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| Data/ | ||
| *_files | ||
| */02_range_maps_files/figure-html/* | ||
| *cache/ | ||
| *cache/ | ||
| *.motus | ||
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| --- | ||
| title: Workflow | ||
| title: Plans | ||
| --- | ||
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| ## Order of operations | ||
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| --- | ||
| title: Selecting Projects | ||
| --- | ||
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| Here we explore a list of open or semi open Motus projects to select more | ||
| project ids which we can use in our pilot study. | ||
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| ## Setup | ||
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| ```{r} | ||
| #| message: false | ||
| source("XX_setup.R") | ||
| library(readxl) | ||
| ``` | ||
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| ## Cleaning and Filtering | ||
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| **Cleaning** | ||
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| The species names (English and scientific) listed here aren't always consistent, | ||
| so we'll omit them and use only the species IDs to match them with the | ||
| NatureCounts metadata from [`XX_setup`](XX_setup.html). | ||
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| Then we consolidate the deployments in those projects as some listed some | ||
| deployments under one species name and others under another (even if the species | ||
| was the same) | ||
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| **Filtering** | ||
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| - keep only `access == 1` which are fully public projects | ||
| - omit species ID 129470 which are listed as attached to a Human...(?) | ||
| - omit projects with non-Canadian species | ||
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| ```{r} | ||
| p_sp <- read_excel("Data/Raw/TagsSpeciesProject.xlsx") |> | ||
| rename_with(.cols = contains("No column"), \(x) "access") |> | ||
| filter(access == 1, # Only completely open projects | ||
| speciesID != 129470) |> # Don't worry about Human tags :D | ||
| select(-speciesName, -motusEnglishName, -access) |> # Species Names are not consistent | ||
| summarize(across(everything(), sum), .by = c("tagProjectID", "speciesID")) |> | ||
| left_join(select(species, species_id, scientific_name, english_name), | ||
| by = c("speciesID" = "species_id")) |> | ||
| mutate(good = !is.na(scientific_name), | ||
| other = str_detect(english_name, "Eurasian|European|Elaenia")) |> | ||
| group_by(tagProjectID) |> | ||
| filter(all(!other | is.na(other))) |> # Omit projects with non-Canadian species | ||
| mutate(good_tags = sum(num_deployments[good]), | ||
| prop_good_tags = good_tags / sum(num_deployments)) |> | ||
| ungroup() |> | ||
| select(-other) |> | ||
| arrange(desc(prop_good_tags)) | ||
| gt(p_sp) |> | ||
| fmt_number("prop_good_tags", decimals = 2) | ||
| ``` | ||
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| ## Summarize | ||
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| Now we can summarize these projects by how many species, deployments (tags) and | ||
| the average number of deployments per species. | ||
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| We'll aim to include projects with a bread of species but also reasonable coverage, | ||
| so we exclude projects with less than 100% passerines and fewer than three species. | ||
| ```{r} | ||
| p <- p_sp |> | ||
| filter(prop_good_tags == 1) |> | ||
| group_by(tagProjectID) |> | ||
| summarize(total_tags = sum(num_deployments), | ||
| n_species = n_distinct(speciesID), | ||
| mean_tags_per_species = mean(num_deployments), | ||
| species = list(unique(english_name))) |> | ||
| filter(n_species > 3) |> | ||
| arrange(desc(mean_tags_per_species), desc(n_species), desc(total_tags)) | ||
| gt(p) |> | ||
| fmt_number(columns = "mean_tags_per_species", decimals = 1) | ||
| ``` | ||
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| ## Data sizes | ||
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| Now, we can check the amount of data per project (see what we're in for!) | ||
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| For reference, 7,006,847,799 bytes is ~ 7 GB | ||
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| ```{r} | ||
| #| cache: true | ||
| dir.create("Data/Temp") | ||
| status <- map( | ||
| set_names(p$tagProjectID), | ||
| \(x) tellme(x, dir = "Data/Temp", new = TRUE)) |> | ||
| list_rbind(names_to = "proj_id") | ||
| unlink("Data/Temp", recursive = TRUE) | ||
| ``` | ||
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| So this, isn't too bad, data-wise, I think we could use all projects. | ||
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| ```{r} | ||
| status |> | ||
| mutate(Megabytes = numBytes / 1000000) |> | ||
| arrange(desc(numBytes)) |> | ||
| gt() |> | ||
| fmt_number(decimals = 0) | ||
| ``` | ||
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