Steve On Data – Steve’s Data Tips and Tricks

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A Comprehensive Guide to Computer Networking in Linux: Commands, Tools, and Best Practices

code

linux

Unlock the power of Linux networking! Explore essential commands, secure remote access, file transfer tools, and best practices for beginners. Master TCP/IP, troubleshoot issues, and secure your Linux network.

Steven P. Sanderson II, MPH

How to Use complete.cases in R With Examples

code

rtip

operations

Learn how to effectively use complete.cases in R with practical examples. Master handling missing values, data cleaning, and advanced applications for better data analysis.

Steven P. Sanderson II, MPH

Breaking In and Out of Looped Code: A Beginner’s Guide to C Loop Control

code

c

Master C programming loop control with break and continue statements. Learn when and how to exit loops early or skip iterations for more efficient code execution.

Steven P. Sanderson II, MPH

A Complete Guide to Using na.rm in R: Vector and Data Frame Examples

code

rtip

operations

Master handling missing values in R with na.rm. Learn practical examples for vectors and data frames, plus best practices for effective data analysis.

Steven P. Sanderson II, MPH

How to Use na.omit in R: A Comprehensive Guide to Handling Missing Values

code

rtip

operations

Learn how to effectively use na.omit in R to handle missing values in vectors, matrices, and data frames. Includes practical examples and best practices for data cleaning.

Steven P. Sanderson II, MPH

Understanding Storage Media in Linux: A Beginner’s Guide

code

linux

Comprehensive guide to Linux storage media management - from mounting devices and creating file systems to troubleshooting issues. Perfect for beginner Linux users.

Steven P. Sanderson II, MPH

How to Use drop_na to Drop Rows with Missing Values in R: A Complete Guide

code

rtip

operations

Learn how to effectively use drop_na in R to clean up missing values in your datasets. Detailed guide with examples, best practices, and troubleshooting tips for R programmers.

Steven P. Sanderson II, MPH

Understanding Switch Statements in C Programming

code

c

Mastering switch statements in C programming: Comprehensive guide with syntax, examples, best practices, and common pitfalls. Perfect for beginner C coders!

Steven P. Sanderson II, MPH

How to Select Row with Max Value in Specific Column in R: A Complete Guide

code

rtip

operations

Discover three powerful methods to select rows with maximum values in R: base R’s which.max(), traditional subsetting, and dplyr’s slice_max(). Comprehensive guide with examples, best practices, and performance considerations.

Steven P. Sanderson II, MPH

How to Find the Column with the Max Value for Each Row in R

code

rtip

operations

Discover efficient ways to identify the column with the maximum value for each row in your R data frames. Explore base R, dplyr, and data.table approaches to boost your data analysis skills.

Steven P. Sanderson II, MPH

A Beginner’s Guide to Package Management in Linux

code

linux

Discover the fundamentals of package management in Linux. Learn how to find, install, remove, and update packages using apt, yum, dpkg, and rpm tools. Understand repositories, dependencies, and common package management tasks.

Steven P. Sanderson II, MPH

How to Find Columns with All Missing Values in Base R

code

rtip

operations

Find out how to easily identify columns in your R data frame that contain only missing (NA) values using base R functions. Streamline your data cleaning process with these simple techniques.

Steven P. Sanderson II, MPH

Mastering For Loops in C: A Comprehensive Beginner’s Guide with Examples

code

c

Unlock the power of for loops in C programming with this comprehensive beginner’s guide. Discover how to use for loops effectively, from simple counting to nested loops, with practical examples. Master the syntax and control structures to write efficient and readable C code.

Steven P. Sanderson II, MPH

How to Find and Count Missing Values in R: A Comprehensive Guide with Examples

code

rtip

operations

Learn how to effectively find and count missing values (NA) in R data frames, columns, and vectors with practical examples and code snippets.

Steven P. Sanderson II, MPH

How to Replace Missing Values in R: A Comprehensive Guide

code

rtip

operations

Struggling with missing values in your R datasets? This in-depth guide covers proven techniques to effectively handle and replace NA values in vectors, data frames, and columns. Learn to use mean, median, and other methods for imputation.

Steven P. Sanderson II, MPH

Mastering Linux: A Beginner’s Guide to Customizing the Bash Prompt

code

linux

Customize your Linux bash prompt with colors, symbols, time, and more. Learn to use PS1 variables, ANSI escape codes, and cursor positioning to create a personalized command line experience.

Steven P. Sanderson II, MPH

How to Interpolate Missing Values in R: A Step-by-Step Guide with Examples

code

rtip

operations

Unlock insights from your data by learning how to interpolate missing values in R. Explore practical examples using the zoo library and na.approx() function. Become a master of handling missing data with this step-by-step guide.

Steven P. Sanderson II, MPH

Mastering While and Do While Loops in C: A Beginner’s Guide

code

c

Learn how to effectively use while and do while loops in C programming to automate repetitive tasks and write cleaner code. This in-depth tutorial for beginners covers syntax, examples, best practices, and practical applications. Master these essential looping constructs to take your C skills to the next level.

Steven P. Sanderson II, MPH

Deleting Multiple Columns in R: A Step-by-Step Guide for Data Frame Manipulation

code

rtip

operations

Learn how to efficiently remove multiple columns from a data frame in Base R using various methods like subset(), select(), the minus sign, and assigning NULL. Includes step-by-step examples for each approach.

Steven P. Sanderson II, MPH

Mastering String Comparison in R: 3 Essential Examples and Bonus Tips

code

rtip

operations

strings

stringr

stringi

Learn how to compare strings in R with 3 practical examples. Discover techniques to compare two strings, compare vectors of strings, and find similarities between string vectors. Boost your R programming skills now!

Steven P. Sanderson II, MPH

A Beginner’s Guide to VI and VIM: Mastering Text Editing in Linux

code

linux

Discover the power of VI and VIM, the essential text editors for Linux beginners. Master modal editing, navigation, and advanced features to boost your productivity on the command line.

Steven P. Sanderson II, MPH

How to Compare Two Columns in R: A Comprehensive Guide with Examples

code

rtip

operations

Learn how to effectively compare two columns in R using various base R functions and techniques. Includes practical examples for R programmers.

Steven P. Sanderson II, MPH

Mastering Conditional Logic and Small Change Operators in C

code

c

Unlock the power of C’s conditional operator, increment/decrement operators, and sizeof() to write more efficient and expressive code. Explore practical examples and best practices for beginner C programmers.

Steven P. Sanderson II, MPH

How to Combine Vectors in R: A Comprehensive Guide with Examples

code

rtip

operations

Learn how to efficiently combine two or more vectors in R using base functions like c(), rbind(), cbind(), and data.frame(). Includes practical examples for R programmers.

Steven P. Sanderson II, MPH

How to Compare Two Vectors in base R With Examples

code

rtip

operations

Learn how to efficiently compare vectors in R using match(), %in%, identical(), and all.equal(). Includes code examples and best practices for beginner R programmers.

Steven P. Sanderson II, MPH

Linux Environment Variables: A Beginner’s Guide to printenv, set, export, and alias

code

linux

Master Linux environment variables with our comprehensive guide. Learn how to use printenv, set, export, and alias commands to customize your Linux environment effectively.

Steven P. Sanderson II, MPH

How to Keep Certain Columns in Base R with subset(): A Complete Guide

code

rtip

operations

Learn how to efficiently keep specific columns in R using subset(). Complete guide with practical examples, best practices, and advanced techniques for data frame manipulation.

Steven P. Sanderson II, MPH

Understanding Logical Operators in C Programming

code

c

Master logical operators in C programming (&&, ||, !) with comprehensive examples, truth tables, and best practices. Perfect guide for beginners to advance their C skills.

Steven P. Sanderson II, MPH

How to Subset a Data Frame in R: 4 Practical Methods with Examples

code

rtip

operations

Master data manipulation in R with this comprehensive guide on subsetting data frames. Explore 4 powerful methods - base R, subset(), dplyr, and data.table - with step-by-step examples. Optimize your workflow and unlock the full potential of your R projects.

Steven P. Sanderson II, MPH

How to Use the Tilde Operator (~) in R: A Comprehensive Guide

code

rtip

operations

Unlock the power of the tilde operator (~) in R programming. Master formula creation, statistical modeling, and data analysis techniques. Includes practical examples and expert tips.

Steven P. Sanderson II, MPH

Understanding Linux Processes and Essential Commands: A Beginner’s Guide

code

linux

Unlock the power of Linux processes with this beginner’s guide. Master essential commands like ps, top, jobs, and bg to effectively manage and monitor your system. Boost your Linux administration skills today.

Steven P. Sanderson II, MPH

Testing Data with If and Else If in C

code

c

Mastering if and else if statements in C programming is essential for decision-making and controlling program flow. Explore relational operators, examples, and best practices to write efficient C code.

Steven P. Sanderson II, MPH

How to Use Dollar Sign ($) Operator in R: A Comprehensive Guide for Beginners

code

rtip

operations

Learn how to effectively use the dollar sign ($) operator in R programming to access data frame columns and list elements. Perfect guide for R beginners with practical examples.

Steven P. Sanderson II, MPH

The Complete Guide to Using setdiff() in R: Examples and Best Practices

code

rtip

operations

Learn how to effectively use the setdiff function in R with practical examples. Master vector comparisons, understand set operations, and solve real-world programming challenges.

Steven P. Sanderson II, MPH

How to Use NOT IN Operator in R: A Complete Guide with Examples

code

rtip

operations

Unlock the power of the NOT IN operator in R with this comprehensive guide. Learn syntax, practical examples, and advanced techniques to master data filtering, vector comparisons, and custom operator creation for better R programming.

Steven P. Sanderson II, MPH

Linux Permissions Explained: A Beginner’s Guide to File Security Commands

code

linux

Master Linux file permissions with this comprehensive guide. Learn essential commands like chmod, umask, su, sudo, and chown to secure your files and manage user access effectively.

Steven P. Sanderson II, MPH

How to Use ‘OR’ Operator in R: A Comprehensive Guide for Beginners

code

rtip

operations

Learn how to effectively use the OR operator in R programming with practical examples. Master boolean logic and conditional filtering for better data manipulation.

Steven P. Sanderson II, MPH

Powering Up Your Variables with Assignments and Expressions in C

code

c

Master C programming operators, compound assignments, and type casting with our comprehensive guide. Perfect for beginners learning variable manipulation and expression evaluation.

Steven P. Sanderson II, MPH

The Ultimate Guide to Creating Lists in R: From Basics to Advanced Examples

code

rtip

lists

operations

Learn how to create and manipulate lists in R with comprehensive examples. Perfect for beginners, covering basic to advanced list operations with practical code samples.

Steven P. Sanderson II, MPH

How to Iterate Over Rows of Data Frame in R: A Complete Guide for Beginners

code

rtip

operations

Learn how to efficiently iterate over rows in R data frames with practical examples and best practices. Perfect for beginners looking to master data manipulation in R programming.

Steven P. Sanderson II, MPH

Mastering Linux Terminal: Clear and History Commands for Beginners

code

rtip

linux

Learn how to efficiently manage your Linux terminal with clear and history commands. Master essential keyboard shortcuts and security best practices for better command-line productivity.

Steven P. Sanderson II, MPH

Enhancing Time Series Analysis: RandomWalker 0.2.0 Release

code

rtip

randomwalker

Explore the latest features in RandomWalker 0.2.0, an R package update that enhances time series analysis with new cumulative functions, interactive plotting, and tools for finance professionals

Steven P. Sanderson II, MPH

Mastering Mathematics in C Programming: A Beginner’s Guide

code

rtip

c

Master mathematical operations in C with our comprehensive guide. Learn arithmetic operators, order of operations, using parentheses, and practical examples for beginner C programmers.

Steven P. Sanderson II, MPH

How to Loop Through List in R with Base R and purrr: A Comprehensive Guide for Beginners

code

rtip

operations

lists

Master list manipulation in R using base loops and purrr. Learn efficient techniques with practical examples for beginners. Boost your data analysis skills today!

Steven P. Sanderson II, MPH

Mastering Column Names in Base R: A Beginner’s Guide

code

rtip

operations

Learn how to efficiently retrieve and sort column names in Base R using functions like sort() and sapply(). Perfect for beginner R programmers!

Steven P. Sanderson II, MPH

Understanding Expansion in the Linux Shell

code

rtip

linux

Discover the power of shell expansion in Linux with our beginner-friendly guide. Learn how to use echo and other commands to enhance your command-line skills.

Steven P. Sanderson II, MPH

How to Loop Through Column Names in Base R with Examples

code

rtip

operations

Discover how to efficiently loop through column names in R using various methods like for loops, lapply(), sapply(), and dplyr. Includes practical examples and best practices for beginner R programmers.

Steven P. Sanderson II, MPH

Interacting with Users: Mastering `scanf()` in C

code

rtip

c

Unlock the power of user input in C programming with this comprehensive guide on the scanf() function. Master syntax, data types, error handling, and more to create interactive, user-friendly apps.

Steven P. Sanderson II, MPH

How to Add Prefix to Column Names in Base R: A Comprehensive Guide for Beginners

code

rtip

operations

Mastering data manipulation in R? Learn how to easily add prefixes to column names using base R functions like paste(), colnames(), and for loops. Practical examples, exercises, and tips for beginner R programmers. Improve data organization and readability. #RProgramming #DataManipulation

Steven P. Sanderson II, MPH

How to Add Suffix to Column Names in Base R: A Beginner’s Guide

code

rtip

operations

Mastering column name management in R? Learn 3 easy methods to add suffixes to data frame columns using base R functions like paste, lapply, and setNames. Practical examples for beginner R programmers.

Steven P. Sanderson II, MPH

Redirection in Linux: A Beginner’s Guide

code

rtip

linux

Unlock the power of Linux with this beginner’s guide to redirection. Learn how to use essential commands like cat, sort, grep, and more to efficiently manipulate data. Discover the difference between pipes and redirection, and combine commands for complex tasks.

Steven P. Sanderson II, MPH

How to Combine Two Data Frames in R with Different Columns Using Base R, dplyr, and data.table

code

rtip

operations

dplyr

datatable

Combine data frames in R with different columns using base R, dplyr, and data.table. Detailed guide for beginner R programmers with practical examples and code. Optimize your data manipulation skills.

Steven P. Sanderson II, MPH

Making Your Programs More Powerful with #include and #define for C

code

rtip

c

Unlock the power of #include and #define in C programming. Master header files, symbolic constants, and macros to organize, optimize, and enhance your C code. Beginner-friendly tips and best practices.

Steven P. Sanderson II, MPH

How to Combine Two Columns into One in R With Examples in Base R and tidyr

code

rtip

operations

tidyr

Streamline your data manipulation in R! Learn how to combine two columns into one using Base R functions like paste() and tidyr’s unite(). Includes step-by-step examples and best practices for beginners.

