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Reading Data
Large data objects will usually be read as values from external files
rather than entered during an R session at the keyboard. R input
facilities are simple and their requirements are fairly strict and even
rather inflexible. If variables are to be held mainly in data frames, as we strongly
suggest they should be, an entire data frame can be read directly with
the read.table() function. There is also a more primitive input
function, scan(), that can be called directly.
There are a few principal functions reading data into R.
read.table, read.csv, for reading tabular data
The read.table function is one of the most commonly used functions for reading data. It has a few important arguments:
- file, the name of a file, or a connection
- header, logical indicating if the file has a header line
- sep, a string indicating how the columns are separated
- colClasses, a character vector indicating the class of each column in the dataset
- nrows, the number of rows in the dataset
- comment.char, a character string indicating the comment character
- skip, the number of lines to skip from the beginning
- stringsAsFactors, should character variables be coded as factors
For small to moderately sized datasets, you can usually call read.table without specifying any other arguments
data <- read.table("foo.txt")
R will automatically skip lines that begin with a # and figure out how many rows there are (and how much memory needs to be allocated). Figure what type of variable is in each column of the table. Telling R all these things directly makes R run faster and more efficiently. read.csv is identical to read.table except that the default separator is a comma. |
