Merge branch 'gh-pages' of https://github.com/OHI-Science/ohiprep_v2023…

… into gh-pages

globalprep/fis/v2023/STEP1_download_saup_match_fao_data_fix.Rmd

@@ -35,7 +35,7 @@ Note: In the future, I think it would be possible to use python to download the
 
 # Updates from previous scenario year
 
-- None. This script wasn't run in v2023.
+- None. This script wasn't run in v2023. Associated data files within the repo were copied from v2022 to v2023.
 
 # Data Source
 

globalprep/fis/v2023/STEP1_download_saup_match_fao_data_fix.html

@@ -9,7 +9,7 @@
 <meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
 
 
-<meta name="author" content="Compiled on Tue Jul 18 09:54:08 2023 by egg" />
+<meta name="author" content="Compiled on Thu Jul 20 15:42:13 2023 by egg" />
 
 
 <title>OHI 2023: Download SAUP production data and match FAO regions</title>
@@ -1666,7 +1666,7 @@
 
 <h1 class="title toc-ignore">OHI 2023: Download SAUP production data and
 match FAO regions</h1>
-<h4 class="author"><em>Compiled on Tue Jul 18 09:54:08 2023 by
+<h4 class="author"><em>Compiled on Thu Jul 20 15:42:13 2023 by
 egg</em></h4>
 
 </div>
@@ -1700,7 +1700,8 @@ <h1>Summary</h1>
 <div id="updates-from-previous-scenario-year" class="section level1">
 <h1>Updates from previous scenario year</h1>
 <ul>
-<li>None. This script wasn’t run in v2023.</li>
+<li>None. This script wasn’t run in v2023. Associated data files within
+the repo were copied from v2022 to v2023.</li>
 </ul>
 </div>
 <div id="data-source" class="section level1">

globalprep/fis/v2023/STEP2a_saup_catch_prep.Rmd

@@ -33,13 +33,10 @@ An example of our aggregation process: New Zealand is located entirely in FAO re
 First, we need to match SAUP EEZ regions to OHI regions, and summarize per region, year, and species.
 
 ## Updates from previous assessment
-v2022: Added new SAUP fisheries data to include year 2019 and added 2019 as a year for RAM data.
+- None. This script wasn't run in v2023. Associated data files within the repo were copied from v2022 to v2023.
 
 ***
 
-# Updates from previous scenario year
-
-- None. This script wasn't run in v2023.
 
 # Data Source
 

globalprep/fis/v2023/STEP2a_saup_catch_prep.html

@@ -9,7 +9,7 @@
 <meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
 
 
-<meta name="author" content="Compiled on Tue Jul 18 09:54:49 2023 by egg" />
+<meta name="author" content="Compiled on Thu Jul 20 15:43:02 2023 by egg" />
 
 
 <title>OHI 2023: Food Provision, Preparing catch data for BBmsy calculations and mean catch (weights in FIS model) and FP weights</title>
@@ -1667,7 +1667,7 @@
 <h1 class="title toc-ignore">OHI 2023: Food Provision, Preparing catch
 data for BBmsy calculations and mean catch (weights in FIS model) and FP
 weights</h1>
-<h4 class="author"><em>Compiled on Tue Jul 18 09:54:49 2023 by
+<h4 class="author"><em>Compiled on Thu Jul 20 15:43:02 2023 by
 egg</em></h4>
 
 </div>
@@ -1701,16 +1701,12 @@ <h1>Summary</h1>
 summarize per region, year, and species.</p>
 <div id="updates-from-previous-assessment" class="section level2">
 <h2>Updates from previous assessment</h2>
-<p>v2022: Added new SAUP fisheries data to include year 2019 and added
-2019 as a year for RAM data.</p>
-<hr />
-</div>
-</div>
-<div id="updates-from-previous-scenario-year" class="section level1">
-<h1>Updates from previous scenario year</h1>
 <ul>
-<li>None. This script wasn’t run in v2023.</li>
+<li>None. This script wasn’t run in v2023. Associated data files within
+the repo were copied from v2022 to v2023.</li>
 </ul>
+<hr />
+</div>
 </div>
 <div id="data-source" class="section level1">
 <h1>Data Source</h1>

globalprep/fis/v2023/STEP2b_species_resilience_lookup_table.Rmd

@@ -19,7 +19,7 @@ This script uses FishBase to find the Resilience for each of the species in the
 
 # Updates from previous scenario year
 
-- None. This script wasn't run in v2023.
+- None. This script wasn't run in v2023. Associated data files within the repo were copied from v2022 to v2023.
 
 # Setup
 

globalprep/fis/v2023/STEP3_calculate_bbmsy.Rmd

@@ -24,7 +24,7 @@ The [`datalimited`](https://github.com/datalimited/datalimited) R package, devel
 
 # Updates from previous scenario year
 
-- None. This script wasn't run in v2023.
+- None. This script wasn't run in v2023. Associated data files within the repo were copied from v2022 to v2023.
 
