tmap vs ggplot2
Side-by-side — pricing, strengths, tradeoffs, and fit.
tmap
Visualize geospatial data with static and interactive maps in R.
ggplot2
Data visualization package based on the grammar of graphics.
At a glance
Open SourceFree Tier
· Not enough data·From $0
Open SourceFree Tier
● Established·From $0
Overview
One-liner
One-liner
Visualize geospatial data with static and interactive maps in R.
One-liner
Data visualization package based on the grammar of graphics.
Maturity
Maturity
Not enough dataMaturity
EstablishedOpen source
Open source
✓ YesOpen source
✓ YesPricing
Free tier
Free tier
✓ AvailableOpen source — free to use
Free tier
✓ AvailableOpen source — free to use
Starts at
Starts at
$0
Starts at
$0
Model
Model
Flat rate
Model
Flat rate
Enterprise
Enterprise
✕ NoneEnterprise
✕ NoneBest for
✓ Best for
R developers who need to create both static and interactive geospatial visualizations
Academic researchers working with geospatial data in R
Web developers integrating interactive maps into their applications
✓ Best for
Researchers who need to create complex, publication-quality plots with ease
Data analysts working with R who require a flexible and customizable plotting system
Academics teaching data visualization in R courses
Not ideal for
✕ Not for
Developers looking for a cloud-based service for map creation
Teams requiring real-time updates to maps without manual intervention
✕ Not for
Developers primarily using Python for data analysis (consider matplotlib or seaborn instead)
Users requiring real-time interactive visualizations without the need for customization
Strengths
↑ Strengths
Support for static and interactive map creation
Integration with Leaflet for web-based visualizations
Comprehensive documentation and examples
↑ Strengths
Grammar of graphics implementation for R
Highly customizable and flexible plotting system
Supports complex multi-layered visualizations
Integration with other tidyverse packages
Weaknesses
↓ Weaknesses
Limited support for complex geospatial data transformations
Performance issues with large datasets
Limited customization options for interactive maps
Dependence on external packages for full functionality
↓ Weaknesses
Steep learning curve for new users
Limited to R language
Performance issues with large datasets
Complex setup for non-standard environments