RStudio Connect 1.6.8 includes additions to custom emails, new user endpoints in the RStudio Connect Server API, support for content descriptions and title changes, and important security and authentication improvements. Updates R Markdown Reports have access to environment variables containing metadata about the report on RStudio Connect. This addition is especially important for custom emails. In case you missed...
This report discusses some problems that can arise when attempting to import PostScript images into R, when the PostScript image contains coordinate transformations that skew ...
Here is the track link. Track Details In this track, you’ll learn the skills needed to get you up and running with data science in R using the tidyverse. The tidyverse is a collection of R packages that share a common design philosophy and are designed to seamlessly work together, all with novices to data science professionals equally benefiting. You’ll begin...
Despite having sailed through the core components of this year’s Talk Like A Pirate Day R post a few months ago, time has been an enemy of late so this will be a short post that others can build off of, especially since there’s lots more knife work ground to cover from the data. DMC-WhAt?... Continue reading →
In a web application, routing is the process of using URLs to drive the user interface. Routing adds more possibilities and flexibility while building a complex and advanced web application, offering dividing app into separated sections. New features Contributing to open source is incorporated into Appsilon mission. Last week we updated i18n internationalization package, now it’s Artykuł Official release of...
Customers lifetime value (LTV or CLV) is one of the cornerstones of product analytics because we need to make a lot of decisions for which the LTV is a necessary or at least very significant factor. In this article, we will focus on products/services/applications with recurring subscription paymentsCustomers lifetime value (LTV or CLV) is one The post LTV prediction...
Today we’re excited to announce Radix, a new R Markdown format optimized for scientific and technical communication. Features of Radix include: Reader-friendly typography that adapts well to mobile devices. Flexible figure layout options (e.g. displaying figures at a larger width than the article text). Tools for making articles easily citeable, as well as for generating Google Scholar compatible citation metadata. The ability to...
Registration is now open for my 1.5-day workshop on deep learning with Keras and TensorFlow using R. It will take place on November 8th & 9th in Munich, Germany. You can read about one participant’s experience in my workshop: Big Data – ...
I have yet another Meetup talk to announce: On Wednesday, October 26th, I’ll be talking about ‘Decoding The Black Box’ at the Frankfurt Data Science Meetup. Particularly cool with this meetup is that they will livestream the event at www.youtube....
![Le Monde puzzle [#1066]](https://xianblog.files.wordpress.com/2015/10/lemondapari.jpeg?w=450&h=299)
The second Le Monde mathematical puzzle in the new competition is sheer trigonometry: When in the above figures both triangles ABC are isosceles and the brown segments are all of length 25cm, find the angle in A and the value of DC², respectively. This could have been solved by R coding the various possible angles
Intro One of the features of functions of smooth package is the ability to work with intermittent data and the data with periodically occurring zeroes. Intermittent time series is a series that has non-zero values occurring at irregular frequency (Svetuknov and Boylan, 2017). Imagine retailer who sells green leap sticks. The demand on such a
I had a great time at the EARL Conference in London last week, and as always came away invigorated by all of the applications of R that were presented there. I'll do a full writeup of the conference later this week, but in the meantime I wanted to share the materials from my own presentation there, "Not Hotdog: Image...
CategoriesData Management Tags Data Manipulation R Programming Tips & Tricks Before running your data analysis, every data scientist needs to make data management, data cleaning and data selection. This can be done in very different ways. For the data scientist – this process is typically quite time comsuming and it does not bring data value in the same degree as AI analysis or data...
CategoriesData Management Tags Data Manipulation R Programming Tips & Tricks I must admit that data cleaning sometimes feels like the necessary data step before the fun and also much more value creating process: Analysis! But in every coding project that a data scientist is involved in, the first step is always to get a clear understanding of the dataset by descriptive statistics and to...
When I first started working as a data scientist (or something like it) I was told to program in C++ and Java. Then R came along and it was liberating, my ability to do data analysis increased substantially. As my applications grew in size and complexity, I started to miss the structure of Java/C++. At the time, Python felt...
Artificial intelligence (AI) has the potential to change industries across the board, yet few organizations are able to capture its value and realize a real return-on-investment. The reality is that the transition to AI and data driven analysis is diff...
We are thrilled to announce our (my and Nina Zumel’s) paper on the dot-pipe has been accepted by the R-Journal! A huge “thank you” to the reviewers and editors for helping us with this! You can find our article here (pdf here)!
We are pleased to announce a new demo Shiny application that uses machine learning to predict annual payments on individual insurance claims for 10 years into the future. This post describes the basics of the model behind the above Shiny app, and walks through the model fitting, prediction, and simulation ideas using a single claim as an example. Background Insurance is the...
Sharing data sets for collaboration or publication has always been challenging, but it’s become increasingly problematic as complex and high dimensional data sets have become ubiquitous in the life sciences. Studies are large and time consuming; data collection takes time, data analysis is a moving target, as is the software used to carry it out. In the vaccine space (where...
The wayback🔗 package has had an update to more efficiently retrieve mementos and added support for working with the Internet Archive’s advanced search+scrape API. Search/Scrape The search/scrape interface lets you examine the IA collections and download what you are after (programmatically). The main function is ia_scrape() but you can also paginate through results with the... Continue reading →
This post will investigate using Principal Components as part of a momentum strategy. Recently, I ran across a post from … Continue reading →
The breakDown package explains predictions from black-box models, such as random forest, xgboost, svm or neural networks (it works for lm and glm as well). As a result you gets decomposition of model prediction that can be attributed to particular variables. The version 0.3 has a new function break_down. It identifies pairwise interactions of variables. … Czytaj dalej Break...
Back in the “olden days” of the 1970s it was apparently not unknown for statisticians to create 3D visualizations using tinkertoys. For some inexplicable reason, the advent...
Here is a link to the podcast. Introducing Renée Teate Hugo: Hi there, Renée, and welcome to DataFramed. Renée: Hi Hugo. Great to be here. Hugo: It's great to have you on the show and I'm really excited to talk about all the things we're gonna talk about today, the podcast that you worked...
This blog post is one of a series highlighting specific images from my book Data Visualization: charts, maps and interactive … More
The shinydashboard provides a well-designed dashboard theme for Shiny apps, and allows for an easy assembly of a dashboard from a couple of basic building blocks. The package is widely used in commercial environments as well, due to its neat features for building convenient and robust layouts. This exercise set will help you practice all Related exercise sets: Spatial Data...
In the post (https://statcompute.wordpress.com/2018/09/15/how-to-avoid-for-loop-in-r), I briefly introduced the idea of vectorization and potential use cases. One might be wondering why we even need the Vectorize() function given the fact that it is just a wrapper and whether there is any material efficiency gain by vectorizing a function. It is true that the Vectorize() function is
Today I am very happy to announce that during my stay in London for the m3 conference, I’ll also be giving a talk at the R-Ladies London Meetup on Tuesday, October 16th, about one of my favorite topics: Interpretable Deep Learning with R, Keras and L...
This tutorial will introduce the Deep Learning classification task with Keras. With focus on one-hot encoding, layer shapes, train & evaluate the model.How to create a sequential model in Keras for R
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