worked with timon to harmonize report output

r_app/2_CI_data_prep.R

@@ -172,7 +172,7 @@ missing_pixels_count <- layer_5_list %>% global(., fun="notNA") %>%
     total_pixels = total_pix_area$notNA,
     missing_pixels_percentage = round(100 -((notNA/total_pix_area$notNA)*100)),
     thres_5perc = as.integer(missing_pixels_percentage < 5),
-    thres_40perc = as.integer(missing_pixels_percentage < 40)
+    thres_40perc = as.integer(missing_pixels_percentage < 45)
     )
 
 # cloud_perc_list <- freq(layer_5_list, usenames = TRUE) %>%
@@ -204,7 +204,7 @@ if(sum(missing_pixels_count$thres_5perc)>1){
 }else if(sum(missing_pixels_count$thres_40perc)>1){
   message("More than 1 image contains clouds, composite made of <40% cloud cover images")
   
-  cloudy_rasters_list <- vrt_list[index_5perc]
+  cloudy_rasters_list <- vrt_list[index_40perc]
   
   rsrc <- sprc(cloudy_rasters_list)
   x <- mosaic(rsrc, fun = "max")
@@ -213,7 +213,7 @@ if(sum(missing_pixels_count$thres_5perc)>1){
 }else if(sum(missing_pixels_count$thres_40perc)==1){
   message("Only 1 image available but contains clouds")
   
-  x <- rast(vrt_list[index_5perc[1]])
+  x <- rast(vrt_list[index_40perc[1]])
   names(x) <-  c("CI")
   
 }else{

r_app/CI_report_dashboard_planet.Rmd

@@ -7,7 +7,7 @@ params:
 output:
   word_document:
     reference_docx: !expr file.path("word-styles-reference-var1.docx")
-    # toc: true
+    toc: true
 editor_options:
   chunk_output_type: console
 ---
@@ -70,7 +70,7 @@ week <- week(today)
 #week = lubridate::week(Sys.time())
 ## week = 26
 #title_var <- paste0("CI dashboard week ", week, " - all pivots dashboard using 3x3 meter resolution")
-subtitle_var <- paste("Report generated on", today)
+subtitle_var <- paste("Report generated on", Sys.Date())
 
 today_minus_1 <- as.character(ymd(today) - 7)
 today_minus_2 <- as.character(ymd(today) - 14)
@@ -118,6 +118,9 @@ last_week_dif_raster_abs <- (CI - CI_m1)
 three_week_dif_raster_abs <- (CI - CI_m3)
 
 AllPivots0 <-st_read(here(data_dir, "pivot_20210625.geojson"))
+AllPivots0$pivot  <- factor(AllPivots0$pivot, levels = 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" ,"2.1", "2.2", "2.3" , "2.4", "2.5", "3.1", "3.2", "3.3", "4.1", "4.2", "4.3", "4.4", "4.5", "4.6", "5.1" ,"5.2", "5.3", "5.4", "6.1", "6.2", "DL1.1", "DL1.3"))
+
+
 joined_spans <-st_read(here(data_dir, "spans2.geojson")) %>% st_transform(crs(AllPivots0))
 
 pivots_dates <- readRDS(here(harvest_dir, "harvest_data_new")) %>% filter(
@@ -125,6 +128,8 @@ pivots_dates <- readRDS(here(harvest_dir, "harvest_data_new")) %>% filter(
                "1.14" , "1.16" , "1.17" , "1.18" ,"2.1", "2.2", "2.3" , "2.4", "2.5", "3.1", "3.2", "3.3",
                "4.1", "4.2", "4.3", "4.4", "4.5", "4.6", "5.1" ,"5.2", "5.3", "5.4", "6.1", "6.2", "DL1.1", "DL1.3") #without 1.6
 )
+pivots_dates$pivot  <- factor(pivots_dates$pivot, levels = 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" ,"2.1", "2.2", "2.3" , "2.4", "2.5", "3.1", "3.2", "3.3", "4.1", "4.2", "4.3", "4.4", "4.5", "4.6", "5.1" ,"5.2", "5.3", "5.4", "6.1", "6.2", "DL1.1", "DL1.3"))
+
 AllPivots <- merge(AllPivots0, pivots_dates, by = "pivot_quadrant") %>%
   rename(pivot = pivot.x) #%>% select(-pivot.y)
 
@@ -133,9 +138,6 @@ AllPivots_merged <- AllPivots %>%
 
 AllPivots_merged <- st_transform(AllPivots_merged, crs = proj4string(CI))
 
