format: Read and Reformat Data

In innager/dcifer: Genetic Relatedness Between Polyclonal Infections

Read and Reformat Data

Description

readDat and readAfreq read data and population allele frequencies from csv files and reformat them for further processing. formatDat and formatAfreq reformat corresponding data frames. Original data are assumed to be in a long format, with one row per allele.

Usage

readDat(sfile, svar, lvar, avar, ...)

formatDat(dlong, svar, lvar, avar)

readAfreq(afile, lvar, avar, fvar, ...)

formatAfreq(aflong, lvar, avar, fvar)

Arguments

sfile	the name of the file containing sample data.
svar	the name of the variable for sample ID.
lvar	the name of the variable for locus/marker.
avar	the name of the variable for allele/haplotype.
...	additional arguments for read.csv().
dlong	a data frame containing sample data.
afile	the name of the file containing population allele frequencies.
fvar	the name of the variable for population allele frequiency.
aflong	a data frame containing population allele frequencies.

Value

For readDat and formatDat, a list with elements corresponding to samples. Each element of the list is itself a list of binary vectors, one vector for each locus. For readAfreq and formatAfreq, a list with elements corresponding to loci. The frequencies at each locus are normalized and sum to 1. Samples, loci, and alleles are ordered by their IDs/names.

See Also

matchAfreq for making sure that the list containing sample data is conformable to provided population allele frequencies.

Examples

sfile <- system.file("extdata", "MozParagon.csv", package = "dcifer")
dsmp  <- readDat(sfile, svar = "sampleID", lvar = "locus", avar = "allele")

# OR, if the dataset is provided as an R data frame, e.g.
dlong <- read.csv(sfile)
# reformat only:
dsmp <- formatDat(dlong, svar = "sampleID", lvar = "locus", avar = "allele")

afile  <- system.file("extdata", "MozAfreq.csv", package = "dcifer")
afreq2 <- readAfreq(afile, lvar = "locus", avar = "allele", fvar = "freq")

# OR, if allele frequencies are provided as an R data frame, e.g.
aflong <- read.csv(afile)
# reformat only:
afreq2 <- formatAfreq(aflong, lvar = "locus", avar = "allele", fvar = "freq")

dsmp2  <- matchAfreq(dsmp, afreq2)

innager/dcifer documentation built on May 21, 2024, 1:23 a.m.