R Data Import Cheat Sheet for Files, Types and Encoding
A Chinese R data-import reference for delimited text, spreadsheets, JSON and statistical files, with encoding, missing-value and column-type checks.
What this reference covers
This reference follows the first stage of an analysis pipeline: selecting a reader for the input format, identifying delimiter and encoding, testing a small sample and checking names, rows, types, dates and missing values. A successful function call does not prove that the data was read correctly.
Separate file facts from inferred types
Record source, generation date, delimiter, encoding, decimal mark and field notes before reading. After import, inspect row and column counts, ranges, duplicate identifiers, date-time values and special missing markers. Explicit rules are safer for amounts, dates, identifiers and multilingual text than relying on the first rows.
Make the import reproducible
Keep a read-only copy of the original, write parameters into a script and save an import log with file checksum, row count and warnings. Use a de-identified sample for initial testing, then rerun the same configuration against the full file after the checks pass.
Maintenance note
Readers, file formats and locale behavior can change. Sensitive or regulated data needs separate access, masking and retention controls. Content review date: 2026-08-23.
Save to your cloud drive
Save the complete collection first so files remain together and are easier to access across devices.
Quark Cloud Drive
RecommendedSave R Data Import Cheat Sheet for Files, Types and Encoding to this cloud drive
Baidu Netdisk
Save R Data Import Cheat Sheet for Files, Types and Encoding to this cloud drive
R data import study guide
Identify the file facts, test a small sample, inspect types and missing values, then record explicit import parameters before reading the complete dataset.
Before you start
- Prepare a PDF reader and a de-identified sample without sensitive fields.
- Record file format, delimiter, encoding, decimal mark, work directory and expected rows.
- Keep the original file read-only and create a separate import log.
Quick start
- 01
Identify the input format
Separate delimited text, spreadsheets, JSON, statistical files and database results before selecting a reader.
- 02
Read a small sample
Import a limited set of records and inspect names, row count, encoding, dates and numeric columns before scaling.
- 03
Check missing values and types
Compare empty strings, special markers, factors or characters, dates, times and numeric precision with the field notes.
- 04
Freeze the parameters
Save path, encoding, delimiter, column types and missing-value rules in a script, then record full-file row count and warnings.
Usage tips
- Preserve the original file and write cleaned output to a separate location.
- Determine actual encoding before changing read parameters for multilingual text.
- Specify types for dates, amounts, ratios and identifiers when inference could be misleading.
Troubleshooting and uninstall
Why is Chinese text garbled after import?
Check actual file encoding and line endings, set the read encoding explicitly and compare the terminal, editor and source file separately.
Why did a numeric column become character data?
Inspect thousands separators, currency symbols, empty strings and abnormal text, clean them deliberately and review missing values created by conversion.
Frequently asked questions
Which formats are covered by the R data-import guide?
It covers delimited text, spreadsheets, JSON, statistical files and other structured inputs with format-specific checks.
Why test a small sample first?
A sample exposes delimiter, encoding, column-name and type-inference problems before the same configuration affects the full dataset.
Which fields should be checked after import?
Check row and column counts, names, ranges, dates, missing markers, encoding, duplicate identifiers and key types.
Does a successful import prove data quality?
No. Business meaning, units, ranges, completeness, duplicate records and sensitive fields require separate validation.