A Fine Slice Of SQL Server
Steven Sanderson reads an Excel file:
Have you ever battled with Excel’s quirky date formats in your R projects? If so, you’re not alone! Those cryptic numbers can be a real headache, but fear not, fellow R warriors! Today, we’ll conquer this challenge and transform those numbers into beautiful, usable dates.
This is a common pain point in a lot of libraries and Steven shows how to solve it in R using a pair of functions.
Comments closedJohn MacKintosh takes us through what’s new in R’s data.table package:
The newest version of data.table has hit CRAN, and there are lots of great new features.
Among them, a
%notin%function, a newletfunction that can be used instead of:=( I wasn’t too fussed about this originally but have tried it a few times today and I may well adopt it – although I do like that:=really stands out in my code when assigning / updating variables).
Read on for the big changes. H/T R-Bloggers.
Comments closedColin Gillespie contrasts two file formats:
The RDS format is a binary file format, native to R. It has been part of R for many years, and provides a convenient method for saving R objects, including data sets.
The obvious question is which file format should you use for storing tabular data? RDS or parquet? For this comparison, I’m interested in the following characteristics:
- the time required to save the file;
- the file size;
- the time required to load the file.
Read on for the throw-down.
Comments closedSebastian Sauer bridges the gap:
However, a disadvantage of Bayes analysis, at least at its current state, is that it has higher technical and computational demands. For beginners in particular, this may present a substantial (entry) burden. Teaching statistics, I have found that students (and many colleagues) have had difficulties installing Stan (particularly the C++ compiler needed in order to run Stan); Stan is the probabilistic programming language which many front-end Bayes engines use such as
brmsin R.Thus, the installation process being not so user-friendly, a burden is placed for beginners which may prevent using Bayes methods.
In that light, this post explores the numerical simarilities of Bayes regression models and Frequentis models. The idea is to use a Frequentist regression model as a proxi for a full Bayesian analysis. The value added is the quick computation and the simple technical setup.
Click through for the conditions where you’ll find very similar results, as well as a few examples of it in action.
Comments closedStephane Laurent does a bit of work:
The ‘RowReorder’ extension of datatables is available in the DT package. This extension allows to reorder the rows of a DT table by dragging and dropping. However, if you enable this extension in a Shiny app for a table using the server-side processing (option
server=TRUEinrenderDT), that won’t work: each time the rows are reordered, they will jump back to their original locations.
Read on to see what you need to do in that case, as well as an example of how to do it. H/T R-Bloggers.
Comments closedSteven Sanderson asks what month it is:
Greetings fellow R enthusiasts! Today, we’re diving into a fundamental task: extracting the month from a date in R. Whether you’re new to R or a seasoned pro, understanding how to manipulate dates is essential. We’ll explore two popular methods: using base R and the powerful lubridate package. So, let’s roll up our sleeves and get started!
Read on for several examples across two solution spaces.
Comments closedSebastian Sauer saves the levels:
The summary table does not show the level
TRUE, as it is not occuring in the data. This can be problematic: If the data is unknown before summarizing and you would expect that both/all levels (TRUE, FALSE) occur. Just imagine that a subsequent function will count the levelTRUEand the levelFALSE. If one level is missing, your system may brake down.
Click through for a solution, where, even if your dataset is missing a particular level (value of a categorical variable), you will still see it in the final output. That way, if you train a model on this data and the new level shows up in your test dataset or in the wild, it won’t cause an error.
Comments closedSteven Sanderson puts on the archaeologist’s fedora and bullwhip:
Greetings, fellow data enthusiasts! Today, we embark on a quest to uncover the earliest date lurking within a column of dates using the power of R. Whether you’re a seasoned R programmer or a curious newcomer, fear not, for we shall navigate through this journey step by step, unraveling the mysteries of date manipulation along the way.
Imagine you have a dataset filled with dates, and you’re tasked with finding the earliest one among them. How would you tackle this challenge? Fear not, for R comes to our rescue with its arsenal of functions and packages.
Click through to see how, keeping those pernicious missing values in mind.
Comments closedRasmus Baath has done a good deed for all:
In statistics, there are a number of classic datasets that pop up in examples, tutorials, etc. There’s the infamous iris dataset (just type
irisin your nearest R prompt), the Palmer penguins (the modern iris replacement), the titanic dataset(s) (I hope you’re not a guy in 3rd class!), etc. While looking for a dataset to illustrate a simple hierarchical model I stumbled upon another one: Thecakedataset in thelme4package which is described as containing “data on the breakage angle of chocolate cakes made with three different recipes and baked at six different temperatures [as] presented in Cook (1938)1”. For me, this raised a lot of questions: Why measure the breakage angle of chocolate cakes? Why was this data collected? And what were the recipes?
Read on as Rasmus unravels the mysteries of the cake dataset with the help of several others. H/T R-Bloggers.
Comments closedSteven Sanderson has ways to track months:
Greetings fellow R enthusiasts! Today, let’s dive into the fascinating world of date calculations. Whether you’re a data scientist, analyst, or just someone who loves coding in R, understanding how to calculate the number of months between dates is a valuable skill. In this blog post, we’ll explore two approaches using both base R and the lubridate package, ensuring you have the tools to tackle any date-related challenge that comes your way.
Read on to see how to do this in base R as well as the lubridate package.
Comments closed