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time. Here,wefocus onconverting texttoPOSIXct objects since this is themostportableway
to store such information.
Underthehood,aPOSIXct objectstores thenumberof seconds thathavepassed since January
1,1970 00:00. Sucha storageformat facilitates the calculation of durations by subtraction of
twoPOSIXctobjects.
When aPOSIXct objectis printed,R shows itin ahuman-readable calenderformat. For
example,the commandSys.time returns thesystem time provided by theoperatingsystemin
POSIXctformat.
current_time <- Sys.time()
class(current_time)
## [1] "POSIXct" "POSIXt"
current_time
## [1] "2013-10-28 11:12:50 CET"
Here,Sys.timeuses thetimezonethatis stored in thelocale settings of themachine running
R.
Converting from acalendertime toPOSIXct and backis notentirely trivial,sincethereare
many idiosyncrasies tohandle incalendersystems. These includeleap days,leapseconds,
daylightsaving times,timezones and so on. Convertingfrom texttoPOSIXct is further
complicated by themany textual conventions of time/date denotation. Forexample,both28
September 1976and1976/09/28indicatethesamedayofthesameyear.Moreover,the
nameof themonth(orweekday) is language-dependent, wherethelanguageis againdefinedin
the operating system's locale settings.
Thelubridatepackage
13
contains a numberof functions facilitatingtheconversion of textto
POSIXctdates.Asanexample,considerthefollowingcode.
library(lubridate)
dates <- c("15/02/2013", "15 Feb 13", "It happened on 15 02 '13")
dmy(dates)
## [1] "2013-02-15 UTC" "2013-02-15 UTC" "2013-02-15 UTC"
Here,thefunctiondmy assumesthatdates aredenoted intheorderday-month-yearand tries to
extractvalid dates. Notethatthecode abovewill only work properly in localesettings where
the nameof thesecond monthis abbreviated to Feb. This holds forEnglish orDutch locales, but
fails forexample inaFrench locale (Février).
Thereare similarfunctions forallpermutations ofd,mandy. Explicitly,all of thefollowing
functions exist.
dmy
myd
ydm
mdy
dym
ymd
So onceitis known in whatorderdays,months and years aredenoted, extraction is very easy.
Note. It is notuncommontoindicate years with twonumbers,leavingoutthe
indication of century. InR,00-68 are interpreted as 2000-2068 and 69-99 as
1969-1999.
dmy("01 01 68")
## [1] "2068-01-01 UTC"
dmy("01 01 69")
## [1] "1969-01-01 UTC"
Anintroductiontodata cleaningwithR 21
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Table2: Day, monthand yearformats recognized byR.
Code description
Example
%a
Abbreviated weekday namein the currentlocale. Mon
%A
Full weekday namein the currentlocale.
Monday
%b
Abbreviated month namein the currentlocale.
Sep
%B
Full month namein thecurrentlocale.
September
%m
Month number(01-12)
09
%d
Day of the month as decimalnumber(01-31).
28
%y
Yearwithout century (00-99)
13
%Y
Yearincluding century.
2013
This behaviour is according tothe 2008 POSIX standard,butone shouldexpect that
this interpretationchanges over time.
Itshouldbenoted thatlubridate(as well asR's basefunctionality) is only capable of
converting certain standard notations. For example, the followingnotation does notconvert.
dmy("15 Febr. 2013")
## Warning: All formats failed to parse. No formats found.
## [1] NA
Thestandard notations thatcan be recognized byR,eitherusinglubridateorR's built-in
functionality areshown inTable 2. Here,thenames of (abbreviated) week ormonth names that
aresought forin the textdependon thelocalesettings of themachinethatis runningR.For
example,ona PC running under aDutch locale,``maandag''will berecognizedas thefirstday of
the weekwhilein English locales ``Monday''will berecognized. If themachine runningRhas
multiple locales installed you may add theargumentlocale to oneofthedmy-likefunctions. In
Linux-alikesystemsyoucanusethecommand locale -ain bashterminaltoseethelistof
installed locales. InWindowsyoucanfind available localesettings under``languageand
regional settings'',undertheconfigurationscreen.
