lumberjack 1.2.0 is on CRAN and paper accepted by JSS
lumberjack is a package that allows you to track (log) changes in data while an R script is running. This allows you to detect exactly which code had what effect...continue reading.
lumberjack is a package that allows you to track (log) changes in data while an R script is running. This allows you to detect exactly which code had what effect...continue reading.
We’re ecstatic to announce the release of googlesheets4 0.2.0 on CRAN: googlesheets4.tidyverse.org googlesheets4 is a package to work with Google Sheets from R. Although version 0.1.0 debuted on CRAN in...continue reading.
Representing words in a numerical format has been a challenging and important first step in building any kind of Machine Learning (ML) system for processing natural language, be it for...continue reading.
tinytest is a relatively new, light-weight (no-dependency) but full-featured unit testing framework for R. It is currently used by 60+ packages, including the famous Rcpp package. The latest version of...continue reading.
R is not only good for analysing and visualizing data, but also for solving maths problems or comparing data with each other. Plus you can use it just like a...continue reading.
COVID-19 Data Forum Webinar – Next Thursday (5/14/20) at Noon Pacific time, the COVID-19 Data Forum, sponsored by R Consortium and the Stanford Data Science Institute will open with a...continue reading.
Background I’ve always found it a bit of a pain to explore and choose from all the different themes available out there for {ggplot2}. Yes I know, I know -...continue reading.
This tutorial is part of a series of R tutorials analyzing covid-19 data. For parts 1 and 2, see the following posts: https://www.sharpsightlabs.com/blog/r-data-analysis-covid-19-part1-data-wrangling/ https://www.sharpsightlabs.com/blog/r-data-analysis-covid-19-part-2-merge-datasets/ Covid19 analysis, part 3: initial data...continue reading.
Read this to find out about a maths game for home schooling, including some probability in R!continue reading.
Table of Contents Is it possible to build a video game in R Shiny? Concept: Card Swiping Project Structure: CSS, JavaScript, and R6 Classes Final Result Resources Is It Possible...continue reading.
How can you frame a data science question according to your client’s needs? In this blog post, our colleague Dominique explains how important it is to think about the business...continue reading.
A lot has been happening in the tidymodels ecosystem lately! There are many possible projects we on the tidymodels team could focus on next; we are interested in gathering community...continue reading.
This post is the latest in a series of post leading up the the dplyr 1.0.0 release on May 15. So far, the series has covered: Major lifecycle changes. New...continue reading.
A new version of sparklyr is now available on CRAN! In this sparklyr 1.2 release, the following new improvements have emerged into spotlight: A registerDoSpark() method to create a foreach...continue reading.
A lot has been happening in the tidymodels ecosystem lately! There are many possible projects we on the tidymodels team could focus on next; we are interested in gathering community...continue reading.
A new version of sparklyr is now available on CRAN! In this sparklyr 1.2 release, the following new improvements have emerged into spotlight: A registerDoSpark() method to create a foreach...continue reading.
May 7th (8:00pm GMT+2) is the next date for a webinar at Why R? Foundation YouTube channel. We will have an amazing talk by Dr. Nina Zumel and Dr. John...continue reading.
This post is to announce that the AzureQstor package is now on GitHub. AzureQstor provides an R interface to Azure queue storage, building on the facilities provided by AzureStor. Queue...continue reading.
Usually you want to store vectors and other objects into variables so you can work with them more easily. Variables are like a box with a name. You can then...continue reading.
There’s an old saying (at least old in data scientist years) that goes, “90% of data science is data wrangling.” This rings particularly true for data science leaders, who watch...continue reading.