Steven P. Sanderson II, MPH

How to Combine Rows with Same Column Values in R

code

rtip

dplyr

datatable

Combining rows with the same column values is a fundamental task in data analysis and manipulation, especially when handling large datasets. This guide is tailored for…

Steven P. Sanderson II, MPH

Working With Linux Commands: A Beginner’s Guide to Essential Tools

code

rtip

Linux, known for its powerful command-line interface, offers a vast array of tools that can significantly enhance your productivity. For beginners, navigating this landscape…

Steven P. Sanderson II, MPH

How to Split Data into Equal Sized Groups in R: A Comprehensive Guide for Beginners

code

rtip

operations

As a beginner R programmer, you’ll often encounter situations where you need to divide your data into equal-sized groups. This process is crucial for various data analysis…

Steven P. Sanderson II, MPH

Understanding Character Variables in C: A Beginner’s Guide

code

rtip

c

Character variables are fundamental building blocks in C programming, serving as the foundation for text processing and string manipulation. For beginner C programmers…

Steven P. Sanderson II, MPH

How to Split a Data Frame in R: A Comprehensive Guide for Beginners

code

rtip

operations

As a beginner R programmer, one of the most crucial skills you’ll need to master is data manipulation. Among the various data manipulation techniques, splitting a data frame…

Steven P. Sanderson II, MPH

Mastering Data Manipulation in R: Comprehensive Guide to Stacking Data Frame Columns

code

rtip

operations

Data manipulation is a crucial skill for any data analyst or scientist, and R provides a powerful set of tools for this purpose. One common task is stacking columns in a…

Steven P. Sanderson II, MPH

Mastering File and Directory Manipulation in Linux: A Beginner’s Guide

code

rtip

linux

Linux file manipulation is a fundamental skill for managing data efficiently. This guide will introduce you to essential commands like cp, mv, mkdir, rm, and ln, which are…

Steven P. Sanderson II, MPH

How to Create Horizontal Boxplots in Base R and ggplot2

code

rtip

viz

ggplot2

Data visualization is a crucial aspect of data analysis, allowing us to understand and communicate complex data insights effectively. Among various visualization techniques…

Steven P. Sanderson II, MPH

Adding Variables to Your C Code: A Beginner’s Guide

code

rtip

c

In the world of C programming, variables play a crucial role. They are human-readable names that refer to specific memory locations where data is stored. Understanding how…

Steven P. Sanderson II, MPH

How to Remove Outliers from Multiple Columns in R: A Comprehensive Guide

code

rtip

operations

Outliers can significantly skew your data analysis results, leading to inaccurate conclusions. For R programmers, effectively identifying and removing outliers is crucial…

Steven P. Sanderson II, MPH

How to Switch Two Columns in R: A Beginner’s Guide

code

rtip

operations

Welcome to the world of R programming, where data manipulation is a crucial skill. One common task you may encounter is the need to switch two columns in a data frame.…

Steven P. Sanderson II, MPH

Mastering Linux Commands: `ls`, `file`, and `less` for Beginners

code

rtip

linux

Thank you for joining me today as we explore the fundamental Linux commands ls, file, and less. These commands are essential for navigating and managing files in a Linux…

Steven P. Sanderson II, MPH

How to Use `cat()` in R to Print Multiple Variables on the Same Line

code

rtip

Printing multiple variables on the same line is a fundamental skill for R programmers. This guide will introduce you to the cat() function, a powerful tool for efficient and…

Steven P. Sanderson II, MPH

Mastering printf() in C: A Beginner’s Guide

code

rtip

In the world of C programming, understanding how to effectively use printf() is crucial for any beginner. As one of the most widely used functions, it plays a pivotal role…

Steven P. Sanderson II, MPH

How to Print All Rows of a Tibble in R: A Beginner’s Guide

code

rtip

operations

In the world of R programming, tibbles are enhanced data frames that provide a more user-friendly way to handle data. Unlike traditional data frames, tibbles come with a set…

Steven P. Sanderson II, MPH

Unveiling ‘RandomWalker’: Your Gateway to Tidyverse-Compatible Random Walks

code

rtip

randomwalk

Welcome to the world of ‘RandomWalker’, an innovative R package designed to simplify the creation of various types of random walks. Developed by myself and my co-author…

Steven P. Sanderson II, MPH

How to Use the duplicated Function in Base R with Examples

code

rtip

operations

duplicated

In data analysis, one of the common tasks is identifying and handling duplicate entries in datasets. Duplicates can arise from various stages of data collection and…

Steven P. Sanderson II, MPH

How to Print Tables in R with Examples Using `table()`

code

rtip

table

Tables are an essential part of data analysis, serving as a powerful tool to summarize and interpret data. In R, the table() function is a versatile tool for creating…

Steven P. Sanderson II, MPH

How to Use `lapply()` Function with Multiple Arguments in R

code

rtip

lapply

R is a powerful programming language primarily used for statistical computing and data analysis. Among its many features, the lapply() function stands out as a versatile…

Steven P. Sanderson II, MPH

How to Exclude Specific Matches in Base R Using grep() and grepl()

code

rtip

To exclude specific matches using the grep() function in Base R, you can use the grepl() function in combination with the ! (NOT) operator. This approach allows you to…

Steven P. Sanderson II, MPH

How to Use grep() and Return Only Substring in R: A Comprehensive Guide

code

rtip

grep

When working with text data in R, you often need to search for specific patterns or extract substrings from larger strings. The grep() function is a powerful tool for…

Steven P. Sanderson II, MPH

Navigating Linux with ‘pwd’, ‘cd’, and ‘ls’: A Beginner’s Guide

code

linux

I have mentioned in my previous linux post that I am on my own personal journey to learn it. I have been using it for sometime but not really understanding the commands. So…

Steven P. Sanderson II, MPH

C Programming Data Types: A Comprehensive Guide to Characters, Integers, and Floating Points

code

c

C programming data types are fundamental building blocks that define how data is stored and manipulated in a program. Understanding these data types is crucial for writing…

Steven P. Sanderson II, MPH

Harness the Full Potential of Case-Insensitive Searches with grep() in R

code

rtip

grep

The grep() function in R is a powerful tool for searching and matching patterns within text data. It is commonly used in data cleaning, manipulation, and text analysis to…

Steven P. Sanderson II, MPH

Mastering the grep() Function in R: Using OR Logic

code

rtip

grep

For R programmers, mastering the built-in functions is key to efficient data manipulation. One such powerful tool is the grep() function, which is commonly used for pattern…

Steven P. Sanderson II, MPH

Mastering grep() in R: A Fun Guide to Pattern Matching and Replacement

code

rtip

grep

Hey there useRs! Today, we’re going back to the wonderful world of grep() - a powerful function for pattern matching and replacement in R. Whether you’re a data wrangling…

Steven P. Sanderson II, MPH

How to use the agrep() function in base R

code

rtip

grep

agrep

The agrep() function in base R is used for approximate string matching, also known as fuzzy matching. Here’s how to use it effectively:

Steven P. Sanderson II, MPH

How to Use grep() for Exact Matching in Base R: A Comprehensive Guide

code

rtip

grep

strings

The grep() function is a powerful tool in base R for pattern matching and searching within strings. It’s part of R’s base package, making it readily available without…

Steven P. Sanderson II, MPH

Understanding Comments in C: Why They Matter and How to Use Them Effectively

c

Comments play a critical role in programming, serving as notes within the source code that explain what the code does, why certain decisions were made, or how a particular…

Steven P. Sanderson II, MPH

Mastering the sapply() Function in R: A Comprehensive Guide for Data Manipulation

code

rtip

sapply

Are you looking to boost your data manipulation skills? Look no further than the powerful sapply() function! In this comprehensive guide, we’ll explore how to leverage…

Steven P. Sanderson II, MPH

Unlocking the Power of the Linux Shell

code

linux

As you go into the world of Linux, you’re bound to encounter the term “shell.” If you’re following along with The Linux Command Line: A Complete Introduction by William…

Steven P. Sanderson II, MPH

Understanding the main() Function in C

code

c

If you’re just starting with C programming, you’ve probably noticed that almost every C program begins with a main() function. But have you ever wondered why this function…

Steven P. Sanderson II, MPH

lapply vs. sapply in R: What’s the Difference?

code

rtip

lapply

sapply

In the world of R programming, understanding the difference between lapply() and sapply() can make your coding life much easier. These two functions are part of R’s powerful …

Steven P. Sanderson II, MPH

grep() vs. grepl() in R

code

rtip

strings

grep

grepl

Hey there, useR’s! Today, we’re going to talk about two super useful functions in R: grep() and grepl(). These functions might sound similar, but they have some key…

Steven P. Sanderson II, MPH

Your First C Adventure: Hello World in VS Code

code

c

Hey there, budding C programmer! Ready to embark on your coding journey? Let’s start with the classic “Hello World” program using Visual Studio Code. Don’t worry if you’re…

Steven P. Sanderson II, MPH

Mastering grepl with Multiple Patterns in Base R

code

rtip

strings

Hello, fellow useRs! Today, we’re going to expand on previous uses of the grepl() function where we looked for a single pattern and move onto to a search for multiple…

Steven P. Sanderson II, MPH

Mastering Matrix Concatenation in R: A Guide to rbind() and cbind()

code

rtip

Hello, fellow useRs! Today, we’re going to discuss the art of concatenating matrices in R. Concatenating matrices is all about combining smaller pieces into a larger whole…

Steven P. Sanderson II, MPH

Opening an Excel Workbook with VBA and Calling it from R

code

rtip

vba

In this post, we’ll cover how to open an Excel workbook using VBA and then call this VBA code from R. This guide will help you automate tasks in Excel directly from R…

Steven P. Sanderson II, MPH

Mastering String Concatenation of Vectors in R: Base R, stringr, stringi, and glue

code

rtip

strings

stringr

stringi

glue

Welcome to another exciting R programming tutorial! Today, we will explore how to concatenate vectors of strings using different methods in R: base R, stringr, stringi, and g…

Steven P. Sanderson II, MPH

Mastering String Concatenation in R: A Comprehensive Guide

code

rtip

strings

stringr

stringi

glue

String concatenation is a fundamental operation in data manipulation and cleaning. If you are working in R, mastering string concatenation will significantly enhance your…

Steven P. Sanderson II, MPH

Mastering Character Counting in R: Base R, stringr, and stringi

code

rtip

strings

stringr

stringi

Counting the occurrences of a specific character within a string is a common task in data processing and text manipulation. Whether you’re working with base R or leveraging…

Steven P. Sanderson II, MPH

How to Check if a String Contains Specific Characters in R: A Comprehensive Guide with Base R, string & stringi

code

rtip

strings

stringr

stringi

Welcome to another exciting blog post where we walk into the world of R programming. Today, we’re going to explore how to check if a string contains specific characters…

Steven P. Sanderson II, MPH

Checking If a Workbook is Open Using VBA and Executing from R

code

rtip

excel

vba

automation

In the world of data analysis and automation, Excel and R are powerful tools that can work in tandem to streamline workflows. One common task is to check if a specific Excel…

Steven P. Sanderson II, MPH

Converting Text to Uppercase with toupper() in R

code

rtip

Greetings, useR! Today, we’re exploring a handy function from base R that will help with string manipulation: toupper(). This little function is the complement to tolower() w…

Steven P. Sanderson II, MPH

Systematic Sampleing in R with Base R

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rtip

In this post, we will explore systematic sampling in R using base R functions. Systematic sampling is a technique where you select every (k^{th}) element from a list or…

Steven P. Sanderson II, MPH

Cluster Sampling in R: A Simple Guide

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rtip

Cluster sampling is a useful technique when dealing with large datasets spread across different groups or clusters. It involves dividing the population into clusters…

Steven P. Sanderson II, MPH

Automate Your Blog Workflow with a Custom R Function: Creating QMD Files

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rtip

automation

As a blogger who uses R for content creation, I’ve found it incredibly useful to automate some of the repetitive tasks. One such task is creating Quarto Markdown (QMD) files…

Steven P. Sanderson II, MPH

How to List All Open Workbooks Using VBA and Call It from R

code

rtip

operations

vba

excel

Hello, fellow R useRs! Today, we’re going to discuss a fascinating topic that bridges the gap between VBA (Visual Basic for Applications) and R. We’ll explore how to get a…

Steven P. Sanderson II, MPH

Mastering String Conversion to Lowercase in R

code

rtip

operations

In data analysis and manipulation, handling text data is a common task. One of the essential operations you might need to perform is converting strings to lowercase. In R…

Steven P. Sanderson II, MPH

Stratified Sampling in R: A Practical Guide with Base R and `dplyr`

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dplyr

Stratified sampling is a technique used to ensure that different subgroups (strata) within a population are represented in a sample. This method is particularly useful when…

Steven P. Sanderson II, MPH

Creating Summary Tables in R with tidyquant and dplyr

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rtip

operations

Creating summary tables is a key part of data analysis, allowing you to see trends and patterns in your data. In this post, we’ll explore how to create these tables using tid…

Steven P. Sanderson II, MPH

Mastering Wildcard Searches in R with `grep()`

code

rtip

operations

strings

In R, finding patterns in text is a common task, and one of the most powerful functions to do this is grep(). This function is used to search for patterns in strings…

Steven P. Sanderson II, MPH

Getting the Workbook Name in VBA and Calling It from R

code

rtip

operations

vba

excel

When working with Excel, it’s often useful to know the name of the workbook you’re working in, especially if you’re managing multiple files. Today, we’ll look at how to…

Steven P. Sanderson II, MPH

Checking if a String Contains Multiple Substrings in R

code

rtip

operations

strings

Hello, fellow R programmers! Today, we’re looking at a practical topic that often comes up when dealing with text data: how to check if a string contains multiple…

Steven P. Sanderson II, MPH

How to Concatenate Strings in R

code

rtip

operations

strings

Hello, R users! Today, we’re going to talk about a fundamental yet essential aspect of data manipulation: concatenating strings. String concatenation is the process of…

Steven P. Sanderson II, MPH

Simplify Regression Modeling with tidyAML’s `fast_regression()`

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rtip

tidyaml

If you’ve ever faced the daunting task of setting up multiple regression models in R, you’ll appreciate the convenience and efficiency that tidyAML brings to the table.…

Steven P. Sanderson II, MPH

VBA: Saving and Closing a Workbook

code

rtip

operations

vba

excel

In this tutorial, you’ll learn how to save and close an Excel workbook using VBA (Visual Basic for Applications) and then doing it from R. We’ll create a simple VBA script…

Steven P. Sanderson II, MPH

How to Extract Substring Starting from the End of a String in R

code

rtip

operations

strings

regex

stringr

stringi

Hey useR’s! Today, we’re going to discuss a neat trick: extracting substrings starting from the end of a string. We’ll cover how to achieve this using base R, stringr, and st…

Steven P. Sanderson II, MPH

tidyAML: Automated Machine Learning with tidymodels

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rtip

tidyaml

Welcome to {tidyAML} which is an R package that makes it easy to use the tidymodels ecosystem to perform automated machine learning (AutoML). This package provides a simple…

Steven P. Sanderson II, MPH

Exploring Random Walks with TidyDensity in R

code

rtip

tidydensity

Welcome back, data enthusiasts! Today, we’re diving into the fascinating world of random walks using the TidyDensity R package. If you’re working with time series data…