If you knowthetextualformatthatis used todescribea datein the input, you may wantto use
R'scorefunctionalitytoconvertfromtextto POSIXct.Thiscanbedonewiththe as.POSIXct
function. Ittakes as arguments acharacter vectorwith time/datestrings and astring
describing theformat.
dates <- c("15-9-2009", "16-07-2008", "17 12-2007", "29-02-2011")
as.POSIXct(dates, format = "%d-%m-%Y")
## [1] "2009-09-15 CEST" "2008-07-16 CEST" NA
NA
In theformatstring, dateandtimefields areindicated by aletterpreceded by apercentsign (%).
Basically,such a%-code tellsR to lookforarangeof substrings. Forexample,the%d indicator
makesR lookfornumbers1-31 where precursorzeros are allowed,so01,02,…31 are
recognized as well. Table2 shows which date-codes arerecognizedbyR. Thecompletelist can
befound by typing?strptime in theRconsole. Strings thatarenot in theexact format
specified by theformatargument(like the third string in the above example) willnot be
converted byas.POSIXct. Impossible dates,such as theleapday in the fourth dateaboveare
alsonot converted.
Finally,to convertdates fromPOSIXctbackto character, onemay usetheformat functionthat
comes with baseR.Itaccepts aPOSIXctdate/timeobjectand anoutputformatstring.
AnintroductiontodatacleaningwithR 22
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mybirth <- dmy("28 Sep 1976")
format(mybirth, format = "I was born on %B %d, %Y")
## [1] "I was born on September 28, 1976"
2.4
character
manipulation
Becauseof themany ways peoplecan writethe samethings down, characterdata canbe
difficultto process. Forexample,considerthefollowing excerpt of adataset withagender
variable.
##
gender
## 1
M
## 2 male
## 3 Female
## 4
fem.
If this would betreatedas afactorvariable withoutany preprocessing,obviously four,nottwo
classes would bestored. Thejob athandis thereforeto automatically recognizefrom the above
datawhether eachelementpertains tomaleorfemale. In statisticalcontexts,classifyingsuch
``messy''textstrings into anumberof fixedcategories is oftenreferred to ascoding.
Belowwediscuss two complementary approaches to stringcoding: stringnormalizationand
approximate textmatching. In particular,the followingtopics arediscussed.
– Removeprepending ortrailing whitespaces.
– Pad strings to acertainwidth.
– Transform toupper/lowercase.
– Search forstrings containing simplepatterns (substrings).
– Approximatematching procedures based onstring distances.
2.4.1 Stringnormalization
String normalization techniques areaimedattransformingavariety ofstrings to asmallersetof
string values whicharemoreeasily processed. By default,Rcomes with extensivestring
manipulationfunctionality thatis based onthetwo basicstringoperations: finding apatternin a
string and replacingonepatternwith another. Wewill deal withR's genericfunctions belowbut
startby pointing outsome commonstringcleaningoperations.
Thestringrpackage
36
offers anumberof functions thatmakesome somestringmanipulation
tasks aloteasier thanthey would bewithR's basefunctions. Forexample,extrawhitespaces at
the beginningorend of astring canbe removed usingstr_trim.
library(stringr)
str_trim(" hello world ")
## [1] "hello world"
str_trim(" hello world ", side = "left")
## [1] "hello world "
str_trim(" hello world ", side = "right")
## [1] " hello world"
Conversely,strings canbepadded with spaces or othercharacters withstr_pad to acertain
width. Forexample,numerical codes areoftenrepresentedwithprepending zeros.
Anintroductiontodata cleaningwithR 23
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str_pad(112, width = 6, side = "left", pad = 0)
## [1] "000112"
Bothstr_trim andstr_padacceptaside argumentto indicatewhethertrimming orpadding
should occuratthebeginning (left), end (right) orboth sides of thestring.
Converting strings to complete upperor lowercasecan bedone withR's built-intoupper and
tolowerfunctions.
toupper("Hello world")
## [1] "HELLO WORLD"
tolower("Hello World")
## [1] "hello world"
2.4.2 Approximate string matching
Thereare twoforms of stringmatching. Thefirst consists of determining whethera(rangeof)
substring(s) occurs withinanotherstring. In this case oneneeds to specify arangeof substrings
(called apattern) to searchforin another string. In thesecond formone defines adistance
metric between strings that measures how``different''two strings are. Belowwewill givea
shortintroductionto patternmatching andstring distances withR.
