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Similarly, to use our function on a vector with more than one element, we should call it

within the lapply() function as follows:

# reverse vector (by words)

lapply(c("the big bang theory", "atmosphere"), reverse_words)

## [[1]]

## [1] "theory bang big the"

## [[2]]

## [1] "atmosphere"

7.2 Matching e-mail addresses

The second practical example that we will discuss consists of matching an email address. We

will work with usual email addresses having one (or a similar variant) of the following forms:

somename@email.com

somename99@email.com

some.name@email.com

some.name@an-email.com

some.name@an.email.com

Since our goal is tomatch an email address, this implies thatweneed to dene acorresponding

regex pattern. If we look at the previous email forms it is possible to see that they have a

general structure that can be broken into three parts. The rst part is the username (e.g.

somename99). The second part is an @ symbol. The third part is the domain name (e.g.

an.email.com).

The username pattern can be dened as:

^([a-z0-9_\\.-]+)

The username pattern starts with a caret ^ to indicate the beginning of the string. Then we

have a group indicated with parentheses. It matches one or more lowercase letters, numbers,

underscores, dots, or hyphens.

The domain name pattern can be dened as:

([\\da-z\\.-]+)\\.([a-z\\.]{2,6})$

The domain name should be one or more lowercase letters, numbers, underscores, dots, or

hyphens. Then another (escaped) dot, followed by an extension of two to six letters or dots.

And nally the end of the string ($).

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The complete regular expression pattern (in R) for an email address is:

"^([a-z0-9_\\.-]+)@([\\da-z\\.-]+)\\.([a-z\\.]{2,6})$"

Let’s test our pattern with a minimalist example:

# pattern

email_pat = = "^([a-z0-9_nn.-]+)@([nnda-znn.-]+)nn.([a-znn.]f2,6g)$"

# string that matches

grepl(pattern = = email_pat, x x = = "gaston@abc.com")

## [1] TRUE

Here is another more real example:

# another string that matches

grep(pattern = email_pat, , x x = "gaston.sanchez@research-center.fr")

## [1] 1

However, if we have a \long" TLD (top-level domain) exceeding six letters, the pattern won’t

match, like in the next example:

# unmatched email (TLD too long)

grep(pattern = email_pat, , x x = "gaston@abc.something")

## integer(0)

Now let’s apply the email pattern to test whether several strings match or not:

# potential email addresses

emails = = c(

"simple@example.com",

"johnsmith@email.gov",

"marie.curie@college.edu",

"very.common@example.com",

"a.little.lengthy.but.ok@dept.example.com",

"disposable.style.email.with+symbol@example.com",

"not_good@email.address")

# detect pattern

str_detect(string=emails, pattern=email_pat)

## [1] TRUE TRUE TRUE TRUE TRUE FALSE FALSE

Note that the two last elements in emails are not well dened email addresses (they don’t

match the espicied pattern). The fth address contains ve sets of strings including a

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+ symbol. In turn, the sixth address has a long domain name (email.address) in which

address exceeds six letters.

7.3 Matching HTML elements

For our third example we will deal with some basic handling of HTML tags. We’ll take the

webpage for the R mailing lists:http://www.r-project.org/mail.html

If you visit the previous webpage you will see that there are four general mailing lists devoted

to R:

 R-announce is where major announcements about the development of R and the

availability of new code.

 R-packages is a list of announcements on the availability of new or enhanced con-

tributed packages

 R-help is the main R mailing list for discussion about problems and solutions using R

 R-devel is a list intended for questions and discussion about code development in R

Additionally, there are several specic Special Interest Group (SIG) mailing lists. The

following table shows the rst 5 groups:

First 5 Special Interest Groups (SIG) in R

Name

Description

R-SIG-Mac

Special Interest Group on Mac ports of R

R-sig-DB

SIG on Database Interfaces

R-SIG-Debian

Special Interest Group for Debian ports of R

R-sig-dynamic-models Special Interest Group for Dynamic Simulation Models

R-sig-Epi

Rfor epidemiological data analysis

As a simple example, suppose we wanted to get the href attributes of all the SIG links. For

instance, the href attribute of the R-SIG-Mac link is:

https://stat.ethz.ch/mailman/listinfo/r-sig-mac

In turn the href attribute of the R-sig-DB link is:

https://stat.ethz.ch/mailman/listinfo/r-sig-db

If we take a peek at the html source-code of the webpage, we’ll see that all the links can be

found on lines like this one:

