The goal of D6recaptureR is to help us during swallow recapturing
You can install the development version of D6recaptureR from GitHub with:
# install.packages("devtools")
devtools::install_github("mariusgrabow/D6recaptureR")This is a basic example which shows you how to handle a recapture:
- 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] 11Imagine 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 nowPlease note, cmr has 12 rows now (one was added with the System date & time)
nrow(cmr)
#> [1] 12Let’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>
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