关于R语言数据存储位置及数据集修改持久性的技术咨询

Hey Chris, let's break down your two R questions clearly—they're super common for new users, so great call asking!

1. Where does R store data? #

• In-memory workspace objects: When you load or create data in R (like using read.csv() to import a CSV, or assigning values to a variable with my_vec <- c(1,2,3)), these live in R's temporary in-memory workspace. You can list all active objects in this workspace with the ls() command, and check your current working directory (the default folder R uses to look for/save files) with getwd().
• Original external files: If you're working with files stored on your computer (like CSV, Excel, or text files), R doesn't move or alter these by default. When you read a file into R, you're creating a copy of that data in memory— the original file stays exactly where you saved it on your hard drive.
• Quick note: When you close your R session, all in-memory data will be lost unless you choose to save your workspace (usually as a .RData file), which reloads automatically next time you open R from that directory.

2. Does modifying a dataset (e.g., adding a column) permanently change the original data? #

Short answer: No, not unless you explicitly write the modified data back to the original file. Here's the full breakdown:

• When you load a dataset into R (e.g., mydata <- read.csv("original_data.csv")), mydata is a copy of the original file stored in R's memory. If you modify this copy—like adding a column with mydata$new_column <- 1:nrow(mydata)—you're only changing the in-memory version. The original original_data.csv file on your computer remains completely untouched.
• The only way to overwrite the original file is if you intentionally run a command like write.csv(mydata, "original_data.csv") to save the modified data back to the original location.

Is assigning data to a new name necessary? #

This is actually a fantastic habit for beginners—and here's why:

• It lets you keep the original dataset intact for reference, debugging, or running alternative analyses later. For example, if you do mydata_modified <- mydata then edit mydata_modified, you still have the unaltered mydata to fall back on if you make a mistake.
• If you accidentally mess up the modified version, you don't have to re-read the original file from scratch (which can be slow for large datasets).
• The only time it might not be strictly necessary is if you're 100% sure you won't need the original data again and want to save a tiny bit of memory—but even then, the peace of mind from keeping the original is almost always worth it.

内容的提问来源于stack exchange，提问作者Chris