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Extracting the Last N’th Row in R Data Frames

code

rtip

operations

Ever wrangled with a data…

Steven P. Sanderson II, MPH

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

code

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…

Steven P. Sanderson II, MPH

Selecting Rows with Specific Values: Exploring Options in R

code

rtip

operations

In R, we often need to filter data frames based on whether a…

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…

Steven P. Sanderson II, MPH

Mastering Rows: Selecting by Index in R

code

rtip

operations

Let’s jump…

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…

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)

code

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…

Steven P. Sanderson II, MPH

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

code

rtip

operations

As a data scientist or analyst, you often encounter situations where you need to combine data from multiple sources.…

Steven P. Sanderson II, MPH

Unveiling Car Specs with Multidimensional Scaling in R

code

rtip

operations

Visualizing similarities between data points can be tricky, especially when dealing with many features. This is where…

Steven P. Sanderson II, MPH

Scaling Your Data in R: Understanding the Range

code

rtip

operations

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…

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

code

rtip

operations

In the realm of text manipulation in R, the gsub() function stands as a powerful tool, allowing you to replace…

Steven P. Sanderson II, MPH

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

code

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 novice or a…

Steven P. Sanderson II, MPH

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

code

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…

Steven P. Sanderson II, MPH

Mastering Data Manipulation in R with the Sweep Function

code

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. Whether you’re a seasoned R programmer or just starting out, understanding…

Steven P. Sanderson II, MPH

Mastering Replacement: Using the replace() Function in R

code

rtip

operations

The replace() function is a handy tool in your R toolbox for…

Steven P. Sanderson II, MPH

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

code

rtip

operations

In the realm of data analysis, understanding how to effectively segment your data is…

Steven P. Sanderson II, MPH

How to Replicate Rows in a Data Frame in R

code

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 embark on a journey to unravel the mysteries of regression residuals and witness how this function revolutionizes the way we visualize and understand…

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…

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…

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

code

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

code

rtip

operations

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() function is here to save the…

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…

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

code

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

code

rtip

operations

Welcome back, fellow R enthusiasts! Today, we’re diving into a fundamental yet crucial aspect of data…

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…

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

code

rtip

operations

The melt() function in the data.table package is an extremely useful tool for reshaping datasets in R. However, for…

Steven P. Sanderson II, MPH

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

code

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

code

rtip

operations

Ah, data! The lifeblood of many an analysis, but sometimes it can feel like you’re lost in a tangled jungle.…

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…

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 your secret weapons for efficiency and elegance, so buckle…

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…

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…

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…

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.…

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…

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…

Steven P. Sanderson II, MPH

Accounts Recievables Pathways in SQL

code

sql

Yesterday I was working on a project that required me…

Steven P. Sanderson II, MPH

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

code

rtip

timeseries

Hi fellow coders…

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…

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…

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…

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…

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 functions, but it’s incredibly handy…

Steven P. Sanderson II, MPH

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

code

rtip

tidyaml

Ever feel like your data is hiding secrets? Like it’s whispering truths but you…

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 the brand new…

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 latest version addresses issue #190, ensuring that all crucial data is now included in the predictions. Let’s dive…

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 is a logical value that…

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…

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 package for generating tidy distribution data…

Steven P. Sanderson II, MPH

Benchmarking the Speed of Cumulative Functions in TidyDensity

code

tidydensity

benchmark

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. Version 1.3.0 includes…

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…

Steven P. Sanderson II, MPH

Unveiling the Smooth Operator: Rolling Averages in R

code

rtip

timeseries

Steven P. Sanderson II, MPH

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

code

rtip

linkedin

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…

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.…

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…

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…

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…

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…

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…

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…

Steven P. Sanderson II, MPH

A Complete Guide to Stepwise Regression in R

rtip

regression

Stepwise regression is…

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…

Steven P. Sanderson II, MPH

Understanding Spline Regression

rtip

regression

Spline regression is particularly useful when the relationship between the independent…

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 supports a wide variety of regression and…

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…

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 when dealing with outliers or influential…

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…

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…

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.…

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…

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…

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…

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…

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…

Steven P. Sanderson II, MPH

Multinomial Distribution in R

rtip

distribution

The multinomial distribution is a…

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 ru…

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…

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…

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…

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…

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…

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 you’re into data, statistics, and R coding, this is a must-know…

