Introduction Greetings, fellow data enthusiasts! Today, we’re thrilled to unveil a fresh wave of functionalities in the ever-evolving TidyDensity package. Buckle up, as we delve into the realm of distribution statistics! This update brings a bo... [Read more...]

Introduction Hello, R enthusiasts! I’m excited to share some fantastic updates to the TidyDensity package. These updates introduce a suite of parameter estimate functions designed to make your data analysis more efficient and insightful. Whethe... [Read more...]

Introduction The latest update the the TidyDensity package introduces several new functions that make it easier to work with data in R. In this article, we’ll take a look at the new AIC functions and how they work. New Functions The set of fu... [Read more...]

Introduction Hello, fellow R enthusiasts! I’m thrilled to share some fantastic updates to the TidyDensity package. These updates bring a wealth of new features, functions, and enhancements, making distribution analysis more comprehensive and ef... [Read more...]

Introduction Hello, R enthusiasts! Today, I’m excited to introduce a new function in the healthyR.data package: get_provider_meta_data(). This function is excellent for anyone working with healthcare datasets, making it easy to fetch and filter... [Read more...]

Introduction Hey, R users! 🌟 Today, we’re going to look at a great new addition to the healthyR.data package—the get_cms_meta_data() function! This function is a helpful tool for retrieving and analyzing metadata from CMS (Centers for Medicare ... [Read more...]

Announcing the Latest Updates to the healthyR.data R Package I’m excited to share the latest updates to the healthyR.data R package! This release brings new functionality and minor improvements, all aimed at making your data management tasks ea... [Read more...]

Introduction Good morning, everyone! Today, we’re going to talk about how to handle rows in your dataset that contain a specific string. This is a common task in data cleaning and can be easily accomplished using both base R and the dplyr packa... [Read more...]

Introduction Splitting numbers into individual digits can be a handy trick in data analysis and manipulation. Today, we’ll explore how to achieve this using base R functions, specifically gsub() and strsplit(). Let’s walk through the process st... [Read more...]

Introduction In data analysis, there are times when you need to split a vector into smaller chunks. Whether you’re managing large datasets or preparing data for parallel processing, breaking down vectors can be incredibly useful. In this post, ... [Read more...]

Introduction Working with vectors is one of the fundamental aspects of R programming. Sometimes, you need to remove specific elements from a vector to clean your data or prepare it for analysis. This post will guide you through several methods ... [Read more...]

Introduction If you’ve ever worked with text data in R, you know how important it is to have powerful tools for pattern matching. One such tool is the gregexpr() function. This function is incredibly useful when you need to find all occurrences... [Read more...]

Introduction Counting words in a string is a common task in data manipulation and text analysis. Whether you’re parsing tweets, analyzing survey responses, or processing any textual data, knowing how to count words is crucial. In this post, we’... [Read more...]

How to Select Columns Containing a Specific String in R Today I want to discuss a common task in data manipulation: selecting columns containing a specific string. Whether you’re working with base R or popular packages like stringr, stringi, or... [Read more...]

Introduction When working with data in R, you might need to check if values across multiple columns are equal. This is a common task in data cleaning and preprocessing. In this blog, I’ll show you how to do this using base R, dplyr, and data.ta... [Read more...]

Introduction When working with data frames in R, it’s common to need to check whether a specific column exists. This is particularly useful in data cleaning and preprocessing, to ensure your scripts don’t throw errors if a column is missing. To... [Read more...]

Introduction Whether you’re doing some data cleaning or exploring your dataset, checking if a column contains a specific string can be a crucial task. Today, I’ll show you how to do this using both str_detect() from the stringr package and base... [Read more...]

Introduction When working with data frames in R, you may often encounter scenarios where you need to collapse or concatenate text values based on groups within your dataset. This could involve combining text from multiple rows into a single row... [Read more...]

Introduction When working with data frames in R, it’s common to need to select specific columns based on their index positions. This task is straightforward in R, especially with base functions. In this article, we’ll explore how to select colu... [Read more...]

Introduction Welcome back, R enthusiasts! Today, we’re going to explore a fundamental task in data analysis: counting the number of missing (NA) values in each column of a dataset. This might seem straightforward, but there are different ways t... [Read more...]