Hey there! Let's work through merging your two datasets to build that final training set you need. Based on what you've shared—you've already deduplicated TrainWithAppevents_rel4, and you have app_labels to tie in—here's a practical, step-by-step approach:

First, confirm that app_id is the shared column between both datasets (your sample data confirms this!). Double-check that the app_id data type matches across both tables (e.g., both are numeric or both are character strings)—mismatched types can break the merge silently.

Choose a join type based on what you want in your final training set:

• Left join: Keep every row from your deduplicated TrainWithAppevents_rel4, even if there's no matching label_id in app_labels (missing labels will show up as NA). This is usually the go-to for training sets when you don't want to lose existing data.
• Inner join: Only keep rows where app_id exists in both datasets. Use this if you only want records with complete label data.
• Full join: Keep all rows from both datasets—rarely needed for training sets, but useful if you need to audit missing matches.

Since you used head(), I'm assuming you're working in R. Here are options using common frameworks:

Using tidyverse (dplyr) #

First load the package if you haven't:

library(tidyverse)

Then run the merge—here's the left join version:

final_train_set <- TrainWithAppevents_rel4 %>%
  left_join(app_labels, by = "app_id")

Swap left_join for inner_join or full_join if you need that strategy instead.

Using base R #

If you prefer base R, use the merge() function:

# Left join (all.x = TRUE keeps all rows from the first dataset)
final_train_set <- merge(TrainWithAppevents_rel4, app_labels, by = "app_id", all.x = TRUE)

# Inner join (default, omit all.x/all.y)
# final_train_set <- merge(TrainWithAppevents_rel4, app_labels, by = "app_id")

For extra large datasets (data.table) #

If your data is huge and you need faster performance with less memory usage, use data.table:

library(data.table)

# Convert data frames to data.tables
setDT(TrainWithAppevents_rel4)
setDT(app_labels)

# Left join equivalent
final_train_set <- TrainWithAppevents_rel4[app_labels, on = "app_id"]

Don't skip this step! Make sure the merge worked as expected:

• Run head(final_train_set) to spot-check that columns from both datasets are present and correctly aligned.
• Use dim(final_train_set) to compare row counts to your original deduplicated dataset (left join should have the same number of rows; inner join will have fewer).
• Check for NA values in label_id (if using left join) and decide if you want to fill them (e.g., with a placeholder like 0) or filter those rows out, depending on your model's needs.

内容的提问来源于stack exchange，提问作者Bak_was