Steven P. Sanderson II, MPH

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

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healthyrts

timeseries

Time series data is essential for understanding trends and making forecasts in…

Steven P. Sanderson II, MPH

Mastering the Art of Drawing Circles in Plots with R

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viz

Steven P. Sanderson II, MPH

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

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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 how to control the size of text and symbols in your…

Steven P. Sanderson II, MPH

Horizontal Legends in Base R

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viz

Creating a horizontal legend in base R can be a useful skill when you want to label multiple categories in a…

Steven P. Sanderson II, MPH

Resizing Legends in R

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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

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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

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viz

As an R programmer, you may want to create added variable plots to…

Steven P. Sanderson II, MPH

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

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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…

Steven P. Sanderson II, MPH

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

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viz

Radar charts, also known as spider, web, polar, or star plots, are a useful…

Steven P. Sanderson II, MPH

Horizontal Boxplots in R using the Palmer Penguins Data Set

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viz

Boxplots are a great way…

Steven P. Sanderson II, MPH

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

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viz

Decision trees are a powerful machine learning algorithm that can be…

Steven P. Sanderson II, MPH

How to Reorder Boxplots in R: A Comprehensive Guide

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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

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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…

Steven P. Sanderson II, MPH

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

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viz

ggplot2

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

Steven P. Sanderson II, MPH

Mastering Data Visualization with Pairs Plots in Base R

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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

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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

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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

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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

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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

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viz

Histograms are powerful tools for visualizing the distribution of a single variable, but what if you want to compare the distributions of…

Steven P. Sanderson II, MPH

How to Create a Histogram with Different Colors in R

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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

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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

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viz

If you’re an R…

Steven P. Sanderson II, MPH

Plotting SVM Decision Boundaries with e1071 in R

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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…

Steven P. Sanderson II, MPH

Creating Population Pyramid Plots in R with ggplot2

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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

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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

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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

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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

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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

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viz

When it comes to conveying…

Steven P. Sanderson II, MPH

Exploring Relationships with Correlation Heatmaps in R

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viz

correlation

Data visualization is a powerful tool for understanding the relationships between variables in a dataset.…

Steven P. Sanderson II, MPH

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

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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

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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

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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

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viz

Steven P. Sanderson II, MPH

Exploring Data Distribution in R: A Comprehensive Guide

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viz

Understanding the distribution of your data is a fundamental step in any data analysis process. It…

Steven P. Sanderson II, MPH

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

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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. If you’re looking to reveal distribution patterns in your data with style and simplicity, then this function might just become your new best friend. In this blog…

Steven P. Sanderson II, MPH

Exploring Box Plots with Mean Values using Base R and ggplot2

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viz

Data visualization is a powerful tool for understanding and interpreting data. In this blog post, we will explore how to create…

Steven P. Sanderson II, MPH

Exploring Data Distribution with Box Plots in R

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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

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Are you tired of dealing with irregularly spaced data points that just don’t seem to fit together? Do you find…

Steven P. Sanderson II, MPH

Exploring the Power of the curve() Function in R

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viz

In the vast world…

Steven P. Sanderson II, MPH

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

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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, which is a powerful tool for solving systems of linear equations. In this blog post, we will explore the purpose of solving systems of equations, explain…

Steven P. Sanderson II, MPH

The `substring()` function in R

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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

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

Steven P. Sanderson II, MPH

Mastering Grouped Counting in R: A Comprehensive Guide

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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

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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…

Steven P. Sanderson II, MPH

Mastering Data Transformation with the scale() Function in R

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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 standardize or normalize your data, helping you unlock deeper…

Steven P. Sanderson II, MPH

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

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As a programmer, you’re well aware of the importance of data…

Steven P. Sanderson II, MPH

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

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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 datasets.…

Steven P. Sanderson II, MPH

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

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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 allows you to read tabular data from a delimited text file and load it into R for further analysis. But fret not, dear reader, as I’ll walk you…

Steven P. Sanderson II, MPH

The `unlist()` Function in R

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Hey fellow R enthusiasts!

