Package 'camtrapDensity'

Check deployment calibration model diagnostic plots

Description

Displays deployment calibration model diagnostic plots and allows users to record interactively whether each deployment is reliable.

Usage

check_deployment_models(package)

Arguments

package

Camera trap data package object, as returned by read_camtrap_dp.

Value

The original package with logical column useDeployment' added to deployments and observations data.

Examples

## Not run: 
    pkg <- read_camtrapDP("./datapackage/datapackage.json")
    pkg_checked <- check_deployment_models(pkg)
  
## End(Not run)

---

Change the units of an REM parameter table

Description

Changes the units of parameters from their current setting to new user-defined units.

Usage

convert_units(
  param,
  radius_unit = c("km", "m", "cm"),
  angle_unit = c("radian", "degree"),
  active_speed_unit = c("km/day", "km/hour", "m/hour", "m/second"),
  overall_speed_unit = c("km/day", "km/hour", "m/hour", "m/second"),
  trap_rate_unit = c("n/day", "n/100day", "n/hour", "n/minute", "n/second"),
  density_unit = c("n/km2", "n/ha", "n/100km2")
)

Arguments

param	An REM parameter dataframe (see details).
radius_unit	A character string giving the output unit of radius.
angle_unit	A character string giving the output unit of angle.
active_speed_unit	A character string giving the output unit of speed while active.
overall_speed_unit	A character string giving the output unit of day range.
trap_rate_unit	A character string giving the output unit of trap rate.
density_unit	A character string giving the output unit of density.

Details

Input dataframe param must contain field unit, and at least one field among estimate, se, lcl95, and ucl95. Row names must be among radius, angle, activity_level, active_speed, overall_speed. Input is typically created with function get_parameter_table.

Value

A replica of input dataframe param with estimate, se and confidence limit values converted to output units.

Examples

## Not run: 
    pkg <- read_camtrapDP("./datapackage/datapackage.json")
    
## End(Not run)
  data(pkg)
  sp <- "Vulpes vulpes"
  trdata <- get_traprate_data(pkg, species=sp)
  radmod <- fit_detmodel(radius~1, pkg, species=sp, order=0)
  angmod <- fit_detmodel(angle~1, pkg, species=sp, unit="radian", order=0)
  spdmod <- fit_speedmodel(pkg, species=sp)
  actmod <- fit_actmodel(pkg, species=sp, reps=100)
  param <- get_parameter_table(trdata, radmod, angmod, spdmod, actmod)
  convert_units(param, radius_unit="m", angle_unit="degree", active_speed_unit="m/second")

---

Correct times for a given deployment in a camera trap datapackage

Description

When a camera trap starts with the wrong time stamp, times in all datapackage tables can be corrected given a reference time recorded by the camera, the correct time for this reference time and the ID of the deployment to correct.

Usage

correct_time(package, depID = NULL, locName = NULL, wrongTime, rightTime)

Arguments

package	Camera trap data package object, as returned by read_camtrap_dp.
depID	A character value giving the deployment ID, to be matched in package$data$deployments$deploymentID.
locName	A character value giving the location name, to be matched in package$data$deployments$locationName.
wrongTime	A character or POSIX reference date-time recorded wrongly by the camera.
rightTime	A character or POSIX value giving the correct date-time when the reference time was recorded.

Details

One, but not both, of depID and locName must be provided, as single text values. If locName is provided, the deployment associated with this in pkg$data$deployments is corrected, but if locName is associated with more than one deployment the function does not run.

Value

As for read_camtrap_dp, with all date-times corrected by the difference between rightTime and wrongTime.

Examples

## Not run: 
  pkg <- read_camtrapDP("./datapackage/datapackage.json")

## End(Not run)
data(pkg)
pkg_corrected <- correct_time(pkg,
                              locName = "S01",
                              wrongTime = "2017-10-02 08:06:43",
                              rightTime = "2017-09-01 10:36:00")

---

Downversion Camtrap DP datapackage

Description

Downversions a Cmatrap DP datapackage to V0 by duplicating dataframe fields whose names differ between versions with V0 fields created in addition to existing V1 fields.