Steven P. Sanderson II, MPH

Using the `FileDateTime` Function in VBA from R

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operations

vba

excel

Welcome back to our series where we explore the synergy between R and VBA! Today, we’re diving into the FileDateTime function in VBA and how you can leverage it within R.…

Steven P. Sanderson II, MPH

Extracting Strings Before a Space in R

code

rtip

operations

strings

Hello, R users! Today, we’ll dive into a common text manipulation task: extracting strings before a space. This is a handy trick for dealing with names, addresses, or any…

Steven P. Sanderson II, MPH

Unleashing the Power of TidyDensity: Simplifying Distribution Analysis in R

code

rtip

tidydensity

If you’re a data scientist or statistician who often deals with probability distributions, you know the importance of seamlessly integrating these functions into your…

Steven P. Sanderson II, MPH

Exploring Linear Models with R and Exporting to Excel

code

rtip

excel

Today, we’re going to walk through an example of fitting a linear model in R, summarizing the results, and exporting the findings to an Excel file. This workflow is useful…

Steven P. Sanderson II, MPH

Automate Your R Scripts with `taskscheduleR`

code

rtip

operations

automation

Today, let’s dive into a nifty R package called taskscheduleR that can automate running your R scripts. Whether you need to execute a task every hour or just once a day, task…

Steven P. Sanderson II, MPH

Mastering Zoom Functionality in Excel with VBA

code

rtip

vba

excel

When working with large datasets in Excel, the ability to zoom in and out quickly can significantly enhance your productivity. If you often find yourself adjusting the zoom…

Steven P. Sanderson II, MPH

How to Extract String After a Specific Character in R

code

rtip

operations

strings

Welcome back, R Programmers! Today, we’ll explore a common task: extracting a substring after a specific character in R. Whether you’re cleaning data or transforming…

Steven P. Sanderson II, MPH

Unlocking the Power of Administrative Data with healthyR.data

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rtip

healthyrdata

Hello, fellow data enthusiasts! Today, I’m excited to share insights into the { healthyR.data } package, an essential tool in the healthyverse that will streamline your data…

Steven P. Sanderson II, MPH

How to Execute VBA Code in Excel via R using RDCOMClient

code

rtip

vba

excel

Steven P. Sanderson II, MPH

Exploring Random Walks and Brownian Motions with healthyR.ts

code

rtip

healthyrts

In the world of time series analysis, Random Walks, Brownian Motion, and Geometric Brownian Motion are fundamental concepts used in various fields, including finance…

Steven P. Sanderson II, MPH

How to Run a Macro When a Cell Value Changes in VBA

code

rtip

vba

Hello, everyone! Today, we’ll be diving into a practical example of how to run a macro when a cell value changes in VBA. This is particularly useful when you need to trigger…

Steven P. Sanderson II, MPH

How to Extract Strings Between Specific Characters in R

code

rtip

operations

Hello, R enthusiasts! Today, we’re jumping into a common text processing task: extracting strings between specific characters. This is a great skill for data cleaning and…

Steven P. Sanderson II, MPH

An Introduction to healthyR.ai

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healthyrai

This post will introduction to the healthyR.ai package. The healthyR.ai package is a collection of functions that I have developed to help me analyze and visualize data. The…

Steven P. Sanderson II, MPH

Writing Excel Spreadsheets to Disk with R and Python

code

rtip

python

excel

When working with data, exporting your results to an Excel file can be very handy. Today, I’ll show you how to write the iris dataset to an Excel file using R and Python. We…

Steven P. Sanderson II, MPH

Practical Examples with healthyR.ts

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healthyrts

Today I am going to go over some quick yet practical examples of ways that you can use the healthyR.ts package. This package is designed to help you analyze time series data…

Steven P. Sanderson II, MPH

Extracting Data from Another Workbook Using VBA and Executing It from R

code

rtip

vba

excel

When working with Excel files, you may need to extract data from one workbook and use it in another. This can be done manually by copying and pasting the data, but it can be…

Steven P. Sanderson II, MPH

How to Add Leading Zeros to Numbers in R

code

rtip

operations

Hello! Today, we’re going to discuss a common yet essential task in data manipulation: adding leading zeros to numbers. This might come in handy when dealing with IDs, ZIP…

Steven P. Sanderson II, MPH

Introducing healthyR.ts: A Comprehensive R Package for Time Series Analysis

code

rtip

healthyrts

Hello everyone,

Steven P. Sanderson II, MPH

Working with Excel Files in R and Python

code

rtip

excel

python

If you often work with Excel files and are looking to streamline your data import and export processes, R and Python offer some powerful packages to help you. Here, I’ll…

Steven P. Sanderson II, MPH

An Introduction to healthyR

code

rtip

healthyr

This article will introduce you to the healthyR package. healthyR is a package that provides functions for analyzing and visualizing health-related data. It is designed to…

Steven P. Sanderson II, MPH

VBA Code to Check if a Sheet Exists

code

rtip

vba

excel

In today’s post we are going to go over VBA code to check if a sheet exists and then we are going to call that function from R using the RDCOMClient package. This can be…

Steven P. Sanderson II, MPH

Extracting Numbers from Strings in R

code

rtip

operations

strings

Hello! Today, we’ll jump into something I think is a pretty neat task in data processing: extracting numbers from strings. We’ll explore three different methods using base…

Steven P. Sanderson II, MPH

Introduction to My Content Series

code

rtip

healthyr

Hello Everyone,

Steven P. Sanderson II, MPH

How to Check if a Character is in a String in R

code

rtip

operations

strings

When working with text data in R, one common task is to check if a character or substring is present within a larger string. R offers multiple ways to accomplish this…

Steven P. Sanderson II, MPH

Introduction of My Content Series

code

rtip

operations

Hello, everyone!

Steven P. Sanderson II, MPH

How to Split a Character String and Get the First Element in R

code

rtip

operations

strings

Hello, R community!

Steven P. Sanderson II, MPH

Unveiling New Tools in the TidyDensity Arsenal: Distribution Parameter Wrangling

code

rtip

tidydensity

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…

Steven P. Sanderson II, MPH

An Overview of the New Parameter Estimate Functions in the `TidyDensity` Package

code

rtip

tidydensity

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…

Steven P. Sanderson II, MPH

An Overview of the New AIC Functions in the `TidyDensity` Package

code

rtip

tidydensity

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…

Steven P. Sanderson II, MPH

Exciting New Updates to TidyDensity: Enhancing Distribution Analysis!

code

rtip

operations

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…

Steven P. Sanderson II, MPH

Introducing `get_provider_meta_data()` in healthyR.data

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rtip

healthyrdata

Hello, R enthusiasts!

Steven P. Sanderson II, MPH

Unveiling the Power of `get_cms_meta_data()` in healthyR.data

code

rtip

healthyrdata

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…

Steven P. Sanderson II, MPH

Update to healthyR.data 1.1.0

code

rtip

healthyrdata

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…

Steven P. Sanderson II, MPH

How to Drop or Select Rows with a Specific String in R

code

rtip

operations

Good morning, everyone!

Steven P. Sanderson II, MPH

How to Split a Number into Digits in R Using `gsub()` and `strsplit()`

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rtip

operations

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 …

Steven P. Sanderson II, MPH

How to Split a Vector into Chunks in R

code

rtip

operations

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…

Steven P. Sanderson II, MPH

How to Remove Specific Elements from a Vector in R

code

rtip

operations

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…

Steven P. Sanderson II, MPH

Mastering `gregexpr()` in R: A Comprehensive Guide

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rtip

operations

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…

Steven P. Sanderson II, MPH

Counting Words in a String in R: A Comprehensive Guide

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rtip

operations

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…

Steven P. Sanderson II, MPH

How to Select Columns Containing a Specific String in R

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rtip

operations

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…

Steven P. Sanderson II, MPH

Checking if Multiple Columns are Equal in R

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rtip

operations

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…

Steven P. Sanderson II, MPH

How to Check if a Column Exists in a Data Frame in R

code

rtip

operations

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…

Steven P. Sanderson II, MPH

How to Check if a Column Contains a String in R

code

rtip

operations

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…

Steven P. Sanderson II, MPH

How to Collapse Text by Group in a Data Frame Using R

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rtip

operations

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…

Steven P. Sanderson II, MPH

How to Select Columns by Index in R (Using Base R)

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rtip

operations

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…

Steven P. Sanderson II, MPH

Counting NA Values in Each Column: Comparing Methods in R

code

rtip

operations

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…

Steven P. Sanderson II, MPH

Exploring Model Selection with TidyDensity: Understanding AIC for Statistical Distributions

code

rtip

tidydensity

In the world of data analysis and statistics, one of the key challenges is selecting the best model to describe and analyze your data. This decision is crucial because it…

Steven P. Sanderson II, MPH

Exploring Data with TidyDensity’s `tidy_mcmc_sampling()`

code

rtip

tidydensity

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_s…

Steven P. Sanderson II, MPH

Estimating Chisquare Parameters with TidyDensity

code

rtip

tidydensity

Hello R users! Today, let’s explore the latest addition to the TidyDensity package: util_chisquare_param_estimate(). This function is designed to estimate parameters for a…

Steven P. Sanderson II, MPH

Introducing `check_duplicate_rows()` from TidyDensity

code

rtip

tidydensity

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…

Steven P. Sanderson II, MPH

Quantile Normalization in R with the {TidyDensity} Package

code

rtip

tidydensity

In data analysis, especially when dealing with multiple samples or distributions, ensuring comparability and removing biases is crucial. One powerful technique for achieving…

Steven P. Sanderson II, MPH

Introducing TidyDensity Version 1.4.0: Enhancing Data Analysis in R

code

rtip

tidydensity

I’m thrilled to announce the release of TidyDensity version 1.4.0, packed with exciting features and improvements to elevate your data analysis experience in R. Let’s dive…

Steven P. Sanderson II, MPH

Exploring `strsplit()` with Multiple Delimiters in R

code

rtip

operations

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…

Steven P. Sanderson II, MPH

Simplifying Data Manipulation: How to Drop Columns from Data Frames in R

code

rtip

operations

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…

Steven P. Sanderson II, MPH

A Practical Guide to Selecting Top N Values by Group in R

code

rtip

operations

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…

Steven P. Sanderson II, MPH

Checking Row Existence Across Data Frames in R

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rtip

operations

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…

Steven P. Sanderson II, MPH

Extracting the Last N’th Row in R Data Frames

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rtip

operations

Ever wrangled with a data frame and needed just the final row? Fear not, R warriors! Today’s quest unveils three mighty tools to conquer this task: base R, the dplyr…

Steven P. Sanderson II, MPH

A Guide to Selecting Rows with NA Values in R Using Base R

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rtip

operations

Dealing with missing data is a common challenge in data analysis and machine learning projects. In R, missing values are represented by NA. Being able to identify and handle…

Steven P. Sanderson II, MPH

Selecting Rows with Specific Values: Exploring Options in R

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rtip

operations

In R, we often need to filter data frames based on whether a specific value appears within any of the columns. Both base R and the dplyr package offer efficient ways to…

Steven P. Sanderson II, MPH

Estimating Chi-Square Distribution Parameters Using R

code

rtip

tidydensity

distribution

In the world of statistics and data analysis, understanding and accurately estimating the parameters of probability distributions is crucial. One such distribution is the…

Steven P. Sanderson II, MPH

Taking the data out of the glue with regex in R

code

rtip

operations

glue

unglue

Regular expressions, or regex, are incredibly powerful tools for pattern matching and extracting specific information from text data. Today, we’ll explore how to harness the…

Steven P. Sanderson II, MPH

Mastering Rows: Selecting by Index in R

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rtip

operations

Let’s jump into data manipulation with R! Selecting specific rows from our datasets is an important skill. Today, we’ll focus on subsetting rows by index, using the trusty…

Steven P. Sanderson II, MPH

A Guide to Removing Multiple Rows in R Using Base R

code

rtip

operations

As data analysts and scientists, we often find ourselves working with large datasets where data cleaning becomes a crucial step in our analysis pipeline. One common task is…

Steven P. Sanderson II, MPH

How to Remove Rows with Some or All NAs in R

code

rtip

operations

Handling missing values is a crucial aspect of data preprocessing in R. Often, datasets contain missing values, which can adversely affect the analysis or modeling process.…

Steven P. Sanderson II, MPH

Data Frame Merging in R (With Examples)

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rtip

operations

Merging multiple data frames is a pivotal skill in data manipulation. Whether you’re handling small-scale datasets or large-scale ones, mastering the art of merging can…

Steven P. Sanderson II, MPH

A Practical Guide to Merging Data Frames Based on Multiple Columns in R

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rtip

operations

As a data scientist or analyst, you often encounter situations where you need to combine data from multiple sources. One common task is merging data frames based on multiple…

Steven P. Sanderson II, MPH

Unveiling Car Specs with Multidimensional Scaling in R

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rtip

operations

Visualizing similarities between data points can be tricky, especially when dealing with many features. This is where multidimensional scaling (MDS) comes in handy. It…

Steven P. Sanderson II, MPH

Scaling Your Data in R: Understanding the Range

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rtip

operations

Today, we’re diving into a fundamental data pre-processing technique: scaling values. This might sound simple, but it can significantly impact how your data behaves in…

Steven P. Sanderson II, MPH

A Practical Guide to Data Normalization in R

code

rtip

operations

Data normalization is a crucial preprocessing step in data analysis and machine learning workflows. It helps in standardizing the scale of numeric features, ensuring fair…

Steven P. Sanderson II, MPH

Mastering Quantile Normalization in R: A Step-by-Step Guide

code

rtip

operations

Quantile normalization is a crucial technique in data preprocessing, especially in fields like genomics and bioinformatics. It ensures that the distributions of different…

Steven P. Sanderson II, MPH

Mastering Text Manipulation in R: A Guide to Using gsub() for Multiple Pattern Replacement

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rtip

operations

In the realm of text manipulation in R, the gsub() function stands as a powerful tool, allowing you to replace specific patterns within strings effortlessly. Whether you’re…

Steven P. Sanderson II, MPH

Mastering the map() Function in R: A Comprehensive Guide

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rtip

operations

In the world of data manipulation and analysis with R, efficiency and simplicity are paramount. One function that epitomizes these qualities is map(). Whether you’re a…

Steven P. Sanderson II, MPH

Wrangling Data with R: A Guide to the tapply() Function

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rtip

operations

Hey R enthusiasts! Today we’re diving into the world of data manipulation with a fantastic function called tapply(). This little gem lets you apply a function of your choice…

Steven P. Sanderson II, MPH

Mastering Data Manipulation in R with the Sweep Function

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rtip

operations

Welcome to another exciting journey into the world of data manipulation in R! In this blog post, we’re going to explore a powerful tool in R’s arsenal: the sweep function.…

Steven P. Sanderson II, MPH

Mastering Replacement: Using the replace() Function in R

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rtip

operations

The replace() function is a handy tool in your R toolbox for modifying specific elements within vectors and data frames. It allows you to swap out unwanted values with new…

