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

Introduction to R for Data Science

Lecturers

dipl. ing Branko Kovač

Data Analyst at CUBE/Data Science Mentor

at Springboard

Data Science zajednica Srbije

[email protected]

dr Goran S. Milovanović

Data Scientist at DiploFoundation

Data Science zajednica Srbije

[email protected]

[email protected]

mailto:[email protected]



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 character vectors

stringA <- "Hello world" stringB <- "Sun shines!" stringA

stringB is.character(stringA) # TRUE

as.character(200*5) as.numeric("1000") as.double("3.14")


# 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)

# Escaping in R: use \, the R escape character

stringA <- 'Hello \"World\"' stringA print(stringA)

writeLines(stringA) # Escaping escaping

writeLines("\\") # nice




# String Concatenation in R stringC <- c(stringA, stringB) # a character

vector of 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="", faster than paste(), base function


strA <- "One" strB <- "Two"

strC <- "Three" paste0(strA, strB, strC)

# the collapse argument is 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




stringA <- "The quick brown fox jumps over the lazy dog"; splitA <- strsplit(stringA," ") # is.list(splitA) == T

splitA <- unlist(strsplit(stringA," ")) # "The quick brown" from "The quick brown fox jumps over the 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 argument exactly,

# otherwise, split is an regular expression; default is: 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 over the lazy dog" paste0(str_split(stringA," ", n=4)[[1]][1:3],collapse=" ")

Strings in R

• Splitting




# default: str_split(string, pattern, n = Inf), where pattern is regex str_split(stringA, boundary("word"))

# very useful: stringA1 <- "The quick brown fox jumps over the lazy dog"

str_split(stringA1, boundary("word")) stringA1 <- "Above all, don't lie to yourself. The man who lies to himself and listens to his own lie comes to a point that he cannot distinguish the

truth within him, or around him, and so loses all respect for himself and for others. And having no respect he ceases to love."

str_split(stringA1, boundary("word")) str_split(stringA1, boundary("word", skip_word_none = F)) # including punctuation and special str_split(stringA1, boundary("line_break"))

writeLines(str_split(stringA1, boundary("line_break"))[[1]])

Strings in R

• Splitting




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




# Replacing characters in 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


# 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




# Remove whitespace stringA <- c(" Remove whitespace ");

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 about this one?

# there are different ways to do it. Try: gsub(" ", "", stringA, fixed=T) # (!(fixed==T)), the first (pattern) argument is regex # in general:

stringA <- "The quick brown fox jumps over the lazy dog The quick brown" gsub("The quick brown", "The slow red", stringA, fixed=T)

Strings in R

• More transforming




# Searching for something in a string # Does a string encompass a substring?

grepl("The quick brown", stringA, fixed = T) grepl("The fast red", stringA, fixed = T)

stringB <- "Uraaaaaaaa" grep("The quick brown", c(stringA, stringB), fixed = T) # where?

stringA <- "The quick brown fox jumps over the lazy dog The quick brown" w <- gregexpr("The quick 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




# first match extraction: str_sub(stringA,b1,b1+e1-1)

# second match extraction: str_sub(stringA,b2,b2+e2-1)

# Ok, but easier and more convenient with {stringr} str_detect(stringA,"The quick brown") # T or F str_locate(stringA,"The quick brown") # first match

str_locate_all(stringA,"The quick brown") # all matches # term frequency, as we know, is very important in text-mining:

term1 <- str_locate_all(stringA,"The quick brown")[[1]] # all matches for term1 ie. "The quick brown" dim(term1)[1] # how many matches = how many rows in the str_locate_all output matrix...

Strings in R

• Search




# 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




# Take home message on encodings # 1. Most of the time, you simply need to know the source encoding

# 2. All of the time *** convert everything to UTF-8*** - as soon as possible # 3. Most {base}, and all {stringr} and {stringi} functions that process strings in R # will convert their output to UTF-8 automatically # Working inside R only, running an English locale, will never cause you any trouble # However, in Data Science you will probably need to do a lot of web-scraping for a living

# - and that's where the fan starts. # God bless iconv() - but don't get to excited, it does not avoid all problems

# Next session: Thurday, June 2, 2016 :: Linear Regression w. R

Strings in R

• Encodings…

Education

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