Usage

downversion_camtrapDP(pkg)

Arguments

pkg	Camera trap data package list, as returned by read_camtrapDP.

Value

A duplicate of the original package with the addition of duplicate of fields with V0 fieldnames to data.

Examples

## Not run: pkgV0 <- downversion_camtrapDP(pkgV1)

---

Fit an activity model

Description

Fits an activity model to data package data and estimates activity level (proportion of time spent active).

Usage

fit_actmodel(
  package,
  species = NULL,
  reps = 999,
  obsdef = c("individual", "sequence"),
  ...
)

Arguments

package	Camera trap data package object, as returned by read_camtrap_dp.
species	A character string indicating species subset to analyse; if NULL runs select_species to get user input.
reps	Number of bootstrap replicates to run.
obsdef	Observation definition, either individual or sequence.
...	Arguments passed to fitact.

Value

An 'actmod' list.

See Also

fitact

Examples

## Not run: 
    pkg <- read_camtrapDP("./datapackage/datapackage.json")
  
## End(Not run)
  data(pkg)
  ## Not run: 
    # With interactive species definition
    act_model <- fit_actmodel(pkg)
  
## End(Not run)
  # With species predefined, reps reduced for speed
  act_model <- fit_actmodel(pkg, species="Vulpes vulpes", reps=100)
  act_model@act

---

Fit a detection function model

Description

Fits a detection function to a data package and estimates effective detection distance (EDD).

Usage

fit_detmodel(
  formula,
  package,
  species = NULL,
  newdata = NULL,
  unit = c("m", "km", "cm", "degree", "radian"),
  ...
)

Arguments

formula	A two sided formula relating radius or angle data to covariates.
package	Camera trap data package object, as returned by read_camtrap_dp.
species	A character string indicating species subset to analyse; if NULL runs select_species to get user input; if "all" all data are used.
newdata	A dataframe of covariate values at which to predict detection distance.
unit	The units in which to return the result.
...	Arguments passed to ds.

Details

The type of detection function (line or point) is determined by the unit argument.

Value

A ddf detection function model list, with additional elements: edd, a vector with estimated and standard error effective detection distance, or the newdata dataframe with EDD estimate and se columns added; proportion_used, the proportion of the observations used to fit the detection function in the case of truncation.

See Also

ds

Examples

## Not run: 
    pkg <- read_camtrapDP("./datapackage/datapackage.json")
    
## End(Not run)
  data(pkg)
  ## Not run: 
    # With interactive species definition
    radius_model <- fit_detmodel(radius~1, pkg, order=0)
  
## End(Not run)
  # With species predefined
  sp <- "Vulpes vulpes"
  radius_model <- fit_detmodel(radius~1, pkg, species=sp, order=0)
  angle_model <- fit_detmodel(angle~1, pkg, species=sp, order=0, unit="degree")
  radius_model$edd
  angle_model$edd
  plot(radius_model, pdf=TRUE)
  plot(angle_model)

---

Estimate average animal speed

Description

Calculates harmonic mean and standard error of animal speed while active from a data package.

Usage

fit_speedmodel(
  package,
  species = NULL,
  formula = speed ~ 1,
  newdata = NULL,
  reps = 1000,
  distUnit = c("m", "km", "cm"),
  timeUnit = c("second", "minute", "hour", "day"),
  ...
)

Arguments

package	Camera trap data package object, as returned by read_camtrap_dp.
species	A character string indicating species subset to analyse; if NULL runs select_species to get user input; if all uses all data.
formula	A formula with speed on the left and covariates on the right.
newdata	A data frame of covariate values at which to predict speeds.
reps	Number of random draws to use for standard calculation.
distUnit	A character string indicating distance unit of speed observations.
timeUnit	A character string indicating time unit of speed observations.
...	Other parameters passed to sbm for covariate modelling (see details).