Steven P. Sanderson II, MPH

R Functions for Getting Objects

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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 functions may sound mysterious, but fear not; we’ll demystify them together and see how they can be incredibly handy tools in your R toolkit. So…

Steven P. Sanderson II, MPH

The replicate() function in R

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As a programmer, you must have encountered situations where you need…

Steven P. Sanderson II, MPH

The intersect() function in R

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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 the common elements shared between two or more vectors in R. Whether you’re a seasoned R programmer or just starting your journey…

Steven P. Sanderson II, MPH

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

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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()

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Steven P. Sanderson II, MPH

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

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

Steven P. Sanderson II, MPH

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

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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

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Steven P. Sanderson II, MPH

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

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If you’ve been working with R for some time…

Steven P. Sanderson II, MPH

How to subset list objects in R

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list

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…

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…

Steven P. Sanderson II, MPH

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

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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

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Steven P. Sanderson II, MPH

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

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As a programmer, you’ll often come…

Steven P. Sanderson II, MPH

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

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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 col…

Steven P. Sanderson II, MPH

A Closer Look at the R Function identical()

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In the realm of programming, R is a widely-used language for statistical computing and data analysis. Within R, there exists a powerful function called identical() that allows programmers to compare objects for exact equality. In this blog post, we will delve into the syntax and…

Steven P. Sanderson II, MPH

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

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Managing files is an essential task for any programmer, and when working with R, the file.rename() function can…

Steven P. Sanderson II, MPH

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

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batchfile

Using a Windows .bat file to execute an R script can be a convenient way to automate tasks and…

Steven P. Sanderson II, MPH

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

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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

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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…

Steven P. Sanderson II, MPH

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

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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 plots…

Steven P. Sanderson II, MPH

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

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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

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Steven P. Sanderson II, MPH

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

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Sampling is a fundamental technique in data analysis and…

Steven P. Sanderson II, MPH

Mastering Data Aggregation with xtabs() in R

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As a programmer, you’re constantly faced with the task of organizing and analyzing…

Steven P. Sanderson II, MPH

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

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As a programmer, it’s crucial to have a deep…

Steven P. Sanderson II, MPH

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

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linear

regression

The lm() function in R is used…

Steven P. Sanderson II, MPH

Pulling a formula from a recipe object

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recipes

tidymodels

The formula() function in R is a generic function that…

Steven P. Sanderson II, MPH

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

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

Steven P. Sanderson II, MPH

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

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In R, the file.info() function is a useful tool for retrieving file information, such as file…

Steven P. Sanderson II, MPH

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

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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…

Steven P. Sanderson II, MPH

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

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In the realm of data analysis and programming…

Steven P. Sanderson II, MPH

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

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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() function is one such gem. This often-overlooked function is a powerful tool that allows you to dynamically call other functions, opening up a world of possibilities for…

Steven P. Sanderson II, MPH

Demystifying Regular Expressions: A Programmer’s Guide for Beginners

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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()

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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?

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excel

openxlsx

xlsx

writexl

When working with data, it is important to be aware of the file size of…

Steven P. Sanderson II, MPH

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

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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…

Steven P. Sanderson II, MPH

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

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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 post will explain how I did it. The…

Steven P. Sanderson II, MPH

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

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The sink() function in R is…

Steven P. Sanderson II, MPH

Update to {TidyDensity}

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tidydensity

To effectively extract insights and communicate findings, you need…

Steven P. Sanderson II, MPH

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

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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 utility that allows you to effortlessly navigate through directories, retrieve file names, and perform various file-related operations. In…

Steven P. Sanderson II, MPH

The `which()` Function in R

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which

Steven P. Sanderson II, MPH

Working with Dates and Times Pt 4

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datetime

Formatting dates is an essential task in data analysis and programming. In R, there are…

Steven P. Sanderson II, MPH

Working with Dates and Times Pt 3

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datetime

Dates and times are essential components in many programming tasks, and R provides various functions and packages to handle them…

Steven P. Sanderson II, MPH

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

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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

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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…

Steven P. Sanderson II, MPH

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

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vba

excel

Yesterday I posted on using VBA to…

Steven P. Sanderson II, MPH

VBA to R and Back Again: Running R from VBA

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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}

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healthyrdata

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

Steven P. Sanderson II, MPH

Maps with {shiny} Pt 2

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shiny

mapping

Steven P. Sanderson II, MPH

Maps with {shiny}

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shiny

mapping

The code is used to create a Shiny app that allows the user to search for…

Steven P. Sanderson II, MPH

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

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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}

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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

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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 two built-in datasets (mtcars or…

Steven P. Sanderson II, MPH

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

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shiny

tidymodels

tidyaml

As data science continues to be a sought-after field…

Steven P. Sanderson II, MPH

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

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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

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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…

Steven P. Sanderson II, MPH

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

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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

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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

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shiny

tidydensity

Shiny is an R package that allows you to create interactive web applications from R code. In this blog post, we’ll…

