Use WRA in national stats

data-raw/get-data.R

@@ -17,9 +17,9 @@ RDI_tab <-
 
 timor_population <-
   readr::read_csv(system.file("timor_census_2022.csv", package = "timor.nutrients")) %>%
-  dplyr::filter(!gender == "Total") %>%
+  dplyr::filter(gender == "Female") %>%
   dplyr::group_by(region) %>%
-  dplyr::filter(age > 5) %>%
+  dplyr::filter(age > 14 & age < 50) %>%
   dplyr::summarise(population = sum(count, na.rm = T)) %>%
   dplyr::add_row(region = "All", population = sum(.$population)) %>%
   dplyr::ungroup()

docs/index.html

@@ -140,7 +140,7 @@ <h1>
 <div id="header">
 <h1 class="title">Modelling scenarios for nutrient-sensitive fisheries management</h1>
 <p class="author"><em>Lorenzo Longobardi</em></p>
-<p class="date"><em>Last update: 2023-12-27</em></p>
+<p class="date"><em>Last update: 2024-01-04</em></p>
 </div>
 <div id="content" class="section level1 hasAnchor" number="1">
 <h1><span class="header-section-number">1</span> Content<a href="index.html#content" class="anchor-section" aria-label="Anchor link to header"></a></h1>

docs/profiles.html

@@ -184,8 +184,8 @@ <h3><span class="header-section-number">5.2.1</span> Clusters<a href="profiles.h
 </div>
 <p>The PERMANOVA analyses (Table 5.1) revealed statistically significant differences between clusters, suggesting robust groupings based on the nutrient profiles. The pseudo-F statistics were remarkably high in all cases, indicating strong differentiation between clusters. Specifically, the R² values were 0.87, 0.88, 0.84, and 0.80 for Atauro AG, Atauro GN, Mainland AG, and Mainland GN respectively, indicating that between 80% to 88% of the variance in nutrient concentrations was explained by the clusters. The high R² values underscore the distinctness of the clusters, reinforcing the validity of the K-means clustering.</p>
 <p>These findings were consistent across all the datasets, with p-values below 0.001, providing clear evidence to reject the null hypothesis of no difference between clusters. Hence, the PERMANOVA results robustly support the effectiveness of the K-means algorithm in capturing meaningful patterns in nutrient profiles.</p>
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+<div class="reactable html-widget html-fill-item" id="htmlwidget-bb7f3bb398f1334b3f2c" style="width:auto;height:auto;"></div>
+<script type="application/json" data-for="htmlwidget-bb7f3bb398f1334b3f2c">{"x":{"tag":{"name":"Reactable","attribs":{"data":{"subset":["atauro_AG","atauro_AG","atauro_AG","atauro_GN","atauro_GN","atauro_GN","mainland_AG","mainland_AG","mainland_AG","mainland_GN","mainland_GN","mainland_GN"],"term":["clusters","Residual","Total","clusters","Residual","Total","clusters","Residual","Total","clusters","Residual","Total"],"df":[4,4522,4526,4,8543,8547,4,6688,6692,4,10433,10437],"SumOfSqs":[10296.92,1543.73,11840.64,12071.28,1712.98,13784.26,16953.41,3325.36,20278.77,12220.31,2999.88,15220.2],"R2":[0.87,0.13,1,0.88,0.12,1,0.84,0.16,1,0.8,0.2,1],"statistic":[7540.63,"NA","NA",15050.53,"NA","NA",8524.22,"NA","NA",10624.96,"NA","NA"],"p.value":["< 0.001",null,null,"< 0.001",null,null,"< 0.001",null,null,"< 0.001",null,null]},"columns":[{"id":"subset","name":"subset","type":"character","align":"center","minWidth":120},{"id":"term","name":"term","type":"character","align":"center"},{"id":"df","name":"df","type":"numeric","align":"center"},{"id":"SumOfSqs","name":"SumOfSqs","type":"numeric","align":"center"},{"id":"R2","name":"R2","type":"numeric","align":"center"},{"id":"statistic","name":"statistic","type":"numeric","align":"center"},{"id":"p.value","name":"p.value","type":"character","align":"center"}],"groupBy":["subset"],"pagination":false,"defaultExpanded":true,"theme":{"color":"#222222","backgroundColor":"#ffffff","borderColor":"#dddddd","borderWidth":"1px","stripedColor":"#dddddd","highlightColor":"#f0f0f0","cellPadding":5,"tableStyle":{"fontSize":14,"borderBottom":"3px solid #222222"},"headerStyle":{"borderWidth":"3px","paddingTop":"12px","verticalAlign":"bottom","textAlign":"bottom","background":"#ffffff","textTransform":"uppercase","borderColor":"#222222","color":"#000000","&:hover":{"background":"#dddddd"},"&[aria-sort='ascending'], &[aria-sort='descending']":{"background":"#5b5e5f","color":"#ffffff"},"borderColor.1":"#333","fontSize":12},"groupHeaderStyle":{"&:not(:empty)":{"paddingBottom":"3px","verticalAlign":"bottom","textAlign":"bottom","backgroundColor":"#ffffff","textTransform":"uppercase","fontSize":12,"color":"#222222"}},"rowSelectedStyle":{"backgroundColor":"#dddddd"},"cellStyle":{"display":"flex","flexDirection":"column","justifyContent":"center"},"inputStyle":{"backgroundColor":"#ffffff","color":"#222222"},"searchInputStyle":{"textTransform":"uppercase","color":"#222222","fontSize":"14px"},"paginationStyle":{"textTransform":"uppercase","fontSize":"14px"},"pageButtonStyle":{"textTransform":"uppercase","fontSize":"14px"}},"dataKey":"d3709fe0b2631e78160e9af712467b91"},"children":[]},"class":"reactR_markup"},"evals":[],"jsHooks":[]}</script>
 <p>Table 5.1: Results of PERMANOVA analysis assessing the homogeneity of nutrient profiles within fishing trip clusters. The analysis was conducted across four datasets: Atauro with all gears (atauro_AG), Atauro with gill nets (atauro_GN), Mainland with all gears (mainland_AG), and Mainland with gill nets (mainland_GN). For each dataset, the term ‘clusters’ represents the within-group sum of squares (SUMOFSQS), which measures the variance within the nutritional profiles, while ‘Residual’ represents the variance between nutritional profiles Degrees of Freedom (DF), R-squared values (R2), and associated statistics indicate the strength and significance of the clustering. The R2 value quantifies the proportion of variance explained by the clusters.</p>
