The problem here isn't R. It's that the spreadsheet lied to you.
The user above has a column of field IDs that are mostly numbers, 1, 2, 3, but then field 4 splits into 4a and 4b. In the spreadsheet, the numbers sit right-aligned, as numbers do, while 4a and 4b sit left-aligned, as text does. That visual inconsistency is the first warning. But the user didn't notice until R read the CSV and silently turned some of those rows into NA. That's not a bug. That's the spreadsheet's quiet coercion at work.
When a column contains mixed data types, numbers in most rows, text in others, the spreadsheet app has to pick a single type for the whole column. It chooses the dominant type, which is numeric, and then it cannot store "4a" as a number. So it either drops the value silently or writes it in a way that R interprets as missing. The alignment shift the user observed? That's the spreadsheet telling you, in its own quiet way, that those cells are now text. But the message is easy to miss when you're focused on the data itself.
The practical fix is straightforward: force the entire column to text before export. In Excel, that means formatting the column as "Text" before typing any values, or using a text import step that reads the column as character. In R, you can specify `colClasses = "character"` in `read.csv()`. That way, "4a" stays "4a", and "1" becomes the character "1" instead of the number 1. You lose numeric behavior, but you gain every row being readable. You can always convert the numeric ones back later.
This is a common pitfall, and it points to a deeper truth: spreadsheets are designed for human eyes, not for programmatic analysis. They optimize for what looks right, not for what a statistical environment expects. The user's experience is not unusual, and it's not their fault. But it is a reminder that when you move data from a spreadsheet into R, you are crossing a boundary where assumptions change. The spreadsheet assumes it can smooth over inconsistencies. R assumes you've been explicit.
If you work with mixed data types, the rule is simple: decide the column's type before you export, not after R rejects your rows. That single step transforms a frustrating debugging session into a clean, repeatable pipeline. And it's exactly the kind of practical shift that separates someone fighting their tools from someone directing them.