 # Setup 
 

globalprep/fis/v2023/STEP3_calculate_bbmsy.html

@@ -9,7 +9,7 @@
 <meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
 
 
-<meta name="author" content="Compiled on Tue Jul 18 09:56:20 2023 by egg" />
+<meta name="author" content="Compiled on Thu Jul 20 15:44:12 2023 by egg" />
 
 
 <title>OHI 2023: Food Provision, Calculating fisheries stock status</title>
@@ -1666,7 +1666,7 @@
 
 <h1 class="title toc-ignore">OHI 2023: Food Provision, Calculating
 fisheries stock status</h1>
-<h4 class="author"><em>Compiled on Tue Jul 18 09:56:20 2023 by
+<h4 class="author"><em>Compiled on Thu Jul 20 15:44:12 2023 by
 egg</em></h4>
 
 </div>
@@ -1686,7 +1686,8 @@ <h1>Summary</h1>
 <div id="updates-from-previous-scenario-year" class="section level1">
 <h1>Updates from previous scenario year</h1>
 <ul>
-<li>None. This script wasn’t run in v2023.</li>
+<li>None. This script wasn’t run in v2023. Associated data files within
+the repo were copied from v2022 to v2023.</li>
 </ul>
 </div>
 <div id="setup" class="section level1">

globalprep/fis/v2023/STEP4a_RAM_data_prep.Rmd

@@ -335,7 +335,7 @@ summary(mod)
 
 ## Identify FAO and OHI regions for RAM stocks
 
-Identify the FAO/OHI regions where each RAM stock is located (fao and ohi regions are assigned to RAM Data in [STEP4b_fao_ohi_rgns.Rmd](https://mazu.nceas.ucsb.edu/rstudio/files/github/ohiprep_v2021/globalprep/fis/v2022/STEP4b_fao_ohi_rgns.html). **Run STEP4b_fao_ohi_rgns.Rmd now.**
+Identify the FAO/OHI regions where each RAM stock is located (fao and ohi regions are assigned to RAM Data in [STEP4b_fao_ohi_rgns.Rmd](https://mazu.nceas.ucsb.edu/rstudio/files/github/ohiprep_v2023/globalprep/fis/v2023/STEP4b_fao_ohi_rgns.html). **Run STEP4b_fao_ohi_rgns.Rmd now.**
 
 
 If there are many differences between RAM spatial file and RAM metadata, check the STEP4_fao_ohi_rgns.Rmd prep again. 
@@ -376,7 +376,7 @@ ram_sp <- ram_bmsy_gf_final %>%
 
 
 # SAUP species, sci name (read in the datatable that includes TaxonKey)
-SAUP_sp <- read_csv(file.path(dir_M,'git-annex/globalprep/fis/v2022/int/stock_catch_by_rgn_taxa.csv')) %>% 
+SAUP_sp <- read_csv(file.path(dir_M,'git-annex/globalprep/fis/v2022/int/stock_catch_by_rgn_taxa.csv')) %>% # use most recent one available
   dplyr::rename(SAUP_scientificname = TaxonName) %>%
   dplyr::select(SAUP_scientificname) %>%
   unique() %>%
@@ -402,7 +402,7 @@ ram_sp_fao_ohi <- tmp %>%
 write_csv(ram_sp_fao_ohi, "int/new_ram_sp.csv")  
 
 ## get SAUP fao_ohi regions
-SAUP_sp_fao_ohi <- read_csv(file.path(dir_M,'git-annex/globalprep/fis/v2022/int/stock_catch_by_rgn_taxa.csv')) %>% 
+SAUP_sp_fao_ohi <- read_csv(file.path(dir_M,'git-annex/globalprep/fis/v2022/int/stock_catch_by_rgn_taxa.csv')) %>% # use most recent one available
   dplyr::rename(SAUP_scientificname = TaxonName) %>%
    dplyr::filter(year > 1979) # %>%
   #filter(str_detect(SAUP_scientificname, "Lepidorhombus"))

globalprep/fis/v2023/STEP4a_RAM_data_prep.html

@@ -9,7 +9,7 @@
 <meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
 
 
-<meta name="author" content="Compiled on Tue Jul 18 09:56:52 2023 by egg" />
+<meta name="author" content="Compiled on Thu Jul 20 15:00:39 2023 by egg" />
 
 
 <title>OHI 2023: Food Provision/Fisheries, Preparing RAM B/Bmsy data</title>
@@ -1666,7 +1666,7 @@
 
 <h1 class="title toc-ignore">OHI 2023: Food Provision/Fisheries,
 Preparing RAM B/Bmsy data</h1>
-<h4 class="author"><em>Compiled on Tue Jul 18 09:56:52 2023 by
+<h4 class="author"><em>Compiled on Thu Jul 20 15:00:39 2023 by
 egg</em></h4>
 
