The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

camtrapdp

CRAN status CRAN checks R-CMD-check codecov repo status DOI

Camtrapdp is an R package to read and manipulate Camera Trap Data Packages (Camtrap DP). Camtrap DP is a data exchange format for camera trap data. With camtrapdp you can read, filter and transform data (including to Darwin Core) before further analysis in e.g. camtraptor or camtrapR.

To get started, see:

Installation

Install the latest released version from CRAN:

install.packages("camtrapdp")

Or the development version from GitHub:

# install.packages("devtools")
devtools::install_github("inbo/camtrapdp")

Usage

With camtrapdp you can read a Camtrap DP dataset into your R environment:

library(camtrapdp)

file <- "https://raw.githubusercontent.com/tdwg/camtrap-dp/1.0/example/datapackage.json"
x <- read_camtrapdp(file)
x
#> A Camera Trap Data Package with 3 tables:
#> • deployments: 4 rows
#> • media: 423 rows
#> • observations: 549 rows
#> 
#> And 1 additional resource:
#> • individuals
#> Use `unclass()` to print the Data Package as a list.

read_camtrapdp() will automatically convert an older version of Camtrap DP to the latest version. It will also make the data easier to use, by assigning taxonomic information (found in the metadata) to the observations and eventIDs (found in the observations) to the media.

To access the data, use one of the accessor functions like locations():

locations(x)
#> # A tibble: 4 × 5
#>   locationID locationName               latitude longitude coordinateUncertainty
#>   <chr>      <chr>                         <dbl>     <dbl>                 <dbl>
#> 1 e254a13c   B_HS_val 2_processiepark       51.5      4.77                   187
#> 2 2df5259b   B_DL_val 5_beek kleine vi…     51.2      5.66                   187
#> 3 ff1535c0   B_DL_val 3_dikke boom          51.2      5.66                   187
#> 4 ce943ced   B_DM_val 4_'t WAD              50.7      4.01                   187

You can also filter data with one of the filter functions, which automatically filter the related data. For example, here we filter observations on scientific name(s) and return the associated events in that subset:

x %>%
  filter_observations(
    scientificName %in% c("Martes foina", "Mustela putorius")
  ) %>%
  events()
#> # A tibble: 4 × 4
#>   deploymentID eventID  eventStart          eventEnd           
#>   <chr>        <chr>    <dttm>              <dttm>             
#> 1 577b543a     976129e2 2020-06-19 22:31:51 2020-06-19 22:31:56
#> 2 577b543a     b4b39b00 2020-06-23 23:33:53 2020-06-23 23:33:58
#> 3 577b543a     5be4f4ed 2020-06-28 22:01:12 2020-06-28 22:01:18
#> 4 577b543a     a60816f2 2020-06-28 23:33:16 2020-06-28 23:33:22

For more functionality, see the function reference.

Meta

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.