Healthcare IT Decoded - Data Visualization using R

Explore healthcare revenue cycle management by tracing the appointment process—from creating an appointment to registration, pre-registration, and insurance eligibility—and understand how scheduling and physician assignment flow through the hospital system.

Explore how health insurance eligibility works, contrasting patient self-checks with hospital workflows. Learn how EDI 270/271, clearinghouses, and TPAs streamline eligibility checks across insurers, including Medicare ABN considerations.

Explore the admissions workflow, including registration vs admission, and how the front desk uses financial counseling to explain ballpark costs and NGO aid.

Explore the outpatient department (OPD) patient journey, from front-office tasks like appointment and registration to diagnostics, prescriptions, and data artifacts from lab, radiology, and pharmacy.

Explore inpatient department workflows from ICU monitoring to discharge summaries, highlighting hourly vitals, SpO2, ABG testing, and the admitting versus final diagnosis narrative.

Explore medical transcription and coding, including ICD diagnosis and procedure codes, why standardization matters for billing and revenue cycle management, and how discharge summaries and DRG weights drive hospital costs.

Discover healthcare it standards from icd and snomed codes to loinc and rxnorm, and learn data exchange via hl7, fhir, and dicom across emr systems.

Explore back office operations in revenue cycle management, where charges, ICD codes, and insurance data feed patient accounting, billing edits, and EDI claim processing (837/835) with AR and appeals.

Identify and map source systems across hospital workflows to prepare data for visualization, from EMR modules like appointment, registration or admission, lab, and vitals.

Identify source systems and extract healthcare data using standards such as EDI, HL7, CCD, Fire, and Dicom, and extract data in JSON, XML, CSV, or other file formats for visualization.

Clean the data after extraction by deduplicating patient IDs across modules and standardizing units. Map terminology with Relma to enable accurate reports and data analysis.

Identify your kpis and build a logical data model before loading data, distinguishing master data from transactional data to focus on the analysis across lab and billing systems.

Identify source systems, build the data model in parallel with cleaning, then load the data using tools like Informatica, DM Express, mirth, cloverleaf, or Rhapsody, supporting HL7, EDI, CCD, fire.

Analyze healthcare data to derive insights and foresights with charts, drag-and-drop reporting, and forecasting using Tableau, Power BI, or R and Python.

Recaps the patient journey, source systems, extraction and transformation, logical and physical models, loading with Informatica, DM Express, or Merge, and generating insights with Tableau, Power BI, R, or Python.

Learn basic math in R, including addition, subtraction, division, and multiplication, and explore operators for remainder, integer division, and exponent, plus assigning variables and viewing results in the console.

Use relational operators in R, such as greater than and less than, to compare values and yield true or false; then check data types with class and L suffix.

Explore logical operators in R, including and, or, not, and xor, on boolean data, with true and false values and a quick explainer of exclusive or.

Explore assignment operators in R, using the equal sign and arrow styles to assign values, and learn valid variable names with letters, digits, dots, underscores, and reserved word constraints.

Explore data types in R, including numeric, integer, boolean, logical, character, and complex, through simple variable assignments. Check types with class() to verify each data type.

Learn how R vectors store multiple values, reference elements by index. Observe how operations are performed element-wise and how type coercion occurs when mixing numeric, logical, character, and complex vectors.

Explore how lists in R differ from vectors, show that lists hold multiple data types and how to create, append, and reference them with double brackets.

Explore how to create and manipulate matrices in R. Learn to specify data, rows, columns, by row, and dimension names, and to index elements for visualization in healthcare IT.

Explore arrays in R, compare them to matrices, and learn to create multi-dimensional arrays with array(), using dim and dimnames to reference data.

Learn the data frame in R by creating it from vectors of names, ages, and marks loaded from files, and reference data with indexing, the dollar operator, and square brackets.

Master for and while loops in R to print and replicate patterns, using print, cat, and rep to control iteration.

Explore built-in R functions like head, tail, str, and summary to inspect data frames. Analyze iris data and the psych package's describe and is.na to check data structure.

Explore nrow and ncol to inspect data frames and matrices. Apply upper and lower to strings, add new columns with results, and split strings with strsplit, then reference list elements.

Master the basics of decision statements in R with if and if else, evaluating conditions and printing results such as i plus ten or i minus ten.