Details

If a formula is provided, the model is fitted using the sbm (size biased model) function, which can be installed using: devtools::source_url("https://raw.githubusercontent.com/MarcusRowcliffe/sbd/master/sbd.r")

Value

List with elements:

• speed: a dataframe containing columns estimate (mean) and se (standard error) speed while active.

• data: a numeric vector of the data from which the estimate is derived.

Examples

## Not run: 
    pkg <- read_camtrapDP("./datapackage/datapackage.json")
  
## End(Not run)
  data(pkg)
  ## Not run: 
    # With interactive species definition
    speed_model <- fit_speedmodel(pkg)
  
## End(Not run)
  # With species predefined
  speed_model <- fit_speedmodel(pkg, species="Vulpes vulpes")
  speed_model$estimate
  hist(speed_model)

---

Gets a set Agouti sequences URLs.

Description

Obtains web addresses for sequences of selected observations, based on criteria defined using fields in the observations table.

Usage

get_agouti_url(package, obsChoice)

Arguments

package	Camera trap data package object, as returned by read_camtrap_dp.
obsChoice	A logical expression using column names from the observations table defining which observations you want to inspect.

Value

A dataframe of Agouti URLs.

Examples

data(pkg)
  get_agouti_url(pkg, speed>1)

---

Get a unit multiplier

Description

Returns a multiplier to convert values from one unit to another, in one of four categories: distance, time, angle, count.

Usage

get_multiplier(unitIN, unitOUT)

Arguments

unitIN	A character vector giving the units of input.
unitOUT	A character vector giving the units of output, the same length as unitIN,

Details

Possible unitIN and unitOUT values are "cm", "m", "km" for distances; "second", "minute", "hour", "day", "100day" for times; "radian", "degree" for angles; "n" for count; "none" for no units. Unit ratios are allowed for rates or densities. In this case, units should be separated with a forward slash (e.g. "n/day", "km/hour", "n/km2"). Input and output types must match.

Value

A vector of numbers giving the amount by which to multiply input values to arrive at unit-converted values.

Examples

get_multiplier(c("m", "m/second"), c("km", "km/day"))

---

Create a parameter table from a set of models

Description

Creates a table of REM parameters taken from models for detection radius, detection angle, speed and activity level.

Usage

get_parameter_table(
  traprate_data,
  radius_model,
  angle_model,
  speed_model,
  activity_model,
  strata = NULL,
  reps = 999
)

Arguments

traprate_data	A dataframe of trap rate data (counts and effort), as returned by get_traprate_data.
radius_model	A detection radius model fitted using fit_detmodel
angle_model	A detection angle model fitted using fit_detmodel
speed_model	A speed model fitted using fit_speedmodel
activity_model	An activity model fitted using fit_actmodel
strata	A dataframe of stratum information passed to get_trap_rate
reps	Number of bootstrap replicates for estimating trap rate error (see get_trap_rate)

Value

A dataframe of unit-harmonised parameter estimates with rows:

• radius: detection radius

• angle: detection angle

• active_speed: speed while active

• activity_level: proportion of time spent active

• overall_speed: long-term average speed (day range) - the product of active_speed and activity_level

• trap_rate: number of camera trap records per unit time

and columns

• estimate: parameter estimates

• se: standard error

• cv: proportional coefficient of variation

• lcl95, ucl95: lower and upper 95% confidence limits

• n: sample size

• unit: the unit of the estimate

Examples

## Not run: 
    pkg <- read_camtrapDP("./datapackage/datapackage.json")
    
## End(Not run)
  data(pkg)
  sp <- "Vulpes vulpes"
  trdata <- get_traprate_data(pkg, species=sp)
  radmod <- fit_detmodel(radius~1, pkg, species=sp, order=0)
  angmod <- fit_detmodel(angle~1, pkg, species=sp, unit="radian", order=0)
  spdmod <- fit_speedmodel(pkg, species=sp)
  actmod <- fit_actmodel(pkg, species=sp, reps=100)
  get_parameter_table(trdata, radmod, angmod, spdmod, actmod)

---

Get average trap rate from REM data

Description

Calculates average trap rate and its bootstrapped error from a table of per-location observation counts and camera time.

