Hey there! Let's walk through how to handle dataset matching in R for your scenario where you have a primary table LN1 and need to link it to related tables like LN1-1, LN4-1, etc. I'll cover both base R and tidyverse (dplyr) approaches so you can pick what works best for you.

First, make sure all your tables are loaded into your R environment. Note that R doesn't allow hyphens in variable names by default, so if your tables are named LN1-1 or LN4-1, you'll need to wrap them in backticks ` whenever you reference them.

# Example: Reading in CSV files (adjust paths as needed)
LN1 <- read.csv("path/to/LN1.csv")
`LN1-1` <- read.csv("path/to/LN1-1.csv")  # Backticks handle the hyphen in the name
`LN4-1` <- read.csv("path/to/LN4-1.csv")

The core idea is matching rows using a common identifier (like an id column, transaction number, etc.). Below are two common methods:

Using Base R's merge() #

Base R has a built-in merge() function that works great for basic matches:

# Inner Join: Only keep rows where the identifier exists in both LN1 and LN1-1
inner_merged <- merge(LN1, `LN1-1`, by = "id")  # Replace "id" with your actual common column

# Left Join: Keep all rows from LN1, and match corresponding rows from LN1-1 (fill with NA if no match)
left_merged <- merge(LN1, `LN1-1`, by = "id", all.x = TRUE)

Using Tidyverse's dplyr #

If you prefer a more readable, pipe-based syntax, use dplyr (you'll need to install it first if you haven't):

# Install and load dplyr if needed
# install.packages("dplyr")
library(dplyr)

# Inner Join
inner_merged_dplyr <- inner_join(LN1, `LN1-1`, by = "id")

# Left Join
left_merged_dplyr <- left_join(LN1, `LN1-1`, by = "id")

To link LN1 with multiple tables (like LN1-1 and LN4-1), you can chain the join operations together with dplyr pipes for clean code:

# Chain left joins to link LN1 with both LN1-1 and LN4-1
full_merged <- LN1 %>%
  left_join(`LN1-1`, by = "id") %>%
  left_join(`LN4-1`, by = "id")

• Different column names for the identifier: If LN1 uses user_id but LN1-1 uses id, specify the mapping in the by parameter:

  merged_diff_cols <- left_join(LN1, `LN1-1`, by = c("user_id" = "id"))
  

• Matching on multiple columns: If you need to match using two or more columns (e.g., id and transaction_date), list all common columns:

  merged_multi_keys <- left_join(LN1, `LN1-1`, by = c("id", "transaction_date"))
  

内容的提问来源于stack exchange，提问作者SATH