1. Respect Rate Limits
Even with built-in rate limiting, be considerate when making requests:
# Good practice: Add additional delays between scholar extractions
scholar_ids <- c("id1", "id2", "id3")
results <- list()
for (id in scholar_ids) {
results[[id]] <- ExtractScholarData(id, max_publications = 20)
Sys.sleep(10) # Additional delay between scholars
}2. Data Caching
Cache results to avoid repeated requests:
# Check if cached data exists
cache_file <- "scholar_cache.rds"
if (file.exists(cache_file)) {
cached_data <- readRDS(cache_file)
} else {
# Extract and cache data
cached_data <- ExtractScholarData("qc6CJjYAAAAJ", max_publications = 50)
saveRDS(cached_data, cache_file)
}3. Error Handling
Use try-catch blocks for robust analysis:
safe_extract <- function(scholar_id) {
tryCatch({
ExtractScholarData(scholar_id, max_publications = 20)
}, error = function(e) {
warning(sprintf("Failed to extract data for %s: %s", scholar_id,
e$message))
NULL
})
}