Introduction to R for Data Science :: Session 5 [Data Structuring: Strings in R]

1. Introduction to R for Data Science
Lecturers
dipl. ing Branko Kovač
Data Analyst at CUBE/Data Science Mentor
at Springboard
Data Science zajednica Srbije
branko.kovac@gmail.com
dr Goran S. Milovanović
Data Scientist at DiploFoundation
Data Science zajednica Srbije
goran.s.milovanovic@gmail.com
goranm@diplomacy.edu

2. Strings in R
• {base} for strings
• {stringr} for strings
• {stringi} for strings
Intro to R for Data Science
Session 5: Structuring Data: Strings in R
# Introduction to R for Data Science
# SESSION 5 :: 26 May, 2016
# Processing strings in R
library(stringr)
# strings in R are charactervectors
stringA <- "Hello world"
stringB <- "Sun shines!"
stringA
stringB
is.character(stringA) # TRUE
as.character(200*5)
as.numeric("1000")
as.double("3.14")
# Introduction to R for Data Science
# SESSION 5 :: 26 May, 2016
# Using " and '
# either:
stringA <- "Hello 'World'"
stringA
# or
stringA <- 'Hello "World"'
stringA # prints:"Hello "World"" - what is
this: ?
print(stringA)
# try:
writeLines(stringA)
print(stringA)
# Escapingin R: use , the R escape
character
stringA <- 'Hello "World"'
stringA
print(stringA)
writeLines(stringA)
# Escapingescaping
writeLines("")# nice

3. Intro to R for Data Science
Session 5: Structuring Data: Strings in R
# Introduction to R for Data Science
# SESSION 5 :: 26 May, 2016
# String Concatenationin R
stringC <- c(stringA,stringB) # a character
vectorof length == 2
length(stringC)
stringC <- paste(stringA,stringB,
sep=",") # length == 1, base
function
writeLines(stringC)
# sep w. collapse (paste args)
stringC <- c(stringA,stringB)
stringC <- paste(stringC,collapse="__")
writeLines(stringC)
# paste0 is paste w. sep="",fasterthan
paste(),base function
# Introduction to R for Data Science
# SESSION 5 :: 26 May, 2016
strA <- "One"
strB <- "Two"
strC <- "Three"
paste0(strA,strB, strC)
# the collapse argumentis used in paste0 as well
strD <- c(strA,strB,strC)
paste0(strD,collapse="-")
# stringr concatenation,also has sep and collapse
as args
str_c(strA,strB,strC)
str_c(strA,strB,strC,sep="...")
str_c(strD,collapse="...")
# both paste {base}and str_c {stringr} are
vectorized
paste("Prefix-",strD, sep="-")
str_c("Prefix-",strD,sep="-")
Strings in R
• Concatenation

4. Intro to R for Data Science
Session 5: Structuring Data: Strings in R
# Introduction to R for Data Science
# SESSION 5 :: 26 May, 2016
stringA <- "The quick brown fox jumps overthe lazy dog";
splitA <- strsplit(stringA," ") # is.list(splitA) == T
splitA <- unlist(strsplit(stringA," "))
# "The quick brown" from "The quick brown fox jumps overthe lazy dog"
splitA <- paste(unlist(strsplit(stringA," "))[1:3],collapse=" ")
# or
splitA <- paste(strsplit(stringA," ")[[1]][1:3],collapse=" ")
# advice:use
splitA <- strsplit(stringA," ",fixed=T) # fixed=T says:match the split argumentexactly,
# otherwise,split is an regularexpression;defaultis: fixed = FALSE
# string split w. {stringr}
is.list(str_split(stringA," "))
# this is interesting:
str_split(stringA," ", n=3)
# "The quick brown" from "The quick brown fox jumps overthe lazy dog"
paste0(str_split(stringA," ", n=4)[[1]][1:3],collapse=" ")
Strings in R
• Splitting

5. Intro to R for Data Science
Session 5: Structuring Data: Strings in R
# Introduction to R for Data Science
# SESSION 5 :: 26 May, 2016
# default: str_split(string,pattern,n = Inf), where pattern is regex
str_split(stringA,boundary("word"))
# very useful:
stringA1 <- "The quick brown fox jumps overthe lazy dog"
str_split(stringA1,boundary("word"))
stringA1 <- "Aboveall, don'tlie to yourself.
The man who lies to himselfand listens to his own lie comes to a pointthat he cannotdistinguish the
truth within him, or around him,and so loses all respectfor himselfand for others.
And having no respecthe ceasesto love."
str_split(stringA1,boundary("word"))
str_split(stringA1,boundary("word",skip_word_none= F)) # includingpunctuation and special
str_split(stringA1,boundary("line_break"))
writeLines(str_split(stringA1,boundary("line_break"))[[1]])
Strings in R
• Splitting

6. Intro to R for Data Science
Session 5: Structuring Data: Strings in R
# Introduction to R for Data Science
# SESSION 5 :: 26 May, 2016
stringA <- c("Belgrade","Zagreb","Ljubljana")# {stringr}
str_sub(stringA,1, 2)
# counting backwards
str_sub(stringA, -3, -1)
# {base}
substr(stringA,1, 3)
# play:
substr(stringA,c(1,2,3),c(2,3,4))
# nope:
substr(stringA, -2, -1) # {base}
Strings in R
• Subsetting strings