Steven P. Sanderson II, MPH

Mastering Data Segmentation: A Guide to Using the cut() Function in R

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rtip

operations

In the realm of data analysis, understanding how to effectively segment your data is paramount. Whether you’re dealing with age groups, income brackets, or any other…

Steven P. Sanderson II, MPH

How to Replicate Rows in a Data Frame in R

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rtip

operations

Are you working with a dataset where you need to duplicate certain rows multiple times? Perhaps you want to create synthetic data by replicating existing observations, or…

Steven P. Sanderson II, MPH

Introducing `plot_regression_residuals()` from tidyAML: Unveiling the Power of Visualizing Regression Residuals

tidyaml

rtip

data-analysis

data-science

Greetings, fellow R enthusiasts! Today, we’re diving into the depths of tidyAML, specifically exploring a new gem in its arsenal: plot_regression_residuals(). Strap in as we…

Steven P. Sanderson II, MPH

Plotting Training and Testing Predictions with tidyAML

tidyaml

rtip

data-analysis

data-science

In the realm of machine learning, visualizing model predictions is essential for understanding the performance and behavior of our algorithms. When it comes to regression…

Steven P. Sanderson II, MPH

Unleash the Power of Your Data: Extend Excel with Python and R!

rtip

excel

python

data-analysis

viz

Have you ever felt limited by Excel’s capabilities? Sure, it’s fantastic for basic tasks and creating clear spreadsheets, but what if your data craves something more? What…

Steven P. Sanderson II, MPH

🚀 Exciting News! 🚀

tidyaml

rtip

data-analysis

data-science

I’m thrilled to announce the latest release of tidyAML, version 0.0.5, now available for download on CRAN or GitHub! 🎉

Steven P. Sanderson II, MPH

Mastering Random Sampling in R with the sample() Function

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rtip

operations

The sample() function in R is a powerful tool that allows you to generate random samples from a given dataset or vector. It’s an essential function for tasks such as data…

Steven P. Sanderson II, MPH

Wrangling Names in R: Your Guide to the make.names() Function

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rtip

operations

Ever tried to use a number or special character as a name for a variable or column in R, only to be met with an error? R has specific rules for what constitutes a valid…

Steven P. Sanderson II, MPH

Taming the Nameless: Using the names() Function in R

code

rtip

operations

dplyr

datatable

Have you ever created a dataset in R and ended up with a bunch of unnamed elements? It can make your code clunky and hard to read. Fear not, fellow R wranglers! The names() f…

Steven P. Sanderson II, MPH

How to Subset Data Frame in R by Multiple Conditions

code

rtip

operations

dplyr

datatable

In data analysis with R, subsetting data frames based on multiple conditions is a common task. It allows us to extract specific subsets of data that meet certain criteria.…

Steven P. Sanderson II, MPH

How to Add New Level to Factor in R

code

rtip

operations

As an R programmer, working with categorical data is a common task, and factors (a data type in R) are used to represent categorical variables. However, sometimes you may…

Steven P. Sanderson II, MPH

How to Rename Factor Levels in R

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rtip

operations

Hey there, fellow R enthusiasts! Today, we’re diving into the world of factors in R and learning how to rename their levels. Factors are essential data structures in R…

Steven P. Sanderson II, MPH

A Beginner’s Guide to Renaming Data Frame Columns in R

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rtip

operations

Welcome back, fellow R enthusiasts! Today, we’re diving into a fundamental yet crucial aspect of data manipulation: renaming data frame columns. Whether you’re just starting…

Steven P. Sanderson II, MPH

Filtering Rows in R Where Column Value is Between Two Values

code

rtip

operations

Filtering data frames in R is a common task in data analysis. Often we want to subset a data frame to only keep rows that meet certain criteria. A useful filtering technique…

Steven P. Sanderson II, MPH

Unlocking Efficiency: How to Set a Data Frame Column as Index in R

code

rtip

operations

In the realm of data manipulation and analysis, efficiency is paramount. One powerful technique to enhance your workflow is setting a column in a data frame as the index.…

Steven P. Sanderson II, MPH

Demystifying the melt() Function in R

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rtip

operations

The melt() function in the data.table package is an extremely useful tool for reshaping datasets in R. However, for beginners, understanding how to use melt() can be tricky.…

Steven P. Sanderson II, MPH

Unveiling the Magic of `dcast` Function in R’s data.table Package

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rtip

operations

Here is a draft blog post on using the dcast function from the data.table package in R:

Steven P. Sanderson II, MPH

Taming the Data Jungle: Filtering data.tables and data.frames in R

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rtip

operations

Ah, data! The lifeblood of many an analysis, but sometimes it can feel like you’re lost in a tangled jungle. Thankfully, R offers powerful tools to navigate this data…

Steven P. Sanderson II, MPH

Demystifying Data Types in R: A Beginner’s Guide with Code Examples

code

rtip

operations

Ever wondered what kind of information your data holds in R? Knowing the data type is crucial for performing the right analysis and avoiding errors. This post will equip you…

Steven P. Sanderson II, MPH

Enhancing Your Plots in R: Adding Superscripts & Subscripts

code

rtip

operations

Hey R enthusiasts! Are you looking to take your data visualization skills to the next level? Well, you’re in the right place because today, we’re diving into the world of…

Steven P. Sanderson II, MPH

Level Up Your Data Wrangling: Adding Index Columns in R like a Pro!

code

rtip

operations

Data wrangling in R is like cooking: you have your ingredients (data), and you use tools (functions) to prepare them (clean, transform) for analysis (consumption!). One…

Steven P. Sanderson II, MPH

Conquering R’s Apply Family: Your Guide to apply(), lapply(), sapply(), and tapply()

code

rtip

operations

Welcome, fellow R warriors! Today, we delve into the heart of vectorized operations with R’s “apply” family: apply(), lapply(), sapply(), and tapply(). These functions are…

Steven P. Sanderson II, MPH

Mastering Date Sequences in R: A Comprehensive Guide

code

rtip

timeseries

In the world of data analysis and manipulation, working with dates is a common and crucial task. Whether you’re analyzing financial data, tracking trends over time, or…

Steven P. Sanderson II, MPH

How to Get First or Last Day of Month in R with lubridate and base R

code

rtip

timeseries

When working with dates in R, you’ll often need to find the first or last day of the current month or any given month. There are a couple easy ways to do this using the…

Steven P. Sanderson II, MPH

From Chaos to Clarity: Mastering Weekly Data Wrangling in R with strftime()

code

rtip

timeseries

Grouping data by week is a common task in data analysis. It allows you to summarize and analyze your data on a weekly basis. In R, there are a few different ways to group…

Steven P. Sanderson II, MPH

Demystifying Dates: Finding the Day of the Week in R with `lubridate`

code

rtip

timeseries

Have you ever stared at a date in R and wondered, “What day of the week was this?!” Fear not, fellow data wranglers! Today, we embark on a journey to conquer this seemingly…

Steven P. Sanderson II, MPH

How to Check if a Column is a Date in R: A Comprehensive Guide with Examples

code

rtip

timeseries

As an R programmer, you may often encounter datasets where you need to determine whether a column contains date values. This task is crucial for data cleaning, manipulation…

Steven P. Sanderson II, MPH

How to Check if Date is Between Two Dates in R

code

rtip

timeseries

Hello fellow R enthusiasts! Today, we’re diving into a common task in data analysis and manipulation: checking if a date falls between two given dates. Whether you’re…

Steven P. Sanderson II, MPH

Simplifying Date Manipulation: How to Get Week Numbers in R

code

rtip

timeseries

When working with dates in R, you may need to extract the week number for any given date. This can be useful for doing time series analysis or visualizations by week.

Steven P. Sanderson II, MPH

Taming Excel Dates in R: From Numbers to Meaningful Dates!

code

rtip

timeseries

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…

Steven P. Sanderson II, MPH

Accounts Recievables Pathways in SQL

code

sql

Yesterday I was working on a project that required me to create a SQL query to generate a table of accounts receivables pathways. I thought it would be interesting to share…

Steven P. Sanderson II, MPH

R for the Real World: Counting those Business Days like a Pro!

code

rtip

timeseries

Hi fellow coders, data wranglers, and all-around R enthusiasts! Have you ever been stuck calculating the number of business days between two dates? You know, like figuring…

Steven P. Sanderson II, MPH

Time Flies? Time Travels! Adding Days to Dates in R (Like a Pro)

code

rtip

timeseries

Ever wished you could skip ahead a few days for that weekend getaway, or rewind to relive a magical moment? While real-life time travel remains a sci-fi dream, in R, adding…

Steven P. Sanderson II, MPH

Mastering Time Manipulation in R: Subtracting Hours with Ease

code

rtip

timeseries

Ever wished you could rewind time in R, not just for debugging, but for actual data analysis? Well, you don’t need plutonium and flux capacitors! Let’s dive into the…

Steven P. Sanderson II, MPH

How to Extract Month from Date in R (With Examples)

code

rtip

timeseries

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…

Steven P. Sanderson II, MPH

Unveiling the Earliest Date: A Journey Through R

code

rtip

timeseries

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…

Steven P. Sanderson II, MPH

Mastering Date Calculations in R: A Guide to Calculating Months with Base R and lubridate

code

rtip

timeseries

Welcome back, fellow R enthusiasts! Today, we’re diving into a common task in data manipulation: subtracting hours from time objects in R. Whether you’re working with…

Steven P. Sanderson II, MPH

Mastering Date Calculations in R: A Guide to Calculating Months with Base R and lubridate

code

rtip

timeseries

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…

Steven P. Sanderson II, MPH

Exploring Data Lengths with R’s lengths() Function

code

rtip

Hey folks! Today, we’re diving into the world of R programming, and our star of the show is the lengths() function. This little gem might not be as famous as some other R…

Steven P. Sanderson II, MPH

Demystifying `bootstrap_stat_plot()`: Your Ticket to Insightful Data Exploration

code

rtip

tidydensity

Ever feel like your data is hiding secrets? Like it’s whispering truths but you just can’t quite grasp them? Well, fear not, fellow data sleuths! Today, we’ll crack the code…

Steven P. Sanderson II, MPH

The new function on the block with tidyAML `extract_regression_residuals()`

code

rtip

tidyaml

Yesterday I discussed the use of the function internal_make_wflw_predictions() in the tidyAML R package. Today I will discuss the use of the function extract_wflw_pred() and…

Steven P. Sanderson II, MPH

Exploring the Enhanced Features of tidyAML’s internal_make_wflw_predictions()

code

rtip

tidyaml

Hey R enthusiasts! Steve here, and today I’m excited to share some fantastic updates about a key function in the tidyAML package – internal_make_wflw_predictions(). The…

Steven P. Sanderson II, MPH

Using .drop_na in Fast Classification and Regression

code

rtip

tidyaml

In the newest release of tidyAML there has been an addition of a new parameter to the functions fast_classification() and fast_regression(). The parameter is .drop_na and it…

Steven P. Sanderson II, MPH

Exploring the Power of tidyAML 0.0.4: Unleashing New Features and Enhancements

code

rtip

tidyaml

Greetings, fellow data enthusiasts! Today, we’re diving into the exciting world of tidyAML 0.0.4, where innovation meets efficiency in the realm of R programming. As we…

Steven P. Sanderson II, MPH

TidyDensity Powers Up with Data.table: Speedier Distributions for Your Data Exploration

code

benchmark

datatable

tidydensity

I’m thrilled to announce a major upgrade to the TidyDensity package that’s sure to accelerate your data analysis workflows. We’ve integrated the lightning-fast data.table pac…

Steven P. Sanderson II, MPH

Benchmarking the Speed of Cumulative Functions in TidyDensity

code

tidydensity

benchmark

Statistical analysis often involves calculating various measures on large datasets. Speed and efficiency are crucial, especially when dealing with real-time analytics or…

Steven P. Sanderson II, MPH

Exploring the Peaks: A Dive into the Triangular Distribution in TidyDensity

code

tidydensity

Welcome back, fellow data enthusiasts! Today, we embark on an exciting journey into the world of statistical distributions with a special focus on the latest addition to the…

Steven P. Sanderson II, MPH

New Horizons for TidyDensity: Version 1.3.0 Release

code

tidydensity

The latest release of the TidyDensity R package brings some major changes and improvements that open up new possibilities for statistical analysis and data visualization.…

Steven P. Sanderson II, MPH

Conquering Daily Data: How to Aggregate to Months and Years Like a Pro in R

code

rtip

timeseries

Taming the beast of daily data can be daunting. While it captures every detail, sometimes you need a bird’s-eye view. Enter aggregation, your secret weapon for transforming…

Steven P. Sanderson II, MPH

Unveiling the Smooth Operator: Rolling Averages in R

code

rtip

timeseries

Ever felt those data points were a bit too jittery? Smoothing out trends and revealing underlying patterns is a breeze with rolling averages in R. Ready to roll? Let’s dive…

Steven P. Sanderson II, MPH

Reflecting on the Past Year: A LinkedIn Year in Review (2023)

code

rtip

linkedin

As we close the door on another year, it’s always a good time to look back and reflect on the past 12 months. And what better way to do that than with a LinkedIn Year in…

Steven P. Sanderson II, MPH

Mastering Lowess Smoothing in R: A Step-by-Step Guide

rtip

timeseries

Hey fellow R enthusiasts! Today, let’s dive into the fascinating world of Lowess smoothing and learn how to harness its power for creating smooth visualizations of your…

Steven P. Sanderson II, MPH

Unlocking the Power of Time: Transforming Data Frames into Time Series in R

rtip

timeseries

Hey there, fellow R enthusiasts! Today, we’re diving into the realm of time series, where data dances along the temporal dimension. To join this rhythmic analysis, we’ll…

Steven P. Sanderson II, MPH

Unveiling the Time Traveler: Plotting Time Series in R

rtip

timeseries

Ready to journey through time with R? Buckle up, because we’re about to explore the art of visualizing time-dependent data, known as time series analysis. Whether you’re…

Steven P. Sanderson II, MPH

Creating Time Series in R with the ts() Function

rtip

timeseries

Time series analysis is a powerful tool in the hands of a data scientist or analyst. It allows us to uncover patterns, trends, and insights hidden within temporal data. In…

Steven P. Sanderson II, MPH

A Gentle Introduction to Poisson Regression for Count Data: School’s Out, Job Offers Incoming!

rtip

regression

Hey data enthusiasts! Today, we’re diving into the fascinating world of count data and its trusty sidekick, Poisson regression. Buckle up, because we’re about to explore how…

Steven P. Sanderson II, MPH

Exploring Variance Inflation Factor (VIF) in R: A Practical Guide

rtip

regression

Hey there fellow R enthusiasts! Today, we’re diving into the fascinating world of Variance Inflation Factor (VIF) and how to calculate it using R. VIF is a crucial metric…

Steven P. Sanderson II, MPH

Demystifying Odds Ratios in Logistic Regression: Your R Recipe for Loan Defaults

rtip

regression

Ever wondered why some individuals default on loans while others don’t? Logistic regression can shed light on this, and calculating odds ratios in R is the secret sauce. So…

