How to install ggalt and handle its dependencies in R?

Install from CRAN with install.packages('ggalt'), but ensure ggplot2 is installed first. For full functionality, you may need to manually install KernSmooth and PROJ.4 libraries, as highlighted in the README examples.

What's the difference between ggalt and other ggplot2 extensions like ggrepel?

ggalt focuses on advanced geoms and coordinate systems for statistical and geographic visualizations, while packages like ggrepel handle label positioning. Choose based on whether you need specialized plots like horizon charts or improved map projections.

How do I create a dumbbell plot with ggalt for comparison data?

Use geom_dumbbell() in your ggplot2 code, specifying x and xend variables for the comparisons. The README includes an example with health data, showing customization options for colors and dot guides.

Does ggalt work with tidyverse packages like dplyr?

Yes, ggalt integrates well with tidyverse workflows; the README demonstrates using dplyr and tidyr for data manipulation before plotting with geoms like geom_horizon or geom_dumbbell.

What are good alternatives to ggalt for interactive R visualizations?

For broader interactivity, consider plotly or shiny directly, as ggalt's plotly support is limited. Packages like gganimate offer animation, while ggalt excels in static, specialized charts.

How to use coord_proj for custom map projections in ggalt?

Replace coord_map with coord_proj in your ggplot code, providing a PROJ.4 string for the projection. The README shows an example with a Winkel tripel projection for world maps.

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ggalt

NOASSERTIONRv0.8.0

A collection of extra coordinate systems, geoms, statistical transformations, scales, and fonts for the ggplot2 R package.

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What is ggalt?

ggalt is an R package that extends the ggplot2 graphics system with additional coordinate systems, geometric objects (geoms), statistical transformations, and scales. It solves the problem of creating specialized visualizations—like horizon charts, improved map projections, and advanced density plots—that are not natively supported in base ggplot2.

Target Audience

R users and data scientists who rely on ggplot2 for data visualization and need specialized plotting capabilities for statistical analysis, geographic mapping, or creating publication-quality custom charts.

Value Proposition

Developers choose ggalt because it provides a curated collection of well-tested, ggplot2-compatible extensions that are easy to integrate, saving time compared to building custom geoms from scratch. Its unique selling point is the combination of advanced statistical visualizations (like bkde/ash) with improved mapping capabilities (coord_proj) in a single package.

Overview

:earth_americas: Extra Coordinate Systems, Geoms, Statistical Transformations & Scales for 'ggplot2'

Use Cases

Best For

Creating horizon charts for time-series data with multiple categories
Generating smoothed density estimates with KernSmooth's bkde algorithm
Producing improved map projections using PROJ.4 coordinate transformations
Making lollipop and dumbbell plots for comparative data visualization
Adding X-spline curves to connect data points with smooth, adjustable paths
Incorporating StateFace font for U.S. state abbreviations in spatial plots

Not Ideal For

Projects relying exclusively on base R graphics or alternative plotting libraries like lattice
Teams requiring extensive interactive dashboards; plotly integration is limited to only a few geoms
Applications with strict dependency management; ggalt requires external libraries like KernSmooth and PROJ.4 that may need manual installation
Users needing comprehensive, beginner-friendly tutorials; the package emphasizes vignettes which might not cover all edge cases

Pros & Cons

Pros

Specialized Geom Collection

Includes horizon charts, X-splines, lollipop plots, and dumbbell plots for niche visualizations, as demonstrated in the README with detailed code examples.

Advanced Statistical Visualizations

Provides smoothed density estimates (bkde) and univariate averaged shifted histograms (ash) using robust algorithms like KernSmooth, offering alternatives to ggplot2's default methods.

Improved Map Projections

Features coord_proj for superior map projections via PROJ.4, addressing limitations in ggplot2's coord_map, as shown in world map examples.

Seamless ggplot2 Integration

Follows the grammar of graphics philosophy, ensuring all geoms and stats work cohesively with existing ggplot2 workflows, reducing integration friction.

Cons

External Dependency Burden

Relies on packages like KernSmooth and PROJ.4, which may require separate installation and can cause setup issues in environments like Docker or restricted servers.

Limited Interactivity Scope

Plotly integration is only supported for a few geoms, restricting the ability to create fully interactive versions of advanced charts like horizon or density plots.

Documentation Gaps

The README notes that coord_proj 'still needs a teensy bit of work,' and users are directed to vignettes for deeper understanding, which may be insufficient for complex use cases.

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