Steven P. Sanderson II, MPH

Exploring Distributions with {shiny} and {TidyDensity}

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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…

Steven P. Sanderson II, MPH

Styling Tables for Excel with {styledTables}

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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}

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readxl

lapply

healthyr

Reading in an Excel file with multiple sheets can be a daunting task, especially for users who are not…

Steven P. Sanderson II, MPH

A New Package for the African Stock Market {BRVM}

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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 package, users can easily retrieve historical stock price…

Steven P. Sanderson II, MPH

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

rtip

shiny

tidydensity

tidyquant

timeseries

Steven P. Sanderson II, MPH

A sample Shiny App to view Forecasts on the AirPassengers Data

rtip

shiny

data

timeseries

Steven P. Sanderson II, MPH

A sample Shiny App to view CMS Healthcare Data

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shiny

data

healthcare

cms

In this blog post, we will be discussing how to create a Shiny application in R that will download…

Steven P. Sanderson II, MPH

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

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timeseries

bootstrap

Time series analysis is a powerful…

Steven P. Sanderson II, MPH

How fast does a compressed file in Part 2

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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 (@mariv…

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 per-se it was a…

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…

Steven P. Sanderson II, MPH

Some Examples of Cumulative Mean with {TidyDensity}

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tidydensity

Cumulative mean is a statistical…

Steven P. Sanderson II, MPH

Getting the CCI30 Index Current Makeup

crypto

cci30

The CCI30 Crypto Index is a cryptocurrency index that tracks the…

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…

Steven P. Sanderson II, MPH

Use of the apply family of functions

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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}

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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 {tidytabl…

Steven P. Sanderson II, MPH

Getting NYS Home Heating Oil Prices with {rvest}

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rvest

If you live in New York and rely on heating oil to keep your home warm during the…

Steven P. Sanderson II, MPH

tidy_bernoulli() with {data.table}

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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}

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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…

Steven P. Sanderson II, MPH

Simple examples of pmap() from {purrr}

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purrr

Steven P. Sanderson II, MPH

Forecasting Timeseries in a list with R

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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

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sql

healthyr

Are you tired of manually manipulating…

Steven P. Sanderson II, MPH

Open a File Folder in R

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shell

When writing a function, it is possible that…

Steven P. Sanderson II, MPH

Quickly Generate Nested Time Series Models

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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}

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healthyrai

preprocessor

There are many…

Steven P. Sanderson II, MPH

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

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healthyrts

timeseries

In time series analysis, it is common to split the data into training and testing sets to…

Steven P. Sanderson II, MPH

Converting a {tidyAML} tibble to a {workflowsets}

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tidyaml

workflowsets

tidymodels

The {tidyAML} package…

Steven P. Sanderson II, MPH

Officially on CRAN {tidyAML}

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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}

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timeseries

healthyrts

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

Steven P. Sanderson II, MPH

An example of using {box}

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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…

Steven P. Sanderson II, MPH

Off to CRAN! {tidyAML}

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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 simplify the process for you! tidyAML is a simple interface for automatic machine learning that fits the tidymodels framework, making it easier for you to solve…

Steven P. Sanderson II, MPH

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

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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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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…

Steven P. Sanderson II, MPH

Creating an R Project Directory

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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

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list

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}

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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…

Steven P. Sanderson II, MPH

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

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list

tidyaml

tidymodels

I am working on finishing up a few things with my new R package {tidyAML} before I release it to CRAN. One…

Steven P. Sanderson II, MPH

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

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healthyr

plots

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

Steven P. Sanderson II, MPH

Attributes in R Functions: An Overview

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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}

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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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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 and improvements, making…

Steven P. Sanderson II, MPH

Service Line Grouping with {healthyR}

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augment

healthyr

Healthcare data analysis can be a complex and time-consuming task, but it doesn’t have to be. Meet {healthyR}…

Steven P. Sanderson II, MPH

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

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transforms

healthyrai

Transforming data refers to the process of changing the…

Steven P. Sanderson II, MPH

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

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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 filter a list by keeping only the elements…

Steven P. Sanderson II, MPH

Making Non Stationary Data Stationary

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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

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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…

Steven P. Sanderson II, MPH

Another Post on Lists

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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}

