genes help researchers identify the malware family and contextualize the attacks , since this is relevant not only to try to put an end to the threat by executing the proper counterfeits in time, but also helps profiling and framing threat actors in some cases.
=> ↺ Day 1 (Advent of Code 2022)
Two years ago, I did part of Advent of Code 2020 using the Rust language. It was a lot of fun, so let's try it again!
=> ↺ Day 2 (Advent of Code 2022)
Left column is "their move": A means Rock, B means Paper, C means Scissors. Right column is "our move": X means Rock, Y means Paper, Z means Scissors.
Each line corresponds to a turn, and we must calculate the total score we get. Picking "Rock" gives 1 point, "Paper" gives 2 points, and "Scissors" gives 3. Losing the round gives 0 points, drawing gives 3, winning it gives 6.
=> ↺ Day 3 (Advent of Code 2022)
I'm not sure where the day 3 challenge is going, because the problem statement for the first part is kinda convoluted.
=> ↺ Day 4 (Advent of Code 2022)
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