Tag: regression

Feed: R-bloggers. Author: R on datawookie. Related To leave a comment for the author, please follow the link and comment on their blog: R on datawookie. R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more... If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook ... Read More

Feed: R-bloggers. Author: R on kieranhealy.org. Data Visualization: A Practical Introduction will begin shipping next week. I’ve written an R package that contains datasets, functions, and a course packet to go along with the book. The socviz package contains about twenty five datasets and a number of utility and convenience functions. The datasets range in size from things with just a few rows (used for purely illustrative purproses) to datasets with over 120,000 observations, for practicing with and exploring. A course packet is also included the package. This is a zipped file containing an R Studio project consisting of nine ... Read More

Feed: SAS Blogs. Author: Rick Wicklin. This article describes best practices and techniques that every data analyst should know before bootstrapping in SAS. The bootstrap method is a powerful statistical technique, but it can be a challenge to implement it efficiently. An inefficient bootstrap program can take hours to run, whereas a well-written program can give you an answer in an instant. If you prefer "instants" to "hours," this article is for you! I’ve compiled dozens of resources that explain how to compute bootstrap statistics in SAS. Overview: What is the bootstrap method? Recall that a bootstrap analysis enables you ... Read More

Feed: R-bloggers. Author: Jakob Gepp. We at STATWORX work a lot with R and we often use the same little helper functions within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier to share and improve those functions, if they are within a package. Up till the 24th December I will present one function each day from helfRlein. So, on the 12th day of Christmas my true love ... Read More

Feed: R-bloggers. Author: Sean Justice. Motivation for Project Around the time that I was selecting a topic for this project, my parents and my hometown found themselves in the path of a Category 1 hurricane. Thankfully, everyone was ok, and there was only minor damage to their property. But this event made me think about how long it had been since the last time my hometown had been in the path of a Category 1 hurricane. I also wanted to study trends in hurricane intensity over time to see if it corresponds to the popular impression that storms have grown ... Read More

Feed: R-bloggers. Author: Daniel Marcelino's Blog. I have several tables that I would like to load as a sole data frame. Derived functions from read. table () have a lot of convenient features, but it seems like there is a lot of steps in the implementation that would slow things down. The gain in performance of reading 29 CSV files (about 2.2 GB) shows quite different picture. While the parallelization process does bring some improvement considering the ‘user time’, i.e. the CPU time charged for the process execution at the machine level, the ‘elapsed time’, i.e. the ‘real’ elapsed time ... Read More

Feed: R-bloggers. Author: kjytay. I’m a casual NBA fan: I don’t have time to watch the games but enjoy viewing the highlights on Instagram/Youtube (especially Shaqtin’ A Fool!); I sometimes read game articles and analyses (e.g. Blogtable). Apart from the game being an amazing visual spectacle, it’s fun to drink in the deluge of stats that each game brings. I’m not even talking about advanced stats and “ABPRmetrics“: there’s something exciting about seeing how many different statistical records can be broken on a given night. As a data/stats person, I’ve been wanting to get my hands on NBA data and ... Read More

Feed: R-bloggers. Author: Jack Talboys. Unsurprisingly we see that the search term ‘Trump’ was most popular around November 2016, the same time as the US Presidential Election of that year where someone with the last name ‘Trump’ performed rather well. Maybe more surprising is how the searches for ‘Trump’ have been relatively low since the election, despite it feeling as if Donald has been a constant fixture in the news. It is possible to download this information as a CSV file, which could then be read into R and we could have some fun with it. Luckily for us, there ... Read More

Feed: R-bloggers. Author: Thinking inside the box. A new version 0.4.7 of RQuantLib reached CRAN and Debian. Following up on the recent 0.4.6 release post which contained a dual call for help: RQuantLib was (is !!) still in need of a macOS library build, but also experienced issues on Windows. Since then we set up a new (open) mailing list for RQuantLib and, I am happy to report, sorted that Windows issue out! In short, with the older g++ 4.9.3 imposed for R via Rtools, we must add an explicit C++11 flag at configuration time. Special thanks to Josh Ulrich ... Read More

Feed: R-bloggers. Author: eoda GmbH. Two years ago, we revealed something sensational: Santa Claus is a data scientist. The secret of his success does not lay in his hundred years of experience as a gift messenger, his flying reindeer or his faithful elves. It is his analytical skills that enables him to make Christmas to the most beautiful time of the year for children all over the world. Since then, a lot has happened in the world of data and algorithms and there are also new, amazing findings about Santa Claus. Is Santa Claus a regular guest at data science ... Read More