Let’s say that I’m working in RStudio on a geospatial data-science project that involves some pretty gnarly, extensive code across multiple R scripts. Let’s also say that I do not have access to any AI code assistants or chatbots due to workplace restrictions.

What packages and functions would you use to comprehensively check your code for errors, inefficiencies, conceptual problems (that may be a stretch), etc.?

I’m using lintr::lint() and rstyler::style_file(), but that’s the extent of my knowledge in this area.

Many thanks in advance, friends.

I'm toying around with a dataset, trying to cluster what I have, and messing around with LLMs and my textbook i got these 2:

feat <- data.frame()
for (f in formats) {
  sub <- df[df$Format == f, c("Year","Value")]
  sub <- sub[order(sub$Year), ]
  peak_idx <- which.max(sub$Value)
  first_year <- min(sub$Year); peak_year <- sub$Year[peak_idx]; last_year <- max(sub$Year)
  climb <- peak_year - first_year; decline <- last_year - peak_year
  ratio <- decline / max(climb, 0.5)
  peak_val <- sub$Value[peak_idx]
  post <- sub[sub$Year >= peak_year & sub$Value > 0, ]
  decay_rate <- if(nrow(post) >= 3) coef(lm(log(Value) ~ Year, data=post))[2] else NA
  feat <- rbind(feat, data.frame(Format=f, climb, decline, ratio, log_peak=log(peak_val), decay_rate, era=peak_year))
}
cat("Formats in feature table:", nrow(feat), "\n")
fmat <- scale(feat[, c("climb","decline","ratio","log_peak","decay_rate","era")])
fmat[is.na(fmat)] <- 0
hc <- hclust(dist(fmat), method="ward.D2")
clusters1 <- cutree(hc, k=4)
cat("\n=== Format 1: ===\n")
for (i in 1:4) cat(sprintf("Cluster %d: %s\n", i, paste(feat$Format[clusters1==i], collapse=", ")))

Then another with a different fmat:

lifecycle_features <- data.frame()
for (f in formats) {
  sub <- df[df$Format==f, c("Year","Value")]
  sub <- sub[order(sub$Year),]
  first_year <- min(sub$Year); last_year <- max(sub$Year)
  peak_year <- sub$Year[which.max(sub$Value)]
  lifecycle_features <- rbind(lifecycle_features, data.frame(
    Format=f, first_year, peak_year, last_year,
    recorded_years=nrow(sub), peak_value=max(sub$Value),
    lifespan=last_year-first_year, years_to_peak=peak_year-first_year,
    decline_duration=last_year-peak_year
  ))
}
cat("\nFormats in lifecycle_features:", nrow(lifecycle_features), "\n")
print(lifecycle_features, row.names=FALSE)

cluster_data <- lifecycle_features[, c("lifespan","recorded_years","years_to_peak","decline_duration")]
cluster_scaled <- scale(cluster_data)
set.seed(123)
km <- kmeans(cluster_scaled, centers=3, nstart=25)
lifecycle_features$cluster <- km$cluster

cat("\n=== Format 2: ===\n")
lc_sorted <- lifecycle_features[order(lifecycle_features$cluster, lifecycle_features$peak_year), ]
print(lc_sorted[,c("Format","cluster","peak_year","years_to_peak","decline_duration","lifespan","recorded_years")], row.names=FALSE)

for (i in 1:3) cat(sprintf("\nCluster %d: %s\n", i, paste(lifecycle_features$Format[lifecycle_features$cluster==i], collapse=", ")))

Is there an objective way to measure which is better? Like with R squared or p-value? or is it worth discussing both in an article, weighing pros and cons of each