Usage

get_trap_rate(traprate_data, strata = NULL, reps = 999)

Arguments

traprate_data	A dataframe containing (at least) columns n and effort, as returned by get_traprate_data; if strata supplied for stratified calculation, must also have column stratumID.
strata	A dataframe with one row per stratum, and columns stratumID and area.
reps	The number of bootstrap replicates to run.

Value

A dataframe with columns: - estimate: average trap rate - se: standard error - cv: proportional coefficient of variation - lcl95, ucl95: lower and upper 95 - n: sample size (number of locations) - unit: the unit of the estimate

Examples

## Not run: 
    pkg <- read_camtrapDP("./datapackage/datapackage.json")
    
## End(Not run)
  data(pkg)
  trdata <- get_traprate_data(pkg, species="Vulpes vulpes")
  get_trap_rate(trdata)

---

Get REM data from a camtrap-dp datapackage

Description

Extracts a data table of observation counts and effort for each camera location in a camtrap-dp data package.

Usage

get_traprate_data(
  package,
  species = NULL,
  unit = c("day", "hour", "minute", "second")
)

Arguments

package	Camera trap data package object, as returned by read_camtrap_dp.
species	A character string indicating species subset to extract data for; if NULL runs select_species to get user input.
unit	The time unit in which to return camera effort.

Value

A tibble with columns:

• locationName: name of the camera location

• effort: the camera time for the location

• unit: the effort time unit

• scientificName: the scientific name of the species data extracted

• n: the observation counts

• stratumID: stratum identifier (only if this column is present in package$data$deployments)

Examples

## Not run: 
    pkg <- read_camtrapDP("./datapackage/datapackage.json")
    
## End(Not run)
  data(pkg)
  ## Not run: 
    # With interactive species definition
    trdata <- get_traprate_data(pkg)
  
## End(Not run)
  # With species predefined
  trdata <- get_traprate_data(pkg, species="Vulpes vulpes")

---

Log-normal confidence interval

Description

Calculates approximate log-normal confidence intervals given estimates and their standard errors.

Usage

lnorm_confint(estimate, se, percent = 95)

Arguments

estimate	Numeric estimate value(s)
se	Standard error(s) of the estimate
percent	Percentage confidence level

Value

A dataframe with a row per estimate input, and columns lcl and ucl (lower and upper confidence limits).

Examples

lnorm_confint(10.13, 3.57)

---

Plot a map of deployments

Description

Creates an OpenStreetMap street or satellite map over-plotted with deployment locations.

Usage

map_deployments(pkg, basemap = c("street", "satellite"), ...)

Arguments

pkg	Camera trap data package object, as returned by read_camtrap_dp.
basemap	Basemap to plot, street (default) or satellite
...	Additional arguments passed to addCircleMarkers

Examples

## Not run: pkg <- read_camtrapDP("./data/datapackage.json")
  data(pkg)
  map_deployments(pkg)

---

Plot a map of deployment trap rates

Description

Creates an OpenStreetMap street or satellite map over-plotted with deployment locations, with points sized in proportion to trap rate for a given species.

Usage

map_traprates(
  pkg,
  species = NULL,
  basemap = c("street", "satellite"),
  maxSize = 25,
  minSize = 3
)

Arguments

pkg	Camera trap data package object, as returned by read_camtrap_dp.
species	A character string indicating species subset to analyse. Use scientific names. If NULL runs select_species to get user input; if all uses all data.
basemap	Basemap to plot, street (default) or satellite
maxSize	Maximum point size to plot.
minSize	Minimum point size to plot.