 </div>
 <div id="xgboost-model" class="section level3 hasAnchor" number="5.2.2">
@@ -201,8 +201,8 @@ <h3><span class="header-section-number">5.2.2</span> XGBoost model<a href="profi
 Figure 5.3: Receiver Operating Characteristic (ROC) Curves for evaluating the performance of a cluster-based XGBoost classification model across four distinct fishing datasets: Atauro with all gears (a), Atauro with gill nets (b), Mainland with all gears (c), and Mainland with gill nets (d). Each curve represents one of the five clusters obtained from the classification, with different colors marking each cluster. Data points on the curves indicate the trade-off between sensitivity (true positive rate) and 1-specificity (false positive rate) for each cluster. The proximity of the curves to the top-left corner reflects the accuracy of the model in classifying the nutritional profiles into the correct clusters.
 </p>
 </div>
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+<div class="reactable html-widget html-fill-item" id="htmlwidget-14cfc8ef927acc7c76b4" style="width:auto;height:auto;"></div>
+<script type="application/json" data-for="htmlwidget-14cfc8ef927acc7c76b4">{"x":{"tag":{"name":"Reactable","attribs":{"data":{"metric":["roc_auc","accuracy","kap","sens","spec","ppv","npv","mcc","j_index","bal_accuracy","detection_prevalence","precision","recall","f_meas"],"Atauro AG":[0.91,0.68,0.58,0.62,0.92,0.63,0.93,0.6,0.53,0.77,0.2,0.63,0.62,0.58],"Atauro GN":[0.92,0.76,0.66,0.73,0.94,0.73,0.93,0.67,0.66,0.83,0.2,0.73,0.73,0.71],"Mainland AG":[0.7,0.42,0.21,0.36,0.84,0.46,0.86,0.24,0.2,0.6,0.2,0.46,0.36,0.35],"Mainland GN":[0.84,0.67,0.51,0.59,0.9,0.66,0.91,0.52,0.49,0.75,0.2,0.66,0.59,0.59]},"columns":[{"id":"metric","name":"metric","type":"character","align":"center","minWidth":120},{"id":"Atauro AG","name":"Atauro AG","type":"numeric","align":"center"},{"id":"Atauro GN","name":"Atauro GN","type":"numeric","align":"center"},{"id":"Mainland AG","name":"Mainland AG","type":"numeric","align":"center"},{"id":"Mainland GN","name":"Mainland GN","type":"numeric","align":"center"}],"pagination":false,"defaultExpanded":true,"theme":{"color":"#222222","backgroundColor":"#ffffff","borderColor":"#dddddd","borderWidth":"1px","stripedColor":"#dddddd","highlightColor":"#f0f0f0","cellPadding":5,"tableStyle":{"fontSize":14,"borderBottom":"3px solid #222222"},"headerStyle":{"borderWidth":"3px","paddingTop":"12px","verticalAlign":"bottom","textAlign":"bottom","background":"#ffffff","textTransform":"uppercase","borderColor":"#222222","color":"#000000","&:hover":{"background":"#dddddd"},"&[aria-sort='ascending'], &[aria-sort='descending']":{"background":"#5b5e5f","color":"#ffffff"},"borderColor.1":"#333","fontSize":12},"groupHeaderStyle":{"&:not(:empty)":{"paddingBottom":"3px","verticalAlign":"bottom","textAlign":"bottom","backgroundColor":"#ffffff","textTransform":"uppercase","fontSize":12,"color":"#222222"}},"rowSelectedStyle":{"backgroundColor":"#dddddd"},"cellStyle":{"display":"flex","flexDirection":"column","justifyContent":"center"},"inputStyle":{"backgroundColor":"#ffffff","color":"#222222"},"searchInputStyle":{"textTransform":"uppercase","color":"#222222","fontSize":"14px"},"paginationStyle":{"textTransform":"uppercase","fontSize":"14px"},"pageButtonStyle":{"textTransform":"uppercase","fontSize":"14px"}},"dataKey":"aeca06704838ec81a3eb516d1a80f6ad"},"children":[]},"class":"reactR_markup"},"evals":[],"jsHooks":[]}</script>
 <p>Table 5.2: Performance Metrics for XGBoost Model Across Fishing Data Subsets. This table provides a comprehensive overview of the predictive performance of an XGBoost classification model for four distinct subsets of fishing data: Atauro with all gears (ATAURO AG), Atauro with gill nets (ATAURO GN), Mainland with all gears (MAINLAND AG), and Mainland with gill nets (MAINLAND GN). Key performance indicators include ROC-AUC (area under the receiver operating characteristic curve), accuracy, Kappa (kap), sensitivity (sens), specificity (spec), positive predictive value (ppv), negative predictive value (npv), Matthew’s correlation coefficient (mcc), Youden’s J index (j_index), balanced accuracy (bal_accuracy), detection prevalence, precision, recall, and F measure (f_meas). The metrics collectively reflect the model’s ability to discriminate between nutritional profiles, its overall accuracy, and the balance between the sensitivity and specificity for each subset.</p>
 <p><br />
 The analysis of SHAP values (see <a href="simple.html#simple">ML model explanation</a>) from gill net models (Figure 5.4), which provide insights into how different factors influence predictions in an XGBoost model, shows how mesh size and habitat together predict nutrient profiles in the Atauro region. It’s found that smaller mesh sizes, specifically below 40 mm, are closely linked with a higher likelihood of predicting nutrient profile NP3 across various habitats like reefs and beaches. These smaller sizes also have a lesser association with NP4, particularly when fishing occurs in deeper waters. In contrast, mesh sizes around 50mm are predominantly associated with nutrient profile NP2 in similar environments, with mangroves also playing a role.</p>