Explore the CDC Wonder death data, including ten year age groups, race, year, number of deaths, population, and crude rate per 100,000, using the California sample and the data tab.

Learn to create a simple vertical bar chart in R that visualizes categorical data, such as deaths by race, with bar height representing the value.

Upload the CDC wonder Excel file, read it into R using readxl, import and preview the data, and prepare to create the bar chart.

Create a basic bar chart in R using ggplot2 to display deaths by race with geom_bar and identity stat, color bars by race, and add a title, subtitle, and source.

Reorder the data using the reorder function to sort the bar chart by the largest value on the left and smallest on the right, demonstrating ascending and descending orders.

Create a stacked bar chart in R with the CDC wonder data, color by age group, and switch to grayscale using ggplot2 with position set to stack.

Learn to build a 100% stacked bar chart in R by swapping stack with fill to show proportional age groups data by year, and explore gray-scale options.

learn to create trellis charts in r, with six small charts by race or by age group and a combined view of both, including saving the plot.

Create a grouped bar chart in R with side-by-side bars showing age groups across years, and apply a manual blue color palette using scale_fill_manual with dodge.

Learn to create a horizontal bar chart in R by flipping the chart and removing a minus sign to place the highest values at the top.

Create a pie chart in R with ggplot to show age group proportions, using polar coordinates and theme_void, and adjust the legend position and colors.

Explore the prostate cancer dataset from Kaggle, detailing diagnosis result benign or malignant, radius, texture, perimeter, smoothness, compactness, fractal dimension, and plan data cleaning for missing values.

Learn to create a scatter plot of compactness versus fractal dimension from the prostate cancer data, read an Excel file in R, and clean and visualize with ggplot.

Compute the mean of compactness and fractal dimension after removing NA values with na.rm. Assign these means to NA entries and prepare numeric data for scatter plots.

Create a basic scatter plot in R of fractal dimension versus compactness using the prostate cancer data, coloring points by diagnosis and applying a minimal theme with labeled axes.

Learn to create a scatter plot of fractal dimension versus compactness in R, add a linear trend line using geom_smooth with method lm, and display the regression line.

Plot a scatter plot in R with multiple trend lines. Split by benign and malignant diagnoses to show two smooth curves and per-group linear trends using lm.

Create a scatter plot with a linear trend line in R, add benign and malignant labels using geom_text_path, and apply geom_label_smooth with method lm to display diagnosis_result.

Learn to create a scatter plot with different point shapes in R using the gguber package, mapping shapes with numeric codes and applying scale_shape_manual for custom shapes.

Explore how a jitter plot extends a scatter plot to reveal overlapping observations, using radius versus texture and benign/malignant diagnosis in R with ggplot and geom_jitter.

Learn to create a bubble plot in R by treating size as area and applying alpha transparency to reveal overlapping points, with adjustable scale and range.

Explore how to visualize diagnostic groups with a scatter plot and ellipse in R, using stat_ellipse and polygon aesthetics to separate benign and malignant points by diagnosis.

Compare a hexbin plot with a scatter plot to visualize a two-dimensional histogram with hexagon bins colored by point counts, using R and ggplot.

Explore a hexbin plot variation by adjusting bins to control hexagon density, using geom_hex with bins in R to reveal different point counts, where lighter colors indicate more points.

Create a violin plot in R with ggplot to visualize the distribution of compactness by diagnosis, using color and jitter to highlight benign versus malignant patterns.

Explore bee swarm plots and violin plots in R using the ggbeeswarm package, showing diagnosis versus area (and compactness) and how to add geom_beeswarm with alpha for clearer, non-overlapping points.

Explore density plots as a smooth alternative to histograms for visualizing the distribution of compactness, using the adjust parameter in ggplot's geom_density to reveal peaks and valleys.

Explore how adding the color parameter to a density plot in R creates two density plots colored by diagnosis result, and how changing the adjust value reveals data variation.

Discover how to create a stacked density plot in R using ggplot2 by setting position to stack, adding fill by diagnosis result, and applying alpha transparency for clarity.

Explore histogram creation in R with ggplot to visualize the distribution of compactness, compare with density plots, and color by diagnosis result to separate benign and malignant groups.

Create a 2d density plot in R with ggplot to explore the relationship between compactness and fractal dimension, then overlay points and color by diagnosis result.

