DataScience4you2me

[This article was first published on #FunDataFriday - Little Miss Data , and kindly contributed to R-bloggers ]. (You can report issue about the content on this page here ) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. WHAT IS IT? #BlackInDataWeek is a free, online data conference taking place November 16-21, 2020. Straight from their website , #BlackInDataWeek is: > “ A week-long celebration to (1) highlight the valuable work and experiences of Black people in the field of Data, (2) provide community, and (3) educational and professional resources. ” WHY IS IT AWESOME? I can’t possibly list all of the reasons why this event is awesome. Just look at the description above, it’s full of goodness! The organizers have created a very exciting event structured to provide community, support and growth for Black people working in data. Also, the sessions are on fire! I’ve added nearly all of them to my calendar. They offer...

[This article was first published on JottR on R , and kindly contributed to R-bloggers ]. (You can report issue about the content on this page here ) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. I presented Future: Simple, Friendly Parallel Processing for R (65 minutes; 59 slides + Q&A slides) at New York Open Statistical Programming Meetup , on November 9, 2020: HTML (incremental Google Slides; requires online access) PDF (flat slides) Video (presentation starts at 0h10m30s, Q&A starts at 1h17m40m) I like to thanks everyone who attented and everyone who asked lots of brilliant questions during the Q&A. I’d also want to express my gratitude to Amada, Jared, and Noam for the invitation and making this event possible. It was great fun. – Henrik Links Relevant packages mentioned in this talk: future package: CRAN , GitHub future.apply package: CRAN , GitHub furrr package: CRAN , GitHub foreach...

[This article was first published on R on OSM , and kindly contributed to R-bloggers ]. (You can report issue about the content on this page here ) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. In our last post , we took our analysis of rolling average pairwise correlations on the constituents of the XLI ETF one step further by applying kernel regressions to the data and comparing those results with linear regressions. Using a cross-validation approach to analyze prediction error and overfitting potential, we found that kernel regressions saw average error increase between training and validation sets, while the linear models saw it decrease. We reasoned that the decrease was due to the idiosyncrasies of the time series data: models trained on volatile markets, validating on less choppy ones. Indeed, we felt we should trust the kernel regression results more than the linear ones precisely because those results followed the commo...

[This article was first published on R – Xi'an's Og , and kindly contributed to R-bloggers ]. (You can report issue about the content on this page here ) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. An intriguing question made a short-lived appearance on the CodeGolf section of Stack Exchange, before being removed , namely the (most concise possible) coding of an arithmetic derivation of the square root of an integer, S, with a 30 digit precision and using only arithmetic operators. I was not aware of the myriad of solutions available, as demonstrated on the dedicated WIkipedia page . And ended playing with three of them during a sleepless pre-election night! The first solution for finding √S is based on a continued fraction representation of the root, with a²≤S and r=S-a². It is straightforward to code-golf: while((r<-S-T*T)^2>1e-9)T=(F<-2*T+r/(2*T+F))-T;F but I found it impossible to reach the 30 dig...

[This article was first published on business-science.io , and kindly contributed to R-bloggers ]. (You can report issue about the content on this page here ) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. This article is part of a R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do common R coding tasks. Learn how to use pivot_wider() and pivot_longer() to format data like a data wizard: Get the Code: GitHub Link Video Tutorial: YouTube Tutorial (Click image to play tutorial) Why Pivot Wider? Pivoting wider is essential for making summary tables that go into reports & help humans (like you and me) understand key information. Let’s say we have some automobile manufacturer data that we want to format into a table that people can read. We can summarize and pivot the data by manufacturer and class to understand the number of vehicle classes that each manufacturer produces. The result i...

[This article was first published on R Views , and kindly contributed to R-bloggers ]. (You can report issue about the content on this page here ) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. This week, the Bay Area useR Group (BARUG) held a mini-conference focused on ROC Curves. Talks discussed the history of the ROC, extending ROC analysis to multiclass problems, various ways to think about and interpret ROC curves, and how to translate concrete business goals into the ROC framework, and pick the optimal threshold for a given problem. Some History I introduced the session with a very brief eclectic “history” of the ROC anchored on a few key papers that seem to me to represent inflection points in its development and adoption. Anecdotal accounts of early ROC such as this brief mention in Deranged Physiology make it clear that Receiver Operating Characteristic referred to the ability of a radar technician, sitting at at ...

[This article was first published on R & Census , and kindly contributed to R-bloggers ]. (You can report issue about the content on this page here ) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Benford’s law attracted lots of attention after the 2020 election, as some people attempted to use this law to question the integrity of the election. Benford’s law states that the frequency of the first digit of a large set of numerical data follows this rule: 30.1% are 1s, 17.6% are 2s, 12.5% are 3s, …, 4.6% are 9s. Therefore it can be used to detect data manipulation that leads to the violaton of the law. I am not going to repudiate those “findings” in the election data but rather provide some examples using census data to show when this law holds and when you should not expect it to work. It is well known that this law works best when the data spread several orders of magnitude and not so well when the numbers are in a narrow r...

[This article was first published on Swimming + Data Science , and kindly contributed to R-bloggers ]. (You can report issue about the content on this page here ) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Today we’re going to use SwimmeR to build a large database of results – that’s the overall goal. Having a more specific goal in mind can be useful though, so here goes. My wife is a college administrator. It’s part of her job to keep up on what Kids These Days (TM) are doing and one thing they’re apparently doing is getting participation trophies. Looking back to my childhood I participated in a lot of swim meets, and swam a lot of races. For my trouble I’ve got piles of ribbons, enough medals to cover my entire head if I were to try and wear them all at once, and even a few plaques – but almost no trophies. My lack of trophies could be due to my modest swimming abilities, but I’ve checked with some of my faster friends...