如何在data.table中按规则聚合重复行？求高效简洁实现方案

Great question! Your current approach works, but you're right—there are much cleaner ways to handle this in data.table, either before you combine the tables (to avoid duplicates entirely) or after combining (with more concise aggregation logic). Let's break down both options:

Option 1: Handle Priority During Merging (Avoid Duplicates Upfront) #

Instead of creating duplicates first with rbindlist, we can join the tables directly and prioritize site_type="type1" values during the merge. This avoids having to clean up duplicates later, which is more efficient.

Using fifelse for Explicit Priority Checks #

# Join dt1 (type1) with dt2 (type2) on site + time, keep type1 values where non-NA
merged <- dt1[dt2, on = .(site, time), 
              `:=`(temp = fifelse(!is.na(temp), temp, i.temp),
                   prec = fifelse(!is.na(prec), prec, i.prec),
                   snow = fifelse(!is.na(snow), snow, i.snow),
                   site_type = "type1")]

# Add rows from dt2 that don't exist in dt1 (unique type2 sites)
final_result <- rbindlist(list(merged, dt2[!dt1, on = .(site, time)]), fill = TRUE)

Using fcoalesce for Cleaner Code #

data.table's fcoalesce function returns the first non-NA value from a set of vectors, which simplifies the priority logic:

# Full join to get all site/time pairs
full_join <- dt1[dt2, on = .(site, time), allow.cartesian = FALSE]

# Coalesce to prioritize type1 values, then group by site/time
final_result <- full_join[, .(site_type = "type1",
                              temp = fcoalesce(temp, i.temp),
                              prec = fcoalesce(prec, i.prec),
                              snow = fcoalesce(snow, i.snow)),
                          by = .(site, time)]

# Add unique type2 rows not present in dt1
final_result <- rbindlist(list(final_result, dt2[!dt1, on = .(site, time)]), fill = TRUE)

Option 2: Clean Up Duplicates After Merging (Concise Aggregation) #

If you already have the combined r1 table, you can skip splitting into duplicate/non-duplicate subsets and use data.table's grouping power directly.

Method A: Sort by Priority, Take First Non-NA Value #

Sort each site/time group to put type1 first, then grab the first non-NA value for each column:

r1_clean <- r1[order(site, time, factor(site_type, levels = c("type1", "type2"))),
               lapply(.SD, function(x) first(na.omit(x))),
               by = .(site, time)]

Method B: Direct Filter + fcoalesce #

Explicitly pull type1 values first, then fall back to type2 using fcoalesce:

r1_clean <- r1[, .(site_type = "type1",
                   temp = fcoalesce(temp[site_type == "type1"], temp[site_type == "type2"]),
                   prec = fcoalesce(prec[site_type == "type1"], prec[site_type == "type2"]),
                   snow = fcoalesce(snow[site_type == "type1"], snow[site_type == "type2"])),
               by = .(site, time)]

Why This Beats Your Original Approach #

• No need to split the data into separate subsets—one grouping operation handles all rows.
• Uses data.table's optimized built-in functions (fcoalesce, first) which are faster and more readable than manual ifelse logic.
• The code scales better if you add more columns later (just extend the fcoalesce calls or keep .SD as-is).

内容的提问来源于stack exchange，提问作者Jed