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xgboost

healthyrai

XGBoost, short for “eXtreme Gradient Boosting,” is a powerful…

Steven P. Sanderson II, MPH

Geometric Brownian Motion with {healthyR.ts}

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timeseries

healthyrts

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

Steven P. Sanderson II, MPH

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

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timeseries

healthyrts

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

Steven P. Sanderson II, MPH

Auto K-Means with {healthyR.ai}

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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…

Steven P. Sanderson II, MPH

The building of {tidyAML}

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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}

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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 machine learning (AutoML). This package provides a simple and intuitive interface that allows users to quickly generate machine learning…

Steven P. Sanderson II, MPH

Reflecting on the Past Year: A LinkedIn Year in Review

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linkedin

Steven P. Sanderson II, MPH

Optimal Break Points for Histograms with {healthyR}

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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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healthyrts

Steven P. Sanderson II, MPH

Brownian Motion

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weeklytip

Brownian motion, also known as the random motion of particles suspended in a fluid, is a phenomenon that was…

Steven P. Sanderson II, MPH

More Randomwalks with {TidyDensity}

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tidydensity

randomwalk

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

Steven P. Sanderson II, MPH

Calendar Heatmap with {healthyR.ts}

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healthyrts

timeseries

Calendar heatmaps are a useful visualization tool for…

Steven P. Sanderson II, MPH

Event Analysis with {healthyR.ts}

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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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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…

Steven P. Sanderson II, MPH

Simulating Time Series Model Forecasts with {healthyR.ts}

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healthyrts

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simulation

Time series models are a powerful tool for forecasting future values…

Steven P. Sanderson II, MPH

Listing Functions and Parameters

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dplyr

I got a little bored one day and…

Steven P. Sanderson II, MPH

Distribution Statistics with {TidyDensity}

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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, manipulating, and…

Steven P. Sanderson II, MPH

Random Walks with {TidyDensity}

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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}

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distributions

tidydensity

In statistics, it is often useful to view different versions of the same…

Steven P. Sanderson II, MPH

Model Scedacity Plots with {healthyR.ts}

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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…

Steven P. Sanderson II, MPH

Simple Moving Average Plots with {healthyR.ts}

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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 standout features is the…

Steven P. Sanderson II, MPH

Distribution Summaries with {TidyDensity}

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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_distribut…

Steven P. Sanderson II, MPH

Mixture Distributions with {TidyDensity}

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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…

Steven P. Sanderson II, MPH

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

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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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histograms

healthyrai

One of the most important steps in data analysis is visualizing the distribution…

Steven P. Sanderson II, MPH

Create Multiple {parsnip} Model Specs with {purrr}

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parsnip

purrr

If you want to generate multiple parsnip model…

Steven P. Sanderson II, MPH

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

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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}

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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…

Steven P. Sanderson II, MPH

Auto KNN with {healthyR.ai}

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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 interface…

Steven P. Sanderson II, MPH

Extract Boilerplate Workflow Metrics with {healthyR.ai}

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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} we can do the same. This post will focus on the…

Steven P. Sanderson II, MPH

Generate Random Walk Data with {healthyR.ts}

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timeseries

healthyrts

Steven P. Sanderson II, MPH

Working with Lists

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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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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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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 this month, and this is a form of what we will go over today although slightly more…

Steven P. Sanderson II, MPH

Data Preprocessing Scale/Normalize with {healthyR.ai}

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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 data before proceeding to the modeling phase. I will discuss one such function…

Steven P. Sanderson II, MPH

Bootstrap Modeling with Base R

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bootstrap

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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…

Steven P. Sanderson II, MPH

Updates to {healthyverse} packages

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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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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}

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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}

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healthyrai

xgboost

Sometimes we may want to…

Steven P. Sanderson II, MPH

Find Skewed Features with {healthyR.ai}

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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_ske…

Steven P. Sanderson II, MPH

Time Series Lag Correlation Plots

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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 looking at a…

Steven P. Sanderson II, MPH

Reading Multiple Files with {purrr}

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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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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

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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_hyperbolic_…

Steven P. Sanderson II, MPH

Discrete Fourier Vec with healthyR.ai

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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

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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 come into play. It allows us to get a hold on what the possible parameter values are by taking repeated samples of the data that is…

Steven P. Sanderson II, MPH

Cumulative Skewness

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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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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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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

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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(), stats::v…

Steven P. Sanderson II, MPH

Time Series Clustering with healthyR.ts

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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…

Steven P. Sanderson II, MPH

healthyR.ai Primer

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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 scratch…

Steven P. Sanderson II, MPH

TidyDensity Primer

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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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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