Thereare several pattern matchingfunctions that come with baseR.The mostused are
probablygrepandgrepl. Both functions takeapattern and acharacter vectoras input. The
outputonly differs in thatgreplreturns a logical index,indicating which elementof theinput
charactervectorcontainsthepattern,while grepreturnsanumericalindex.Youmaythinkof
grep(...)aswhich(grepl(...)).
In the mostsimplecase, the pattern tolookforis asimplesubstring. Forexample,usingthe
dataof theexample onpage23,wegetthefollowing.
gender <- c("M", "male ", "Female", "fem.")
grepl("m", gender)
## [1] FALSE TRUE TRUE TRUE
grep("m", gender)
## [1] 2 3 4
Notethattheresult is case sensitive: thecapitalMin the firstelementofgenderdoes notmatch
the lowercasem. There areseveralways to circumventthis casesensitivity. Eitherby case
normalization orby theoptional argumentignore.case.
grepl("m", gender, ignore.case = TRUE)
## [1] TRUE TRUE TRUE TRUE
grepl("m", tolower(gender))
## [1] TRUE TRUE TRUE TRUE
Obviously,looking fortheoccurrenceofm orM inthegendervectordoes notallowus to
determinewhich strings pertain tomale and which not. Preferably wewould liketo search for
strings that startwith anm orM.Fortunately,thesearch patterns thatgrepaccepts allow for
suchsearches. Thebeginning of astring is indicated with acaret(̂).
grepl("^m", gender, ignore.case = TRUE)
## [1] TRUE TRUE FALSE FALSE
Anintroductiontodata cleaningwithR 24
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Indeed, thegrepl function nowfinds only the firsttwo elements ofgender. The caretis an
exampleof aso-called meta-character. Thatis,itdoes not indicatethecaret itself but
something else,namely thebeginning of astring. Thesearch patterns thatgrep,grepl(and
subandgsub)understandhavemoreofthesemeta-characters,namely:
. \ | ( ) [ { ^ $ * + ?
If you need to search astring forany of thesecharacters,youcanuse the optionfixed=TRUE.
grepl("^", gender, fixed = TRUE)
## [1] FALSE FALSE FALSE FALSE
This will makegrepl orgrepignoreany meta-characters in thesearch string.
Search patterns using meta-characters arecalled regularexpressions. Regularexpressions offer
powerful andflexible ways tosearch (and alter)text. Adiscussion of regularexpressions is
beyond thescopeof theselecture notes. However,aconcise description of regularexpressions
allowed byR's built-in string processingfunctions can befoundby typing?regex attheR
command line. Thebooks by Fitzgerald
10
or Friedl
11
provideathoroughintroduction to the
subjectof regular expression. Ifyoufrequently haveto deal with ``messy'' textvariables,
learning towork withregularexpressions is aworthwhile investment. Moreover,sincemany
popularprogramming languages support somedialectof regexps,it is an investment thatcould
pay off severaltimes.
Wenowturn ourattention to thesecond method of approximate matching,namely string
distances. Astringdistanceis an algorithm orequation thatindicates howmuch two strings
differ from each other. An importantdistancemeasureis implementedby theR's nativeadist
function. This functioncounts howmany basicoperations areneeded to turn onestring into
another. Theseoperations include insertion,deletion orsubstitution of asingle character
19
.For
example
adist("abc", "bac")
##
[,1]
## [1,]
2
Theresultequals two sinceturning"abc"into"bac" involves two charactersubstitutions:
abcbbcbac.
Usingadist,wecan comparefuzzy textstrings to alist of knowncodes. Forexample:
codes <- c("male", "female")
D <- adist(gender, codes)
colnames(D) <- codes
rownames(D) <- gender
D
##
male female
## M
4
6
## male
1
3
## Female
2
1
## fem.
4
3
Here,adist returns the distancematrix betweenourvectorof fixedcodes and theinput data.
For readability weadded row-and columnnames accordingly. Now,to find outwhichcode
matches best with our rawdata, weneedto find the index of thesmallestdistanceforeach row
ofD.This can bedoneas follows.
