2015 10-7-9am regex-functions-loops.key
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This document provides an introduction to regular expressions (regex) for text search and pattern matching. It explains that regex allows for powerful text searches beyond simple keywords. Various special symbols and constructs are demonstrated that allow matching complex patterns and variants in text. Examples show matching names, sequences, microsatellite repeats and more with regex. Functions, loops and logical operators in R programming are also briefly covered.
![Regular expressions (regex):
Text search on steroids.
Regular expression Finds
David David
Dav(e|(id)) David, Dave
Dav(e|(id)|(ide)|o) David, Dave, Davide, Davo
At{1,2}enborough
Attenborough,
Atenborough
Atte[nm]borough
Attenborough,
Attemborough
At{1,2}[ei][nm]bo{0,1}ro((ugh)|w){0,1}
Atimbro,
attenbrough,
ateinborow
Easy counting, replacing all with “Sir David Attenborough”](https://image.slidesharecdn.com/2015-10-7-9amregex-functions-loops-151007091911-lva1-app6891/85/2015-10-7-9am-regex-functions-loops-key-2-320.jpg)
![Regex Special symbols
Regular expression Finds Example
[aeiou] any single vowel “e”
[aeiou]*
between 0 and infinity
vowels vowels, e.g.’
“eeooouuu"
[aeoiu]{1,3} between 1 and 3 vowels “oui”
a|i one of the 2 characters “"
((win)|(fail))
one of the two
words in ()
fail](https://image.slidesharecdn.com/2015-10-7-9amregex-functions-loops-151007091911-lva1-app6891/85/2015-10-7-9am-regex-functions-loops-key-3-320.jpg)
![More Regex Special symbols
• Google “Regular expression cheat sheet”
• ?regexp
Synonymous with
[:digit:] [0-9]
[A-z] [A-z], ie [A-Za-z]
s whitespace
. any single character
.+ one to many of anything
b* between 0 and infinity letter ‘b’
[^abc] any character other than a, b or c.
( (
[:punct:]
any of these: ! " # $ % & ' ( ) * + , - . /
: ; < = > ? @ [ ] ^ _ ` { |](https://image.slidesharecdn.com/2015-10-7-9amregex-functions-loops-151007091911-lva1-app6891/85/2015-10-7-9am-regex-functions-loops-key-4-320.jpg)
![Functions
• R has many. e.g.: plot(), t.test()
• Making your own:
tree_age_estimate <- function(diameter, species) {
growth_rate <- growth_rates[ species ]
age_estimate <- diameter / growth_rate
return(age_estimate)
}
> tree_age_estimate(25, “White Oak”)
+ 66
> tree_age_estimate(60, “Carya ovata”)
+ 190](https://image.slidesharecdn.com/2015-10-7-9amregex-functions-loops-151007091911-lva1-app6891/85/2015-10-7-9am-regex-functions-loops-key-15-320.jpg)
![“for”
Loop
> possible_colours <- c('blue', 'cyan', 'sky-blue', 'navy blue',
'steel blue', 'royal blue', 'slate blue', 'light blue', 'dark
blue', 'prussian blue', 'indigo', 'baby blue', 'electric blue')
> possible_colours
[1] "blue" "cyan" "sky-blue" "navy blue"
[5] "steel blue" "royal blue" "slate blue" "light blue"
[9] "dark blue" "prussian blue" "indigo" "baby blue"
[13] "electric blue"
> for (colour in possible_colours) {
+ print(paste("The sky is oh so, so", colour))
+ }
[1] "The sky is so, oh so blue"
[1] "The sky is so, oh so cyan"
[1] "The sky is so, oh so sky-blue"
[1] "The sky is so, oh so navy blue"
[1] "The sky is so, oh so steel blue"
[1] "The sky is so, oh so royal blue"
[1] "The sky is so, oh so slate blue"
[1] "The sky is so, oh so light blue"
[1] "The sky is so, oh so dark blue"
[1] "The sky is so, oh so prussian blue"
[1] "The sky is so, oh so indigo"
[1] "The sky is so, oh so baby blue"](https://image.slidesharecdn.com/2015-10-7-9amregex-functions-loops-151007091911-lva1-app6891/85/2015-10-7-9am-regex-functions-loops-key-18-320.jpg)
![for (letter in LETTERS) {
begins_with <- paste("^", letter, sep="")
matches <- grep(pattern = begins_with,
x = ants$genus)
print(paste(length(matches), "begin with", letter))
}
> LETTERS
[1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S"
[20] "T" "U" "V" "W" "X" "Y" "Z"
> ants <- read.table("https://goo.gl/3Ek1dL")
> colnames(ants) <- c("genus", “species")
> head(ants)
genus species
1 Anergates atratulus
2 Camponotus sp.
3 Crematogaster scutellaris
4 Formica aquilonia
5 Formica cunicularia
6 Formica exsecta
What does this loop do?](https://image.slidesharecdn.com/2015-10-7-9amregex-functions-loops-151007091911-lva1-app6891/85/2015-10-7-9am-regex-functions-loops-key-21-320.jpg)
2015 10-7-9am regex-functions-loops.key
- 1. Regular expressions (regex): Text search on steroids.
