No `by` argument when merging lists of data.tables

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Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
45/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Stale
Tech stack
r
Domain
data

Research direction

Start by reproducing the examples around merge(dt1, some_dts, by = "idcol") and cbindlist(all_dts), then inspect the merge and cbindlist entry points. Done means merging a list of data.tables by idcol produces the same result as the sequential merge, preserving unmatched rows with NAs instead of recycling values.

Written by the indexing model from the issue text.

Description

Hello, I'd like to request the incorporation of a by column when merging lists of data.tables. Here is a minimum reproducible example of the desired behavior:

dt1 <- data.table::data.table(idcol = c(1:10),
                              spp = c("a", "b", "a", "b", "c", "c", "d", "a", "d", "c"),
                              value = sample(1:100, 10))
dt2 <- data.table::data.table(idcol = c(1:10),
                              temp_c = sample(5:15, 10),
                              elevation_m = sample(0:500, 10))
dt3 <- data.table::data.table(idcol = c(1, 5, 2, 6, 8),
                              tree_spp = c("fir", "pine", "pine", "fir", "cedar"))

# Option 1 - Correct
# This works fine - we get the expected output
dt.x <- merge(dt1, dt2)
dt.y <- merge(dt.x, dt3, all = TRUE)

dt.y # All records merged, we've got NAs for cases where no tree_spp was recorded for a given record

# Option 2 - Incorrect
# This succeeds, but we get incorrect merging, as it recycles missing values
some_dts <- list(dt2, dt3)
merge(dt1, some_dts, by = "idcol") # Incorrect output - the tree_spp col recycles values instead of NAs - not merging by "idcol"

# Option 3 - Incorrect
# This also fails :( we get the recycling error
all_dts <- list(dt1, dt2, dt3)
data.table::cbindlist(all_dts) # Fails with error

# Ideal situation:
# merge(dt.x, list_of_dts, by = "idcol") # == dt.y
# OR
# data.table::cbindlist(all_dts, by = "idcol") # == dt.y

Thank you to those who maintain this excellent package.

EDIT: In meantime, I have discovered this solution, so perhaps this is low priority:

dt.y <- purrr::reduce(all_dts, merge, by = "idcol", all.x = TRUE)

I will leave this up in case anyone has a similar issue down the line.

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