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. It offers an interactive wizard, multi-indicator downloads with automatic joining, and maps that draw every region at each spatial level.

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:

install.packages("inkaR")

Or get the latest development version from GitHub:

# install.packages("devtools")
# devtools::install_github("ofurkancoban/inkaR")
install.packages("inkaR", repos = c('https://ofurkancoban.r-universe.dev', 'https://cloud.r-project.org'))

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"
)

# One column per indicator, named by its short ID; GDP is in thousand euros:
df_wide$gdp_per_capita <- df_wide$bip / df_wide$xbev

3. Professional Mapping

inkaR integrates seamlessly with sf and ggplot2 to render premium maps. Boundaries come from Eurostat GISCO with the same territorial status as INKAR, so every region is drawn; get_inkar_geometry() returns them as an sf object for your own analysis.

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

Available Spatial Levels

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

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

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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 0.7.0. https://CRAN.R-project.org/package=inkaR

BibTeX:

@Manual{,
  title = {inkaR: Download and Analyze Spatial Development Data from BBSR INKAR},
  author = {Omer Furkan Coban},
  year = {2026},
  note = {R package version 0.7.0},
  url = {https://CRAN.R-project.org/package=inkaR},
}