epi_visualize

Creates visualizations for prevalence mapping, epidemic curves, or general plots (scatter, boxplot).

Integrates methods for epidemiological analysis, modeling, and visualization, including functions for summary statistics, SIR (Susceptible-Infectious-Recovered) modeling, DALY (Disability-Adjusted Life Years) estimation, age standardization, diagnostic test evaluation, NLP (Natural Language Processing) keyword extraction, clinical trial power analysis, survival analysis, SNP (Single Nucleotide Polymorphism) association, and machine learning methods such as logistic regression, k-means clustering, Random Forest, and Support Vector Machine (SVM). Includes datasets for prevalence estimation, SIR modeling, genomic analysis, clinical trials, DALY, diagnostic tests, and survival analysis. Methods are based on Gelman et al. (2013) <doi:10.1201/b16018> and Wickham et al. (2019, ISBN:9781492052040>.

Esther Atsabina Wanjala

EpidigiR

Digital Epidemiological Analysis and Visualization Tools

epi_visualize function

<dl><dt>data</dt>
<dd>Input data frame or SpatialPolygonsDataFrame with relevant columns.</dd>
<dt>x</dt>
<dd>X-axis column name (character, e.g., "region").</dd>
<dt>y</dt>
<dd>Y-axis column name (character, e.g., "prevalence", optional).</dd>
<dt>type</dt>
<dd>Plot type: "map", "curve", "scatter", "boxplot".</dd>
<dt>...</dt>
<dd>Additional plotting parameters (e.g., main, xlab).</dd></dl>

Arguments

Flexible Epidemiological Visualization — epi_visualize

<dl>

<dt>data</dt>
<dd>Input data frame or SpatialPolygonsDataFrame with relevant columns.</dd>


<dt>x</dt>
<dd>X-axis column name (character, e.g., "region").</dd>


<dt>y</dt>
<dd>Y-axis column name (character, e.g., "prevalence", optional).</dd>


<dt>type</dt>
<dd>Plot type: "map", "curve", "scatter", "boxplot".</dd>


<dt>...</dt>
<dd>Additional plotting parameters (e.g., main, xlab).</dd>

</dl>

epi_visualize: Flexible Epidemiological Visualization

Description

Usage

Value

Arguments