Merge branch 'gh-pages' of https://github.com/OHI-Science/ohiprep_v2024…

… into gh-pages

globalprep/le/v2024/README.md

@@ -228,10 +228,11 @@ Initially, there are three scripts, each with different starting units of value,
             -   \*\*Citation for paper\*\*  
 
             -   Melnychuk, M. C., Clavelle, T., Owashi, B., and Strauss, K. 2016. Reconstruction of global ex-vessel prices of fished species. - ICES Journal of Marine Science. <doi:10.1093/icesjms/fsw169>.
+
 -   `eco_tour_prep.Rmd`
     -   Tourism Revenue in USD per Country per Year (2008 - 2019)
 
-    -   Tourism direct GDP as a proportion of total GDP (indicator 8.9.1): **UNWTO Dept. of Statistics (UN Tourism)**
+    -   **Tourism direct GDP as a proportion of total GDP (indicator 8.9.1):** **UNWTO Dept. of Statistics (UN Tourism)**
 
         -   <https://www.unwto.org/tourism-statistics/economic-contribution-SDG>
 

globalprep/le/v2024/eco/eco_tour_prep.Rmd

@@ -31,15 +31,15 @@ editor_options:
 
 ### Tourism direct GDP as a proportion of total GDP 
 
-Tourism direct GDP as a proportion of total GDP (indicator 8.9.1): UNWTO Dept. of Statistics (UN Tourism)
+**Tourism direct GDP as a proportion of total GDP (indicator 8.9.1): UNWTO Dept. of Statistics (UN Tourism)**
 
 -   <https://www.unwto.org/tourism-statistics/economic-contribution-SDG> 
 -   A big issue with this data set is that it does not have data for
 mainland China. Thus, I was instructed to gapfill using another
 data source. I found tourism revenue data on the website for the
 National Bureau of Statistics for China
 
--   China Gapfilling -- °ºNational Data: National Bureau of Statistics of China (NBS) <https://data.stats.gov.cn/english/easyquery.htm?cn=C01>
+-   **China Gapfilling -- National Data: National Bureau of Statistics of China (NBS)** <https://data.stats.gov.cn/english/easyquery.htm?cn=C01>
 
 -   Made an account to download data:
 -   Email:
@@ -48,8 +48,8 @@ National Bureau of Statistics for China
 -   Security Question: who has influenced you the most?
 -   Answer: Melanie Frazier
 
--   Returned to home page, clicked “Annual”, clicked “Tourism” from options on the left, clicked “Year” dropdown menu and selected “LATEST20”, then clicked the download button and selected “csv”. File appeared as “Annual.csv”, which I then renamed to `eco_tour_china_all_metrics_2004-2023.csv` 
--   Added to new file in `_raw_data` on Mazu → NBS_China
+-   Return to home page, click “Annual”, click “Tourism” from options on the left, click “Year” dropdown menu and selected “LATEST20”, then clicked the download button and select “.csv”. File appears as “Annual.csv” -- rename this to `eco_tour_china_all_metrics_[start-year]-[end-year].csv`, e.g.,  `eco_tour_china_all_metrics_2004-2023.csv` 
+-   Add new file to `_raw_data/NBS_China` folder on Mazu
 
 -   For this tourism revenue data used for China, I wanted to discern whether or not these numbers included Macao and Hong Kong, as that can sometimes be the case (and would significantly impact my data processing methods). Thankfully, the website where I downloaded the data had information on this: 
 
@@ -93,6 +93,13 @@ Has sector-level data:
 
 -   Downloaded from NOAA Office for Coastal Management; Digital Coast website: <https://coast.noaa.gov/digitalcoast/data/>.
 
+
+
+### Coastal population proportion
+
+
+
+
 ### Setup
 
 ```{r}

globalprep/np/v2024/STEP1b_np_seaweeds_prep.Rmd

@@ -27,6 +27,8 @@ This analysis converts FAO mariculture data into one of the data layers used to
 
 -   Updating script throughout to use tidyverse style, reproducible file paths, updated read and write functions
 
+-   Added more comments/documentation to explain certain steps
+
 ------------------------------------------------------------------------
 
 # Data Source
@@ -635,11 +637,11 @@ np_new <- readr::read_csv(here(current_np_dir, "output", "np_seaweed_harvest_ton
 
 # Compare yield data for Saint Lucia
 np_old_sl <- np_old %>% 
-  dplyr::filter(rgn_id == 122, year == 2021) %>% 
+  dplyr::filter(rgn_id == 122, year == (as.integer(data_years[2]) - 1)) %>% 
   dplyr::select(rgn_id, taxa_code, tonnes)
 
 np_new_sl <- np_new %>% 
-  filter(rgn_id == 122, year == 2022) %>% 
+  filter(rgn_id == 122, year == as.integer(data_years[2])) %>% 
   select(rgn_id, taxa_code, tonnes)
 
 yield_sl <- np_old_sl %>% 
@@ -660,17 +662,17 @@ yield_sl <- np_old_sl %>%
 
 # Compare yield data for Vietnam - Vietnam score decreased
 np_old_vietnam <- np_old %>% 
-  filter(rgn_id == 206, year == 2020) %>% 
+  filter(rgn_id == 206, year == (as.integer(data_years[2]) - 1)) %>% 
   select(rgn_id, taxa_code, year, tonnes)
 
 np_new_vietnam <- np_new %>% 
-  filter(rgn_id == 206, year == 2021) %>% 
+  filter(rgn_id == 206, year == as.integer(data_years[2])) %>% 
   select(rgn_id, taxa_code, year, tonnes)
 
 yield_vietnam <- np_old_vietnam %>% 
   full_join(np_new_vietnam, by = c("rgn_id","taxa_code")); View(yield_vietnam) 
 
-
+# v2024: huge increase in Elkhorn seamoss: +129091.7 from 2021 to 2022 
 
 
 # ================= v2022 and v2023 code: =====================================