Explore the heart failure dataset from the UCI repository, featuring 299 patients and 13 clinical features such as age, anemia, creatinine, ejection fraction, high blood pressure, and death status.

Explore box plots and jitter plots in R to visualize death event versus serum creatinine, converting death event to a categorical factor and layering jitter with ggplot2.

Explore the empirical cumulative distribution function (ecdf) plot in ggplot to visualize how heart data, such as age, ejection fraction, and death versus survival, distribute across outcomes in R.

Explore ridgeline plots, also called joy plots, to study the distribution of numeric data. Use R with g ridges, mapping ejection fraction to x and death_event to fill.

Learn to interpret a bean plot that visualizes data distribution, comparing two groups: survived and died, using R's bin plot package, with global mean, group medians, colors, and transparency.

Discover the rain cloud plot, a distribution visualization that combines half violin and box plots with dots, using ggdist and tidyquant in R for ejection fraction versus death event.

Explore q-q plots (quantile-quantile plots) to assess normality, compare samples, and identify skewness and dispersion patterns using a red reference line in R.

Explore qq plots of serum creatinine data, compare sample and theoretical quantiles to a line for death-event groups, note skewness and gaps, and plot side-by-side charts in R using gridExtra.

Create q-q plots in R with ggplot2, mapping serum creatinine as sample and theoretical values on the x-axis, add a red qq line, and facet by death_event to compare groups.

Learn to create side-by-side QQ plots and histograms in R with gridExtra, then use grid.arrange to compare serum creatinine and ejection fraction data.

Explore the California CABG readmissions dataset from data.ca.gov, analyzing readmissions for isolated CABG complications across gender, race, payer, age, and post-operative outcomes.

Explore how to construct a tornado butterfly chart by pivoting CABG complication data into left and right wings, comparing readmission yes and no across age strata.

Build a butterfly chart in R using the readmissions CABG data, filter for complications and age, and pivot wider to compare readmission yes and no, while handling package issues.

Load tidy R package, pivot wider, and build a butterfly chart in ggplot by mapping strata to x and using yes and no bars, then reorder and apply minimal theme.

Explore the United States measles data set with four columns: state, week number, year, and total cases. Learn to analyze and visualize this data in R for healthcare IT insights.

Create a heat map of measles data in R by aggregating cases by state and year with a geom_tile visualization and color scale.

Explore a worldwide covid dataset with date reported, country code, region, and daily and cumulative cases and deaths from a csv file and regional codes such as mro and wpro.

Create a heat map of covid data in R by loading a csv, selecting ten countries from a predefined vector, converting the date, and filtering with %in% for year-month aggregation.

Create a heatmap of covid data using R by filtering ten countries, aggregating new cases by country and year-month, and refining the visualization with date formatting and x-axis label readability.

Create an interactive heat map of COVID data in R using Plotly and HTML widgets, convert a ggplotly heatmap to an interactive widget, and save it as heatmap.html.

Build a geographical heat map of covid cases by country using sf and R Natural Earth data, merging boundaries and totals to produce an interactive map.

Learn to build a calendar heat map in R using COVID data for India 2021, including data filtering, package setup, color gradients, and orientation options.

Explore the correlation matrix in R, using Pearson correlation to measure linear relationships between variables, visualize with ggplot and plotly, and assess significance with p-values and hierarchical clustering.

Build a correlation matrix for heart failure data in R, ensuring numeric data, compute correlations and p-values, and visualize with ggplot core plot for interactive exploration.

Explore the Kaggle Gapminder data for 1952–2007, including country, year, population, continent, life expectancy, and GDP per capita, to build visualizations in R.

Create a basic line chart of life expectancy over years for Argentina using Gapminder data in R with ggplot2, adding points and configurable line types.

Create a multiple line chart in R by filtering data for three countries and mapping color to country in ggplot.

Create an animated line chart in R showing life expectancy trends for Argentina, Australia, and India using gganimate and gifski, with transition_reveal, customizable duration, width, and height.

Discover how to build area charts in R by layering a geom_area with custom fills and transparency, then enhance with gradient patterns using ggpattern on Argentina life expectancy data.

Learn to create multiple area charts in R to compare three countries, using facet_wrap by country, a viridis color palette, and proper fill settings for clear, stacked visualization.

Create step charts in R with ggplot to visualize abrupt data changes. Color by country using colorbrewer palettes for clear, discrete-interval comparisons.