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.

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

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

install.packages("inkaR")

And the development version from GitHub with:

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

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() 

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`

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")

• 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().