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Workflow

Wolf Missotten

2026-09-09

Source: vignettes/workflow.Rmd
workflow.Rmd

Camtraptor is an R package to explore and visualize Camera Trap Data Package (Camtrap DP) datasets. This vignette walks you through the workflow of the package.

Coming from camtraptor version 0.28 or earlier? Check out News to see what changed. Deprecated functions will throw a warning pointing you towards their replacements.

Setup

Load the package:

library(camtraptor)
#> 
#> Attaching package: 'camtraptor'
#> The following object is masked from 'package:base':
#> 
#>     contributors

Workflow

Reading data

To start your data analysis you first need data.

Here, the function example_dataset() is used to load an example Camera Trap Data Package dataset that is included in the camtraptor package. This dataset is derived from a study on detecting invasive muskrat and coypu populations using camera traps.

To read your own locally stored dataset, use read_camtrapdp():

x <- read_camtrapdp("path/to/datapackage.json")

In this vignette the example dataset will be used.

Exploring data

Now that you read in your data, you can start exploring it. A Camtrap DP dataset consists of three tables: deployments, media and observations. For more details on the data structure, see the Camtrap DP website. You can access each table directly by using deployments(), media() and observations(). Let’s take a look at the deployments table:

deployments(x)
#> # A tibble: 4 × 24
#>   deploymentID locationID locationName  latitude longitude coordinateUncertainty
#>   <chr>        <chr>      <chr>            <dbl>     <dbl>                 <dbl>
#> 1 00a2c20d     e254a13c   B_HS_val 2_p…     51.5      4.77                   187
#> 2 29b7d356     2df5259b   B_DL_val 5_b…     51.2      5.66                   187
#> 3 577b543a     ff1535c0   B_DL_val 3_d…     51.2      5.66                   187
#> 4 62c200a9     ce943ced   B_DM_val 4_'…     50.7      4.01                   187
#> # ℹ 18 more variables: deploymentStart <dttm>, deploymentEnd <dttm>,
#> #   setupBy <chr>, cameraID <chr>, cameraModel <chr>, cameraDelay <dbl>,
#> #   cameraHeight <dbl>, cameraDepth <dbl>, cameraTilt <dbl>,
#> #   cameraHeading <dbl>, detectionDistance <dbl>, timestampIssues <lgl>,
#> #   baitUse <lgl>, featureType <fct>, habitat <chr>, deploymentGroups <chr>,
#> #   deploymentTags <chr>, deploymentComments <chr>

To get a quick overview of which species were recorded, use taxa():

taxa(x)
#> # A tibble: 10 × 5
#>    scientificName     taxonID  taxonRank vernacularNames.eng vernacularNames.nld
#>    <chr>              <chr>    <chr>     <chr>               <chr>              
#>  1 Anas platyrhynchos https:/… species   mallard             wilde eend         
#>  2 Anas strepera      https:/… species   gadwall             krakeend           
#>  3 Ardea              https:/… genus     great herons        reigers            
#>  4 Ardea cinerea      https:/… species   grey heron          blauwe reiger      
#>  5 Aves               https:/… class     bird sp.            vogel              
#>  6 Homo sapiens       https:/… species   human               mens               
#>  7 Martes foina       https:/… species   beech marten        steenmarter        
#>  8 Mustela putorius   https:/… species   European polecat    bunzing            
#>  9 Rattus norvegicus  https:/… species   brown rat           bruine rat         
#> 10 Vulpes vulpes      https:/… species   red fox             vos

Filtering data

Now that you have explored the dataset, you can start filtering. Use filter_deployments() to select specific deployment locations and filter_observations() to select specific species. For example, filtering for two specific locations and mallard (Anas platyrhynchos):

x_filtered <- x %>%
  filter_deployments(
    locationName == c(
      "B_HS_val 2_processiepark",
      "B_DM_val 4_'t WAD"
      )
    ) %>%
  filter_observations(scientificName == "Anas platyrhynchos")

filter_deployments() filters the deployments table to the two selected locations, and automatically removes any associated observations and media that do not belong to these deployments. filter_observations() then filters the remaining observations to only keep mallard (Anas platyrhynchos) observations.

Want to filter out timelapse observations? Use filter_out_timelapse() as a shortcut for filter_observations(x, captureMethod != "timelapse").

Summarizing data

Before visualizing, the data first needs to be summarized.

Use summarize_observations() to get an overview of the observations per deployment:

summ_obs_filtered <- summarize_observations(x_filtered)
summ_obs_filtered
#> # A tibble: 1 × 10
#> # Groups:   deploymentID, latitude, longitude, scientificName [1]
#>   deploymentID latitude longitude scientificName     n_scientificName n_events
#>   <chr>           <dbl>     <dbl> <chr>                         <int>    <int>
#> 1 00a2c20d         51.5      4.77 Anas platyrhynchos                1        6
#> # ℹ 4 more variables: n_observations <int>, sum_count <int>,
#> #   rai_observations <dbl>, rai_count <dbl>

Use summarize_deployments() to get an overview of the deployments:

summ_depl_filtered <- summarize_deployments(x_filtered)
summ_depl_filtered
#> # A tibble: 2 × 4
#> # Groups:   deploymentID, latitude, longitude [2]
#>   deploymentID latitude longitude effort_duration       
#>   <chr>           <dbl>     <dbl> <Duration>            
#> 1 00a2c20d         51.5      4.77 2789044s (~4.61 weeks)
#> 2 62c200a9         50.7      4.01 1903602s (~3.15 weeks)

Visualizing data

Use map_summary() to visualize a summary on a map.

Visualize the number of mallard observations per deployment:

map_summary(summ_obs_filtered, feature = "n_observations")

As you can see, in only one of the two deployments was mallard observed.

You can also visualize the daily effort per deployment:

map_summary(summ_depl_filtered, feature = "effort_duration")

Putting it all together

In practice you’d chain everything into a single pipeline, e.g.:

x %>%
  filter_deployments(
    locationName == c(
      "B_HS_val 2_processiepark",
      "B_DM_val 4_'t WAD"
      )
    ) %>%
  filter_observations(scientificName == "Anas platyrhynchos") %>%
  summarize_observations() %>%
  map_summary(feature = "n_observations")

For more information about visualization, check out the vignette vignette("visualize-deployment-features").