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7.3.1 Getting SIG links

The rst step is to create avector ofcharacter strings that will contain the lines ofthe mailing

lists webpage. We can create this vector by simply passing the URL name to readLines():

# read html content

mail_lists = = readLines("http://www.r-project.org/mail.html")

Once we’ve read the HTML content of the R mailing lists webpage, the next step is to dene

our regex pattern that matches the SIG links.

^.*<td> *<a href="(https.*)">.*$

Let’s examine the proposed pattern. By using the caret (^) and dollar sign ($) we can

describe our pattern as an entire line. Next to the caret we match anything zero or more

times followed by a <td> tag. Then there is a blank space matched zero or more times,

followed by an anchor tag with its href attribute. Note that we are using double quotation

marks to match the href attribute ("(https.*)"). Moreover, the entire regex pattern is

surrounded by single quotations marks . Here is how we can get the SIG links:

# SIG s href pattern

sig_pattern = = ^.*<td> *<a a href="(https.*)">.*$

# find SIG href attributes

sig_hrefs = = grep(sig_pattern, mail_lists, value = TRUE)

# let s see first 5 elements (shorten output)

shorten_sigs = rep("", 5)

for (i in n 1:5) ) f

shorten_sigs[i] = toString(sig_hrefs[i], width=70)

shorten_sigs

## [1] "

<td><a href=\"https://stat.ethz.ch/mailman/listinfo/r-sig-mac\">...."

## [2] "

<td><a href=\"https://stat.ethz.ch/mailman/listinfo/r-sig-db\"><...."

## [3] "

<td><a href=\"https://stat.ethz.ch/mailman/listinfo/r-sig-debia...."

## [4] "

<td><a href=\"https://stat.ethz.ch/mailman/listinfo/r-sig-dynam...."

## [5] "

<td><a href=\"https://stat.ethz.ch/mailman/listinfo/r-sig-epi\">...."

We need to get rid of the extra html tags. We can easily extract the names of the note les

using the sub() function (since there is only one link per line, we don’t need to use gsub(),

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# get first matched group

sub(sig_pattern, "nn1", sig_hrefs)

## [1] "https://stat.ethz.ch/mailman/listinfo/r-sig-mac"

## [2] "https://stat.ethz.ch/mailman/listinfo/r-sig-db"

## [3] "https://stat.ethz.ch/mailman/listinfo/r-sig-debian"

## [4] "https://stat.ethz.ch/mailman/listinfo/r-sig-dynamic-models"

## [5] "https://stat.ethz.ch/mailman/listinfo/r-sig-epi"

## [6] "https://stat.ethz.ch/mailman/listinfo/r-sig-ecology"

## [7] "https://stat.ethz.ch/mailman/listinfo/r-sig-fedora"

## [8] "https://stat.ethz.ch/mailman/listinfo/r-sig-finance"

## [9] "https://stat.ethz.ch/mailman/listinfo/r-sig-geo"

## [10] "https://stat.ethz.ch/mailman/listinfo/r-sig-gr"

## [11] "https://stat.ethz.ch/mailman/listinfo/r-sig-gui"

## [12] "https://stat.ethz.ch/mailman/listinfo/r-sig-hpc"

## [13] "https://stat.ethz.ch/mailman/listinfo/r-sig-jobs"

## [14] "https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models"

## [15] "https://stat.ethz.ch/mailman/listinfo/r-sig-mediawiki"

## [16] "https://stat.ethz.ch/mailman/listinfo/r-sig-networks"

## [17] "https://stat.ethz.ch/mailman/listinfo/r-sig-phylo"

## [18] "https://stat.ethz.ch/mailman/listinfo/r-sig-qa"

## [19] "https://stat.ethz.ch/mailman/listinfo/r-sig-robust"

## [20] "https://stat.ethz.ch/mailman/listinfo/r-sig-s"

## [21] "https://stat.ethz.ch/mailman/listinfo/r-sig-teaching"

## [22] "https://stat.ethz.ch/mailman/listinfo/r-sig-wiki"

As you can see, we are using the regex pattern nn1 in the sub() function. Generally speaking

nnN is replaced with the N-th group specied in the regular expression. The rst matched

group is referenced by nn1. In our example, the rst group is everything that is contained in

the curved brackets, that is: (https.*), which are in fact the links we are looking for.