Examples

## Not run: pkg <- read_camtrapDP("./data/datapackage.json")
  data(pkg)
  map_traprates(pkg, species="Vulpes vulpes")

---

Merge Camtrap DP datapackages

Description

Merges a list of several Camtrap DP datapackages into a single datapackage.

Usage

merge_camtrapDP(pkgs)

Arguments

pkgs	A list of camera trap data packages, as returned by read_camtrapDP.

Details

Original datapackage metadata are stored in named top-level components of the output list, along with a single data component with the usual dataframes merged across datapackages, and additional packageName fields indicating the datapackage from which data row originates.

Value

A single datapackage with component dataframes merged.

Examples

## Not run: bigpkg <- merge_camtrapDP(list(pkg1, pkg2))

---

Data and metadata from an example study exported from the Agouti camera trap data management platform in camtrap-DP format. Metadata includes study name, authors, location and other details. Data is held in element data, itself a list holding dataframes deployments, media and observations. See https://tdwg.github.io/camtrap-dp for details.

Description

Data and metadata from an example study exported from the Agouti camera trap data management platform in camtrap-DP format. Metadata includes study name, authors, location and other details. Data is held in element data, itself a list holding dataframes deployments, media and observations. See https://tdwg.github.io/camtrap-dp for details.

Format

A list holding study data and metadata.

---

Plot a deployment Gantt chart

Description

Plots an interactive Gantt chart illustrating deployment times (black lines) and the occurrence of observations within those deployments (orange bars). Useful for checking errors in specification of deployment start and end dates, and visualising spatiotemporal distribution of observations.

Usage

plot_deployment_schedule(package)

Arguments

package

Camera trap data package object, as returned by read_camtrap_dp.

Examples

## Not run: pkg <- read_camtrapDP("./data/datapackage.json")
  data(pkg)
  plot_deployment_schedule(pkg)

---

Create a datapackage from csv files

Description

Reads csv data from a folder into a list that functions like a camtrap-dp object for the purposes of density estimation.

Usage

read_camtrap_dp_csv(
  folder,
  tryFormats = c("%Y-%m-%d %H:%M:%OS", "%Y/%m/%d %H:%M:%OS",
    "%Y:%m:%d %H:%M:%OS")
)

Arguments

folder	A character string giving the folder in which the csv files sit.
tryFormats	A character string defining date-time format, passed to as.POSIXct.

Details

The folder must contain three csv files: deployments.csv, media.csv and observations.csv, each of which must contain at least the following fields:

• deployments: deploymentID, locationID, locationName, longitude, latitude,start,end

• media: mediaID, deploymentID, sequenceID, timestamp, filePath, fileName

• observations: observationID, deploymentID, sequenceID, mediaID, timestamp, scientificName, count, speed, radius, angle

Value

As for read_camtrap_dp but with reduced package metadata.

Examples

## Not run: pkg <- read_camtrap_dp_csv("./data")

---

Read a Camptrap DP datapackage

Description

Reads the metadata and csv files from a Camtrap DP version 1 datapackage as exported from Agouti.

Usage

read_camtrapDP(
  file,
  media = TRUE,
  recalc = TRUE,
  addV0 = TRUE,
  dtFormat = "YmdHMSz"
)

Arguments

file	Path to a datapackage.json file.
media	Logical defining whether to read media table.
recalc	Logical defining whether to recalculate event; if TRUE, calls recalc_events.
addV0	Logical defining whether to add camtrapDP version 0 fields, for compatibility with functions using this data model.
dtFormat	Character format used to read date-time fields, passed to parse_date_time

Value

A list of data package metadata components and a data component with dataframes: deployments, media, observations (containing event observations) and positions (containing media observations). If the media argument is FALSE, the media component is NULL. The positions may also be NULL if there are no media observations in the input.

Examples

## Not run: pkg <- read_camtrapDP("./data/datapackage.json")

---

Recalculate event observations

Description

Recalculates event observation values (individualPositionRadius, individualPositionAngle, individualSpeed) from media observations.