Steven P. Sanderson II, MPH

Decoding the Mystery: How to Interpret Regression Output in R Like a Champ

rtip

regression

Ever run an R regression and stared at the output, feeling like you’re deciphering an ancient scroll? Fear not, fellow data enthusiasts! Today, we’ll crack the code and turn…

Steven P. Sanderson II, MPH

Conquering Unequal Variance with Weighted Least Squares in R: A Practical Guide

rtip

regression

Tired of your least-squares regression model giving wonky results because some data points shout louder than others? Meet Weighted Least Squares (WLS), the superhero of…

Steven P. Sanderson II, MPH

Exploring TidyAML: Simplifying Regression Analysis in R

rtip

regression

If you’re a data enthusiast diving into the world of regression analysis in R, you’ve likely encountered the challenges of managing code complexity and juggling different…

Steven P. Sanderson II, MPH

Unraveling Patterns: A Step-by-Step Guide to Piecewise Regression in R

rtip

regression

Hey there, fellow R enthusiasts! Today, let’s embark on a fascinating journey into the realm of piecewise regression using R. If you’ve ever wondered how to uncover hidden…

Steven P. Sanderson II, MPH

A Complete Guide to Stepwise Regression in R

rtip

regression

Stepwise regression is a powerful technique used to build predictive models by iteratively adding or removing variables based on statistical criteria. In R, this can be…

Steven P. Sanderson II, MPH

Unveiling the Magic of Polynomial Regression in R: A Step-by-Step Guide

rtip

regression

Hey folks! 👋 Today, let’s embark on a coding adventure and explore the fascinating world of Polynomial Regression in R. Whether you’re new to R or a seasoned coder, we’re…

Steven P. Sanderson II, MPH

Understanding Spline Regression

rtip

regression

Spline regression is particularly useful when the relationship between the independent and dependent variables is not adequately captured by a linear model. It involves…

Steven P. Sanderson II, MPH

tidyAML: Now supporting gee models

rtip

tidyaml

regression

classification

I am happy to announce that a new version of tidyAML is now available on CRAN. This version includes support for gee models. This is a big step forward for tidyAML as it now…

Steven P. Sanderson II, MPH

Navigating Quantile Regression with R: A Comprehensive Guide

rtip

regression

Quantile regression is a robust statistical method that goes beyond traditional linear regression by allowing us to model the relationship between variables at different…

Steven P. Sanderson II, MPH

Understanding and Implementing Robust Regression in R

rtip

regression

If you’re familiar with linear regression in R, you’ve probably encountered the traditional lm() function. While this is a powerful tool, it might not be the best choice…

Steven P. Sanderson II, MPH

Unveiling Power Regression: A Step-by-Step Guide in R

rtip

regression

In the realm of statistics, power regression stands out as a versatile tool for exploring the relationship between two variables, where one variable is the power of the…

Steven P. Sanderson II, MPH

Unveiling the Magic of LOESS Regression in R: A Step-by-Step Guide with mtcars

rtip

regression

If you’ve ever found yourself grappling with noisy data and yearning for a smoother representation, LOESS regression might be the enchanting solution you’re seeking. In this…

Steven P. Sanderson II, MPH

Logarithmic Regression in R: A Step-by-Step Guide with Prediction Intervals

rtip

regression

Logarithmic regression is a statistical technique used to model the relationship between a dependent variable and an independent variable when the relationship is…

Steven P. Sanderson II, MPH

Mastering Exponential Regression in R: A Step-by-Step Guide

rtip

regression

Hey folks, welcome back to another exciting R programming journey! Today, we’re diving into the fascinating world of exponential regression using base R. Exponential…

Steven P. Sanderson II, MPH

Quadratic Regression in R: Unveiling Non-Linear Relationships

rtip

regression

In the realm of data analysis, quadratic regression emerges as a powerful tool for uncovering the hidden patterns within datasets that exhibit non-linear relationships.…

Steven P. Sanderson II, MPH

{healthyR.ts} New Features: Unlocking More Power

rtip

healthyrts

timeseries

My R package {healthyR.ts} has been updated to version 0.3.0; you can install it from either CRAN, r-universe or GitHub. Let’s go over some of the changes and improvements.

Steven P. Sanderson II, MPH

How to Perform Multiple Linear Regression in R

rtip

regression

Multiple linear regression is a powerful statistical method that allows us to examine the relationship between a dependent variable and multiple independent variables.

Steven P. Sanderson II, MPH

How to Predict a Single Value Using a Regression Model in R

rtip

regression

Regression models are a powerful tool for predicting future values based on historical data. They are used in a wide range of industries, including finance, healthcare, and…

Steven P. Sanderson II, MPH

Unlocking the Power of Prediction Intervals in R: A Practical Guide

rtip

Prediction intervals are a powerful tool for understanding the uncertainty of your predictions. They allow you to specify a range of values within which you are confident…

Steven P. Sanderson II, MPH

How to Simulate & Plot a Bivariate Normal Distribution in R: A Hands-on Guide

rtip

distribution

Welcome to the fascinating world of bivariate normal distributions! In this blog post, we’ll embark on a journey to understand, simulate, and visualize these distributions…

Steven P. Sanderson II, MPH

Demystifying Data: A Comprehensive Guide to Calculating and Plotting Cumulative Distribution Functions (CDFs) in R

rtip

In the realm of statistics, a cumulative distribution function (CDF) serves as a crucial tool for understanding the behavior of data. It provides a comprehensive picture of…

Steven P. Sanderson II, MPH

Introducing TidyDensity’s New Powerhouse: The convert_to_ts() Function

rtip

tidydensity

timeseries

If you’re an R enthusiast like me, you know that data manipulation is at the core of everything we do. The ability to transform your data swiftly and efficiently can make or…

Steven P. Sanderson II, MPH

Fitting a Distribution to Data in R

rtip

distribution

The gamma distribution is a continuous probability distribution that is often used to model waiting times or other positively skewed data. It is a two-parameter…

Steven P. Sanderson II, MPH

Understanding the Triangular Distribution and Its Application in R

rtip

distribution

As an R programmer and enthusiast, I’m excited to delve into the fascinating world of probability distributions. One of the lesser-known but incredibly useful distributions…

Steven P. Sanderson II, MPH

Multinomial Distribution in R

rtip

distribution

The multinomial distribution is a probability distribution that describes the probability of obtaining a specific number of counts for k different outcomes, when each…

Steven P. Sanderson II, MPH

Randomness in R: runif(), punif(), dunif(), and quinf()

rtip

distribution

Randomness is an essential part of many statistical and machine learning tasks. In R, there are a number of functions that can be used to generate random numbers, but the run…

Steven P. Sanderson II, MPH

Plotting Log Log Plots In Base R

rtip

viz

A log-log plot is a type of graph where both the x-axis and y-axis are in logarithmic scales. This is particularly useful when dealing with data that spans several orders of…

Steven P. Sanderson II, MPH

Plotting a Logistic Regression In Base R

rtip

viz

Logistic regression is a statistical method used for predicting the probability of a binary outcome. It’s a fundamental tool in machine learning and statistics, often…

Steven P. Sanderson II, MPH

What’s a Bland-Altman Plot? In Base R

rtip

viz

Before we dive into the code, let’s briefly understand what a Bland-Altman plot is. It’s a graphical method to visualize the agreement between two measurement techniques…

Steven P. Sanderson II, MPH

Creating a Scree Plot in Base R

rtip

viz

A scree plot is a line plot that shows the eigenvalues or variance explained by each principal component (PC) in a Principal Component Analysis (PCA). It is a useful tool…

Steven P. Sanderson II, MPH

How to Create a Bubble Chart in R using ggplot2

rtip

viz

ggplot2

Bubble charts are a great way to visualize data with three dimensions. The size of the bubbles represents a third variable, which can be used to show the importance of that…

Steven P. Sanderson II, MPH

Creating Pareto Charts in R with the qcc Package

rtip

viz

A Pareto chart is a type of bar chart that shows the frequency of different categories in a dataset, ordered by frequency from highest to lowest. It is often used to…

Steven P. Sanderson II, MPH

Mastering Interaction Plots in R: Unveiling Hidden Relationships

rtip

viz

In the world of data analysis, uncovering hidden relationships between variables is often the key to making informed decisions. Interaction plots in R can be your secret…

Steven P. Sanderson II, MPH

Making Time Series Stationary Made Easy with `auto_stationarize()`

rtip

healthyrts

timeseries

When working with time series data, one common challenge is dealing with non-stationary data. Non-stationary time series can be a headache for analysts, but fear not…

Steven P. Sanderson II, MPH

Testing stationarity with the ts_adf_test() function in R

rtip

healthyrts

timeseries

Hey there, R enthusiasts! Today, we’re going to dive into the fascinating world of time series analysis using the ts_adf_test() function from the healthyR.ts R library. If…

Steven P. Sanderson II, MPH

Analyzing Time Series Growth with `ts_growth_rate_vec()` in healthyR.ts

rtip

healthyrts

timeseries

Time series data is essential for understanding trends and making forecasts in various fields, from finance to healthcare. Analyzing the growth rate of time series data is a…

Steven P. Sanderson II, MPH

Mastering the Art of Drawing Circles in Plots with R

rtip

viz

As an R programmer, you may want to draw circles in plots to highlight certain data points or to create visualizations. Here are some simple steps to draw circles in plots…

Steven P. Sanderson II, MPH

How to Use cex to Change the Size of Plot Elements in base R

rtip

viz

Let’s dive into the world of R and explore how to use cex to change the size of plot elements in base R. Whether you’re a seasoned R user or just starting out, understanding…

Steven P. Sanderson II, MPH

Horizontal Legends in Base R

rtip

viz

Creating a horizontal legend in base R can be a useful skill when you want to label multiple categories in a plot without taking up too much vertical space. In this blog…

Steven P. Sanderson II, MPH

Resizing Legends in R

rtip

viz

Changing the size of the legend on a plot in R can be a handy skill, especially when you want to enhance the readability and aesthetics of your visualizations. In this blog…

Steven P. Sanderson II, MPH

Mastering Legends in R: Drawing Them Outside the Plot

rtip

viz

Legends are an essential part of data visualization. They help us understand the meaning behind the colors and shapes in our plots. But what if your legend is too big or…

Steven P. Sanderson II, MPH

Introduction

rtip

viz

As an R programmer, you may want to create added variable plots to visualize the relationship between a predictor variable and the response variable while controlling for…

Steven P. Sanderson II, MPH

Creating Stacked Dot Plots in R: A Guide with Base R and ggplot2

rtip

viz

Stacked dot plots are a type of plot that displays frequencies using dots, piled one over the other. In R, there are several ways to create stacked dot plots, including…

Steven P. Sanderson II, MPH

Creating Interactive Radar Charts in R with the ‘fmsb’ Library

rtip

viz

Radar charts, also known as spider, web, polar, or star plots, are a useful way to visualize multivariate data. In R, we can create radar charts using the fmsb library. Here…

Steven P. Sanderson II, MPH

Horizontal Boxplots in R using the Palmer Penguins Data Set

rtip

viz

Boxplots are a great way to visualize the distribution of a numerical variable. They show the median, quartiles, and outliers of the data, and can be used to compare the…

Steven P. Sanderson II, MPH

Plotting Decision Trees in R with `rpart` and `rpart.plot`

rtip

viz

Decision trees are a powerful machine learning algorithm that can be used for both classification and regression tasks. They are easy to understand and interpret, and they…

Steven P. Sanderson II, MPH

How to Reorder Boxplots in R: A Comprehensive Guide

rtip

viz

Boxplots are a great way to visualize the distribution of a dataset. However, sometimes the default ordering of boxplots may not be ideal for the data being presented. In…

Steven P. Sanderson II, MPH

Enhancing Your Data Visualizations with Base R: Overlaying Points and Lines

rtip

viz

Data visualization is a crucial aspect of data analysis and exploration. It allows us to gain insights, spot trends, and communicate our findings effectively. In R, there…

Steven P. Sanderson II, MPH

Mastering Data Visualization with ggplot2: A Guide to Using facet_grid()

rtip

viz

ggplot2

Data visualization is a crucial tool in the data scientist’s toolkit. It allows us to explore and communicate complex patterns and insights effectively. In the world of R…

Steven P. Sanderson II, MPH

Mastering Data Visualization with Pairs Plots in Base R

rtip

viz

Data visualization is a crucial tool in data analysis, allowing us to gain insights from our data quickly. One of the fundamental techniques for exploring relationships…

Steven P. Sanderson II, MPH

Creating Confidence Intervals for a Linear Model in R Using Base R and the Iris Dataset

rtip

viz

Linear regression is a fundamental statistical technique used to model the relationship between a dependent variable and one or more independent variables. While fitting a…

Steven P. Sanderson II, MPH

Mastering Data Visualization in R: Plotting Predicted Values with the mtcars Dataset

rtip

viz

Data visualization is a powerful tool in a data scientist’s toolkit. It not only helps us understand our data but also presents it in a way that is easy to comprehend. In…

Steven P. Sanderson II, MPH

Exploring Data with Scatter Plots by Group in R

rtip

viz

Data visualization is a powerful tool for gaining insights from your data. Scatter plots, in particular, are excellent for visualizing relationships between two continuous…

Steven P. Sanderson II, MPH

Mastering Histogram Breaks in R: Unveiling the Power of Data Visualization

rtip

viz

Histograms are a fundamental tool in data analysis and visualization, allowing us to explore the distribution of data quickly and effectively. While creating a histogram in…

Steven P. Sanderson II, MPH

Histograms with Two or More Variables in R

rtip

viz

Histograms are powerful tools for visualizing the distribution of a single variable, but what if you want to compare the distributions of two variables side by side? In this…

Steven P. Sanderson II, MPH

How to Create a Histogram with Different Colors in R

rtip

viz

Histograms are a fantastic way to visualize the distribution of data. They provide insights into the underlying patterns and help us understand our data better. But what if…

Steven P. Sanderson II, MPH

How to Plot Multiple Plots on the Same Graph in R

rtip

viz

Data visualization is a crucial aspect of data analysis. In R, the flexibility and power of its plotting capabilities allow you to create compelling visualizations. One…

Steven P. Sanderson II, MPH

Exploring the Third Dimension with R: A Guide to the persp() Function

rtip

viz

If you’re an R enthusiast looking to take your data visualization to the next level, you’re in for a treat. In this blog post, we’re going to dive into the world of 3D…

Steven P. Sanderson II, MPH

Plotting SVM Decision Boundaries with e1071 in R

rtip

viz

Support Vector Machines (SVM) are a powerful tool in the world of machine learning and classification. They excel in finding the optimal decision boundary between different…

Steven P. Sanderson II, MPH

Creating Population Pyramid Plots in R with ggplot2

rtip

viz

Are you interested in visualizing demographic data in a unique and insightful way? Population pyramids are a fantastic tool for this purpose! They allow you to compare the…

