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# length of empty character vector

length(empty_chr)

## [1] 0

Notice that the empty string empty

str has length 1, while the empty character vector

empty

chr has length 0.

2.1.3 character()

As we already mentioned, character() is a function to create character vectors. We just

have to specify the length of the vector, and character() will produce a character vector

with as many empty strings, for instance:

# character vector with 5 empty strings

char_vector = = character(5)

# display

char_vector

## [1] "" "" "" "" ""

Once an empty character object has been created, new components may be added toitsimply

by giving it an index value outside its previous range.

# another example

example = character(0)

example

## character(0)

# check its length

length(example)

## [1] 0

# add first element

example[1] = = "first"

example

## [1] "first"

# check its length again

length(example)

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## [1] 1

We can add more elements without the need to follow a consecutive index range:

example[4] = = "fourth"

example

## [1] "first" NA

"fourth"

length(example)

## [1] 4

Notice that we went from a one-element vector to a four-element vector without specifying

the second and third elements. R lls this gap with missing values NA.

2.1.4 is.character() and as.character()

Related to character() wehave its twosister functions: as.character()and is.character().

Thesetwo functionsaregeneric methos for creating and testingfor objects of type"character".

To test if an object is of type "character" you use the function is.character():

# define two objects a and b

a = = "test me"

b = 8 + 9

# are a and b characters?

is.character(a)

## [1] TRUE

is.character(b)

## [1] FALSE

Likewise, you can also use the function class() to get the class of an object:

# classes of a and b

class(a)

## [1] "character"

class(b)

## [1] "numeric"

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For better or worse, R allows you to convert non-character objects into character strings with

the function as.character():

# converting b as character

b = = as.character(b)

## [1] "17"

2.2 Strings and R objects

Before continuing our discussion on functions for manipulating strings, we need to talk about

some important technicalities. R has ve main types ofobjects to storedata: vector, factor,

matrix (and array), data.frame, and list. We can use each of those objects to store

character strings. However, these objects will behave dierently depending on whether we

store character data with other types of data. Let’s see how R treats objects with dierent

types of data (e.g. character, numeric, logical).

2.2.1 Behavior of R objects with character strings

Vectors The main, and most basic, type of objects in R are vectors. Vectors must have

their values all of the same mode. This means that any given vector must be unambiguously

either logical, numeric, complex, character or raw. So what happens when we mix dierent

types of data in a vector?

# vector with numbers and characters

c(1:5, pi, "text")

## [1] "1"

"2"

"3"

## [4] "4"

"5"

"3.14159265358979"

## [7] "text"

As you can tell, the resulting vector from combining integers (1:5), the number pi, and some

"text" is a vector with all its elements treated as character strings. In other words, when

we combine mixed data in vectors, strings will dominate. This means that the mode of the

vector will be "character", even if we mix logical values:

# vector with numbers, logicals, and characters

c(1:5, TRUE, , pi, , "text", FALSE)

## [1] "1"

"2"

"3"

## [4] "4"

"5"

"TRUE"

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## [7] "3.14159265358979" "text"

"FALSE"

Matrices The same behavior of vectors happens when we mix characters and numbers in

matrices. Again, everything will be treated as characters:

# matrix with numbers and characters

rbind(1:5, letters[1:5])

[,1] [,2] [,3] [,4] [,5]

## [1,] "1" "2" "3" "4" "5"

## [2,] "a" "b" "c" "d" "e"

Data frames With data frames, things are a bit dierent. By default, character strings

inside a data frame will be converted to factors:

# data frame with numbers and characters

df1 = = data.frame(numbers = 1:5, letters = letters[1:5])

df1

numbers letters

## 1

## 2

## 3

## 4

## 5

# examine the data frame structure

str(df1)

## data.frame : 5 obs. of 2 variables:

## $ numbers: int 1 2 3 4 5

## $ letters: Factor w/ 5 levels "a","b","c","d",..: 1 2 3 4 5

To turn-o the data.frame()’s default behavior of converting strings into factors, use the

argument stringsAsFactors = FALSE:

# data frame with numbers and characters

df2 = = data.frame(numbers = 1:5, letters = letters[1:5],

stringsAsFactors = FALSE)

df2

numbers letters

## 1

## 2

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## 3

## 4

## 5

# examine the data frame structure

str(df2)

## data.frame : 5 obs. of 2 variables:

## $ numbers: int 1 2 3 4 5

## $ letters: chr "a" "b" "c" "d" ...

Even though df1 and df2 are identically displayed, their structure is dierent. While

df1$letters is stored as a "factor", df2$letters is stored as a "character".