Usage

recalc_events(pkg)

Arguments

pkg	Camera trap data package list, as returned by read_camtrapDP.

Details

The process requires media data and fails if this is not available in the datapackage. The datapackage should also contain positions data, but the original package is simply returned unmodified if the positions data table contains no data. In recalculating speeds, observations with zero time difference (hence infinite speed) impute time elapsed from average frame rate.

Value

A duplicate of the original package with event observation values recalculated.

Examples

## Not run: pkg2 <- recalc_events(pkg2)

---

Fit a random encounter model

Description

Estimates REM density given a dataframe of parameters and their errors.

Usage

rem(parameters)

Arguments

parameters

A dataframe containing REM parameter estimates with (at least) rows: radius: effective detection radius angle: effective detection angle overall_speed: average animal speed (day range) trap_rate: animal observations per unit time and columns: estimate: numeric parameter estimate se: numeric parameter standard error unit: character parameter units (see convert_units for allowable values)

Value

A dataframe with the input parameters plus estimated density and its errors.

Examples

## Not run: 
    pkg <- read_camtrapDP("./datapackage/datapackage.json")
    
## End(Not run)
  data(pkg)
  sp <- "Vulpes vulpes"
  trdata <- get_traprate_data(pkg, species=sp)
  radmod <- fit_detmodel(radius~1, pkg, species=sp, order=0)
  angmod <- fit_detmodel(angle~1, pkg, species=sp, unit="radian", order=0)
  spdmod <- fit_speedmodel(pkg, species=sp)
  actmod <- fit_actmodel(pkg, species=sp, reps=100)
  param <- get_parameter_table(trdata, radmod, angmod, spdmod, actmod)
  rem(param)

---

Integrated random encounter model density estimate

Description

Estimates animal density for a given species given a camtrapDP datapackage. Models for detection radius and angle, speed and/or activity level can be fitted externally and provided as arguments, or are fitted internally if not provided (NULL default). Input units are assumed to be distance in m and time in seconds.

Usage

rem_estimate(
  package,
  check_deployments = TRUE,
  species = NULL,
  radius_model = NULL,
  angle_model = NULL,
  speed_model = NULL,
  activity_model = NULL,
  strata = NULL,
  reps = 999
)

Arguments

package	Camera trap data package object, as returned by read_camtrap_dp.
check_deployments	Logical indicating whether to check deployment calibration model diagnostic plots. If TRUE (default) runs check_deployment_models; radius, angle and speed data from any excluded deployments are then dropped from analysis. If FALSE all data are used.
species	A character string indicating species subset to analyse; if NULL runs select_species to get user input.
radius_model	A detection function model for radii fitted using fit_detmodel.
angle_model	A detection function model for angles fitted using fit_detmodel with unit argument "radian" or "degree".
speed_model	A speed model fitted using fit_speedmodel.
activity_model	An activity model fitted using fitact or fit_actmodel.
strata	A dataframe of stratum areas, passed to get_trap_rate.
reps	Number of bootstrap replicates for error estimation.

Value

A dataframe containing estimates and their errors for density and all contributing parameters.

Examples

# Load data
  ## Not run: 
    pkg <- read_camtrapDP("./datapackage/datapackage.json")
    
## End(Not run)
  data(pkg)
  # Sense check deployment schedules
  plot_deployment_schedule(pkg)
 ## Not run: 
   # Sense check deployment calibration model diagnostic plots
   pkg_checked <- check_deployment_models(pkg)
   # Fully automated analysis (interactive species definition by default;
   # reps reduced to limit run time).
   res <- rem_estimate(pkg_checked, check_deployments=FALSE, reps=100)
  
## End(Not run)
  # Automated analysis with species predefined and no deployment checking
  sp <- "Vulpes vulpes"
  res <- rem_estimate(pkg, species=sp, check_deployments=FALSE, reps=100)
  # Inspect results
  res$estimates

  # Analysis with radius model fitted separately
  radmod <- fit_detmodel(radius~1, pkg, species=sp, truncation=15, order=0)
  res <- rem_estimate(pkg, check_deployments = FALSE, species = sp,
                      radius_model = radmod, reps=100)
  res$estimates

---

Select a species name

Description

Presents a table of species names with observation count for each and allows the user to interactively select one.

