A Fine Slice Of SQL Server
Steven Sanderson performs some sampling:
In the area of statistical modeling and Bayesian inference, Markov Chain Monte Carlo (MCMC) methods are indispensable tools for tackling complex problems. The new
tidy_mcmc_sampling()function in the TidyDensity R package simplifies MCMC sampling and visualization, making it accessible to a broader audience of data enthusiasts and analysts.
Read on for a brief primer on MCMC and an example of how the tidy_mcmc_sampling() function works.
Steven Sanderson looks for dupes:
Today, we’re diving into a useful new function from the TidyDensity R package:
check_duplicate_rows(). This function is designed to efficiently identify duplicate rows within a data frame, providing a logical vector that flags each row as either a duplicate or unique. Let’s explore how this function works and see it in action with some illustrative examples.
Read on to see how it works. Though I am curious about whether there’s an option to ignore certain columns, such as row IDs or other “non-essential” columns you don’t want to include for comparison. Also, checking how it handles NA or NULL would be interesting.
Comments closedSteven Sanderson achieves normality:
In data analysis, especially when dealing with multiple samples or distributions, ensuring comparability and removing biases is crucial. One powerful technique for achieving this is quantile normalization. This method aligns the distributions of values across different samples, making them more similar in terms of their statistical properties.
Read on to see how you can use the TidyDensity package to pull this off.
Comments closedSteven Sanderson shows off some more complex string splitting scenarios in R:
In data preprocessing and text manipulation tasks, the
strsplit()function in R is incredibly useful for splitting strings based on specific delimiters. However, what if you need to split a string using multiple delimiters? This is wherestrsplit()can really shine by allowing you to specify a regular expression that defines these delimiters. In this blog post, we’ll dive into how you can usestrsplit()effectively with multiple delimiters to parse strings in your data.
Read on for two examples of complex scenarios.
Comments closedJameson Marriott fixes a spacing issue:
I recently noticed that ggplot2 spaces date axes literally even when grouped by month. I’ve been using ggplot2 extensively for years and I don’t remember noticing before, so this is not really a big deal, but now that I know it bugs me a lot. Take a look below.
I don’t think I had noticed this before either, though now that Jameson has pointed it out, it certainly is annoying. H/T R-Bloggers.
Comments closedSteven Sanderson performs feature selection:
As an R programmer, one of the fundamental tasks you’ll encounter is manipulating data frames. Whether you’re cleaning messy data or preparing it for analysis, knowing how to drop unnecessary columns is a valuable skill. In this guide, we’ll walk through the process of dropping columns from data frames in R, using simple examples to demystify the process.
Read on for three ways of doing this.
Comments closedSteven Sanderson searches for subsets:
In data analysis, there often arises a need to extract the top N values within each group of a dataset. Whether you’re dealing with sales data, survey responses, or any other type of grouped data, identifying the top performers or outliers within each group can provide valuable insights. In this tutorial, we’ll explore how to accomplish this task using three popular R packages: dplyr, data.table, and base R. By the end of this guide, you’ll have a solid understanding of various approaches to selecting top N values by group in R.
Read on for the three examples.
Comments closedRuss Hyde shares items of interest:
R 4.4.0 (“Puppy Cup”) was released on the 24th April 2024 and it is a beauty. In time-honoured tradition, here we summarise some of the changes that caught our eyes. R 4.4.0 introduces some cool features (one of which is experimental) and makes one of our favourite {rlang} operators available in base R. There are a few things you might need to be aware of regarding handling
NULLandcomplexvalues.The full changelog can be found at the r-release ‘NEWS’ page and if you want to keep up to date with developments in base R, have a look at the r-devel ‘NEWS’ page.
Read on for a big note on tail-call recursion, an operator for coalescing, and a few more neat features.
Comments closedSteven Sanderson wants to check for row existence:
Hello, fellow R users! Today, we’re going to explore a common scenario you might encounter when working with data frames: checking if a row from one data frame exists in another. This is a handy skill that can help you compare datasets and verify data integrity.
There are several ways to do it. Click through for two of those ways.
Comments closedSteven Sanderson takes a slice:
Explanation: – We use
nrow(my_df)to get the total number of rows in the data frame. – Then, we use indexing ([nrow(my_df), ]) to extract the last row.
Read on for a simple example of getting the last row using base R, dplyr, and data.table. Then, we kick it up a notch to get the second-to-last row, with the idea being that you could substitute “second-to-last” with any arbitrary number.
Comments closed