 </div>
@@ -2029,7 +2029,7 @@ <h2>Gapfill RAM data when there are missing years</h2>
 <div id="identify-fao-and-ohi-regions-for-ram-stocks" class="section level2">
 <h2>Identify FAO and OHI regions for RAM stocks</h2>
 <p>Identify the FAO/OHI regions where each RAM stock is located (fao and
-ohi regions are assigned to RAM Data in <a href="https://mazu.nceas.ucsb.edu/rstudio/files/github/ohiprep_v2021/globalprep/fis/v2022/STEP4b_fao_ohi_rgns.html">STEP4b_fao_ohi_rgns.Rmd</a>.
+ohi regions are assigned to RAM Data in <a href="https://mazu.nceas.ucsb.edu/rstudio/files/github/ohiprep_v2023/globalprep/fis/v2023/STEP4b_fao_ohi_rgns.html">STEP4b_fao_ohi_rgns.Rmd</a>.
 <strong>Run STEP4b_fao_ohi_rgns.Rmd now.</strong></p>
 <p>If there are many differences between RAM spatial file and RAM
 metadata, check the STEP4_fao_ohi_rgns.Rmd prep again.</p>
@@ -2071,7 +2071,7 @@ <h2>Standardize species names</h2>
 <span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a><span class="co"># SAUP species, sci name (read in the datatable that includes TaxonKey)</span></span>
-<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a>SAUP_sp <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(<span class="fu">file.path</span>(dir_M,<span class="st">&#39;git-annex/globalprep/fis/v2022/int/stock_catch_by_rgn_taxa.csv&#39;</span>)) <span class="sc">%&gt;%</span> </span>
+<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a>SAUP_sp <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(<span class="fu">file.path</span>(dir_M,<span class="st">&#39;git-annex/globalprep/fis/v2022/int/stock_catch_by_rgn_taxa.csv&#39;</span>)) <span class="sc">%&gt;%</span> <span class="co"># use most recent one available</span></span>
 <span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">rename</span>(<span class="at">SAUP_scientificname =</span> TaxonName) <span class="sc">%&gt;%</span></span>
 <span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(SAUP_scientificname) <span class="sc">%&gt;%</span></span>
 <span id="cb7-15"><a href="#cb7-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">unique</span>() <span class="sc">%&gt;%</span></span>
@@ -2097,7 +2097,7 @@ <h2>Standardize species names</h2>
 <span id="cb7-35"><a href="#cb7-35" aria-hidden="true" tabindex="-1"></a><span class="fu">write_csv</span>(ram_sp_fao_ohi, <span class="st">&quot;int/new_ram_sp.csv&quot;</span>)  </span>
 <span id="cb7-36"><a href="#cb7-36" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb7-37"><a href="#cb7-37" aria-hidden="true" tabindex="-1"></a><span class="do">## get SAUP fao_ohi regions</span></span>
-<span id="cb7-38"><a href="#cb7-38" aria-hidden="true" tabindex="-1"></a>SAUP_sp_fao_ohi <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(<span class="fu">file.path</span>(dir_M,<span class="st">&#39;git-annex/globalprep/fis/v2022/int/stock_catch_by_rgn_taxa.csv&#39;</span>)) <span class="sc">%&gt;%</span> </span>
+<span id="cb7-38"><a href="#cb7-38" aria-hidden="true" tabindex="-1"></a>SAUP_sp_fao_ohi <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(<span class="fu">file.path</span>(dir_M,<span class="st">&#39;git-annex/globalprep/fis/v2022/int/stock_catch_by_rgn_taxa.csv&#39;</span>)) <span class="sc">%&gt;%</span> <span class="co"># use most recent one available</span></span>
 <span id="cb7-39"><a href="#cb7-39" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">rename</span>(<span class="at">SAUP_scientificname =</span> TaxonName) <span class="sc">%&gt;%</span></span>
 <span id="cb7-40"><a href="#cb7-40" aria-hidden="true" tabindex="-1"></a>   dplyr<span class="sc">::</span><span class="fu">filter</span>(year <span class="sc">&gt;</span> <span class="dv">1979</span>) <span class="co"># %&gt;%</span></span>
 <span id="cb7-41"><a href="#cb7-41" aria-hidden="true" tabindex="-1"></a>  <span class="co">#filter(str_detect(SAUP_scientificname, &quot;Lepidorhombus&quot;))</span></span>

globalprep/prs_alien/v2023/prs_alien_data_prep.Rmd

@@ -265,7 +265,7 @@ clean_output <- binded_df %>%
     country = str_replace_all(country, "St. Lucia", "Saint Lucia"),
     country = str_replace_all(country, "&", "and"),
     country = str_replace_all(country, "Congo - Kinshasa", "Democratic Republic of the Congo"),
-    country = str_replace_all(country, "Côte d’Ivoire", "	Ivory Coast"),
+    country = str_replace_all(country, "Côte d’Ivoire", "Ivory Coast"),
     country = str_replace_all(country, "Curaçao", "Curacao"),
     country = str_replace_all(country, "Myanmar \\(Burma\\)", "Myanmar"),
     country = str_replace_all(country, "Saint Martin \\(French part\\)", "Northern Saint-Martin"),