Getting started with epiviz

Introduction

The epiviz package provides epidemiological visualization functions for creating both static (ggplot2) and interactive (plotly) charts commonly used in public health surveillance and outbreak investigation. This guide introduces you to the package using the built-in lab_data dataset.

Prerequisites

library(epiviz)
library(dplyr)
library(lubridate)

The lab_data dataset

lab_data is a synthetic laboratory dataset included with epiviz for demonstration purposes. It contains simulated laboratory detection data with typical epidemiological variables:

# Explore the structure of lab_data
glimpse(epiviz::lab_data)

## Rows: 32,560
## Columns: 8
## $ date_of_birth         <date> 1938-10-05, 1957-04-04, 1927-06-24, 1962-06-14,…
## $ sex                   <fct> Female, Male, Male, Male, Male, Male, Male, Male…
## $ organism_species_name <fct> KLEBSIELLA PNEUMONIAE, KLEBSIELLA PNEUMONIAE, ST…
## $ specimen_date         <date> 2020-05-24, 2023-07-08, 2023-02-24, 2023-08-26,…
## $ lab_code              <fct> BI20985, JH70033, CU5997, ES3851, YA29556, QF111…
## $ local_authority_name  <chr> "Worthing", "Reading", "Plymouth", "Cheshire Wes…
## $ local_authority_code  <chr> "E07000229", "E06000038", "E06000026", "E0600005…
## $ region                <chr> "South East", "South East", "South West", "North…

The dataset includes: - Patient demographics: date_of_birth, sex - Laboratory information: organism_species_name, specimen_date, lab_code - Geographic data: local_authority_name, local_authority_code, region

Example 1: Regional distribution of detections

When analyzing laboratory surveillance data, we often want to understand the geographic distribution of detections. Here we’ll create a simple column chart showing detections by region for a specific time period.

# Filter to a specific time period and aggregate by region
regional_detections <- epiviz::lab_data %>%
  filter(
    specimen_date >= as.Date("2023-01-01"),
    specimen_date <= as.Date("2023-01-31")
  ) %>%
  count(region, name = "detections") %>%
  arrange(desc(detections)) %>%
  slice(1:6) %>%  # Keep top 6 regions for readability
  mutate(
    # Handle long region names for better display
    region = ifelse(region == "Yorkshire and Humber", 
                   "Yorkshire and\nHumber", region)
  )

col_chart(
  dynamic = FALSE,  # Create static ggplot chart
  params = list(
    df = regional_detections,
    x = "region",           # Variable for x-axis
    y = "detections",       # Variable for y-axis
    fill_colours = "#007C91",  # Single color for all bars
    chart_title = "Laboratory detections by region (January 2023)",
    x_axis_title = "Region",
    y_axis_title = "Number of detections",
    x_axis_label_angle = -45,  # Rotate labels for readability
    show_gridlines = FALSE     # Remove grid lines for cleaner look
  )
)

Example 2: Temporal trends in detections

Time series analysis is fundamental in epidemiological surveillance. Here we’ll create a line chart showing monthly trends in detections over a two-year period.

# Aggregate detections by month
monthly_detections <- epiviz::lab_data %>%
  filter(
    specimen_date >= as.Date("2022-01-01"),
    specimen_date <= as.Date("2023-12-31")
  ) %>%
  mutate(
    specimen_month = floor_date(specimen_date, "month")
  ) %>%
  count(specimen_month, name = "detections")

line_chart(
  dynamic = FALSE,  # Create static ggplot chart
  params = list(
    dfr = monthly_detections,   # Note: use 'dfr' parameter for line_chart
    x = "specimen_month",      # Date variable for x-axis
    y = "detections",          # Count variable for y-axis
    line_colour = c("#007C91"), # Color for the line (vector format)
    line_type = c("solid")     # Line type
  )
)

Tips for getting started

1. Start with static charts: Use dynamic = FALSE initially to create ggplot2 charts, then switch to dynamic = TRUE for interactive plotly charts when you need zooming, hovering, or filtering capabilities.

2. Filter your data: The lab_data dataset is quite large. Always filter to specific time periods, regions, or organisms to create readable visualizations.

3. Check your data structure: Use glimpse() or str() to understand your data before passing it to visualization functions.

4. Parameter naming: Most functions use a params list to organize parameters. This keeps function calls clean and allows for easy parameter reuse.

5. Color consistency: Use consistent color schemes across your visualizations. The package provides sensible defaults, but you can customize colors using the *_colours parameters.

Next steps

• Explore the function-specific vignettes for detailed examples of each visualization type
• Try setting dynamic = TRUE in the examples above to see interactive versions
• Experiment with different time periods and filters to explore the lab_data dataset