reading_data_I.pdf

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Reading and Writing Data Part I
Roger D. Peng, Associate Professor of Biostatistics
Johns Hopkins Bloomberg School of Public Health
Reading Data
There are a few principal functions reading data into R.
·
read.table
,
read.csv
, for reading tabular data
·
readLines
, for reading lines of a text file
·
source
, for reading in R code files (
inverse
of
dump
)
·
dget
, for reading in R code files (
inverse
of
dput
)
·
load
, for reading in saved workspaces
·
unserialize
, for reading single R objects in binary form
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Writing Data
There are analogous functions for writing data to files
·
write.table
·
writeLines
·
dump
·
dput
·
save
·
serialize
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Reading Data Files with read.table
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?
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read.table
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 #
·
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.
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