Anintroductiontodata cleaningwithR 25
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i <- apply(D, 1, which.min)
data.frame(rawtext = gender, coded = codes[i])
##
rawtext coded
## 1
M
male
## 2
male
male
## 3 Female female
## 4
fem. female
Weuseapply to applywhich.min to every row ofD.Notethat inthecaseof multipleminima,
the firstmatchwill bereturned. At the end of this subsectionweshowhowthis codecan be
simplified with thestringdistpackage.
Finally,wemention threemore functions based onstring distances. First, theR-built-in function
agrepissimilartogrep,butitallowsonetospecifyamaximumLevenshteindistancebetween
the inputpatternand the found substring. Theagrepfunction allows forsearching for regular
expression patterns,which makes itvery flexible.
Secondly,thestringdistpackage
32
offers afunction calledstringdistwhichcan computea
variety of string distancemetrics,someofwhichare likely to provideresults thatarebetterthan
adist's.Mostimportantly,thedistancefunctionusedby adistdoesnotallowforcharacter
transpositions,which is acommon typographical error. Usingtheoptimal stringalignment
distance (thedefault choice forstringdist) we get
library(stringdist)
stringdist("abc", "bac")
## [1] 1
Theansweris now1 (not2as withadist),sincetheoptimal stringalignmentdistanceallowsfor
transpositions of adjacentcharacters:
abc bac.
Thirdly,thestringdist packageprovides afunctioncalledamatch,which mimics the
behaviourofR'smatchfunction: itreturns an index totheclosestmatch within amaximum
distance. Recall thegenderandcode exampleof page25.
# this yields the closest match of 'gender' in 'codes' (within a distance of 4)
(i <- amatch(gender,codes,maxDist=4))
## [1] 1 1 2 2
# store results in a data.frame
data.frame(
rawtext = gender
, code = codes[i]
)
##
rawtext
code
## 1
M
male
## 2
male
male
## 3 Female female
## 4
fem. female
2.5 Character encoding issues
Acharacter encoding systemis asystemthatdefines how to translateeach characterofa given
alphabetintoacomputerbyteorsequence of bytes
.Forexample,ASCIIis anencoding
Infact,the definitioncanbe moregeneral,forexample toincludeMorse code. However,we limitourselvestocom-
puterizedcharacterencodings.
Anintroductiontodata cleaningwithR 26
system thatprescribes howtotranslate 127 characters into single bytes (wherethefirstbitof
eachbyte is necessarily 0). TheASCII characters include the upperand lowercaseletters of the
Latinalphabet (a-z,A-Z),Arabicnumbers (0-9),anumberof punctuation characters and a
numberof invisible so-calledcontrol characters such as newlineand carriagereturn.
Although itis widely used,ASCII is obviously incapableof encoding characters outsidethe
Latinalphabet,soyoucansay ``hello'', butnot``㗾㔀㔄㗼 㔎㗼㔍''in this encoding. Forthis reason,a
numberof characterencodingsystems have been developed thatextendASCII orreplaceitall
together. Somewell-known schemes includeUTF-8andlatin1. The characterencoding
schemethatis used by defaultby youroperating system is defined in yourlocalesettings.
MostUnix-alikes useUTF-8 by defaultwhileolderWindowsapplications,including theWindows
version ofR uselatin1. TheUTF-8 encodingstandard is widely used to encodeweb pages:
according toa frequently repeated survey ofw3techs
35
,about75%ofthe10 million most
visitedweb pages are encoded inUTF-8.
Youcan find outthecharacterencoding of your system by typing(not copy-pasting!) a
non-ASCIIcharacterand askfor the encoding scheme, likeso.
Encoding("Queensrÿche")
## [1] "unknown"
If the answeris"unknown",this means that the local nativeencoding is used. The default
encoding used by yourOS can berequested by typing
Sys.getlocale("LC_CTYPE")
## [1] "en_US.UTF-8"
attheR command-line.