Steven P. Sanderson II, MPH

Mastering Data Visualization in R: How to Plot a Subset of Data

rtip

viz

Data visualization is a powerful tool for gaining insights from your data. In R, you have a plethora of libraries and functions at your disposal to create stunning and…

Steven P. Sanderson II, MPH

Exploring Multivariate Data with Principal Component Analysis (PCA) Biplot in R

rtip

viz

When it comes to analyzing multivariate data, Principal Component Analysis (PCA) is a powerful technique that can help us uncover hidden patterns, reduce dimensionality, and…

Steven P. Sanderson II, MPH

When to use Jitter

rtip

viz

As an R programmer, one of the most useful functions to know is the jitter function. The jitter function is used to add random noise to a numeric vector, which can be…

Steven P. Sanderson II, MPH

Kernel Density Plots in R

rtip

viz

Kernel Density Plots are a type of plot that displays the distribution of values in a dataset using one continuous curve. They are similar to histograms, but they are even…

Steven P. Sanderson II, MPH

Creating Eye-Catching Data Visualizations with Lollipop Charts in R using ggplot2

rtip

viz

When it comes to conveying information effectively, data visualization is a powerful tool that can make complex data more accessible and understandable. One captivating type…

Steven P. Sanderson II, MPH

Exploring Relationships with Correlation Heatmaps in R

rtip

viz

correlation

Data visualization is a powerful tool for understanding the relationships between variables in a dataset. One of the most common and insightful ways to visualize…

Steven P. Sanderson II, MPH

Visualizing Categorical Data in R: A Guide with Engaging Charts Using the Iris Dataset

rtip

viz

Categorical data is a type of data that represents distinct groups or categories. Visualizing categorical data can provide valuable insights and help in understanding…

Steven P. Sanderson II, MPH

Enhancing Your Histograms in R: Adding Vertical Lines for Better Insights

rtip

viz

Are you tired of looking at plain, vanilla histograms that just show the distribution of your data without any additional context? If so, you’re in for a treat! In this blog…

Steven P. Sanderson II, MPH

How to Plot Multiple Histograms with Base R and ggplot2

rtip

viz

Histograms are a powerful tool for visualizing the distribution of numerical data. They allow us to quickly understand the frequency distribution of values within a dataset.…

Steven P. Sanderson II, MPH

Plotting Multiple Lines on a Graph in R: A Step-by-Step Guide

rtip

viz

Graphs are powerful visual tools for analyzing and presenting data. In this blog post, we will explore how to plot multiple lines on a graph using base R. We will cover two…

Steven P. Sanderson II, MPH

Exploring Data Distribution in R: A Comprehensive Guide

rtip

viz

Understanding the distribution of your data is a fundamental step in any data analysis process. It gives you insights into the spread, central tendency, and overall shape of…

Steven P. Sanderson II, MPH

Unveiling Data Distribution Patterns with `stripchart()` in R

rtip

viz

Data visualization is a powerful tool that allows us to uncover patterns and insights within datasets. One such tool in the R programming arsenal is the stripchart() function…

Steven P. Sanderson II, MPH

Exploring Box Plots with Mean Values using Base R and ggplot2

rtip

viz

Data visualization is a powerful tool for understanding and interpreting data. In this blog post, we will explore how to create box plots with mean values using both base R…

Steven P. Sanderson II, MPH

Exploring Data Distribution with Box Plots in R

rtip

viz

Are you ready to dive into the world of data visualization in R? One powerful tool at your disposal is the box plot, also known as a box-and-whisker plot. This versatile…

Steven P. Sanderson II, MPH

Mastering Data Approximation with R’s approx() Function

rtip

Are you tired of dealing with irregularly spaced data points that just don’t seem to fit together? Do you find yourself struggling to interpolate or smooth your data for…

Steven P. Sanderson II, MPH

Exploring the Power of the curve() Function in R

rtip

viz

In the vast world of R programming, there are numerous functions that provide powerful capabilities for data visualization and analysis. One such function that often goes…

Steven P. Sanderson II, MPH

Solving Systems of Equations in R using the `solve()` Function

rtip

linearequations

In mathematical modeling and data analysis, it is often necessary to solve systems of equations to find the values of unknown variables. R provides the solve() function…

Steven P. Sanderson II, MPH

The `substring()` function in R

rtip

The substring() function in R is used to extract a substring from a character vector. The syntax of the function is:

Steven P. Sanderson II, MPH

pmax() and pmin(): Finding the Parallel Maximum and Minimum in R

rtip

Title: Unleashing the Power of pmax() and pmin() Functions in R

Steven P. Sanderson II, MPH

Mastering Grouped Counting in R: A Comprehensive Guide

rtip

As data-driven decision-making becomes more critical in various fields, the ability to extract valuable insights from datasets has never been more important. One common task…

Steven P. Sanderson II, MPH

Mastering Data Visualization: A Guide to Harnessing the Power of R’s par() Function

rtip

When it comes to data visualization in R, the par() function is an indispensable tool that often goes overlooked. This function allows you to control various graphical…

Steven P. Sanderson II, MPH

Mastering Data Transformation with the scale() Function in R

rtip

Data analysis often requires preprocessing and transforming data to make it more suitable for analysis. In R, the scale() function is a powerful tool that allows you to…

Steven P. Sanderson II, MPH

Enhance Your Plots with the `text()` Function in R

rtip

As a programmer, you’re well aware of the importance of data visualization. A well-crafted plot can convey complex information with clarity and impact. In R, creating…

Steven P. Sanderson II, MPH

Exploring R’s Versatile str() Function: Unraveling Your Data with Ease!

rtip

Welcome, fellow data enthusiasts, to another exciting blog post! Today, we’re diving deep into R’s invaluable str() function – a powerful tool for gaining insight into your…

Steven P. Sanderson II, MPH

A Handy Guide to read.delim() in R - Unraveling the Magic of Reading Tabular Data

rtip

Welcome, data enthusiasts! If you’re diving into the realm of data analysis with R, one function you’ll undoubtedly encounter is read.delim(). It’s an essential tool that…

Steven P. Sanderson II, MPH

The `unlist()` Function in R

rtip

Hey fellow R enthusiasts!

Steven P. Sanderson II, MPH

R Functions for Getting Objects

rtip

Welcome, fellow programmers, to this exciting journey into the world of R functions! Today, we’ll explore four powerful functions: get(), get0(), dynGet(), and mget(). These…

Steven P. Sanderson II, MPH

The replicate() function in R

rtip

As a programmer, you must have encountered situations where you need to repeat a task multiple times. Repetitive tasks are not only tedious but also prone to errors. What if…

Steven P. Sanderson II, MPH

The intersect() function in R

rtip

Welcome to another exciting blog post where we delve into the world of R programming. Today, we’ll be discussing the intersect() function, a handy tool that helps us find…

Steven P. Sanderson II, MPH

Unleashing the Power of Cumulative Mean in R: A Step-by-Step Guide

rtip

cumulative

As data-driven decision-making continues to shape our world, the need for insightful statistical analysis becomes ever more apparent. One crucial tool in a programmer’s…

Steven P. Sanderson II, MPH

Summarizing Data in R: tapply() vs. group_by() and summarize()

rtip

Are you tired of manually calculating summary statistics for your data in R? Look no further! In this blog post, we will explore two powerful ways to summarize data: using…

Steven P. Sanderson II, MPH

Unraveling Data Insights with R’s fivenum(): A Programmer’s Guide

rtip

As a programmer and data enthusiast, you know that summarizing data is essential to gain insights into its distribution and characteristics. R, being a powerful and…

Steven P. Sanderson II, MPH

How to Calculate Percentage by Group in R using Base R, dplyr, and data.table

rtip

Calculating percentages by group is a common task in data analysis. It allows you to understand the distribution of data within different categories. In this blog post…

Steven P. Sanderson II, MPH

Harness the Power of `paste()` and `cat()` in R: Combining and Displaying Text Like a Pro

rtip

As a programmer in R, you’ll often find yourself working with textual data and need to manipulate or display it in various ways. Two essential functions at your disposal for…

Steven P. Sanderson II, MPH

Simplify Your Code with R’s Powerful Functions: with() and within()

rtip

If you’ve been working with R for some time, you might have come across situations where your code becomes cumbersome due to repetitive references to data frames or list…

Steven P. Sanderson II, MPH

How to subset list objects in R

rtip

lists

subset

If you’re an aspiring data scientist or R programmer, you must be familiar with the powerful data structure called “lists.” Lists in R are collections of elements that can…

Steven P. Sanderson II, MPH

Efficiently Finding Duplicate Rows in R: A Comparative Analysis

rtip

benchmark

dplyr

datatable

In data analysis and manipulation tasks, it’s common to encounter situations where we need to identify and handle duplicate rows in a dataset. In this blog post, we will…

Steven P. Sanderson II, MPH

Finding Duplicate Values in a Data Frame in R: A Guide Using Base R and dplyr

rtip

benchmark

In data analysis and programming, it’s common to encounter situations where you need to identify duplicate values within a dataset. Whether you’re a beginner or an…

Steven P. Sanderson II, MPH

Covariance in R with the `cov()` Function

rtip

In the world of data analysis, understanding the relationship between variables is crucial. One powerful tool for measuring this relationship is the covariance. Today, we’ll…

Steven P. Sanderson II, MPH

Simplifying File Existence Checking in R with file.exists()

rtip

As a programmer, you’ll often come across situations where you need to check whether a file exists before performing any operations on it. Thankfully, the R programming…

Steven P. Sanderson II, MPH

Exploring Data with colMeans() in R: A Programmer’s Guide

rtip

As a programmer, working with data is a crucial aspect of our work. In R, there are numerous functions available that simplify data analysis tasks. One such function is colMe…

Steven P. Sanderson II, MPH

A Closer Look at the R Function identical()

rtip

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…

Steven P. Sanderson II, MPH

Simplifying File Management in R: Introducing file.rename()

rtip

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…

Steven P. Sanderson II, MPH

How to Use a Windows .bat File to Execute an R Script

rtip

batchfile

Using a Windows .bat file to execute an R script can be a convenient way to automate tasks and streamline your workflow. In this blog post, we will explain each line of a…

Steven P. Sanderson II, MPH

Exploring Rolling Correlation with the rollapply Function: A Powerful Tool for Analyzing Time-Series Data

rtip

timeseries

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…

Steven P. Sanderson II, MPH

The ave() Function in R

rtip

In the world of data analysis and statistics, grouping data based on certain criteria is a common task. Whether you’re working with large datasets or analyzing trends within…

Steven P. Sanderson II, MPH

Visualization in R: Unleashing the Power of the abline() Function

rtip

abline

viz

Welcome to the world of data visualization in R! In this blog post, we will explore the abline() function, a versatile tool that allows you to add straight lines to your…

Steven P. Sanderson II, MPH

Bootstrap Function in R: Resampling with the lapply and sample Functions

rtip

bootstrap

lapply

sample

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.…

Steven P. Sanderson II, MPH

Mastering Repetition with R’s rep() Function: A Programmer’s Guide

rtip

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…

Steven P. Sanderson II, MPH

Unleashing the Power of Sampling in R: Exploring the Versatile sample() Function

rtip

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…

Steven P. Sanderson II, MPH

Mastering Data Aggregation with xtabs() in R

rtip

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…

Steven P. Sanderson II, MPH

Mastering the Power of R’s diff() Function: A Programmer’s Guide

rtip

As a programmer, it’s crucial to have a deep understanding of the tools at your disposal. In the realm of data analysis and manipulation, R stands as a powerhouse. One…

Steven P. Sanderson II, MPH

Introduction to Linear Regression in R: Analyzing the mtcars Dataset with lm()

rtip

linear

regression

The lm() function in R is used for fitting linear regression models. It stands for “linear model,” and it allows you to analyze the relationship between variables and make…

Steven P. Sanderson II, MPH

Pulling a formula from a recipe object

rtip

recipes

tidymodels

The formula() function in R is a generic function that is used to create and manipulate formulas. Formulas are used to specify the relationship between variables in…

Steven P. Sanderson II, MPH

Simplifying Model Formulas with the R Function ‘reformulate()’

rtip

As a programmer, you may come across various scenarios where you need to create complex model formulas in R. However, constructing these formulas can often be challenging…

Steven P. Sanderson II, MPH

Understanding the file.info() Function in R: Listing Files by Date

rtip

In R, the file.info() function is a useful tool for retrieving file information, such as file attributes and metadata. It allows programmers to gather details about files…

Steven P. Sanderson II, MPH

Simplifying Data Transformation with pivot_longer() in R’s tidyr Library

rtip

tidyr

In the world of data analysis and manipulation, tidying and reshaping data is often an essential step. R’s tidyr library provides powerful tools to efficiently transform and…

Steven P. Sanderson II, MPH

Sorting, Ordering, and Ranking: Unraveling R’s Powerful Functions

rtip

In the realm of data analysis and programming, organizing and sorting data efficiently is crucial. In R, a programming language renowned for its data manipulation…

Steven P. Sanderson II, MPH

The `do.call()` function in R: Unlocking Efficiency and Flexibility

rtip

As a programmer, you’re always on the lookout for tools that can enhance your productivity and make your code more efficient. In the world of R programming, the do.call() fun…

Steven P. Sanderson II, MPH

Demystifying Regular Expressions: A Programmer’s Guide for Beginners

rtip

regex

Regular expressions, often abbreviated as regex, are powerful tools used in programming to match and manipulate text patterns. While they might seem intimidating at first…

Steven P. Sanderson II, MPH

Simplifying Logical Operations with the R Function any()

rtip

Programming is often about making decisions based on certain conditions. In the world of R, there are numerous functions that can help us simplify our code and make it more…

Steven P. Sanderson II, MPH

Why Check File Size Output for Different Methods?

rtip

excel

openxlsx

xlsx

writexl

When working with data, it is important to be aware of the file size of the data you are working with. This is especially true when you are working with large datasets, as…

Steven P. Sanderson II, MPH

Comparing R Packages for Writing Excel Files: An Analysis of writexl, openxlsx, and xlsx in R

rtip

excel

openxlsx

xlsx

writexl

In the realm of data analysis and manipulation, R has become a popular programming language due to its extensive collection of packages and libraries. One common task is…

Steven P. Sanderson II, MPH

Exploring Data with TidyDensity: A Guide to Using `tidy_empirical()` and `tidy_four_autoplot()` in R

rtip

tidydensity

dplyr

purrr

Yesterday I had the need to see data that had a grouping column in it. I wanted to use the tidy_four_autoplot() function on it from the {TidyDensity} library on it. This…

Steven P. Sanderson II, MPH

What is the `sink()` function? Capturing Output to External Files

rtip

The sink() function in R is used to divert R output to an external connection. This can be useful for a variety of purposes, such as exporting data to a file, logging R…

Steven P. Sanderson II, MPH

Update to {TidyDensity}

rtip

tidydensity

To effectively extract insights and communicate findings, you need powerful tools that simplify the process and present data in an engaging manner. If you’re a programmer…

Steven P. Sanderson II, MPH

Mastering File Manipulation with R’s `list.files()` Function

rtip

files

When it comes to working with files in R, having a powerful tool at your disposal can make a world of difference. Enter the list.files() function, a versatile and handy…

Steven P. Sanderson II, MPH

The `which()` Function in R

rtip

which

As a programmer, one of the most important tasks is to extract valuable insights from data. To make this process efficient, it is crucial to have a reliable tool at your…

Steven P. Sanderson II, MPH

Working with Dates and Times Pt 4

rtip

datetime

Formatting dates is an essential task in data analysis and programming. In R, there are various ways to manipulate and present dates according to specific requirements. In…

Steven P. Sanderson II, MPH

Working with Dates and Times Pt 3

rtip

datetime

Dates and times are essential components in many programming tasks, and R provides various functions and packages to handle them effectively. In this post, we’ll explore…

Steven P. Sanderson II, MPH

Working with Dates and Times Pt 2: Finding the Next Mothers Day with Simplicity

rtip

datetime

Mother’s Day is a special occasion to honor and appreciate the incredible women in our lives. As programmers, we can use our coding skills to make our lives easier when it…

Steven P. Sanderson II, MPH

Working with Dates and Times Pt 1

rtip

datetime

In this post, we will cover the basics of handling dates and times in R using the as.Date, as.POSIXct, and as.POSIXlt functions. We will use the example code below to…

Steven P. Sanderson II, MPH

VBA to R and Back Again: Running R from VBA Pt 2

rtip

vba

excel

Yesterday I posted on using VBA to execute R code that is written inside of the VBA script. So today, I will go over a simple example on executing an R script from VBA. So…

Steven P. Sanderson II, MPH

VBA to R and Back Again: Running R from VBA

rtip

vba

excel

Today I am going to briefly go over an extremely simple example of running some R code via Excel VBA.

