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
Writing Data
There are analogous functions for writing data to files
write.table
writeLines
dump
dput
save
serialize
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 s
kip, the number of lines to skip from the beginning
stringsAsFactors, should character variables be coded as factors?
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.
Reading in Larger Datasets with read.table
With much larger datasets, doing the following things will make your life easier and will prevent R from choking.
Read the help page for read.table, which contains many hints
Make a rough calculation of the memory required to store your dataset. If the dataset is larger than the amount of RAM on your computer, you can probably stop right here.
Set comment.char = "" if there are no commented lines in your file.
Use the colClasses argument. Specifying this option instead of using the default can make ’read.table’ run MUCH faster, often twice as fast. In order to use this option, you have to know the class of each column in your data frame. If all of the columns are “numeric”, for example, then you can just set colClasses = "numeric". A quick an dirty way to figure out the classes of each column is the following:
initial <- read.table("datatable.txt", nrows = 100)
classes <- sapply(initial, class)
tabAll <- read.table("datatable.txt", colClasses = classes)
Set nrows. This doesn’t make R run faster but it helps with memory usage. A mild overestimate is okay. You can use the Unix tool wc to calculate the number of lines in a file.
Know Thy System
In general, when using R with larger datasets, it’s useful to know a few things about your system.
How much memory is available?
What other applications are in use?
Are there other users logged into the same system?
What operating system? Is the OS 32 or 64 bit?
Calculating Memory Requirements
Calculating Memory Requirements I have a data frame with 1,500,000 rows and 120 columns, all of which are numeric data. Roughly, how much memory is required to store this data frame? 1,500,000 × 120 × 8 bytes/numeric = 1440000000 bytes = 1440000000 / bytes/MB = 1,373.29 MB = 1.34 GB
Textual Formats
dumping and dputing are useful because the resulting textual format is edit-able, and in the case of corruption, potentially recoverable.
Unlike writing out a table or csv file, dump and dput preserve the metadata (sacrificing some readability), so that another user doesn’t have to specify it all over again.
Textual formats can work much better with version control programs like subversion or git which can only track changes meaningfully in text files
Textual formats can be longer-lived; if there is corruption somewhere in the file, it can be easier to fix the problem
Textual formats adhere to the “Unix philosophy”
Downside: The format is not very space-efficient
dput-ting R Objects
Another way to pass data around is by deparsing the R object with dput and reading it back in using dget.
> y <- data.frame(a = 1, b = "a")
> dput(y) structure(list(a = 1, b = structure(1L, .Label = "a", class = "factor")), .Names = c("a", "b"), row.names = c(NA, -1L), class = "data.frame")
> dput(y, file = "y.R")
> new.y <- dget("y.R")
> new.y
a b
1 1 a
Dumping R Objects
Multiple objects can be deparsed using the dump function and read back in using source
> x <- "foo"
> y <- data.frame(a = 1, b = "a")
> dump(c("x", "y"), file = "data.R")
> rm(x, y)
> source("data.R")
> y
a b
1 1 a
> x
[1] "foo"
Interfaces to the Outside World
Data are read in using connection interfaces. Connections can be made to files (most common) or to other more exotic things.
file, opens a connection to a file
gzfile, opens a connection to a file compressed with gzip
bzfile, opens a connection to a file compressed with bzip2
url, opens a connection to a webpage
File Connections
> str(file) function (description = "", open = "", blocking = TRUE, encoding = getOption("encoding"))
description is the name of the file
open is a code indicating
“r” read only
“w” writing (and initializing a new file)
“a” appending
“rb”, “wb”, “ab” reading, writing, or appending in binary mode (Windows)
Connections
In general, connections are powerful tools that let you navigate files or other external objects. In practice, we often don’t need to deal with the connection interface directly.
con <- file("foo.txt", "r")
data <- read.csv(con)
close(con)
is the same as
data <- read.csv("foo.txt")