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For ordinary text files, use read.csv() for comma-separated data and read.delim() for tab-separated data. Before importing, confirm the file’s delimiter, decimal mark, header row, quoting, missing-value markers, encoding, and row-name conventions; after import, check that R interpreted the columns as intended.
Identify the file structure before importing
Start by identifying the file format and inspecting a few lines in a text editor or other suitable viewer. A file extension is a clue, not a guarantee: files called CSV can use different separators and decimal conventions, and a spreadsheet or statistical-software file may contain information that a plain text export does not preserve.
R Core Team’s R Data Import/Export manual describes simple text files as the easiest form to import, often suitable for small or medium-scale problems. The manual is for R version 4.6.1 and is dated 2026-06-24.
Choose a reader for delimited text
| File structure | Typical function | Key point |
|---|---|---|
| Comma-separated text | read.csv() |
Convenient defaults for comma-separated files with a header row. |
| Tab-separated text | read.delim() |
Convenient defaults for tab-separated files with a header row. |
| Other or unusual delimited text | read.table() |
General-purpose tabular reader with more import settings to specify. |
For a CSV named sales.csv in the current working directory:
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sales <- read.csv("sales.csv")For tab-separated data named sales.tsv:
sales <- read.delim("sales.tsv")Use an explicit path if the file is elsewhere, for example "data/sales.csv". read.table() is the general reader documented in R’s utils package; the simpler functions provide defaults for common text formats. Specify the reader’s arguments when the file’s structure differs from those defaults.
Match import settings to the file
Import problems often arise because the reader’s assumptions do not match the file. Check these settings rather than trying functions at random:
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- Separator: Confirm whether fields are divided by commas, tabs, semicolons, or another character. A semicolon-separated file should not be treated as comma-separated simply because it has a
.csvextension. - Decimal mark: In some regional formats, a comma marks decimals and a semicolon separates fields.
read.csv2()uses semicolons as separators and commas as decimal marks by default. Use it when those conventions match the file, not as a universal CSV reader. - Header: Confirm whether the first row contains column names. If the file has no header, tell the reader not to interpret its first data row as names.
- Quoting: Check whether text fields containing separators or line breaks are enclosed in quotes, and whether the quote style matches the reader’s settings.
- Missing values: Check how the file marks missing data—such as blank fields or a text token—and configure the import accordingly. Otherwise, a missing marker can be read as ordinary text.
- Encoding: CSV files do not store an encoding declaration. If names or other non-ASCII text display incorrectly, verify the file’s encoding and set the import encoding as appropriate.
- Row names: Check whether the first column contains identifiers that should be row names or a regular data column. Choose the row-name convention deliberately.
Check the result and column types
After reading the file, inspect its dimensions, names, sample rows, and types before relying on it:
dim(sales)
names(sales)
head(sales)
str(sales)Look for shifted columns, unexpected names, text where numbers are expected, and missing values that were imported as strings. With read.table(), columns are initially read as character and converted by type.convert() when colClasses is not specified. When you know the intended types—or need to control memory use—set colClasses deliberately and verify the result.
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Import an Excel spreadsheet
The R Data Import/Export manual discusses two broad options: export selected spreadsheet data to tab- or comma-separated text and read it with read.delim() or read.csv(), or use a direct-reading approach such as the readxl package. Exporting to text is straightforward, but it may not preserve spreadsheet features such as multiple sheets, cell types, or metadata in the same way as a direct reader.
Package capabilities can change. Check the current package documentation for the workbook formats and features it supports, then choose based on what the workflow needs to retain, the dependencies and platform requirements, and the reproducibility of the import settings. The manual’s package discussion reflects its own version and publication context; it is not a guarantee of present-day support for every Excel format.
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Read files saved by R
Use readRDS() to restore the single R object written to an .rds file:
sales <- readRDS("sales.rds")Use load() for an .RData or .rda workspace file created with save(). It restores one or more saved objects into the chosen environment, rather than returning one object in the same way as readRDS():
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load("analysis.RData")Choose the method based on how the file was saved and whether it contains one object or multiple workspace objects.
Import statistical-software files or use a database connection
For data from statistical software, use an import route designed for the source format when labels, types, or other format-specific details matter. R’s data-import manual covers several statistical systems. Check the relevant reader’s current documentation for supported formats and what information it preserves; exporting to plain text is another option when the data can be reduced to a table and the export’s limitations are acceptable.
For relational databases, connect through an appropriate database interface rather than treating the database as a file to load wholesale. R’s manual notes that larger databases are commonly managed through a database management system (DBMS). A database-backed workflow can retrieve the data needed for analysis without assuming that the entire dataset should fit in memory at once.
Plan for large files and reproducible imports
R’s table readers can use surprisingly much memory on large files, as the official documentation warns. A whole-file import may therefore be unsuitable as file size grows. Consider whether the data can be filtered or managed through a DBMS, and choose a workflow that fits available memory and the scale of the analysis.
For a repeatable import, record the file path and the settings that determine interpretation: separator, decimal mark, header, quoting, missing-value representation, encoding, row names, and column classes where applicable. That makes the import easier to check and rerun when the file or analysis changes.
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