docs_book/02-nation_stats.Rmd

@@ -2,9 +2,9 @@
 
 ## Timor-Est SSF nutritional scenario
 
-The table uses the [EC dataset][data] and summarizes the main statistics on nutrient supply for each region. Below is a description of each table' column:
+The table uses the [EC dataset][data] and summarizes the main statistics on nutrient supply for each region related to **WRA**, the number of woman of reproductive age (15-49 years old). Below is a description of each table' column:
 
--   **MUNICIPALITY (POPULATION)**: Municipality and number of people \> 5 years old in 2022.
+-   **MUNICIPALITY (POPULATION)**: Municipality and WRA number in 2022.
 
 -   **NUTRIENT**: Nutrient of reference
 
@@ -18,7 +18,7 @@ The table uses the [EC dataset][data] and summarizes the main statistics on nutr
 
 The 20% of RNIs values was take as reference in consideration of the fact that [an 'adequate diet' is expected to comprise 5 food group](https://www.fao.org/documents/card/en/c/cc4576en). RNIs were then converted from grams to kg (dividing by 1000) and the requirements was calculated as: $\frac{Anuual\ supply\ (kg)}{(RNI\times 0.20) \ / 1000} /365$
 
--   **POPULATION MEETING RNI REQUIREMENTS**: Percentage of the population meeting the RNI requirements in each municipality: $\frac{Number\ of\ people\ supplied\ daily}{Municipality\ population} \times 100$
+-   **POPULATION MEETING RNI REQUIREMENTS**: Percentage of the WRA population meeting the RNI requirements in each municipality: $\frac{Number\ of\ people\ supplied\ daily}{Municipality\ population} \times 100$
 
 ```{r echo=FALSE, message=FALSE, warning=FALSE}
 color_pal <- c("#f5fcdf", "#f2fbd2", "#c9ecb4", "#93d3ab", "#35b0ab")