-AllPivots_merged$pivot <- as.factor(AllPivots_merged$pivot)
-AllPivots_merged$pivot  <- ordered(AllPivots_merged$pivot, levels = 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" ,"2.1", "2.2", "2.3" , "2.4", "2.5", "3.1", "3.2", "3.3", "4.1", "4.2", "4.3", "4.4", "4.5", "4.6", "5.1" ,"5.2", "5.3", "5.4", "6.1", "6.2", "DL1.1", "DL1.3"))
-
 pivot_names <- unique(CI_quadrant$pivot)
 
 ```
@@ -243,7 +245,7 @@ ci_plot <- function(pivotName){
 
    CImap_m2 <- create_CI_map(singlePivot_m2, AllPivots2,  joined_spans2, show_legend= T, legend_is_portrait = T, week = week_minus_2, age = age  -2)
    CImap_m1 <- create_CI_map(singlePivot_m1, AllPivots2,  joined_spans2, show_legend= F, legend_is_portrait = F, week = week_minus_1, age = age  -1)
-   CImap <- create_CI_map(singlePivot, AllPivots2,  joined_spans2, show_legend= F, legend_is_portrait = F, week = week_minus_1, age = age )
+   CImap <- create_CI_map(singlePivot, AllPivots2,  joined_spans2, show_legend= F, legend_is_portrait = F, week = week, age = age )
 
 
    CI_max_abs_last_week <- create_CI_diff_map(abs_CI_last_week,AllPivots2,  joined_spans2,  show_legend = T, legend_is_portrait = T, week_1 = week, week_2 = week_minus_1, age = age)
@@ -282,6 +284,8 @@ cum_ci_plot <- function(pivotName){
 
    if(length(unique_seasons) == 3) {
      unique_seasons <- unique_seasons[c(2,3)]
+   } else if(length(unique_seasons) == 4) {
+     unique_seasons <- unique_seasons[c(3,4)]
    } else {
      unique_seasons <- unique_seasons
    }
@@ -307,7 +311,11 @@ cum_ci_plot <- function(pivotName){
           labs(title = paste("14 day rolling MEAN CI rate - Pivot ", pivotName))+
           # scale_y_continuous(limits=c(0.5,3), breaks = seq(0.5, 3, 0.5))+
           scale_x_date(date_breaks = "1 month", date_labels =  "%m-%Y") +
-          theme(axis.text.x = element_text(angle = 60, hjust = 1))
+          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)) +
+          guides(color = guide_legend(nrow = 2, byrow = TRUE))
   # options(repr.plot.width = 2, repr.plot.height = 2)
   subchunkify(g, 3.2, 10)
 
@@ -343,28 +351,22 @@ tm_shape(CI, unit = "m")+
   tm_shape(AllPivots)+ tm_borders( col = "black") +
     tm_text("pivot_quadrant", size = .6, col = "black")
 
-
-
-# tm_shape(last_week_dif_raster_abs, unit = "m")+
-#     tm_raster(breaks = c(-3,-2,-1,0,1,2, 3), palette = "RdYlGn", midpoint = NA,legend.is.portrait = F) +
-#   tm_layout(legend.outside = TRUE,legend.outside.position = "bottom",legend.show = T, main.title = "Overview all fields - CI difference")+
-#    tm_scale_bar(position = c("right", "top"), text.color = "black") +
-#
-#    tm_compass(position = c("right", "top"), text.color = "black") +
-#   tm_shape(AllPivots)+ tm_borders( col = "black") +
-#     tm_text("pivot_quadrant", size = .6, col = "black")
-
-
-
-# tm_shape(last_week_dif_raster, unit = "m")+
-#     tm_raster(breaks = c(-Inf,-50,-25,-5,5,25, Inf), palette = "RdYlGn", midpoint = NA,legend.is.portrait = T) +
-#   tm_layout(legend.outside = TRUE,legend.outside.position = "right",legend.show = F, main.title = "Overview all fields - CI difference %")+
-#    tm_scale_bar(position = c("right", "top"), text.color = "black") +
-#
-#    tm_compass(position = c("right", "top"), text.color = "black") +
-#   tm_shape(AllPivots)+ tm_borders( col = "black") +
-#     tm_text("pivot_quadrant", size = .6, col = "black")
 ```
+\newpage
+
+```{r echo=FALSE, fig.height=7.3, fig.width=9, message=FALSE, warning=FALSE}
+
+ tm_shape(last_week_dif_raster_abs, unit = "m")+
+     tm_raster(breaks = c(-3,-2,-1,0,1,2, 3), palette = "RdYlGn", midpoint = NA,legend.is.portrait = F) +
+   tm_layout(legend.outside = TRUE,legend.outside.position = "bottom",legend.show = T, main.title = "Overview all fields - CI difference")+
+    tm_scale_bar(position = c("right", "top"), text.color = "black") +
+
+    tm_compass(position = c("right", "top"), text.color = "black") +
+   tm_shape(AllPivots)+ tm_borders( col = "black") +
+     tm_text("pivot_quadrant", size = .6, col = "black")
+
+```
+
 # Estate fields
 \newpage