wip

laravel_app/app/Livewire/Projects/ReportManager.php

@@ -47,7 +47,7 @@ public function saveProjectReport()
         $this->validate([
             'formData.week' => ['required', 'integer', 'min:1', 'max:53'],
             'formData.year' => 'required|integer|min:2020|max:'.now()->addYear()->year,
-            'formData'      => [new AllMosaicsPresentRule($this->project_id)],
+//            'formData'      => [new AllMosaicsPresentRule($this->project_id)],
         ]);
 
         $newReport = Project::find($this->project_id)

laravel_app/app/Livewire/Projects/Tabs/Report.php

@@ -50,7 +50,7 @@ public function saveProjectReport()
         $this->validate([
             'formData.end_date' => ['required','date','before:today'],
             'formData.offset' => 'required|integer|min:1|max:1000',
-            'formData'      => [new AllMosaicsPresentRule($this->project->id)],
+//            'formData'      => [new AllMosaicsPresentRule($this->project->id)],
         ]);
 
         $newReport = Project::find($this->project->id)

r_app/2_CI_data_prep.R

@@ -782,7 +782,7 @@ pivot_stats_long <- pivot_stats2 %>%
 # 
 # pivot_select_model_Data_2022 <- harvesting_data %>% filter(Year == 2022) %>% pull(field)
 
-pivot_select_model_Data_2023 <- harvesting_data %>% filter(year == 2023) %>% filter(!is.na(season_start))  %>% pull(sub_field)
+# pivot_select_model_Data_2023 <- harvesting_data %>% filter(year == 2023) %>% filter(!is.na(season_start))  %>% pull(sub_field)
 
 pivot_select_model_Data_2024 <- harvesting_data %>% filter(year == 2024)%>% filter(!is.na(season_start)) %>% pull(sub_field)
 
@@ -832,13 +832,14 @@ extract_CI_data <- function(field_names, harvesting_data, field_CI_data, season)
 # message('2021')
 # Data_2022 <- map(pivot_select_model_Data_2022, ~ extract_CI_data(.x, harvesting_data = harvesting_data, field_CI_data = pivot_stats_long, season = 2022)) %>% list_rbind()
 # message('2022')
-Data_2023 <- map(pivot_select_model_Data_2023, ~ extract_CI_data(.x, harvesting_data = harvesting_data, field_CI_data = pivot_stats_long, season = 2023)) %>% list_rbind()
+# Data_2023 <- map(pivot_select_model_Data_2023, ~ extract_CI_data(.x, harvesting_data = harvesting_data, field_CI_data = pivot_stats_long, season = 2023)) %>% list_rbind()
-message('2023')
+# message('2023')
 Data_2024 <- map(pivot_select_model_Data_2024, ~ extract_CI_data(.x, harvesting_data = harvesting_data, field_CI_data = pivot_stats_long, season = 2024)) %>% list_rbind()
 message('2024')
 
 
-CI_all <- rbind(Data_2023, Data_2024)
+#CI_all <- rbind(Data_2023, Data_2024)
+CI_all <-  Data_2024
 message('CI_all created')
 #CI_all <-  Data_2023
 

r_app/CI_report_dashboard_planet.Rmd

@@ -272,10 +272,29 @@ ci_plot <- function(pivotName){
 
  }
 
-
-
-
 cum_ci_plot <- function(pivotName){
+  # pivotName <- "4042903"
+  # Generate a sequence of dates for the last 12 months
+  end_date <- Sys.Date()
+  start_date <- end_date %m-% months(11) # 11 months ago from end_date
+  date_seq <- seq.Date(from = start_date, to = end_date, by = "month")
+  midpoint_date <- start_date + (end_date - start_date) / 2
+  # Create the empty plot
+  g <- ggplot() +
+    scale_x_date(limits = c(start_date, end_date), date_breaks = "1 month", date_labels = "%m-%Y") +
+    scale_y_continuous(limits = c(0, 4)) +
+    labs(title = paste("14 day rolling MEAN CI rate - Field ", pivotName),
+         x = "Date", y = "CI Rate") +
+    theme(axis.text.x = element_text(angle = 60, hjust = 1),
+          legend.justification = c(1, 0), legend.position = c(1, 0),
+          legend.title = element_text(size = 8),
+          legend.text = element_text(size = 8)) +
+    annotate("text", x = midpoint_date, y = 2, label = "No data available", size = 6, hjust = 0.5)
+  subchunkify(g, 3.2, 10)
+}
+
+
+cum_ci_plot2 <- function(pivotName){
 
