Tabulated Data I/O

Data input/output (I/O) moves information between files on disk and objects in R. For tabular data, each row represents an observation and each column represents a variable. Importing a file creates a data frame that you can inspect and analyze; exporting writes information back to disk.

Tabular text files

A tabular text file stores column values separated by a character such as a comma, semicolon or tab. CSV is a common format for exchanging tables with other software. Inspect the file before importing it: identify its column separator, decimal character, header, introductory lines and text encoding.

Task	General R function
Read a table from a text file	read.table()
Write a table to a text file	write.table()

The following is a syntax sketch. Replace the example path and settings with those appropriate for your file:

df <- read.table(file = "path/to/file.txt", header = TRUE, sep = ";", dec = ",")
write.table(df, file = "path/to/output.txt", sep = ";", dec = ",", row.names = FALSE)

read.csv() and read.csv2() are convenient variants of read.table() with defaults for common CSV arrangements. An import still needs checking: rows, column names, missing values, identifiers, numeric types and units should agree with the source.

Saved R objects

An RDS file stores a single R object, including its types. For example, it can preserve a prepared data frame for later work in R. A CSV file stores text that another import must interpret again.

Task	R function
Save an R object	saveRDS()
Restore that object	readRDS()

saveRDS(df, file = "path/to/prepared.rds")
df <- readRDS("path/to/prepared.rds")

Use text formats for exchanging tables, and RDS for preserving an R object for later use in R. Keep the source and preparation notes with either format so that the data remain understandable.

We will first download and organize a small example file, then explain its import on the R Syntax and Help page. The CSV I/O example subsequently applies the workflow to the official land-use data.