R语言合并DataFrame后出现重复条目问题求助

Hey there! Let's break down why you're seeing duplicate entries after merging your DataFrames, and how to fix it.

Why Duplicates Happen #

From your code, I suspect two main culprits:

• Non-unique dates in U.NO2.ab.03: Using unique(NO2.ab.03) only removes rows where all columns are identical. If NO2.ab.03 has multiple rows with the same Date.Local but different values in other columns, unique() will keep all those rows. When you merge, this creates a cartesian product for each matching date, leading to duplicates.
• Implicit merge key: You didn't specify the by argument in merge(). While R will auto-match columns with the same name (here Date.Local), this can lead to unexpected behavior if there are hidden duplicates in either DataFrame's key column.

Step-by-Step Fix #

1. Ensure Unique Dates in Your NO2 Data
   First, clean NO2.ab.03 to make sure each Date.Local has only one corresponding value. Choose an aggregation method that makes sense for your data (e.g., first(), mean(), or sum()):

   # Using dplyr for cleaner aggregation (install if needed: install.packages("dplyr"))
   library(dplyr)
   
   U.NO2.ab.03 <- NO2.ab.03 %>%
     group_by(Date.Local) %>%
     summarise(across(everything(), first)) # Replace `first()` with sum/mean if needed
   

   If you don't want to use dplyr, base R works too:

   # Keep the first occurrence of each Date.Local
   U.NO2.ab.03 <- NO2.ab.03[!duplicated(NO2.ab.03$Date.Local), ]
   

2. Merge Explicitly with Clean Data
   Now merge with a clear by parameter, and use all.x = TRUE to keep all dates from FiresNearLA.ab.03 (your original left join intent):

   # Explicitly merge on Date.Local, keep all rows from FiresNearLA.ab.03
   ind <- merge(FiresNearLA.ab.03, U.NO2.ab.03, by = "Date.Local", all.x = TRUE)
   
   # Replace NA values with 0
   ind[is.na(ind)] <- 0
   

3. Verify No Duplicates Remain
   If you still see duplicates, check which dates are causing the issue:

   # Find duplicate dates
   dup_dates <- ind$Date.Local[duplicated(ind$Date.Local)]
   print(dup_dates)
   
   # Inspect those rows to trace the source
   ind[ind$Date.Local %in% dup_dates, ]
   

   This will help you see if duplicates are coming from FiresNearLA.ab.03 instead, and you can clean that DataFrame similarly.

内容的提问来源于stack exchange，提问作者JaElf