Publications by Ramon Rodriguez-Santana, MBA,MPH
Machine Learning Regression Models in R
Machine Leaning Regression Regression analysis in machine learning (ML) is a method used to examine the connection between independent variables and a dependent variable. This type of analysis it is known as predictive modeling, in which an algorithm or method is used to predict continuous outcomes. Here are the steps for conducting a ML regression...
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Using the {tidycensus} package in R to estimate number of four Connecticut Hispanic subgroups populations
Basic usage of {tidycensus} package. tidycensus enables users to interact with specific US Census Bureau data APIs. It retrieves data frames compatible with tidyverse and integrates a straightforward geography feature. Here is an example on how to use this package. Load libraries. library(tidyverse) ## ── Attaching core tidyverse packages ─...
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Hierarchical cluster analysis of prescription counts by town of residence in R
Hierarchical cluster analysis example Here are the steps on how to create a Hierarchical cluster analysis: Set working directory where your data file is located. setwd("~/Documents/CT 2023 prescription counts by town") Load libraries. library(tidyverse) # data manipulation ## ── Attaching core tidyverse packages ─────────...
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K-Means and K-Medoids clustering of prescription counts by Connecticut towns
R Markdown Here are the steps on how to K-Means and K-Medoids clustering: K-Means clustering Set working directory where data file is located. setwd("~/Documents/CT 2023 prescription counts by town") Load libraries. library(tidyverse) # data manipulation ## ── Attaching core tidyverse packages ───────────────�...
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Create maps in R by merging shapefile with data source file
Create maps by merging shapefile with data source file Here are the steps on how to create the maps: Set working directory of where shapefile and data source file is located. setwd("~/Documents/GIS Shapefiles/OD Drug Deaths Resident Town 2022") Load libraries. library(tidyverse) ## ── Attaching core tidyverse packages ───────�...
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Local Moran and Local Getis-Ord Maps of OD Deaths by Town of Residence using the {rgeoda} package in R
Using the {rgeoda} package to create Local Moran and Local Getis-Ord Maps Here are the steps for making a cluster map and a hot spots map using the {rgeoda} package: Set working directory. setwd("~/Documents/GIS Shapefiles/OD Drug Deaths Resident Town 2022") Load libraries. library(tidyverse) ## ── Attaching core tidyverse packages ──�...
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Create publication-ready analytical and summary tables using {gtsummary} package in R
Create publication-ready analytical and summary tables using {gtsummary} package. Using the {gtsummary} package offers a stylish and adaptable method for producing analytical and summary tables that are ready for publication. This package uses sensible defaults with fully customizable features to summarize datasets, regression models, and more. Her...
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Build a HeatMap in R using the {leaflet} and {leaflet.extras} packages
Creating a HeatMap from 2012 Starbucks locations in CT, MA and RI Here are the steps on how to build the HeatMap: Upload libraries. library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.4 ✔ readr 2.1.4 ## ✔ for...
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Detecting anomalies in your data using Benford’s Law and R
Detecting anomalies in your data using Benford’s Law. Analyzing large amounts of data in search of anomalies can be a frustrating task. You need techniques that allow you to quickly evaluate data in a way that highlights potential anomalies and prevents you from conducting analysis that are meaningless. Here are the steps on how to perform a Benf...
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Impute missing values using {missForest} package in R
Nonparametric missing value imputation using Random Forest In this presentation we will learn how to impute missing values using the {missForest} package. This package uses a random forest trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data. https://cran.r-projec...
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