Steven P. Sanderson II, MPH

Updates to {healthyR.data}

rtip

healthyrdata

Introducing the Updated {healthyR.data} Package: Your Ultimate Health Data Companion

Steven P. Sanderson II, MPH

Maps with {shiny} Pt 2

rtip

shiny

mapping

The code provided at the end of this post is an example of how to create a simple Shiny app in R that utilizes the OpenStreetMap (OSM) API to create a map of amenities in a…

Steven P. Sanderson II, MPH

Maps with {shiny}

rtip

shiny

mapping

The code is used to create a Shiny app that allows the user to search for a type of amenity (such as a pharmacy) in a particular city, state, and country, and then display…

Steven P. Sanderson II, MPH

How to Download a File from the Internet using download.file()

rtip

shiny

readxl

excel

The download.file() function in R is used to download files from the internet and save them onto your computer. Here’s a simple explanation of how to use it:

Steven P. Sanderson II, MPH

Extracting a model call from a fitted workflow in {tidymodels}

rtip

shiny

tidymodels

In this post, we are using a package called tidymodels, which provides a suite of tools for modeling and machine learning.

Steven P. Sanderson II, MPH

Building models with {shiny} and {tidyAML} Part 4

rtip

shiny

tidymodels

tidyaml

This is a Shiny app for building models using the {tidyAML} which is based on the tidymodels package in R. The app allows you to upload your own data or choose from one of…

Steven P. Sanderson II, MPH

Building models with {shiny} and {tidyAML} Part 3

rtip

shiny

tidymodels

tidyaml

As data science continues to be a sought-after field, creating a reliable and accurate model is essential. While there are various machine learning algorithms available, the…

Steven P. Sanderson II, MPH

Building models with {shiny} and {tidyAML} Part 2

rtip

shiny

tidymodels

tidyaml

Yesterday I spoke about building tidymodels models using my package {tidyAML} and {shiny}. I have made an update to it, and will continue to make updates to it this week.

Steven P. Sanderson II, MPH

Building models with {shiny} and {tidyAML} Part 1

rtip

shiny

tidymodels

tidyaml

Welcome to the {tidyAML} Model Builder, a Shiny web application that allows you to build predictive models using the tidyAML and Parsnip packages in R.

Steven P. Sanderson II, MPH

Exploring Distributions with {shiny}, {TidyDensity} and {plotly} Part 5

rtip

shiny

tidydensity

plotly

I have been writing about using the {TidyDensity} package with shiny for the last few posts, and this one is the last. This post will go over the app and discuss how to…

Steven P. Sanderson II, MPH

Exploring Distributions with {shiny} and {TidyDensity} Part 4

rtip

shiny

tidydensity

If you’re new to data science or statistics, you may have heard about probability distributions. Probability distributions are mathematical functions that help us understand…

Steven P. Sanderson II, MPH

Exploring Distributions with {shiny} and {TidyDensity} Part 3

rtip

shiny

tidydensity

In the previous post we allowed users to choose a distribution and a plot type. Now, we want to allow users to download a .csv file of the data that is generated.

Steven P. Sanderson II, MPH

Exploring Distributions with {shiny} and {TidyDensity} Part 2

rtip

shiny

tidydensity

Shiny is an R package that allows you to create interactive web applications from R code. In this blog post, we’ll explore the different components of a Shiny application…

Steven P. Sanderson II, MPH

Exploring Distributions with {shiny} and {TidyDensity}

rtip

shiny

tidydensity

Shiny is an R package that allows you to build interactive web applications using R code. TidyDensity is an R package that provides a tidyverse-style interface for working…

Steven P. Sanderson II, MPH

Styling Tables for Excel with {styledTables}

rtip

excel

In the analytics realm whether some like it or not, Excel is huge and maybe King. This is due to the fact of the shear volume of people using it. Microsoft has positioned…

Steven P. Sanderson II, MPH

Reading in Multiple Excel Sheets with lapply and {readxl}

rtip

readxl

lapply

healthyr

Reading in an Excel file with multiple sheets can be a daunting task, especially for users who are not familiar with the process. In this blog post, we will walk through a…

Steven P. Sanderson II, MPH

A New Package for the African Stock Market {BRVM}

rtip

brvm

markets

This R package provides a user-friendly interface for accessing data from the {BRVM}, which is a regional stock exchange serving multiple West African countries. With this…

Steven P. Sanderson II, MPH

Looking at Daily Log Returns with tidyquant, TidyDensity, and Shiny

rtip

shiny

tidydensity

tidyquant

timeseries

In this blog post, we’ll walk through how to create a shiny application that allows users to analyze the weekly returns of FAANG stocks (AAPL, AMZN, FB, GOOGL, and NFLX)…

Steven P. Sanderson II, MPH

A sample Shiny App to view Forecasts on the AirPassengers Data

rtip

shiny

data

timeseries

Hello! In this code, we are making a program that will help us predict the number of air passengers in the future. Let me explain what each part of the code does, step by…

Steven P. Sanderson II, MPH

A sample Shiny App to view CMS Healthcare Data

rtip

shiny

data

healthcare

cms

In this blog post, we will be discussing how to create a Shiny application in R that will download and extract data from a zip file and allow users to choose which data they…

Steven P. Sanderson II, MPH

A Bootstrapped Time Series Model with auto.arima() from {forecast}

rtip

timeseries

bootstrap

Time series analysis is a powerful tool for understanding and predicting patterns in data that vary over time. In this tutorial, we will use the AirPassengers dataset to…

Steven P. Sanderson II, MPH

How fast does a compressed file in Part 2

rtip

benchmark

arrow

duckdb

datatable

readr

Yesterday I posted on performing a benchmark on reading in a compressed .csv.gz file of a 2,000 by 2,000 data.frame. It was brought to my attention by someone on Mastadon (@m…

Steven P. Sanderson II, MPH

How fast does a compressed file in?

rtip

benchmark

I received an email over the weekend in regards to my last post not containing the reading in of gz compressed file(s) for the benchmarking. While this was not an over site…

Steven P. Sanderson II, MPH

How fast do the files read in?

rtip

benchmark

I will demonstrate how to generate a 1,000 row and column matrix with random numbers in R, and then save it in different file formats. I will also show how to get the file…

Steven P. Sanderson II, MPH

Some Examples of Cumulative Mean with {TidyDensity}

rtip

tidydensity

Cumulative mean is a statistical measure that calculates the mean of a set of numbers up to a certain point in time or after a certain number of observations. It is also…

Steven P. Sanderson II, MPH

Getting the CCI30 Index Current Makeup

crypto

cci30

The CCI30 Crypto Index is a cryptocurrency index that tracks the performance of the top 30 cryptocurrencies by market capitalization. It was created in 2017 by a team of…

Steven P. Sanderson II, MPH

Use of the apply family of functions

thanks

I am thrilled to announce that the R universe of packages {healthyverse} has surpassed 60,000 downloads! Thank you to everyone who has downloaded and used these packages…

Steven P. Sanderson II, MPH

Use of the apply family of functions

code

rtip

apply

In this post I will talk about the use of the R functions apply(), lapply(), sapply(), tapply(), and vapply() with examples.

Steven P. Sanderson II, MPH

Multiple Solutions to speedup tidy_bernoulli() with {data.table}

code

rtip

tidydensity

datatable

I had just recently posted on making an attempt to speedup computations with my package {TidyDensity} using a purely data.table solution, yes of course I can use {dtplyr} or …

Steven P. Sanderson II, MPH

Getting NYS Home Heating Oil Prices with {rvest}

code

rtip

rvest

If you live in New York and rely on heating oil to keep your home warm during the colder months, you know how important it is to keep track of heating oil prices.…

Steven P. Sanderson II, MPH

tidy_bernoulli() with {data.table}

code

rtip

datatable

So I was challanged by Adrian Antico to learn data.table, so yesterday I started with a single function from my package {TidyDensity} called tidy_bernoulli().

Steven P. Sanderson II, MPH

Simple examples of imap() from {purrr}

code

rtip

purrr

The imap() function is a powerful tool for iterating over a list or a vector while also keeping track of the index or names of the elements. This function applies a given…

Steven P. Sanderson II, MPH

Simple examples of pmap() from {purrr}

code

rtip

purrr

The pmap() function in R is part of the purrr library, which is a package designed to make it easier to work with functions that operate on vectors, lists, and other types…

Steven P. Sanderson II, MPH

Forecasting Timeseries in a list with R

code

rtip

purrr

timeseries

In this article, we will discuss how to perform an ARIMA forecast on nested data or data that is in a list using R programming language. This is a common scenario in which…

Steven P. Sanderson II, MPH

Text Processing Made Easy with {healthyR}’s sql_left(), sql_mid(), and sql_right() Functions in R

code

rtip

sql

healthyr

Are you tired of manually manipulating text data in R? Do you find yourself frequently needing to extract substrings from long strings or to grab just the first few…

Steven P. Sanderson II, MPH

Open a File Folder in R

code

rtip

shell

When writing a function, it is possible that you may want to ask the user where they want the data stored and if they want to open the file folder after the download has…

Steven P. Sanderson II, MPH

Quickly Generate Nested Time Series Models

code

rtip

timeseries

autoarima

There are many approaches to modeling time series data in R. One of the types of data that we might come across is a nested time series. This means the data is grouped…

Steven P. Sanderson II, MPH

Data Preppers with {healthyR.ai}

code

rtip

healthyrai

preprocessor

There are many different methods that one can choose from in order to model their data. This brings with it a fundamental issue of how to prepare your data for the specified…

Steven P. Sanderson II, MPH

Calibrate and Plot a Time Series with {healthyR.ts}

code

rtip

healthyrts

timeseries

In time series analysis, it is common to split the data into training and testing sets to evaluate the accuracy of a model. However, it is important to ensure that the model…

Steven P. Sanderson II, MPH

Converting a {tidyAML} tibble to a {workflowsets}

code

rtip

tidyaml

workflowsets

tidymodels

The {tidyAML} package is an R package that provides a set of tools for building regression/classification models on the fly with minimal input required. In this post we will…

Steven P. Sanderson II, MPH

Officially on CRAN {tidyAML}

code

rtip

tidyaml

I’m excited to announce that the R package {tidyAML} is now officially available on CRAN! This package is designed to make it easy for users to perform automated machine…

Steven P. Sanderson II, MPH

Moving Average Plots with {healthyR.ts}

code

rtip

timeseries

healthyrts

Are you interested in visualizing time series data in a clear and concise way? The R package {healthyR.ts} provides a variety of tools for time series analysis and…

Steven P. Sanderson II, MPH

An example of using {box}

code

rtip

box

Today I am going to make a short post on the R package {box} which was showcased to me quite nicely by Michael Miles. It was informative and I was able to immediately see…

Steven P. Sanderson II, MPH

Off to CRAN! {tidyAML}

code

rtip

tidyaml

tidymodels

Are you tired of spending hours tuning and testing different machine learning models for your regression or classification problems? The new R package {tidyAML} is here to…

Steven P. Sanderson II, MPH

Get the Current Hospital Data Set from CMS with {healthyR.data}

code

rtip

healthyrdata

Getting data for health care in the US can sometimes be hard. With my R package {healthyR.data} I am hoping to alleviate some of that pain.