ForRtobeableto correctly readinatextfile,it mustunderstandwhich characterencoding
schemewas usedto store it. By default,R assumes thatatextfile is stored intheencoding
schemedefined by theoperatingsystem'slocalesetting. This may failwhen thefile was not
generated on the same computerthatR is running on butwas obtained fromtheweb for
example. To makethings worse,itis impossibleto determineautomatically with certainty from
afilewhat encoding schemehas been used(althoughforsome encodings it is possible). This
means thatyoumay run into situations whereyouhaveto tellRliterally in which encoding afile
has beenstored. Onceafilehas been read intoR,a charactervectorwill internally be translated
to eitherUTF-8orlatin1.
ThefileEncoding argument ofread.table and its relatives tellsR whatencoding scheme
was used to storethefile. ForreadLines thefileencoding mustbe specified when thefileis
opened,beforecallingreadLines,as in theexamplebelow.
# 1. open a connection to your file, specifying its encoding
f <- file("myUTF16file.txt", encoding = "UTF-16")
# 2. Read the data with readLines. Text read from the file is converted to
# uft8 or latin1
input <- readLines(f)
# close the file connection.
close(f)
When reading thefile,R will nottranslate the encoding toUTF-8orlatin1 by itself,butinstead
relies onanexternaliconvlibrary. Depending on theoperating system,R eitheruses the
conversion service offered by theOS, oruses athird-party library included withR.R'siconv
function allows users to translatecharacterrepresentations,becauseof theOS-dependencies
AnintroductiontodatacleaningwithR 27
justmentioned,not all translations willbepossibleonall operating systems. Withiconvlist()
you can checkwhatencodings can be translated by youroperating system. Theonly encoding
systems thatis guaranteed to beavailable onall platforms areUTF-8andlatin1.
Anintroductiontodata cleaningwithR 28
Exercises
Exercise 2.1. Typeconversions.
a. Load the builtinwarpbreaksdata set. Find out,ina single command, which columns of
warpbreaksareeithernumericorinteger.
b. Isnumeric anaturaldata type for thecolumns whichare storedas such? Converttointeger
whennecessary. (See also?warpbreaksfor an explanation of the data).
c. Error messages inR sometimes reportthe underlying typeof an objectrather thanthe
user-level class. Derive fromthe followingcodeanderror messagewhat the underlying type
of anR functionis.
mean[1]
## Error: object of type 'closure' is not subsettable
Confirmyour answer usingtypeof.
Exercise 2.2. Typethe following code inyourRterminal.
v <- factor(c("2", "3", "5", "7", "11"))
a. Convertvtocharacter withas.character. Explain whatjusthappened.
b. Convertvtonumericwithas.numeric. Explain whatjusthappened.
c. Howwouldyouconvert the values ofv tointegers?
Exercise 2.3. Inthis exercise we'll usereadLinestoread in anirregular textfile. The filelooks like
this (withoutnumbering).
1
// Survey data. Created : 21 May 2013
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// Field 1: Gender
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// Field 2: Age (in years)
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// Field 3: Weight (in kg)
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M;28;81.3
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male;45;
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Female;17;57,2
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fem.;64;62.8
Youmay copy the text from thispdffile ina textfile calledexample.txt or download the file from
our Github page.
a. Readthe complete file usingreadLines.
b. Separatethevector of lines intoavector containing comments andavector containing
the data. Hint: usegrepl.
c. Extract the date fromthe firstcommentline.
d. Readthe dataintoamatrix as follows.
(a) Splitthecharacter vectors in the vector containingdata lines by semicolon (;)using
strsplit.
(b) Find the maximumnumber offields retrievedbysplit. Append rows thatare shorter
withNA's.
(c) Useunlist andmatrix to transformthedata to row-column format.
e. Fromcomment lines2-4,extract the names of thefields. Setthese ascolnamesfor the
matrixyoujustcreated.
Exercise 2.4. We will coercethe columns of the data of the previous exercise to astructureddata
set.
a. Coerce the matrixtoadata.frame,makingsure allcolumns arecharactercolumns.
Anintroductiontodata cleaningwithR 29
b. Use astringdistance techniquetotransformtheGender column intoafactor variable
with labelsman andwoman.
c. Coerce theAgecolumntointeger.
d. Coerce theweightcolumn tonumeric. Hint: usegsub to replace comma's with aperiod.
AnintroductiontodatacleaningwithR 30
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