Adopt "age" as a term for "days since release event".

src/requirements.Rmd

@@ -1353,8 +1353,9 @@ Depending on the needs and hypotheses, the calculation could also aggregate days
 # the package will have to perform. Only, using data extractors
 # as mrr_surveys() et al.
 
-## Days since release event for each surveyed population
-surveys_days <- 
+## Days since release event (age) for each surveyed
+## population of sterile males.
+surveys_ages <- 
   cub_adult_data_raw |> 
   select(
     survey = `Survey date`,
@@ -1367,9 +1368,29 @@ surveys_days <-
   pivot_longer(
     cols = yellow:pink,
     names_to = "population",
-    values_to = "day"
+    values_to = "age"
   ) |> 
-  filter(!is.na(day))
+  filter(!is.na(age))
+
+
+captures_by_age <- 
+  cub_adult_data_clean |> 
+  inner_join(
+    ## Implicitly filters sterile males
+    surveys_ages,
+    by = c("survey", "population")
+  ) |>
+  select(-sex) |> 
+  mutate(
+    species = factor(species),
+    population = factor(population)
+  ) |> 
+  ## Keep observations up to the last age for which there is at least one
+  ## observation of a sterile male.
+  group_by(age) |> 
+  filter(sum(n) > 0) |> 
+  ungroup()
+
 
 ## Use reported distances (based on rings) to the single
 ## release point
@@ -1378,32 +1399,19 @@ traps_distances <-
   select(trap = `Trap Id`, d_m = `Distance (m)`) |> 
   unique()
 
-## Mean dispersal distances by day and release
+
+## Captures by trap, age and population
 cub_dispersal_dat <- 
-  cub_adult_data_clean |>
-  filter(
-    sex == "male",
-    # species == "aeg",  # This is redundant with not wild next
-    population != "wild"
-  ) |>
-  select(-species, -sex, -duration) |> 
+  captures_by_age |> 
   left_join(
     traps_distances,
     by = "trap"
   ) |> 
-  inner_join(
-    surveys_days,
-    by = c("survey", "population")
-  ) |> 
-  select(-survey) |> 
+  select(-survey, -species, -duration) |> 
   mutate(
     population = fct_inorder(population)
-  ) |> 
-  ## Keep observations up to the last day for which there is at least one
-  ## observation of a sterile male.
-  group_by(day) |> 
-  filter(sum(n) > 0) |> 
-  ungroup()
+  )
+
 ```
 
 
@@ -1412,12 +1420,12 @@ cub_dispersal_dat <-
 ## of MRR_template.xls.
 
 cub_dispersal_dat |> 
-  filter(day == 7, population == "pink") |> 
+  filter(age == 7, population == "pink") |> 
   print(n = 21)
 
-## MDD by day and population
+## MDD by age and population
 cub_dispersal_dat |> 
-  group_by(day, population) |> 
+  group_by(age, population) |> 
   ## We have 21 surveys in each group (1 for each trap)
   summarise(
     mdd = ifelse(sum(n) > 0, sum(n * d_m)/sum(n), NA),
@@ -1429,7 +1437,7 @@ cub_dispersal_dat |>
   )
 
 
-## MDD by population, across days.
+## MDD by population, across ages.
 cub_dispersal_dat |> 
   group_by(population) |> 
   ## We have 21 surveys in each group (1 for each trap)
@@ -1438,15 +1446,15 @@ cub_dispersal_dat |>
     .groups = "drop"
   )
 
-## MDD by day, across populations.
+## MDD by ages, across populations.
 cub_dispersal_dat |> 
-  group_by(day) |> 
+  group_by(age) |> 
   summarise(
     mdd = sum(n * d_m)/sum(n),
     .groups = "drop"
   ) 
 
-## MDD across days and populations.
+## MDD across ages and populations.
 cub_dispersal_dat |> 
   summarise(
     mdd = sum(n * d_m)/sum(n),
@@ -1457,15 +1465,15 @@ cub_dispersal_dat |>
 
 
 
-Define a function `mrr_dispersal()` which computes the MDD estimates by day and population.
+Define a function `mrr_dispersal()` which computes the MDD estimates by age and population.
 
 
 Input:
 ```{r}
 print_variable_table(
   "x", "Either a `mrrsit` object or the outcome of a `adult_surveys()` call.",
-  "pool", "Character or NULL (default). Any of `populations`, `days` or `all`.",
-  "by", "Character or NULL. Any of `populations`, `days` or `all` (default). Alternative to `pool`.",
+  "pool", "Character or NULL (default). Any of `populations`, `ages` or `all`.",
+  "by", "Character or NULL. Any of `populations`, `ages` or `all` (default). Alternative to `pool`.",
   header = FALSE
 )
 ```
@@ -1479,7 +1487,7 @@ Only one of either the dual arguments `pool` or `by` should be implemented. I th
   
     ```{r}
     cub_dispersal_dat |> 
-      group_by(day, population) |> 
+      group_by(age, population) |> 
       ## We have 21 surveys in each group (1 for each trap)
       summarise(
         mdd = ifelse(sum(n) > 0, sum(n * d_m)/sum(n), NA),
@@ -1487,11 +1495,11 @@ Only one of either the dual arguments `pool` or `by` should be implemented. I th
       )
     ```
 
-  For `by = days` or `pool = populations` counts are pooled across populations and MDDs are computed by days alone :
+  For `by = ages` or `pool = populations` counts are pooled across populations and MDDs are computed by ages alone :
   
     ```{r}
     cub_dispersal_dat |> 
-      group_by(day) |> 
+      group_by(age) |> 
       ## We have 21 surveys in each group (1 for each trap)
       summarise(
         mdd = ifelse(sum(n) > 0, sum(n * d_m)/sum(n), NA),
@@ -1499,7 +1507,7 @@ Only one of either the dual arguments `pool` or `by` should be implemented. I th
       )
     ```
   
-  For `by = populations` or `pool = days` counts are pooled across days and MDDs are computed by populations alone :
+  For `by = populations` or `pool = ages` counts are pooled across ages and MDDs are computed by populations alone :
   
     ```{r}
     cub_dispersal_dat |>