7. Intro to R for Data Science
Session 5: Structuring Data: Strings in R
# Introduction to R for Data Science
# SESSION 5 :: 26 May, 2016
# Replacingcharactersin strings
stringB <- stringA # just a copy of stringA
str_sub(stringB,1,2)<- "00"
stringB
# {base}
stringB <- stringA # just a copy of stringA
substr(stringB,1,3)<- "WowWow" # check the
result!
stringB
substr(stringB,1,4)<- "WoWWow" # check the
result!
stringB
substr(stringB,1,6)<- "WowWow" # check the
result!
stringB
Strings in R
• Subsetting strings
# Introduction to R for Data Science
# SESSION 5 :: 26 May, 2016
# UPPER CASE to lower case and vice versa
in R
stringA <- "ABRACADABRA"
# {base}
tolower(stringA)
stringA <- tolower(stringA)
toupper(stringA)
stringA <- toupper(stringA)
# {stringr}
str_to_lower(stringA)
stringB <- str_to_lower(stringA)
str_to_upper(stringA)
# capitalize first letter
str_to_title(stringB)
• Transforming strings

8. Intro to R for Data Science
Session 5: Structuring Data: Strings in R
# Introduction to R for Data Science
# SESSION 5 :: 26 May, 2016
# Remove whitespace
stringA <- c(" Removewhitespace ");
str_trim(stringA)
# remove leading whitespace
str_trim(stringA,side="left")
# remove trailing whitespace
str_trim(stringA,side="right")
# remove all whitespace?
stringA <- c(" Remove whitespace ") # how aboutthis one?
# there are differentways to do it. Try:
gsub(" ", "", stringA,fixed=T) # (!(fixed==T)),the first (pattern) argumentis regex
# in general:
stringA <- "The quick brown fox jumps overthe lazy dog The quick brown"
gsub("Thequick brown","The slow red", stringA,fixed=T)
Strings in R
• More transforming

9. Intro to R for Data Science
Session 5: Structuring Data: Strings in R
# Introduction to R for Data Science
# SESSION 5 :: 26 May, 2016
# Searchingfor somethingin a string
# Does a string encompass a substring?
grepl("Thequick brown",stringA,fixed = T)
grepl("Thefastred", stringA, fixed = T)
stringB <- "Uraaaaaaaa"
grep("Thequick brown",c(stringA,stringB),fixed = T)
# where?
stringA <- "The quick brown fox jumps overthe lazy dog The quick brown"
w <- gregexpr("Thequick brown",stringA)
str(w)
b1 <- w[[1]][1] # first match starts at
b2 <- w[[1]][2] # second match starts at
# now, match.length is an attribute of w[[1]], not w itself:
e1 <- attr(w[[1]],"match.length",exact= T)[1]
e2 <- attr(w[[1]],"match.length",exact= T)[2]
Strings in R
• Search

10. Intro to R for Data Science
Session 5: Structuring Data: Strings in R
# Introduction to R for Data Science
# SESSION 5 :: 26 May, 2016
# first match extraction:
str_sub(stringA,b1,b1+e1-1)
# second matchextraction:
str_sub(stringA,b2,b2+e2-1)
# Ok, but easierand more convenientwith {stringr}
str_detect(stringA,"The quickbrown") # T or F
str_locate(stringA,"The quickbrown") # first match
str_locate_all(stringA,"The quickbrown") # all matches
# term frequency,as we know,is very importantin text-mining:
term1 <- str_locate_all(stringA,"The quickbrown")[[1]]# all matches for term1 ie. "The quick
brown"
dim(term1)[1] # how many matches = how many rows in the str_locate_alloutputmatrix...
Strings in R
• Search

11. Intro to R for Data Science
Session 5: Structuring Data: Strings in R
# Introduction to R for Data Science
# SESSION 5 :: 26 May, 2016
# Sorting strings in R
letters
str_sort(letters,locale="en")# locale = en
str_sort(letters,locale="haw")# locale = Hawaiian
# backwards
str_sort(letters,decreasing= T)
# handy:
stringA <- c("New York","Paris",NA, "Moscow","Tokyo")
str_sort(stringA,na_last=T)
# [1] "Moscow" "New York" "Paris" "Tokyo" NA
str_sort(stringA,na_last=F)
# [1] NA "Moscow" "New York" "Paris" "Tokyo"
# {base}
sort(stringA)
sort(stringA,decreasing=T)
Strings in R
• Sorting strings

12. Intro to R for Data Science
Session 5: Structuring Data: Strings in R
# Introduction to R for Data Science
# SESSION 5 :: 26 May, 2016
# Take home messageon encodings
# 1. Most of the time, you simply need to know the source encoding
# 2. All of the time *** converteverythingto UTF-8*** - as soon as possible
# 3. Most {base},and all {stringr} and {stringi} functions thatprocessstrings in R
# will converttheir outputto UTF-8 automatically
# Working inside R only, running an English locale,will nevercause you any trouble
# However,in Data Science you will probably needto do a lot of web-scraping fora living
# - and that's where the fan starts.
# God bless iconv()- but don'tget to excited,it does not avoid all problems
# Next session:Thurday,June2, 2016 :: LinearRegressionw. R
Strings in R
• Encodings…