A Fine Slice Of SQL Server
Before we start anything, I’d like to mention that most of the hard work came from nsaunders and his great blog post Idle thoughts lead to R internals: how to count function arguments.
Let’s get started.
The aim of this blog is to capture the number of arguments present in each function with packages of the tidyverse.
Click through to see the code, as well as some methods of visualizing the results (methods which you can use in other situations).
Comments closedHolger K. von Jouanne-Diedrich uses fractal math to create realistic-looking artificial islands:
Here we will turn this principle on its head and use it to actually create realistic-looking landmasses with R. The inspiration for this came from chapter 4 “Infinite Detail” of the book “Math Bytes” by my colleague Professor T. Chartier from Davidson College in North Carolina.
The idea is to start with some very simple form, like a square, and add more detail step-by-step. Concretely, we go through every midpoint of our ever more complex polygon and shift it by a random amount. Because the polygon will be getting more and more intricate we have to adjust the absolute amount by which we shift the respective midpoints.
Click through for the code.
Comments closedLaura Ellis shows how to use the usmap package in R:
Today, I’d like to share the package ‘usmap’ which enables incredibly easy and fast creation of US maps in R.
In honor of US Thanksgiving tomorrow, I’m going to make this blog Thanksgiving themed! In this tutorial, we will use the gTrendsR package to pull US Google search results on the keyword “thanksgiving” and plot the popularity by state.
Click through for that demo, as well as links to more demos on map usage.
Comments closedSebastian Sauer has a two-parter on waffle plots. The first part is an introduction:
A waffle diagram is a variant of (stacked) bar plots or pie plots. They do not have great perceptual properties, I’d suspect, but for some purposes they may be adequate. This is best explored by example. This post draws heavily from the introduction of hrbrmstr to his Waffle package.
The second part uses emojifont to show pictograms as well:
A Pictogram may be defined as a (statistical) diagram using icons or similar “iconic” graphics to illstrate stuff. The waffle plot (see this post) is a nice object where to combine waffle and pictorgrams. Originally, this post was inspired by HRBRMSTR waffle package, see this post, but I could not get it running.
Maybe the easiest way is to work through an example (spoiler: see below for what we’re heading at).
This type of plot doesn’t work for everything, but I can think of a few places where it’d be the right choice.
Comments closedMarina Wyss walks us through debugging techniques in R:
There are many ways to approach these problems when they arise. For example, condition handling using tools like try(), tryCatch(), and withCallingHandlers() can increase your code’s robustness by proactively steering error handling.
R also includes several advanced debugging tools that can be very helpful for quickly and efficiently locating problems, which will be the focus of this article. To illustrate, we’ll use an example adapted from an excellent paper by Roger D. Peng, and show how these tools work along with some updated ways to interact with them via RStudio. In addition to working with errors, the debugging tools can also be used on warnings by converting them to errors via options(warn = 2).
Read on for a survey of what’s available in R. It’s a lot more than writing a bunch of print statements. H/T R-Bloggers
Comments closedEvgeni Chasnovski has a new CRAN package:
I am glad to announce that my latest, long written R package ‘pdqr’ is accepted to CRAN. It provides tools for creating, transforming and summarizing custom random variables with distribution functions (as base R ‘p*()’, ‘d*()’, ‘q*()’, and ‘r*()’ functions). You can read a brief overview in one of my previous posts.
Click through for a description of the package.
Comments closedSebastian Sauer takes us through two of the most important assumptions of linear models:
Additivity and linearity as the second most important assumptions in linear models
We assume that \(y\) is a linear function of the predictors. If y is not a linear function of the predictors, we cannot expect the model to deliver correct insights (predictions, causal coefficients). Let’s check an example.
Read on to understand what this means, as well as the most important assumption.
Comments closedHong Ooi announces changes to several AzureR packages:
AzureVM 2.1.0
You can now create VM scalesets with attached data disks. In addition, you can specify the disk type (Standard_LRS, StandardSSD_LRS, or Premium_LRS) for the OS disk and, for a Linux Data Science Virtual Machine, the supplied data disk. This enables using VM sizes that don’t support Premium storage.
Click through for the full set of updates.
Comments closedAnisa Dhana shows off several tidyverse methods for combining data sets together:
semi_join
Thesemi_joinfunction is different than the previous examples of joins. A semi join creates a new dataset in which there are all rows from thedata1where there is a corresponding matching value indata2. Still, instead of the final dataset merging both the first (data1) and second (data2) datasets, it only contains the variables from the first one (data1).
Most of this looks like standard SQL joins, but read through to the end for a bonus which doesn’t typically appear in relational database products.
Comments closedThe R-hub blog has an interesting post on creating mocks in R for unit testing:
In some of these cases, the programming concept you’re after is mocking, i.e. making a function act as if something were a certain way! In this blog post we shall offer a round-up of resources around mocking, or not mocking, when unit testing an R package.
It’s interesting watching data scientists work through the same sorts of problems which traditional developers have hit, whether that be testing, deployment, or source control management. H/T R-bloggers
Comments closed