case3 start

.gitattributes

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+*.png filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text

case_study_3/.gitignore

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+case_study_3_reproducible_cache
+case_study_3_additional_cache

case_study_3/case_study_3_additional.Rmd

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+---
+title: "Additional exploration"
+output: github_document
+---
+
+<!---# Source - https://stackoverflow.com/a
+# Posted by Thomas
+# Retrieved 2025-12-09, License - CC BY-SA 3.0 -->
+
+
+First some code from the reproducible example is repeated to use the same time frame
+
+```{r echo=FALSE}
+knitr::opts_chunk$set(cache = TRUE)
+invisible(knitr::purl("case_study_3_reproducable.Rmd", output="temp.R", quiet=TRUE))
+knitr::read_chunk("temp.R")
+```
+
+```{r ref.label='requiredPkg'}
+```
+```{r ref.label='datetime'}
+```
+
+```{r echo=FALSE}
+unlink("temp.R")
+```
+Lets take a bit wider interval to see the context
+
+
+```{r interv}
+small_scale_radar_interval<-lubridate::interval(time-hours(10), time+hours(10))
+```
+## Robin Radars located at sea
+
+To investigate the high densities at sea we analyse two robin radars located there. 
+
+```{r uvaRR}
+require(uvaRR)
+trks <- purrr::map(
+  c("rws01", "borssele"),
+  ~ rr_track(
+    database = .x,
+    ts =c(int_start(small_scale_radar_interval), int_end(small_scale_radar_interval)) ,
+    interv = 60 * 60,
+    geom = T,
+    classification = c("BIRD"),
+    movement_metrics = c("dir", "dist", "edist", "spd", "str", "z")
+  ) |>
+    tibble::add_column(radar = .x)
+) |>
+  bind_rows() |>
+  mutate(trajectory = sf::st_as_sfc(trajectory, crs = 4326)) |>
+  st_as_sf() |>
+  mutate(
+    radarid_unique = trajectory_radarid |>
+      purrr::map(
+        ~ unlist(purrr::map(
+          purrr::map(strsplit(gsub("\\{|\\}", "", .x), ","), as.numeric),
+          unique
+        ))
+      ),
+    radarid_unique_chr = purrr::map_chr(
+      purrr::map(radarid_unique, as.character),
+      paste,
+      collapse = " "
+    ),
+    radar_type = case_match(
+      radarid_unique_chr,
+      c("11 14", "5 8") ~ "mixed",
+      c("14", "8") ~ "vertical",
+      c("11", "5") ~ "horizontal"
+    )
+  )
+```
+There is an increased density of birds in Luchterduinen (`rws01`) while this is absent from Borssele.
+```{r rrplot}
+ggplot(trks) +
+  geom_histogram(aes(x = timestamp_start, fill = classification)) +
+  facet_grid(radar ~ .) +
+  geom_vline(xintercept = time)
+ggplot(trks) +
+  geom_histogram(aes(x = timestamp_start, fill = radar_type)) +
+  facet_grid(radar ~ .) +
+  geom_vline(xintercept = time)
+```
+A directional histogram seems to confirm that the movements are vey directional corresponding to migratory movements
+```{r dirhist}
+ggplot(
+  trks |>
+    dplyr::filter(
+      radar_type != "vertical",
+      timestamp_start <
+        lubridate::ceiling_date(time + lubridate::hours(2), "hours"),
+      timestamp_start >
+        lubridate::floor_date(time - lubridate::hours(2), "hours")
+    )
+) +
+  geom_histogram(aes(x = dir, fill = radar_type), binwidth = 30) +
+  facet_grid(radar ~ lubridate::floor_date(timestamp_start, "hour")) +
+  coord_polar() +
+  scale_x_continuous(limits = c(0, 360), breaks = 0:3*90)
+```
+
+
+## MR1 Radars
+
+
+```{r mr1}
+bs_radar_databases = c("sbrs_kid_bs291", "sbrs_ams_bs290")
+bs_data <- purrr::map(
+  bs_radar_databases, ~
+    DBI::dbGetQuery(
+      as.is = F,
+      uvaAuth::get_connection_postgresql(.x),
+      glue::glue(
+        "select * from collection left join rf_classification on rf_classification.echo = collection.row
+      left join rfclasses on rfclasses.id = rf_classification.class  where time_stamp between '{format(lubridate::int_start(small_scale_radar_interval))}' and '{format(lubridate::int_end(small_scale_radar_interval))}';
+"
+      )
+    ) |>
+      dplyr::mutate(
+        speed = feature3,
+        azimuth = feature2,
+        altitude_AGL = feature1
+      ) %>%
+      dplyr::select(-starts_with("feature")) %>%
+      tibble::add_column(database = .x)
+) |>
+  bind_rows()
+
+```
+
+```{r explore}
+bs_data_filtered<-bs_data |> dplyr::filter(!name%in%c('insect', 'undefined', 'nonbio','precipitation'))
+table(bs_data$name)
+
+ggplot(bs_data_filtered) +
+  geom_histogram(aes(x = time_stamp, fill = name)) +
+  facet_grid(database ~ .) +
+  geom_vline(xintercept = time)
+```
+
+Here we see that the radar in Kijkduin has a migratory peak in the morning while that is absent from Amsterdam.
+
+Lets explore the directions in Kijkduin:
+```{r dirhistBs}
+ggplot(
+  bs_data_filtered |>
+    dplyr::filter(
+     database=='sbrs_kid_bs291',
+      time_stamp <
+        lubridate::ceiling_date(time + lubridate::hours(2), "hours"),
+      time_stamp >
+        lubridate::floor_date(time - lubridate::hours(2), "hours")
+    )
+) +
+  geom_histogram(aes(x = azimuth, fill = name), binwidth = 15) +
+  facet_wrap(. ~ lubridate::floor_date(time_stamp, "hour")) +
+  coord_polar() +
+  scale_x_continuous(limits = c(0, 360), breaks = 0:3*90)
+```