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inkaR

Professional R Interface for BBSR INKAR Spatial Development Data

The inkaR package provides a professional, fast, and feature-rich R interface to download and analyze spatial development indicators from the BBSR INKAR (Indikatoren und Karten zur Raum- und Stadtentwicklung) database.

Designed for researchers and data scientists, inkaR abstracts away the complex JSON API of INKAR into clean, analytical data frames. Version 0.6.1 introduces a premium interactive wizard, multi-indicator support with automatic joining, and high-end visualization themes.

Deeply Integrated Features 💎

  • 🪄 Interactive Selection Wizard: Run inkaR() without arguments for a guided terminal session.
  • 🔗 Multi-Indicator Support: Download and merge multiple variables at once (Vertical or Horizontal joins).
  • 🔍 Bilingual Fuzzy Search: Intelligent, error-tolerant search for both German and English indicator names.
  • ⭐ Usage History & Favorites: Highlighting frequently used indicators for a personalized experience.
  • 🎨 Professional Visualizations: Dedicated ggplot2 themes (theme_inkaR) for publication-ready maps.
  • 🚀 Optimized Performance: Intelligent persistent caching and parallel API discovery.

Installation 📦

You can install the released version of inkaR from CRAN with:

Or get the latest development version from GitHub:

# install.packages("devtools")
devtools::install_github("ofurkancoban/inkaR")

Quick Start

1. Interactive Selection (Wizard Mode)

Simply call inkaR() in an interactive R session. A professional selection wizard will guide you through: - Indicator Discovery: Search with keywords (supports fuzzy matching). - Spatial Level Selection: Automatically probes the API for available levels (Districts, States, etc.). - Year Selection: Choose specific years or download the entire time series.

library(inkaR)
# Launch the Interactive Wizard
df <- inkaR()

2. Analytical Multi-Indicator Download

You can download multiple datasets and join them automatically. Choose between a “Long” (stacked) format or a “Wide” (analytical) format with indicators as columns.

# Horizontal Join: Indicators as side-by-side columns
df_wide <- inkaR(
  variable = c("bip", "xbev"), 
  level    = "KRE", 
  year     = 2021, 
  lang     = "en", 
  format   = "wide"
)

# Ready for direct calculation:
# df_wide$bip_per_capita <- df_wide$bip / df_wide$`Total population`

3. Professional Mapping

inkaR integrates seamlessly with sf and ggplot2 to render premium maps.

# Plot with the premium High-End theme (Dark or Light mode)
plot_inkar(df_wide, mode = "dark")

Available Spatial Levels

  • KRE: Districts (Kreise / Kreisfreie Städte)
  • GEM: Municipalities (Gemeinden)
  • ROR: Spatial Planning Regions (Raumordnungsregionen)
  • BLD: Federal States (Bundesländer)
  • BND: Federal Territory (Bund)

You can explore the full spatial hierarchy via get_geographies().

Scientific Attribution 🎓

If you use inkaR in your research, please cite it to support the project and ensure reproducibility:

APA Style: > Coban, O. F. (2026). inkaR: Download and Analyze Spatial Development Data from BBSR INKAR. R package version 0.6.6. https://cran.r-project.org/web/packages/inkaR

BibTeX:

@Manual{,
  title = {inkaR: Download and Analyze Spatial Development Data from BBSR INKAR},
  author = {Omer Furkan Coban},
  year = {2026},
  note = {R package version 0.6.6},
  url = {https://cran.r-project.org/web/packages/inkaR},
}