Usage

select_species(package, species = NULL)

Arguments

package	Camera trap data package object, as returned by read_camtrap_dp.
species	NULL (default) to select interactively, or character vector giving either one or more valid species names found within package$data$observations$scientificName, or the string "all" to return all available species names.

Value

A character vector of one or more scientific species names.

Examples

## Not run: 
    pkg <- read_camtrapDP("./data/datapackage.json")
    select_species(pkg)
    # If provided, a valid species name is simply passed through
    select_species(pkg, "Vulpes vulpes")
    # Providing a species name that isn't found in the data throws an error
    select_species(pkg, "Vulpes vuppes")
  
## End(Not run)

---

Take a time slice of a data package

Description

Discards any observations, media, or deployments that fall wholly outside the time range defined by start and end. When start or end are not specified, no slicing is applied to start or end points respectively. Slicing applies to all deployments by default, or can be applied to only a subset of deployments specified by depChoice. The fully default behaviour for this function is therefore to do nothing (the datapackage is returned unchanged).

Usage

slice_camtrap_dp(
  package,
  start = NULL,
  end = NULL,
  depChoice = NULL,
  suffix = ""
)

Arguments

package	Camera trap data package object, as returned by read_camtrap_dp.
start, end	Single character or POSIXct values defining the time range within which to slice the package.
depChoice	A logical expression using column names from the deployments table defining which deployments to slice.
suffix	A character value to be added to the package name.

Value

As for read_camtrap_dp, with all data tables reduced according to the choice criteria.

Examples

## Not run: 
    pkg <- read_camtrapDP("./datapackage/datapackage.json")
    
## End(Not run)
  data(pkg)
# Slicing the whole package to mid October 2017
  subpkg <- slice_camtrap_dp(pkg,
                              start = "2017/10/10",
                              end = "2017/10/20")
# Slicing only deployments at location "S03" to a specific start time/date
  subpkg <- slice_camtrap_dp(pkg,
                              start = "2017/10/15 14:30:00",
                              depChoice = locationName=="S03")

---

Subset a camera trap datapackage deployments

Description

Select a subset of deployments from a datapackage defined by a choice based on columns in the deployments table.

Usage

subset_deployments(package, choice, suffix = "")

Arguments

package	Camera trap data package object, as returned by read_camtrap_dp.
choice	A logical expression using column names from the deployments table.
suffix	A character value to be added to the package name.

Value

As for read_camtrap_dp, with all data tables reduced according to the choice criteria at the deployment level.

Examples

# subset excluding a location and including only October 2017
  ## Not run: 
    pkg <- camtraptor::read_camtrapDP("./datapackage/datapackage.json")
    
## End(Not run)
  data(pkg)
  subpkg <- subset_deployments(pkg,
                               locationName != "S01" &
                               start >= as.POSIXct("2017-10-01", tz="UTC") &
                               end <= as.POSIXct("2017-10-31", tz="UTC"))

---

Write REM results to csv file

Description

Writes one or more REM estimate tables to a single csv file, with identifying columns added for project, datapackage, sampling design, sampling effort, project location, project dates and species. Input must be REM analysis object(s) created using rem_estimate. The resulting file name is taken from the project and current date, and the file is saved to the working directory.

Usage

write_rem_csv(...)

Arguments

...	One or more REM analysis objects, separated by commas.

Value

None - creates a csv file.

Examples

## Not run: 
   foxREM <- rem_estimate(pkg_checked, check_deployments=FALSE, species="Vulpes vulpes")
   hhogREM <- rem_estimate(pkg_checked, check_deployments=FALSE, species="Erinaceus europaeus")
   write_rem_csv(foxREM, hhogREM)
   
## End(Not run)

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