   # pivotName = "1.1"
   data_ci <- CI_quadrant %>% filter(Field == pivotName)

r_app/parameters_project.R

@@ -8,15 +8,15 @@ if(project_dir == "chemba"){
   field_boundaries_sf <- st_read(here(data_dir, "pivot.geojson")) %>% dplyr::select(pivot, pivot_quadrant)%>% 
     mutate(sub_area = case_when(pivot %in% c("1.1", "1.2", "1.3", "1.4", "1.6", "1.7", "1.8", "1.9", "1.10", "1.11", "1.12", "1.13", "1.14" , "1.16" , "1.17" , "1.18" , "6.1", "6.2", "DL1.1", "DL1.3") ~ "estate",
                                 TRUE ~ "Cooperative")) 
-  names(field_boundaries_sf) <- c("Field", "subField", "geometry", "sub_area")
+  names(field_boundaries_sf) <- c("field", "sub_field", "geometry", "sub_area")
   
   field_boundaries <- field_boundaries_sf %>% vect()
-  names(field_boundaries) <- c("Field", "subField", "sub_area")
+  names(field_boundaries) <- c("field", "sub_field", "sub_area")
   
   # field_boundaries_merged <- st_read(here(data_dir, "pivot.geojson")) %>% dplyr::select(pivot, pivot_quadrant)   %>% group_by(pivot) %>% summarise() %>% vect()
   
   joined_spans <-st_read(here(data_dir, "span.geojson")) %>% st_transform(crs(field_boundaries_sf))
-  names(joined_spans) <- c("Field", "area", "radius", "spans", "span", "geometry")
+  names(joined_spans) <- c("field", "area", "radius", "spans", "span", "geometry")
   
   
   pivots_dates0 <- readRDS(here(harvest_dir, "harvest_data_new")) %>% filter(
@@ -46,7 +46,7 @@ if(project_dir == "chemba"){
   library(readxl)
   message("Yield data for Xinavane")
   field_boundaries_sf <- st_read(here(data_dir, "pivot.geojson")) %>% dplyr::select(-Pivot)
-  names(field_boundaries_sf) <- c("Field", "subField", "geometry")
+  names(field_boundaries_sf) <- c("field", "sub_field", "geometry")
   
   field_boundaries <- field_boundaries_sf %>% vect() 
   
@@ -57,34 +57,34 @@ if(project_dir == "chemba"){
                               col_types = c("numeric", "text", "skip", "text", "numeric", "numeric", "numeric", "numeric", "date", 
                               "numeric", "skip", "numeric")) %>%
     rename(
-      Year = 1,
+      year = 1,
-      Field = 2,
+      field = 2,
       sub_area = 3,
       hectares = 4,
       tons = 5,
       tcha = 6,
       tchy = 7, 
-      Season_end = 8,
+      season_end = 8,
-      Age = 9,
+      age = 9,
       ratoon = 10
     ) %>%
-    mutate(Season_end = ymd(Season_end),
+    mutate(Season_end = ymd(season_end),
-           Season_start = as.Date(Season_end - (duration(months = Age))),
+           Season_start = as.Date(season_end - (duration(months = age))),
            subField = Field) #don't forget to add 2022 as a year for the 'curent' season
   
   pivots_dates0 <- pivots_dates0 %>%
-    mutate(Year = Year + 6)
+    mutate(year = year + 6)
   
   # Add 6 years to Season_end column
   pivots_dates0 <- pivots_dates0 %>%
-    mutate(Season_end = Season_end + years(6))
+    mutate(season_end = season_end + years(6))
   
   # Add 6 years to Season_start column
   pivots_dates0 <- pivots_dates0 %>%
-    mutate(Season_start = Season_start + years(6))
+    mutate(season_start = season_start + years(6))
   
   
-  harvesting_data <- pivots_dates0 %>% dplyr::select(c("Field","subField", "Year", "Season_start","Season_end", "Age" , "sub_area", "tcha"))
+  harvesting_data <- pivots_dates0 %>% dplyr::select(c("field","sub_field", "year", "season_start","season_end", "age" , "sub_area", "tcha"))
 
   
 } else {