Steven P. Sanderson II, MPH

Creating and Predicting Fast Regression Parsnip Models with {tidyAML}

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rtip

tidyaml

I am almost ready for a first release of my R package {tidyAML}. The purpose of this is to act as a way of quickly generating models using the parsnip package and keeping…

Steven P. Sanderson II, MPH

Creating an R Project Directory

code

rtip

When working in R I find it best to create a new project when working on something. This keeps all of the data and scripts in one location. This also means that if you are…

Steven P. Sanderson II, MPH

Subsetting Named Lists in R

code

rtip

lists

In R, lists are a fundamental data structure that allows us to store multiple objects of different data types under a single name. Often times, we want to extract certain…

Steven P. Sanderson II, MPH

Cumulative Measurement Functions with {TidyDensity}

code

rtip

tidydensity

If you’re looking for an easy-to-use package to calculate cumulative statistics in R, you may want to check out the TidyDensity package. This package offers several…

Steven P. Sanderson II, MPH

The Argument Matcher: A Function for Selecting the Right Arguments {tidyAML}

code

rtip

lists

tidyaml

tidymodels

I am working on finishing up a few things with my new R package {tidyAML} before I release it to CRAN. One of those things is the ability of a user to build a model using a…

Steven P. Sanderson II, MPH

Diverging Lollipop Chart: A Visual Tool for Comparing Data with {healthyR}

code

rtip

healthyr

plots

A diverging lollipop chart is a useful tool for comparing data that falls into two categories, usually indicated by different colors. This type of chart is particularly…

Steven P. Sanderson II, MPH

Attributes in R Functions: An Overview

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rtip

metadata

attributes

R is a powerful programming language that is widely used for data analysis, visualization, and machine learning. One of the features of R that makes it versatile and…

Steven P. Sanderson II, MPH

Median: A Simple Way to Detect Excess Events Over Time with {healthyR}

code

rtip

timeseries

healthyr

As we collect data over time, it’s important to look for patterns and trends that can help us understand what’s happening. One common way to do this is to look at the median…

Steven P. Sanderson II, MPH

{healthyR.ts}: The New and Improved Library for Time Series Analysis

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rtip

timeseries

healthyrts

Are you looking for a powerful and efficient library for time series analysis? Look no further than {healthyR.ts}! This library has recently been updated with new functions…

Steven P. Sanderson II, MPH

Service Line Grouping with {healthyR}

code

weeklytip

augment

healthyr

Healthcare data analysis can be a complex and time-consuming task, but it doesn’t have to be. Meet {healthyR}, your new go-to R package for all things healthcare data…

Steven P. Sanderson II, MPH

Transforming Your Data: A Guide to Popular Methods and How to Implement Them with {healthyR.ai}

code

rtip

transforms

healthyrai

Transforming data refers to the process of changing the scale or distribution of a variable in order to make it more suitable for analysis. There are many different methods…

Steven P. Sanderson II, MPH

Simplifying List Filtering in R with purrr’s keep()

code

rtip

lists

purrr

The {purrr} package in R is a powerful tool for working with lists and other data structures. One particularly useful function in the package is keep(), which allows you to…

Steven P. Sanderson II, MPH

Making Non Stationary Data Stationary

code

rtip

lists

timeseries

In the most basic sense for time series, a series is stationary if the properties of the generating process (the process that generates the data) do not change over time…

Steven P. Sanderson II, MPH

ADF and Phillips-Perron Tests for Stationarity using lists

code

rtip

lists

timeseries

lapply

A time series is a set of data points collected at regular intervals of time. Sometimes, the data points in a time series change over time in a predictable way. This is…

Steven P. Sanderson II, MPH

Another Post on Lists

code

rtip

lists

Manipulating lists in R is a powerful tool for organizing and analyzing data. Here are a few common ways to manipulate lists:

Steven P. Sanderson II, MPH

Boilerplate XGBoost with {healthyR.ai}

code

rtip

xgboost

healthyrai

XGBoost, short for “eXtreme Gradient Boosting,” is a powerful and popular machine learning library that is specifically designed for gradient boosting. It is an open-source…

Steven P. Sanderson II, MPH

Geometric Brownian Motion with {healthyR.ts}

code

rtip

timeseries

healthyrts

Geometric Brownian motion (GBM) is a widely used model in financial analysis for modeling the behavior of stock prices. It is a stochastic process that describes the…

Steven P. Sanderson II, MPH

Augmenting a Brownian Motion to a Time Series with {healthyR.ts}

code

rtip

timeseries

healthyrts

Time series analysis is a crucial tool for forecasting and understanding trends in various industries, including finance, economics, and engineering. However, traditional…

Steven P. Sanderson II, MPH

Auto K-Means with {healthyR.ai}

code

rtip

kmeans

healthyrai

Today’s post is going to center around the automatic k-means functionality of {healthyR.ai}. I am not going to get into what it is or how it works, but rather the function…

Steven P. Sanderson II, MPH

The building of {tidyAML}

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rtip

tidyaml

purrr

Yesterday I posted on An Update to {tidyAML} where I was discussing some of my thought process and how things could potentially work for the package.

Steven P. Sanderson II, MPH

An Update on {tidyAML}

code

rtip

tidyaml

automl

I have been doing a lot of work on a new package called {tidyAML}. {tidyAML} is a new R package that makes it easy to use the {tidymodels} ecosystem to perform automated…

Steven P. Sanderson II, MPH

Reflecting on the Past Year: A LinkedIn Year in Review

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linkedin

As we close the door on another year, it’s always a good time to look back and reflect on the past 12 months. And what better way to do that than with a LinkedIn Year in…

Steven P. Sanderson II, MPH

Optimal Break Points for Histograms with {healthyR}

code

rtip

healthyr

histograms

Histogram binning is a technique used in data visualization to group continuous data into a set of discrete bins, or intervals. The purpose of histogram binning is to…

Steven P. Sanderson II, MPH

New Release of {healthyR.ts}

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rtip

healthyrts

Steven P. Sanderson II, MPH

Brownian Motion

code

rtip

weeklytip

Brownian motion, also known as the random motion of particles suspended in a fluid, is a phenomenon that was first described by Scottish botanist Robert Brown in 1827. It…

Steven P. Sanderson II, MPH

More Randomwalks with {TidyDensity}

code

rtip

tidydensity

randomwalk

Random walks are a mathematical concept that have found various applications in fields such as economics, biology, and computer science. At a high level, a random walk…

Steven P. Sanderson II, MPH

Calendar Heatmap with {healthyR.ts}

code

rtip

healthyrts

timeseries

Calendar heatmaps are a useful visualization tool for understanding patterns and trends in time series data. They are particularly useful for displaying daily data, as they…

Steven P. Sanderson II, MPH

Event Analysis with {healthyR.ts}

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rtip

healthyrts

timeseries

Time-to-event analysis, also known as survival analysis, is a statistical technique used to analyze the length of time until an event occurs. This type of analysis is often…

Steven P. Sanderson II, MPH

Gartner Magic Chart and its usefulness in healthcare analytics with {healthyR}

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rtip

healthyr

The Gartner Magic Chart is a powerful tool for analyzing healthcare data and identifying trends and patterns that can inform decision making. It was developed by Dr. James…

Steven P. Sanderson II, MPH

Simulating Time Series Model Forecasts with {healthyR.ts}

code

weeklytip

healthyrts

timeseries

simulation

Time series models are a powerful tool for forecasting future values of a time-dependent variable. These models are commonly used in a variety of fields, including finance…

Steven P. Sanderson II, MPH

Listing Functions and Parameters

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rtip

dplyr

I got a little bored one day and decided I wanted to list out all of the functions inside of a package along with their parameters in a tibble. Not sure if this serves any…

Steven P. Sanderson II, MPH

Distribution Statistics with {TidyDensity}

code

rtip

tidydensity

If you’re working with statistical distributions in R, you may be interested in the {TidyDensity} package. This package provides a set of functions for creating…

Steven P. Sanderson II, MPH

Random Walks with {TidyDensity}

code

rtip

randomwalk

tidydensity

Random walks are a type of stochastic process that can be used to model the movement of a particle or system over time. At each time step, the position of the particle is…

Steven P. Sanderson II, MPH

Viewing Different Versions of the Same Statistical Distribution with {TidyDensity}

code

rtip

distributions

tidydensity

In statistics, it is often useful to view different versions of the same statistical distribution. For example, when working with the normal distribution, it may be helpful…

Steven P. Sanderson II, MPH

Model Scedacity Plots with {healthyR.ts}

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rtip

timeseries

healthyrts

Scedacity plots are a useful tool for evaluating the performance of time series models and identifying trends or patterns in the data. They are a type of scatter plot that…

Steven P. Sanderson II, MPH

Simple Moving Average Plots with {healthyR.ts}

code

rtip

timeseries

healthyrts

The R package {healthyR.ts}, is an R package that allows users to easily plot and analyze their time series data. The package includes a variety of functions, but one of the…

Steven P. Sanderson II, MPH

Distribution Summaries with {TidyDensity}

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rtip

tidydensity

{TidyDensity} is an R package that provides tools for working with probability distributions in a tidy data format. One of the key functions in the package is tidy_distributi…

Steven P. Sanderson II, MPH

Mixture Distributions with {TidyDensity}

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rtip

tidydensity

mixturemodels

A mixture distribution is a type of probability distribution that is created by combining two or more simpler distributions. This allows us to model complex data that may…

Steven P. Sanderson II, MPH

Create QQ Plots for Time Series Models with {healthyR.ts}

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rtip

timeseries

healthyrts

A Q-Q plot, or quantile-quantile plot, is a graphical tool for comparing two sets of data to assess whether they come from the same distribution. In the context of time…

Steven P. Sanderson II, MPH

Create a Faceted Historgram Plot with {healthyR.ai}

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rtip

histograms

healthyrai

One of the most important steps in data analysis is visualizing the distribution of your data. This can help you identify patterns, outliers, and trends in your data, and…

Steven P. Sanderson II, MPH

Create Multiple {parsnip} Model Specs with {purrr}

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rtip

parsnip

purrr

If you want to generate multiple parsnip model specifications at the same time then it’s really not to hard. This sort of thing is being addressed in an upcoming package of…

Steven P. Sanderson II, MPH

Z-Score Scaling Step Recipe with {healthyR.ai}

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rtip

healthyrai

recipes

Sometimes one may find it useful or necessary to scale their data during a modeling or analysis phase. One of these such transformations is the z-score scaling.

Steven P. Sanderson II, MPH

Naming Items in a List with {purrr}, {dplyr}, or {healthyR}

code

rtip

purrr

healthyr

Many times when we are working with a data set we will want to break it up into groups and place them into a list and work with them in that fashion. With this it can be…

Steven P. Sanderson II, MPH

Auto KNN with {healthyR.ai}

code

weeklytip

knn

healthyrai

Minimal coding ML is not something that is unheard of and is rather prolific, think h2o and pycaret just to name two. There is also no shortage available for R with the h2o i…

Steven P. Sanderson II, MPH

Extract Boilerplate Workflow Metrics with {healthyR.ai}

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rtip

healthyrai

When working with the {tidymodels} framework there are ways to pull model metrics from a workflow, since {healthyR.ai} is built on and around the {tidyverse} and {tidymodels}…

Steven P. Sanderson II, MPH

Generate Random Walk Data with {healthyR.ts}

code

rtip

timeseries

healthyrts

Generating random walk data for timesieries analysis does not have to be difficult, and in fact is not. It can be generated for multiple simulations and have a tidy output.…

Steven P. Sanderson II, MPH

Working with Lists

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rtip

lists

lapply

In R there are many times where we will work with lists. I won’t go into why lists are great or really the structure of a list but rather simply working with them.

Steven P. Sanderson II, MPH

Default Metric Sets with {healthyR.ai}

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rtip

healthyrai

When modeling it is always good to understand your model performance against some metric The {tidymodels} package {yardstick} is a great resource for this.

Steven P. Sanderson II, MPH

Summary Statistics with {TidyDensity}

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weeklytip

tidydensity

datatable

Many times someone may want to see a summary or cumulative statistic for a given set of data or even from several simulations of data. I went over bootstrap plotting earlier…

Steven P. Sanderson II, MPH

Data Preprocessing Scale/Normalize with {healthyR.ai}

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rtip

healthyrai

recipes

A large portion of data modeling occurrs not only in the data cleaning phase but also in the data preprocessing phase. This can include things like scaling or normalizing dat…

Steven P. Sanderson II, MPH

Bootstrap Modeling with Base R

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bootstrap

rtip

I have previously written about bootstrap modeling with {purrr} and {modelr} here. What if you would like to do some simple bootstrap modeling without importing a library?…

Steven P. Sanderson II, MPH

Updates to {healthyverse} packages

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weeklytip

healthyverse

I have made several updates to {healthyverse}, this has resulted in new releases to CRAN for {healthyR.ai}, {healthyR.ts}, and {TidyDesnsity}.

Steven P. Sanderson II, MPH

Bootstrap Modeling with {purrr} and {modler}

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rtip

purrr

modelr

Many times in modeling we want to get the uncertainty in the model, well, bootstrapping to the rescue!

Steven P. Sanderson II, MPH

Cumulative Harmonic Mean with {TidyDensity}

code

rtip

tidydensity

There can be times in which you may want to see a cumulative statistic, maybe in this particular case it is the harmonic mean. Well with the {TidyDensity} it is possible…

Steven P. Sanderson II, MPH

Auto Prep data for XGBoost with {healthyR.ai}

code

rtip

healthyrai

xgboost

Sometimes we may want to quickly format some data in order to just pass it through some algorithm just to see what happens, how crazy are things, just to get an idea of what…

Steven P. Sanderson II, MPH

Find Skewed Features with {healthyR.ai}

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rtip

healthyrai

skew

Sometimes we may want to quickly find skewed features in a data set. This is easily achiveable using the {healthyR.ai} library. There is a simple function called hai_skewed_f…

Steven P. Sanderson II, MPH

Time Series Lag Correlation Plots

code

rtip

healthyrts

weeklytip

In time series analysis there is something called a lag. This simply means we take a look at some past event from some point in time t. This is a non-statistical method for…

Steven P. Sanderson II, MPH

Reading Multiple Files with {purrr}

code

rtip

purrr

There may be times when you have multiple structured files in the same folder, maybe they are .csv files. For this short tip, we will say that they are.

Steven P. Sanderson II, MPH

Mapping K-Means with healthyR.ai

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rtip

healthyrai

kmeans

K-Means is a clustering algorithm that can be used to find potential clusters in your data.

Steven P. Sanderson II, MPH

Hyperbolic Transform with healthyR.ai

code

rtip

healthyrai

In data modeling there can be instanes where you will want some sort of hyperbolic transformation of your data. In {healthyR.ai} this is easy with the use of the function hai…

Steven P. Sanderson II, MPH

Discrete Fourier Vec with healthyR.ai

code

rtip

healthyrai

Sometimes in modeling you may want to get a discrete 1/0 vector of a fourier transform of some input vector. With {healthyR.ai} we can do this easily.

Steven P. Sanderson II, MPH

Bootstrapping and Plots with TidyDensity

code

tidydensity

bootstrap

weeklytip

Many times in the real world we have a data set which is actually a sample as we typically do not know what the actual population is. This is where bootstrapping tends to…

Steven P. Sanderson II, MPH

Cumulative Skewness

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rtip

In this post we will make a function cum_skewness() that will generate a vector output of the cumulative skewness of some given vector. The full function call is simply:

Steven P. Sanderson II, MPH

PCA with healthyR.ai

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rtip

healthyrai

weeklytip

In this post we are going to talk about how you can perform principal component analysis in R with {healthyR.ai} in a tidyverse compliant fashion.

Steven P. Sanderson II, MPH

Control Charts in healthyR.ai

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rtip

healthyrai

Sometimes you may be working with a time series or some process data and you will want to make a control chart. This is simple to do with the {healthyR.ai} package.

Steven P. Sanderson II, MPH

Cumulative Variance

code

rtip

cumulative

sapply

lapply

This is going to be a simple example on how we can make a function in #base #r that will crate a cumulative variance function. From base R we are going to use seq_along(), st…

Steven P. Sanderson II, MPH

Time Series Clustering with healthyR.ts

code

weeklytip

healthyrts

There are two components to time-series clustering with {healthyR.ts}. There is the function that will create the clustering data along with a slew of other information and…

Steven P. Sanderson II, MPH

healthyR.ai Primer

code

weeklytip

healthyrai

This is going to serve as a sort of primer for my r packge {healthyR.ai}. The goal of this package is to help with producing uniform machine learning/ai models either from…

Steven P. Sanderson II, MPH

TidyDensity Primer

code

weeklytip

tidydensity

This is going to serve as a sort of primer for the {TidyDensity} package.

Steven P. Sanderson II, MPH

Simple lapply()

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rtip

weeklytip

This is a simple lapply example to start things off.

Steven P. Sanderson II, MPH

Welcome To Steve On Data

news

This is the first post in a Quarto blog. Welcome!

Steven P. Sanderson II, MPH