The flatfile data format

Distance allows loading data as a "flat file" and analyse data (and obtain abundance estimates) straight away, provided that the format of the flat file is correct. One can provide the file as, for example, an Excel spreadsheet using readxl::read_xls in or CSV using read.csv.

Details

Each row of the data table corresponds to either: (1) an observation or (2) a sample (transect) without observations. In either case the following columns must be present:

• distance observed distance to object

• object a unique identifier for each observation (only required when using dht2)

• Sample.Label identifier for the sample (transect id)

• Effort effort for this transect (e.g. line transect length or number of times point transect was visited)

• Region.Label label for a given stratum (see below)

• Area area of the strataWhen the row represents a transect without observations,distanceand any other observation-specific covariates (includingsizeand detection function covariates) take the valueNA`.

Note that in the simplest case (one area surveyed only once) there is only one Region.Label and a single corresponding Area duplicated for each observation.

The example given below was provided by Eric Rexstad. Additional examples can be found at http://examples.distancesampling.org/.

Examples

if (FALSE) { # \dontrun{
library(Distance)
# Need to have the readxl package installed from CRAN
require(readxl)

# Need to get the file path first
minke.filepath <- system.file("minke.xlsx", package="Distance")

# Load the Excel file, note that col_names=FALSE and we add column names after
minke <- read_xlsx(minke.filepath, col_names=FALSE)
names(minke) <- c("Region.Label", "Area", "Sample.Label", "Effort",
                  "distance")
# One may want to call edit(minke) or head(minke) at this point
# to examine the data format

## perform an analysis using the exact distances
pooled.exact <- ds(minke, truncation=1.5, key="hr", order=0)
summary(pooled.exact)


## Try a binned analysis
# first define the bins
dist.bins <- c(0,.214, .428,.643,.857,1.071,1.286,1.5)
pooled.binned <- ds(minke, truncation=1.5, cutpoints=dist.bins, key="hr",
                    order=0)

# binned with stratum as a covariate
minke$stratum <- ifelse(minke$Region.Label=="North", "N", "S")
strat.covar.binned <- ds(minke, truncation=1.5, key="hr",
                         formula=~as.factor(stratum), cutpoints=dist.bins)

# Stratified by North/South
full.strat.binned.North <- ds(minke[minke$Region.Label=="North",],
                  truncation=1.5, key="hr", order=0, cutpoints=dist.bins)
full.strat.binned.South <- ds(minke[minke$Region.Label=="South",],
                     truncation=1.5, key="hr", order=0, cutpoints=dist.bins)

## model summaries
model.sel.bin <- data.frame(name=c("Pooled f(0)", "Stratum covariate",
                                   "Full stratification"),
                            aic=c(pooled.binned$ddf$criterion,
                                  strat.covar.binned$ddf$criterion,
                                  full.strat.binned.North$ddf$criterion+
                                  full.strat.binned.South$ddf$criterion))

# Note model with stratum as covariate is most parsimonious
print(model.sel.bin)
} # }