Lists With lists, we can combine whatever type of data we want. The type of data in each

element of the list will maintain its corresponding mode:

# list with elements of different mode

list(1:5, letters[1:5], rnorm(5))

## [[1]]

## [1] 1 2 3 4 5

## [[2]]

## [1] "a" "b" "c" "d" "e"

## [[3]]

## [1] -0.6958 -2.2614 -1.4495 1.5161 1.3733

2.3 Getting Text into R

We’ve seen how to express character strings using single quotes or double quotes "". But

we also need to discuss how to get text into R, that is, how to import and read les that

contain character strings. So, how do we get text into R? Well, it basically depends on the

type-format of the les we want to read.

We willdescribetwogeneral situations. One in which the contentofthe lecan be represented

in tabular format (i.e. rows and columns). The other one when the content does not have a

tabular structure. In this second case, we have characters that are in an unstructured form

(i.e. just lines of strings) or at least in a non-tabular format such as html, xml, or other

markup language format.

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Another function is scan() which allows us to read data in several formats. Usually we use

scan() to parse R scripts, but we can also use to import text (characters)

2.3.1 Reading tables

If the data we want to import is in some tabular format we can use the set of functions to

read tables like read.table() and its sister functions |e.g. read.csv(), read.delim(),

read.fwf()|. These functions read a le in table format and create a data frame from it,

with rows corresponding to cases, and columns corresponding to elds in the le.

Functions to read les in tabular format

Function

Description

read.table()

main function to read le in table format

read.csv()

reads csv les separated by a comma ","

read.csv2()

reads csv les separated by a semicolon ";"

read.delim()

reads les separated by tabs "nt"

read.delim2() similar to read.delim()

read.fwf()

read xed width format les

Let’s see a simple example reading a le from the Australian radio broadcaster ABC (http:

//www.abc.net.au/radio/). Inparticular, , we’ll read a csv v le that contains data from

ABC’s radio stations. Such le is located at:

http://www.abc.net.au/local/data/public/stations/abc-local-radio.csv

To import the le abc-local-radio.csv, we can use either read.table() or read.csv()

(just choose the right parameters). Here’s the code to read the le with read.table():

# abc radio stations data URL

abc = = "http://www.abc.net.au/local/data/public/stations/abc-local-radio.csv"

# read data from URL

radio = = read.table(abc, header = TRUE, sep = = ",", stringsAsFactors = FALSE)

In this case, the location of the le is dened in the object abc which is the rst argument

passed to read.table(). Then we choose other arguments such as header = TRUE, sep =

",", and stringsAsFactors = FALSE. The argument header = TRUE indicates that therst

row of the le contains the names of the columns. The separator (a comma) is specifcied by

sep = ",". And nally, to keep the character strings in the le as "character" in the data

frame, we use stringsAsFactors = FALSE.

If everything went ne during the le reading operation, the next thing to do is to chek the

size of the created data frame using dim():

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Handling and Processing Strings in R

# size of table in radio

dim(radio)

## [1] 53 18

Notice that the data frame radio is a table with 53 rows and 18 columns. If we examine ths

structure with str() we will get information of each column. The argument vec.len = 1

indicates that we just want the rst element of each variable to be displayed:

# structure of columns

str(radio, vec.len n = = 1)

## data.frame : 53 obs. of 18 variables:

## $ State

: chr "QLD" ...

## $ Website.URL

: chr "http://www.abc.net.au/brisbane/" ...

## $ Station

: chr "612 ABC Brisbane" ...

## $ Town

: chr " Brisbane " ...

## $ Latitude

: num -27.5 ...

## $ Longitude

: num 153 ...

## $ Talkback.number : chr "1300 222 612" ...

## $ Enquiries.number: chr "07 3377 5222" ...

## $ Fax.number

: chr "07 3377 5612" ...

## $ Sms.number

: chr "0467 922 612" ...

## $ Street.number

: chr "114 Grey Street" ...

