November 2019
Debugging in R: How to Easily and Efficiently Conquer Errors in Your Code
When you write code, you’re sure to run into problems from time to time. Debugging is the process of finding errors in your code to figure out why it’s behaving in unexpected ways. This typically involves: Runni...
Learning R: Data Wrangling in Password Hacking Game
Data Scientists know that about 80% of a Data Science project consists of preparing the data so that they can be analyzed. Building Machine Learning models is the fun part that only comes afterwards! This process is called Data Wrangling (or Data Munging). If you want to use some Base R ... [Read more...]
Hangman game with R
Hangman is a classic word game in which you need to need to guess as many possible letters in word, so you can guess the word, before running out of tries (lives). Upon running out of tries, you are hanged!…Read more ›
ttdo 0.0.4: Extension
A first update release to the still very new (and still very small) ttdo package arrived on CRAN today. Introduced about two months ago in September, the ttdo package extends the most excellent (and very minimal / zero depends) unit testing package t... [Read more...]
When rvest is not enough
An intorduction to CasperJS for R users
Web scraping is a data mining technique that allows to transform data from unstructured (widely available on the interent under the form of webpages) to structured datasets. This is mostly done exploiting the fact that webpages have some sort of structure, in the ... [Read more...]
R Consortium Infrastructure Steering Committee Chair Wins 2019 COPSS Presidents’ Award
Congratulations to our very own Hadley Wickham, Infrastructure Steering Committee Chairperson, for winning the “Nobel Prize of Statistics.” The award is given to a person under the age of 41,...
The post R Consortium Infrastructure Steering Committee Chair Wins 2019 COPSS Presidents’ Award appeared first on R Consortium.
Customer Churn Modeling using Machine Learning with parsnip
This article comes from Diego Usai, a student in Business Science University. Diego has completed both 101 (Data Science Foundations) and 201 (Advanced Machine Learning & Business Consulting) courses. Diego shows off his progress in this Customer...
Shadowing your ggplot lines. Forecasting confidence interval use case.
Yesterday I was asked to easily plot confidence intervals at ggplot2 chart. Then I came up with this shadowing ggplot2 feature called geom_ribbon().
It’s not a trivial issue as long as you need to gather your data in order to achieve a tidy format. When you already have ...
October 2019: “Top 40” New R Packages
Two Hundred twenty-three new packages made it to CRAN in October. Here are my “Top 40” picks in ten categories: Computational Methods, Data, Genomics, Machine Learning, Mathematics, Medicine, Pharmacology, Statistics, Utilities, and Visualization.
Computational Methods
admmDensestSubmatrix v0.1.0: Implements a method to identify the densest sub-matrix in a given or sampled binary ...
Eigenvectors from Eigenvalues – a NumPy implementation
I was intrigued by the recent splashy result showing how eigenvectors can be computed from eigenvalues alone. The finding was covered in Quanta magazine and the original paper is pretty easy to understand, even for a non-mathematician. Being a non-mathematician myself, I tend to look for insights and understanding via ...
Exploratory Data Analysis of Cell Phone Usage with R: Part 1
In this post, we will analyze data from my cell phone provider on my phone usage. The data contain information on my mobile data use, phone calls, and text messages. We will use exploratory data analysis to understand how my phone usage varies across t...
Using R: From gather to pivot
Since version 1.0.0, released in September, the tidyr package has a new replacement for the gather/spread pair of functions, called pivot_longer/pivot_wider. (See the blog post about the release. It can do a lot of cool things.) Just what we needed, another pair of names for melt/cast, ... [Read more...]
Quicker knitr kables in RStudio notebook
The setup The RStudio notebook is a great interactive tool to build a statistical report. Being able to see statistics and graphs right on the fly probably has saved me countless hours, especially when building complex reports. However, one thing that has always bothered me was the way tables are ...
RcppEigen 0.3.3.7.0
A new minor release 0.3.3.7.0 of RcppEigen arrived on CRAN today (and just went to Debian too) bringing support for Eigen 3.3.7 to R.
This release comes almost a year after the previous minor release 0.3.3.5.0. Besides the upgrade to the new upstream... [Read more...]
essurvey release
The new essurvey 1.0.3 is here! This release is mainly about downloading weight data from the European Social Survey (ESS), which has been on the works since 2017! As usual, you can install from CRAN or Github with:
# From CRAN
install.packages("essurvey")
# or development version from Github
devtools::install_github("ropensci/essurvey")
# and load
library(essurvey)
set_email("[email protected]")
Remember to set your registered email with set_email to download ESS data. ... [Read more...]My R Style Guide
This is my take on an R style guide. As such, this is going to be a very long post (long enough to warrant a table of content). Still, I hope it - or at least some parts of it - are useful to some people out there. Another guide ...
Practical Data Science with R, 2nd Edition, IS OUT!!!!!!!
Practical Data Science with R, 2nd Edition author Dr. Nina Zumel, with a fresh author’s copy of her book!
Working with Statistics Canada Data in R, Part 2: Retrieving CANSIM Data
Back to Working with Statistics Canada Data in R, Part 1.Forward to Working with Statistics Canada Data in R, Part 3. Most CANSIM data can be accessed in two formats: as data tables and, at a much more granular level, as individual data vectors. This post is structured accordingly: Searching for ...
Working with Statistics Canada Data in R, Part 2: Retrieving CANSIM Data
A detailed tutorial on using R to retrieve and clean Statistics Canada CANSIM data, step-by-step.
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