Part3: Reading and Writing Data in R

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.   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.  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:    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                                     = 1440000000/220 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 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. 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 usingsource. >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 Data
 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).
  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")

2. source, for reading in R code files (inverse of dump)  

3. dget, for reading in R code files (inverse of dput)

4. load, for reading in saved workspaces

5. unserialize, for reading single R objects in binary form

6. readLines, for reading lines of a text file

  The readLines function can be used to simply read lines of a text file and store them in a character vector.

> con <- gzfile("words.gz")
> x <- readLines(con, 10)
> x
[1] "1080"
"10-point" "10th"
"11-point"
[5] "12-point" "16-point" "18-point" "1st"
[9] "2"
"20-point"

readLines can be useful for reading in lines of webpages

## This might take time

con <- url("http://www.jhsph.edu", "r")
x <- readLines(con)
> head(x)
[1] "<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.0 Transitional//EN\">"
[2] ""
[3] "<html>"
[4] "<head>"
[5] "\t<meta http-equiv=\"Content-Type\" content=\"text/html;    charset = utf-8

Writing Data

There are analogous functions for writing data to files

• write.table
• writeLines

writeLines takes a character vector and writes each element one line at a time to a text file.

• dump
• dput
• save
• serialize