Lists

Lists are generic vectors that can hold other things.

sites <- c("a", "b", "c")
notes <- "It was a good day in the field today. Warm, sunny, lots of gators"
helpers <- 4
field_notes <- list(sites, notes, helpers)
field_notes[1]
field_notes[[1]]

We can also give the values names:

field_notes <- list(sites=sites, notes=notes, helpers=helpers)
field_notes$sites
field_notes$helpers

Objects - e.g., Bioconductor

We’ve looked at several kinds of objects so far including vectors, data frames, and lists, but we can also make arbitrary objects that store whatever kinds of data we need.

As an example we’ll briefly look at Bioconductor, which is used for working with genomic data in R.

Install Bioconductor if you haven’t already.

source("https://bioconductor.org/biocLite.R")
biocLite("ShortRead")

I’ve downloaded data on the coding regions of the E. coli genome from Genbank in a format called FASTA, which is one format used for storing nucleotide sequences.

library(ShortRead)
reads <- readFasta("data/ecoli_coding.fasta")
reads

This tells us that reads is it’s own special kind of object called a ShortRead.

str(read)

Using str we can see that inside this object are other kinds of objects, including something called DNAStringSet, which holds groups of sequences.

reads@sread
reads@sread@ranges
reads@sread@ranges@start

We can use functions to access and work with pieces of these objects.

sequences <- sread(reads)
sequences
sequences[1]

These kinds of objects can then be used with special functions designed to work with specific kinds of data.

firstseq <- sequences[[1]]
reverse(firstseq)
complement(firstseq)
reverseComplement(firstseq)
alphabetFrequencies(firstseq)]
translate(firstseq)

• Someone has probably already written it. See if you can use their work.
• Parsing and manipulating data is hard to get right in all cases, it’s best to rely on existing tools to handle this when possible

reverse(sequences)
complement(sequences)
reverseComplement(sequences)