- 2. Regular expressions (regex): Text search on steroids. Regular expression Finds David David Dav(e|(id)) David, Dave Dav(e|(id)|(ide)|o) David, Dave, Davide, Davo At{1,2}enborough Attenborough, Atenborough Atte[nm]borough Attenborough, Attemborough At{1,2}[ei][nm]bo{0,1}ro((ugh)|w){0,1} Atimbro, attenbrough, ateinborow Easy counting, replacing all with “Sir David Attenborough”
- 3. Regex Special symbols Regular expression Finds Example [aeiou] any single vowel “e” [aeiou]* between 0 and infinity vowels vowels, e.g.’ “eeooouuu" [aeoiu]{1,3} between 1 and 3 vowels “oui” a|i one of the 2 characters “" ((win)|(fail)) one of the two words in () fail
- 4. More Regex Special symbols • Google “Regular expression cheat sheet” • ?regexp Synonymous with [:digit:] [0-9] [A-z] [A-z], ie [A-Za-z] s whitespace . any single character .+ one to many of anything b* between 0 and infinity letter ‘b’ [^abc] any character other than a, b or c. ( ( [:punct:] any of these: ! " # $ % & ' ( ) * + , - . / : ; < = > ? @ [ ] ^ _ ` { |
- 6. You want to scan a protein sequence database for a particular binding site.Type a single regular expression that will match the first two of the following peptide sequences, but NOT the last one: "HATSOMIKTIP" "HAVSONYYIKTIP" "HAVSQMIKTIP"
- 7. (rubular)
- 8. Variants of a microsatellite sequence are responsible for differential expression of vasopressin receptor, and in turn for differences in social behaviour in voles & others. Create a regular expression that finds AGAGAGAGAGAGAGAG dinucleotide microsatellite repeats with lengths of 5 to 500
- 9. Again Make a regular expression • matching “LMTSOMIKTIP” and “LMVSONYYIKTIP” but not “LMVSQMIKTIP” • matching all variants of “ok” (e.g., “O.K.”,“Okay”…)
- 11. Ok… so how do we use this? • ?grep • ?gsub
- 12. Which species names include ‘y’? Create a vector with only species names, but replace all ‘y’ with ‘Y! ants <- read.table("https://goo.gl/3Ek1dL") colnames(ants) <- c("genus", "species") Remove all vowels Replace all vowels with ‘o’
- 14. Functions
- 15. Functions • R has many. e.g.: plot(), t.test() • Making your own: tree_age_estimate <- function(diameter, species) { growth_rate <- growth_rates[ species ] age_estimate <- diameter / growth_rate return(age_estimate) } > tree_age_estimate(25, “White Oak”) + 66 > tree_age_estimate(60, “Carya ovata”) + 190
- 16. Make a function • That converts fahrenheit to celsius (subtract 32 then divide the result by 1.8)
- 17. Loops
- 18. “for” Loop > possible_colours <- c('blue', 'cyan', 'sky-blue', 'navy blue', 'steel blue', 'royal blue', 'slate blue', 'light blue', 'dark blue', 'prussian blue', 'indigo', 'baby blue', 'electric blue') > possible_colours [1] "blue" "cyan" "sky-blue" "navy blue" [5] "steel blue" "royal blue" "slate blue" "light blue" [9] "dark blue" "prussian blue" "indigo" "baby blue" [13] "electric blue" > for (colour in possible_colours) { + print(paste("The sky is oh so, so", colour)) + } [1] "The sky is so, oh so blue" [1] "The sky is so, oh so cyan" [1] "The sky is so, oh so sky-blue" [1] "The sky is so, oh so navy blue" [1] "The sky is so, oh so steel blue" [1] "The sky is so, oh so royal blue" [1] "The sky is so, oh so slate blue" [1] "The sky is so, oh so light blue" [1] "The sky is so, oh so dark blue" [1] "The sky is so, oh so prussian blue" [1] "The sky is so, oh so indigo" [1] "The sky is so, oh so baby blue"
- 19. What does this loop do? for (index in 10:1) { print(paste(index, "mins befo lunch")) }
- 20. Again • What does the following code do (decompose on pen and paper)
- 21. for (letter in LETTERS) { begins_with <- paste("^", letter, sep="") matches <- grep(pattern = begins_with, x = ants$genus) print(paste(length(matches), "begin with", letter)) } > LETTERS [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" [20] "T" "U" "V" "W" "X" "Y" "Z" > ants <- read.table("https://goo.gl/3Ek1dL") > colnames(ants) <- c("genus", “species") > head(ants) genus species 1 Anergates atratulus 2 Camponotus sp. 3 Crematogaster scutellaris 4 Formica aquilonia 5 Formica cunicularia 6 Formica exsecta What does this loop do?
- 25. If/else
- 30. going further