A Fine Slice Of SQL Server
The
datasetRhelps you generate a random datasets for your R project. It provides a preset random list of values with different data types (interval, ordinal, nominal values). It includes also a function for imputing a NULL, NA or missing values.
Click through to see how it works, the types of random data you can generate, and more.
1 CommentSteven Sanderson checks two objects:
In the realm of programming, R is a widely-used language for statistical computing and data analysis. Within R, there exists a powerful function called
identical()that allows programmers to compare objects for exact equality. In this blog post, we will delve into the syntax and usage of theidentical()function, providing clear explanations and practical examples along the way.
You can also take a look at the documentation for this function to see a few more examples.
Comments closedBob Rudis creates an R package:
HTTP Headers Hashing (HHHash) is a technique developed by Alexandre Dulaunoy to generate a fingerprint of an HTTP server based on the headers it returns. It employs one-way hashing to generate a hash value from the list of header keys returned by the server. The HHHash value is calculated by concatenating the list of headers returned, ordered by sequence, with each header value separated by a colon. The SHA256 of this concatenated list is then taken to generate the HHHash value. HHHash incorporates a version identifier to enable updates to new hashing functions.
Read on to see when it might be useful and other things you should know about the package. H/T R-Bloggers.
Comments closedPeter Baranovskiy doesn’t want bog standard but is okay with mostly standard:
There are multiple ways to style or theme a Shiny app. A high-level overview is available in the Mastering Shiny book by Hadley Wickham. Here I’ll show the easiest way to do this. If you need to build an entirely – or mostly – new Shiny theme (e.g. a corporate theme), this post is probably not for you. In that case
bslibmay be the best starting point. Otherwise, if you are generally happy with a pre-made theme and just want to tweak some of its elements, read on.This post is based on an actual app, so that you can see how it all works. Here’s the app’s source.
Click through to see what kinds of changes you can make without a major overhaul.
Comments closedSteven Sanderson renames a file:
Managing files is an essential task for any programmer, and when working with R, the
file.rename()function can become your best friend. In this blog post, we’ll explore the ins and outs offile.rename(), discuss its syntax, provide real-life examples, and share some best practices to empower you in your file management endeavors. So grab a cup of coffee and let’s dive into the world offile.rename()!
Click through for two examples. But do read the documentation if you’re running on Windows and dealing with symbolic links.
Comments closedSteven Sanderson tries out a function:
In the world of data analysis, time-series data is a common sight. Whether it’s stock prices, weather patterns, or website traffic, understanding the relationship between variables over time is crucial. One valuable technique in this domain is calculating rolling correlation, which allows us to examine the evolving correlation between two variables as our data moves through time. In this blog post, we will delve into the
rollapplyfunction and its capabilities, exploring its applications through a series of practical examples. So, let’s get started!
Click through for an example of how it works.
Comments closedSteven Sanderson makes a list and checks it twice (or n number of times with replacement):
Bootstrap resampling is a powerful technique used in statistics and data analysis to estimate the uncertainty of a statistic by repeatedly sampling from the original data. In R, we can easily implement a bootstrap function using the lapply, rep, and sample functions. In this blog post, we will explore how to write a bootstrap function in R and provide an example using the “mpg” column from the popular “mtcars” dataset.
Read on for the process.
Comments closedSteven Sanderson shows off a function in R:
As a programmer, you’re constantly faced with the need to repeat tasks efficiently. Repetition is a fundamental concept in programming, and R provides a powerful tool to accomplish this: the
rep()function. In this blog post, we will explore the syntax of therep()function and delve into several examples to showcase its versatility and practical applications. Whether you’re working with data manipulation, generating sequences, or creating repeated patterns,rep()will become your go-to function for mastering repetition in R.
Read on for the basic syntax, as well as several examples of how to use the rep() function.
Steven Sanderson gathers a sample:
Sampling is a fundamental technique in data analysis and statistical modeling. It allows us to draw meaningful insights and make inferences about a larger population based on a representative subset. In the world of R programming, the
sample()function stands as a versatile tool that enables us to create random samples efficiently. In this post, we will explore thesample()function and its various applications through a series of plain English examples.
Click through for those examples.
Comments closedSteven Sanderson shows off the ability to perform crosstabs in R:
As a programmer, you’re constantly faced with the task of organizing and analyzing data. One powerful tool in your R arsenal is the xtabs() function. In this blog post, we’ll explore the versatility and simplicity of xtabs() for aggregating data. We’ll use the
mtcarsdataset and thehealthyR.data::healthyR_datadataset to illustrate its functionality. Get ready to dive into the world of data aggregation with xtabs()!
Click through for two examples of it creating a crosstab (or, in SSRS/Power BI terms, a matrix) from your data.
Comments closed