7.4 Text Analysis of BioMed Central Journals

For our last application we will work analyzing some text data. We will analyze the catalog

of journals from the BioMed Central (BMC), a scientic publisher that specializes in

open access journal publication. You can nd more informaiton of BMC at: : http://www.

biomedcentral.com/about/catalog

The datawith the journal catalogis availablein csv format at:http://www.biomedcentral.

com/journals/biomedcentraljournallist.txt

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To import the data in R we can read the le with read.table(). Just specify the URL

location of the le and pass it to read.table(). Don’t forget to set the arguments sep =

"," and stringsAsFactors = FALSE

# link of data set

url = = "http://www.biomedcentral.com/journals/biomedcentraljournallist.txt"

# read data (stringsAsFactors=FALSE)

biomed = = read.table(url, header = TRUE, sep = ",", stringsAsFactors = FALSE)

We can check the structure of the data with the function str():

# structure of the dataset

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

## data.frame : 336 obs. of 7 variables:

## $ Publisher

: chr "BioMed Central Ltd" ...

## $ Journal.name : chr "AIDS Research and Therapy" ...

## $ Abbreviation : chr "AIDS Res Ther" ...

## $ ISSN

: chr "1742-6405" ...

## $ URL

: chr "http://www.aidsrestherapy.com" ...

## $ Start.Date

: int 2004 2011 ...

## $ Citation.Style: chr "BIOMEDCENTRAL" ...

As you can see, the data frame biomed has 336 observations and 7 variables. Actually, all

the variables except for Start.Date are in character mode.

7.4.1 Analyzing Journal Names

We will do a simple analysis of the journal names. The goal is to study what are the more

common terms used in the title of the journals. We are going to keep things at a basic

level but for a more formal (and sophisticated) analysis you can check the package tm |text

mining| (by Ingo Feinerer).

To have a better idea of what the data looks like, let’s check the rst journal names.

# first 5 journal names

head(biomed$Journal.name, 5)

## [1] "AIDS Research and Therapy"

## [2] "AMB Express"

## [3] "Acta Neuropathologica Communications"

## [4] "Acta Veterinaria Scandinavica"

## [5] "Addiction Science & Clinical Practice"

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As you can tell, the fth journal "Addiction Science & Clinical Practice" has an am-

persand & symbol. Whether to keep the ampersand and other punctutation symbols depends

on the objectives of the analysis. In our case, we will remove those elements.

Preprocessing

The preprocessing steps implies to get rid of the punctuation symbols. For convenient reasons

it is always recommended to start working with a small subset of the data. In this way we

can experiment at a small scale until we are condent with the right manipulations. Let’s

take the rst 10 journals:

# get first 10 names

titles10 = biomed$Journal.name[1:10]

titles10

## [1] "AIDS Research and Therapy"

## [2] "AMB Express"

## [3] "Acta Neuropathologica Communications"

## [4] "Acta Veterinaria Scandinavica"

## [5] "Addiction Science & Clinical Practice"

## [6] "Agriculture & Food Security"

## [7] "Algorithms for Molecular Biology"

## [8] "Allergy, Asthma & Clinical Immunology"

## [9] "Alzheimer s Research & Therapy"

## [10] "Animal Biotelemetry"

We want to get rid of the ampersand signs &, as well as other punctuation marks. This can be

done with str

replace

all() and replacing the pattern [[:punct:]] with empty strings

"" (don’t forget to load the stringr package)

# remove punctuation

titles10 = = str_replace_all(titles10, pattern = = "[[:punct:]]", "")

titles10

## [1] "AIDS Research and Therapy"

## [2] "AMB Express"

## [3] "Acta Neuropathologica Communications"

## [4] "Acta Veterinaria Scandinavica"

## [5] "Addiction Science Clinical Practice"

## [6] "Agriculture Food Security"

## [7] "Algorithms for Molecular Biology"

## [8] "Allergy Asthma Clinical Immunology"

## [9] "Alzheimers Research Therapy"

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Handling and Processing Strings in R

100

## [10] "Animal Biotelemetry"

We succesfully replaced the punctuation symbols with empty strings, but now we have extra

whitespaces. To remove the whitespaces we will use again str

replace

all() to replace any

one or more whitespaces nns+ with a single blank space " ".

