Aggregate & Group Data

For performance reasons, the API does not allow to aggregate data on the fly. Instead, you can post-process the data on your own. This page provides some examples on how to aggregate and group data.

Mean DBH grouped by tree species in NRW

In this example, we will query meassured tree data from the inventory archive in North Rhine-Westphalia (NRW). We will then calculate the mean diameter at breast height (DBH) for each tree species.

# Install necessary packages if not already installed
if (!require(httr)) install.packages("httr")
if (!require(jsonlite)) install.packages("jsonlite")
if (!require(dplyr)) install.packages("dplyr")

# Load the libraries
library(httr)
library(jsonlite)
library(dplyr)

# Set headers for the API request
headers = c(
  'apikey' = 'eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.ewogICJyb2xlIjogImFub24iLAogICJpc3MiOiAiVEZNIiwKICAiaWF0IjogMTczOTkxOTYwMCwKICAiZXhwIjogMTg5NzY4NjAwMAp9.L28Sk6wzRLoUh1wLz_TjeY_rtUp3UX3-6UttadUEoC0',
  'Accept-Profile' = 'inventory_archive'
)

# Make the API request
res <- VERB("GET", url = "https://ci.thuenen.de/rest/v1/cluster?states_affected=cd.{5}&select=cluster_name,plot!fk_plot_cluster(plot_name,tree(dbh,tree_species))&plot.tree.dbh=not.is.null", add_headers(headers))


# Parse JSON response
data <- content(res, as = "parsed")


# Initialize empty data frame
tree_data <- data.frame(dbh = numeric(), tree_species = numeric())

# Loop through each cluster
for (cluster in data) {
  # Loop through each plot in the cluster
  for (plot in cluster$plot) {
    # Check if the plot has tree data
    if (length(plot$tree) > 0) {
      # Extract tree data safely
      dbh_values <- sapply(plot$tree, function(x) {
        if (!is.null(x$dbh)) as.numeric(x$dbh) else NA
      })
      species_values <- sapply(plot$tree, function(x) {
        if (!is.null(x$tree_species)) as.numeric(x$tree_species) else NA
      })
      
      # Only add data if both vectors have the same length
      if (length(dbh_values) == length(species_values) && length(dbh_values) > 0) {
        plot_trees <- data.frame(
          dbh = dbh_values,
          tree_species = species_values
        )
        # Bind the rows to the main data frame
        tree_data <- rbind(tree_data, plot_trees)
      }
    }
  }
}

# Calculate statistics grouped by tree species (only if we have data)
if (nrow(tree_data) > 0) {
  tree_stats <- tree_data %>%
    group_by(tree_species) %>%
    summarise(
      count = n(),
      mean_dbh = round(mean(dbh, na.rm = TRUE), 2),
      sd_dbh = round(sd(dbh, na.rm = TRUE), 2)
    )

  # Print results for each tree species
  for (i in 1:nrow(tree_stats)) {
    cat("\nTree species:", tree_stats$tree_species[i], "\n")
    cat("Number of trees:", tree_stats$count[i], "\n")
    cat("Mean DBH:", tree_stats$mean_dbh[i], "cm\n")
    cat("Standard deviation DBH:", tree_stats$sd_dbh[i], "cm\n")
  }
} else {
  cat("No tree data found in the response.\n")
}

Source Code (R)