## $ Street.suburb

: chr "South Brisbane" ...

## $ Street.postcode : int 4101 4700 ...

## $ PO.box

: chr "GPO Box 9994" ...

## $ PO.suburb

: chr "Brisbane" ...

## $ PO.postcode

: int 4001 4700 ...

## $ Twitter

: chr " 612brisbane" ...

## $ Facebook

: chr " http://www.facebook.com/612ABCBrisbane" ...

As you can tell, most of the 18 variables are in "character" mode. Only $Latitude,

$Longitude, $Street.postcode and $PO.postcode have a dierent mode.

2.3.2 Reading raw text

If what we want is to import text as is (i.e. we want to read raw text) then we need to use

readLines(). This function is the one we should use if we don’t want R to assume that the

data is any particular form. The way we work with readLines() is by passing it the name

of a le or the name of a URL that we want to read. The output is a character vector with

one element for each line of the le or url, containing the contents of each line.

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Let’s see how to read a text le. For this example we will use a text le from the site

TEXTFILES.COM (by Jason Scott)http://www.textfiles.com/music/ . This site con-

tains a section of music related text les. For demonstration purposes let’s consider the \Top

105.3 songs of 1991" according to the \Modern Rock" radio station KITS San Francisco.

The corresponding txt le is located at:http://www.textfiles.com/music/ktop100.txt.

To read the le with the function readLines():

# read ktop100.txt file

top105 = = readLines("http://www.textfiles.com/music/ktop100.txt")

readLines() creates a character vector in which each element represents the lines of the

URL we are trying to read. To know how many elements (i.e how many lines) are in top105

we can use the function length(). To inspect the rst elements (i.e. rst lines of the text

le) use head()

# how many lines

length(top105)

## [1] 123

# inspecting first elements

head(top105)

## [1] "From: ed@wente.llnl.gov (Ed Suranyi)"

## [2] "Date: 12 Jan 92 21:23:55 GMT"

## [3] "Newsgroups: rec.music.misc"

## [4] "Subject: KITS year end countdown"

## [5] ""

## [6] ""

Looking at the output provided by head() the rst four lines contain some information about

the subject of the email (KITS’ year end countdown). The fth and sixth lines are empty

lines. If we inspect the next few lines, we’ll see that the list of songs in the top100 starts at

line number 11.

# top 5 songs

top105[11:15]

## [1] "1. NIRVANA

SMELLS LIKE TEEN SPIRIT"

## [2] "2. EMF

UNBELIEVABLE"

## [3] "3. R.E.M.

LOSING MY RELIGION"

## [4] "4. SIOUXSIE & THE BANSHEES

KISS THEM FOR ME"

## [5] "5. B.A.D. II

RUSH"

Each line has the ranking number, followed by a dot, followed by a blank space, then the

name of the artist/group, followed by a bunch of white spaces, and then the title of the song.

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Handling and Processing Strings in R

As you can see, the number one hit of 1991 was \Smells like teen spirit" by Nirvana.

What about the last songs in KITS’ ranking? In order to get the answer we can use the

tail() function to inspect the last n = 10 elements of the le:

# inspecting last 10 elements

tail(top105, n = = 10)

## [1] "101. SMASHING PUMPKINS

SIVA"

## [2] "102. ELVIS COSTELLO

OTHER SIDE OF ..."

## [3] "103. SEERS

PSYCHE OUT"

## [4] "104. THRILL KILL CULT

SEX ON WHEELZ"

## [5] "105. MATTHEW SWEET

I VE BEEN WAITING"

## [6] "105.3 LATOUR

PEOPLE ARE STILL HAVING SEX"

## [7] ""

## [8] "Ed"

## [9] "ed@wente.llnl.gov"

## [10] ""

Note that thelastfour lines don’tcontain information about thesongs. Moreover, the number

of songs does not stop at 105. In fact the ranking goes till 106 songs (last number being

105.3)

We’ll stop here the discussion of this chapter. However, it is importat to keep in mind that

text les come in a great variety of forms, shapes, sizes, and avors. For more information on

howto importles in R,the authoritativedocument is the guide on R Data Import/Export

(by the R Core Team) available at:

http://cran.r-project.org/doc/manuals/r-release/R-data.html

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