# trim extra whitespaces

titles10 = = str_replace_all(titles10, pattern = = "nns+", , " ")

titles10

## [1] "AIDS Research and Therapy"

## [2] "AMB Express"

## [3] "Acta Neuropathologica Communications"

## [4] "Acta Veterinaria Scandinavica"

## [5] "Addiction Science Clinical Practice"

## [6] "Agriculture Food Security"

## [7] "Algorithms for Molecular Biology"

## [8] "Allergy Asthma Clinical Immunology"

## [9] "Alzheimers Research Therapy"

## [10] "Animal Biotelemetry"

Once we have a better idea of how to preprocess the journal names, we can proceed with all

the 336 titles.

# remove punctuation symbols

all_titles = = str_replace_all(biomed$Journal.name, pattern = = "[[:punct:]]", "")

# trim extra whitespaces

all_titles = = str_replace_all(all_titles, pattern = "nns+", " ")

The next step is to split up the titles into its dierent terms (the output is a list).

# split titles by words

all_titles_list = = str_split(all_titles, pattern = = " ")

# show first 2 elements

all_titles_list[1:2]

## [[1]]

## [1] "AIDS"

"Research" "and"

"Therapy"

## [[2]]

## [1] "AMB"

"Express"

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Handling and Processing Strings in R

101

Summary statistics

So far we have a list that contains the words ofeach journal name. Wouldn’t be interesting to

know more about the distribution of the number of terms in each title? This means that we

need to calculate how many words are in each title. To get these numbers let’s use length()

within sapply(); and then let’s tabulate the obtained frequencies:

# how many words per title

words_per_title = = sapply(all_titles_list, length)

# table of frequencies

table(words_per_title)

## words_per_title

## 17 108 81 55 33 31

We can also express the distribution as percentages, and we can get some summary statistics

with summary()

# distribution

100 * * round(table(words_per_title)/length(words_per_title), 4)

## words_per_title

## 5.06 32.14 24.11 16.37 9.82 9.23 1.79 1.19 0.30

# summary

summary(words_per_title)

Min. 1st Qu. Median

Mean 3rd Qu.

Max.

1.00

2.00

3.00

3.36

4.00

9.00

Looking at summary statistics we can say that around 30% of journal names have 2 words.

Likewise, the median number of words per title is 3 words.

Interestingly the maximum value is 9 words. What is the journal with 9 terms in its title?

We can nd the longest journal name as follows:

# longest journal

all_titles[which(words_per_title == 9)]

## [1] "Journal of Venomous Animals and Toxins including Tropical Diseases"

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Handling and Processing Strings in R

102

7.4.2 Common words

Remember that our main goal with this example is to nd out what words are the most

common in the journal titles. To answer this question we rst need to create something like

adictionary of words. How do get such dictionary? Easy, we just have to obtain a vector

containing all the words in the titles:

# vector of words in titles

title_words = = unlist(all_titles_list)

# get unique words

unique_words = unique(title_words)

# how many unique words in total

num_unique_words = = length(unique(title_words))

num_unique_words

## [1] 441

Applying unique() to the vector title

words we get the desired dictionary of terms, which

has a total of 441 words.

Once we have the unique words, we need to count how many times each of them appears in

the titles. Here’s a way to do that:

# vector to store counts

count_words = = rep(0, num_unique_words)

# count number of occurrences

for (i in n 1:num_unique_words) ) f

count_words[i] = = sum(title_words == unique_words[i])

An alternative simpler way to count the number of word occurrences is by using the table()

function on title

words:

# table with word frequencies

count_words_alt = = table(title_words)

In any of both cases (count

words or count

words

alt), we can examine the obtained fre-

quencies with a simple table:

# table of frequencies

table(count_words)

## count_words

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Handling and Processing Strings in R

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