2015 11-17-programming inr.key

Programming in R
(and some other stuff)
y.wurm@qmul.ac.uk
https://wurmlab.github.io

© National Geographic
Atta leaf-cutter ants

Oecophylla Weaver ants
© ameisenforum.de

© ameisenforum.de
Fourmis tisserandes

© ameisenforum.de
Oecophylla Weaver ants

Tofilski et al 2008
Forelius pusillus

Tofilski et al 2008
Forelius pusillus hides the nest entrance at night

Avant
Workers staying outside die
« preventive self-sacriﬁce »
Tofilski et al 2008
Forelius pusillus hides the nest entrance at night

Dorylus driver ants: ants with no home
© BBC

Animal biomass (Brazilian rainforest)
from Fittkau & Klinge 1973
Other insects Amphibians
Reptiles
Birds
Mammals
Earthworms
Spiders
Soil fauna excluding
earthworms,
ants & termites
Ants & termites

We use modern technologies to
understand insect societies.
• evolution of social behaviour
• molecules involved in social behaviour
• consequences of environmental change

This changes
everything.
Any lab can
sequence
anything!

http://gregoryzynda.com/ncbi/genome/python/2014/03/31/ncbi-genome.html

Big data is invading biology
• Genomics
• Cancer genomics
• Biodiversity assessments
• Stool microbiome sequencing
• Personalized medicine
• Sensor networks - e.g tracking microclimates, recording sounds
• Huge medical studies
• Aerial surveys (Drones) - e.g. crop productivity; rainforest cover
• Camera traps

Learning to deal with big data takes time

Practicals
• Aim: get relevant data handling skills
• Doing things by hand:
• impossible?
• slow,
• error-prone,
• Automate!
• Basic programming
• in R
• no stats!

Practicals: contents
• Done:
• data accessing/subsetting
• New:
• search/replace
• regular expressions
• New:
• functions
• loops
• Friday: (Introduction to Unix & High performance computing)
Text search on steroids
Reusable pieces of work
Repeating the same thing many times

• create a variable that contains the number 35
• create a variable that contains the string “I love tofu”
• give me a vector containing the sequence of numbers
from 5 to 11
• access the second number
• replace the second number with 42
• add 5 to the second number
• now add 5 to all numbers
• now add an extra number: 1999
• can you sum all the numbers?

• creating a vector
> my_vector <- c(5, 6, 7, 8, 9, 10, 11)
> my_vector <- 5:11
> my_vector <- seq(from=5, to=11, by=1)
> my_vector
[1] 5 6 7 8 9 10 11
> length(my_vector)
[1] 7
> (10 > 30) 
[1] FALSE
> my_vector > 8 
[1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE
> my_vector[my_vector > 8] 
9 10 11
> other_vector <- my_vector[my_vector > 8]
> other_vector
9 10 11
> other_vector + 3
• give me a vector containing numbers from 5 to 11 (3 variants)

• accessing a subset
• of a vector
> big_vector <- 150:100
> big_vector
[1] 150 149 148 147 146 145 144 143 142 141 140 139 138 137 136 135 13
[20] 131 130 129 128 127 126 125 124 123 122 121 120 119 118 117 116 11
[39] 112 111 110 109 108 107 106 105 104 103 102 101 100
> big_vector[5]
146
> mysubset <- big_vector[my_vector]
> mysubset
[1] 146 145 144 143 142 141 140
> big_vector > 130
[1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[13] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
[25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE FALSE
> subset(x = big_vector, subset = big_vector > 140)
[1] 150 149 148 147 146 145 144 143 142 141
> big_vector[big_vector >= 140]
[1] 150 149 148 147 146 145 144 143 142 141 140
> my_vector
[1] 5 6 7 8 9 10 11

Regular expressions (regex):
Text search on steroids.

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”
Yes: ”HATSOMIKTIP"
yes: ”HAVSONYYIKTIP"
not: ”HAVSQMIKTIP"

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
Yes: ”HATSOMIKTIP"
yes: ”HAVSONYYIKTIP"
not: ”HAVSQMIKTIP"

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: ! " # $ % & ' ( ) * + , - . /
: ; < = > ? @ [ ] ^ _ ` { |

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"

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

Again
Make a regular expression
• matching “LMTSOMIKTIP” and “LMVSONYYIKTIP” but not
“LMVSQMIKTIP”
• matching all variants of “ok” (e.g., “O.K.”,“Okay”…)

Ok… so how do we use this?
• ?grep
• ?gsub

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’

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

Make a function
• That converts fahrenheit to celsius
(subtract 32 then divide the result by 1.8)

“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"

What does this loop do?
for (index in 10:1) {
print(paste(index, "mins befo lunch"))
}

Again
• What does the following code do (decompose on pen and
paper)

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?

Jasmin
Zohren
Bruno
Vieira
Rodrigo
Pracana
James
Wright

2015 11-17-programming inr.key

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2015 11-17-programming inr.key