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D6recaptureR

The goal of D6recaptureR is to help us during swallow recapturing

Installation

You can install the development version of D6recaptureR from GitHub with:

# install.packages("devtools")
devtools::install_github("mariusgrabow/D6recaptureR")

Example

This is a basic example which shows you how to handle a recapture:

  1. You need a dataframe named cmr (case-sensitive) from the last years (provided by Marius). Here, we will work with one example from one bird (included in the package)

Please note: In this example, cmr has 11 rows

library(D6recaptureR)

cmr<-D6recaptureR::cmr_filter
nrow(cmr)
#> [1] 11

Imagine you recaptured bird (VH59051) and would like to know the capture history:

(You can write vh59051 or VH59051, the package corrects to Uppercase)

re(vh59051)
#> Adding missing grouping variables: `ring_id`
#> # A tibble: 11 × 9
#> # Groups:   ring_id [1]
#>    ring_id date       time   sex   tars_mm weight_g blood_infection
#>    <chr>   <date>     <time> <chr>   <dbl>    <dbl> <chr>          
#>  1 VH59051 2020-06-06 12:40  f        11.5     20.1 <NA>           
#>  2 VH59051 2020-06-06 17:06  f        11.5     20.1 <NA>           
#>  3 VH59051 2020-06-06 17:30  f        11.5     20.1 <NA>           
#>  4 VH59051 2020-06-17 10:37  f        11.5     19.9 <NA>           
#>  5 VH59051 2020-06-17 14:43  f        11.5     19.7 <NA>           
#>  6 VH59051 2020-06-17 15:15  f        11.5     19.3 <NA>           
#>  7 VH59051 2021-05-26 17:49  f        11       19.8 y              
#>  8 VH59051 2021-06-07 16:06  f        11.5     19.7 y              
#>  9 VH59051 2022-05-26 10:50  f        11.4     20.6 n              
#> 10 VH59051 2022-05-26 14:45  f        11.3     20.6 n              
#> 11 VH59051 2022-06-09 13:08  f        11.3     19.4 n              
#>    infection_type tag_id
#>    <chr>           <dbl>
#>  1 <NA>               NA
#>  2 <NA>               NA
#>  3 <NA>               NA
#>  4 <NA>               NA
#>  5 <NA>               NA
#>  6 <NA>               NA
#>  7 haemoproteus       NA
#>  8 haemoproteus       NA
#>  9 none               NA
#> 10 none               NA
#> 11 none               NA
#> observation was added into global environment. cmr hat 1 more row now

Please note, cmr has 12 rows now (one was added with the System date & time)

nrow(cmr)
#> [1] 12

Let’s look at the last observation, which we just added. Here, there is data missing, although we might collected it manually on the field sheets. However, this should save us a lot of time because we don’t have to screen a pile of paper.

dplyr::slice_tail(cmr,n=1)
#> # A tibble: 1 × 9
#> # Groups:   ring_id [1]
#>   ring_id date   time            sex   tars_mm weight_g blood_infection
#>   <chr>   <date> <time>          <chr>   <dbl>    <dbl> <chr>          
#> 1 VH59051 NA     10:52:55.912993 <NA>       NA       NA <NA>           
#> # ℹ 2 more variables: infection_type <chr>, tag_id <dbl>