Tag: packages

Feed: R-bloggers. Author: R Views. In previous posts, we covered how to run a Monte Carlo simulation and how to visualize the results. Today, we will wrap that work into a Shiny app wherein a user can build a custom portfolio, and then choose a number of simulations to run and a number of months to simulate into the future. A link to that final Shiny app is here and here is a snapshot: We will use RMarkdown to build our Shiny application by inserting runtime: shiny into the yaml header. This will alert the server (or our laptop) that ... Read More

Feed: R-bloggers. Author: rOpenSci - open tools for open science. R packages are widely used in science, yet the code behind them often does not come under scrutiny. To address this lack, rOpenSci has been a pioneer in developing a peer review process for R packages. The goal of pkginspector is to help that process by providing a means to better understand the internal structure of R packages. It offers tools to analyze and visualize the relationship among functions within a package, and to report whether or not functions’ interfaces are consistent. If you are reviewing an R package (maybe ... Read More

Feed: R-bloggers. Author: business-science.io - Articles. The goal is simple: to educate and empower future data scientists so they can help organizations gain data-driven results. This is why it was a no-brainer when the opportunity came up for Business Science to partner with Method Data Science, the go-to data science accelerator for aspiring data scientists. Now Method Data Scientists will get exclusive lectures from Business Science Instructors and have discounted access to Business Science University, the revolutionary online education platform for learning data science for business, along with instructor trainings as part of the Method Data Science accelerator program. This ... Read More

Feed: R-bloggers. Author: nsaunders. I love it when researchers take the time to share their knowledge of the computational tools that they use. So first, let me point you at Environmental Computing, a site run by environmental scientists at the University of New South Wales, which has a good selection of R programming tutorials. One of these is Making maps of your study sites. It was written with the specific purpose of generating simple, clean figures for publications and presentations, which it achieves very nicely. I’ll be honest: the sole motivator for this post is that I thought it would ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Kyle Weller. Did you know that you can execute R and Python code remotely in SQL Server from Jupyter Notebooks or any IDE? Machine Learning Services in SQL Server eliminates the need to move data around. Instead of transferring large and sensitive data over the network or losing accuracy on ML training with sample csv files, you can have your R/Python code execute within your database. You can work in Jupyter Notebooks, RStudio, PyCharm, VSCode, Visual Studio, wherever you want, and then send function execution to SQL Server bringing intelligence to ... Read More

Feed: R-bloggers. Author: filip Schouwenaars. The problem pkgdown is an R package that can create a beautifully looking website for your own R package. Built and maintained by Hadley Wickham and his gang of prolific contributors, this package can parse the documentation files and vignettes for your package and builds a website from them with a single command: build_site(). This is what such a pkgdown-generated website looks like in action. The html files that pkgdown generated are stored in a docs folder. If your source code is hosted on GitHub, you just have to commit this folder to GitHub, navigate ... Read More

Feed: All - O'Reilly Media. Author: James Furbush. Metal tubes (source: BrooklynJohn via Pixabay) Attend the Strata Data Conference to learn the skills and technologies of data engineering. As the the data space has matured, data engineering has emerged as a separate and related role that works in concert with data scientists. Ian Buss, principal solutions architect at Cloudera, notes that data scientists focus on finding new insights from a data set, while data engineers are concerned with the production readiness of that data and all that comes with it: formats, scaling, resilience, security, and more. Ryan Blue, a senior ... Read More

Feed: Databasejournal.com - Feature Database Articles. Author: . Typical big data applications have a load phase, where a snapshot of data is extracted from operational systems, transformed and cleaned, and then loaded into your big data appliance. Analysts and data scientists can then execute business analytics (BI) queries against the data store in search of patterns and potential marketing opportunities and cost savings. One disadvantage of this architecture is that your appliance becomes more like a large data warehouse, in that it does not contain the most up-to-date data. IBM now provides an option to configure its Db2 version 11 ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Kateryna Lytvynova. The best trained soldiers can’t fulfill their mission empty-handed. Data scientists have their own weapons — machine learning (ML) software. There is already a cornucopia of articles listing reliable machine learning tools with in-depth descriptions of their functionality. Our goal, however, was to get the feedback of industry experts. And that’s why we interviewed data science practitioners — gurus, really —regarding the useful tools they choose for theirprojects. The specialists we contacted have various fields of expertise and are working in such companies as Facebook and Samsung. Some of them represent AI startups ... Read More

Feed: R-bloggers. Author: Topics in R. Given the vast amount of R packages available today, it makes sense (at least to me, as a trained economist) to ask a simple yet difficult question: How much value has been created by all those packages? As all R stuff on CRAN is open-source (which is a blessing), there is no measureable GDP contribution in terms of market value that we can use to provide a quick answer. But all of us R users know the pleasant feeling, if not to say the excitement, of finding a package that provides exactly the functionality ... Read More