forgot to save last R file
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Timon
parent
6c00c57b37
commit
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@ -33,6 +33,7 @@ laravel_storage_dir <- here("laravel_app","storage","app")
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#preparing directories
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project = "chemba"
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new_project_question = TRUE
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planet_tif_folder <- here(laravel_storage_dir, project, "merged_tif")
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merged_final <- here(laravel_storage_dir, project,"merged_final_tif")
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@ -52,6 +53,7 @@ dir.create(here(cumulative_CI_vals_dir))
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dir.create(here(weekly_CI_mosaic))
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dir.create(here(daily_vrt))
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dir.create(merged_final)
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dir.create(harvest_dir)
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@ -257,27 +259,14 @@ extract_rasters_daily <- function(file, field_geojson, quadrants = TRUE, save_di
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# pivot_sf_q <- st_read(here("..", "Data", "pivot_20210625.geojson")) %>% dplyr::select(pivot, pivot_quadrant) %>% vect()
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pivot_sf <- st_read(here(data_dir, "pivot_20210625.geojson")) %>% dplyr::select(pivot, pivot_quadrant) %>% group_by(pivot) %>% summarise() %>% vect()
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message("pivot loaded")
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if (!file.exists(here(cumulative_CI_vals_dir,"combined_CI_data.rds"))) {
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print("combined_CI_data.rds does not exist. Running this part of the script...")
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}
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raster_files_NEW <- list.files(merged_final,full.names = T, pattern = ".tif")
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filtered_files <- map(dates$days_filter, ~ raster_files_NEW[grepl(pattern = .x, x = raster_files_NEW)]) %>%
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compact() %>%
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flatten_chr()
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walk(filtered_files, extract_rasters_daily, field_geojson= pivot_sf_q, quadrants = TRUE, daily_CI_vals_dir)
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message("after walk")
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pivots_dates0 <- readRDS(here(harvest_dir, "harvest_data_new")) %>% filter(
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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",
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"1.14" , "1.16" , "1.17" , "1.18" ,"2.1", "2.2", "2.3" , "2.4", "2.5", "3.1", "3.2", "3.3",
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"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")
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)
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# pivots_dates_long <- pivots_dates0 %>%
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# select(c("pivot_quadrant", "season_start_2021", "season_end_2021", "season_start_2022", "season_end_2022", "season_start_2023", "season_end_2023")) %>%
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# pivot_longer(cols = c("season_start_2021", "season_end_2021", "season_start_2022", "season_end_2022", "season_start_2023", "season_end_2023")) %>%
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# separate(pivot_quadrant, into = c("name", "Year"), sep = "\\.")
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harvesting_data <- pivots_dates0 %>%
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select(c("pivot_quadrant", "season_start_2021", "season_end_2021", "season_start_2022", "season_end_2022", "season_start_2023", "season_end_2023", "season_start_2024", "season_end_2024")) %>%
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pivot_longer(cols = starts_with("season"), names_to = "Year", values_to = "value") %>%
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@ -285,32 +274,77 @@ harvesting_data <- pivots_dates0 %>%
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mutate(name = str_to_title(name)) %>%
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pivot_wider(names_from = name, values_from = value) %>%
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rename(Field = pivot_quadrant)
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# extracted_values <- list.files("C:\\Users\\timon\\Resilience BV\\4002 CMD App - General\\4002 CMD Team\\4002 TechnicalData\\04 WP2 technical\\DetectingSpotsR\\EcoFarm\\planet\\extracted",
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# pattern ="_quadrant", full.names = TRUE)
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extracted_values <- list.files(here(daily_CI_vals_dir), full.names = TRUE)
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#get CI values for this week only
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pivots_data_present <- unique(pivots_dates0$pivot_quadrant)
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quadrant_list <- pivots_data_present
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#If run for the firsttime, it will extract all data since the start and put into a table.rds. otherwise it will only add on to the existing table.
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if (new_project_question == TRUE) {
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print("combined_CI_data.rds does not exist. Preparing combined_CI_data.rds file for all available images.")
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walk(raster_files_NEW, extract_rasters_daily, field_geojson= pivot_sf_q, quadrants = TRUE, daily_CI_vals_dir)
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extracted_values <- list.files(here(daily_CI_vals_dir), full.names = TRUE)
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pivot_stats <- extracted_values %>%
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map(readRDS) %>% list_rbind() %>%
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group_by(pivot_quadrant) %>%
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summarise(across(everything(), ~ first(na.omit(.))))
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saveRDS(pivot_stats, here(cumulative_CI_vals_dir,"combined_CI_data.rds")) #used to save the rest of the data into one file
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pivot_stats2 <- pivot_stats
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print("All CI values extracted from allhistoric images")
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} else {
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print("combined_CI_data.rds exists, adding the latest image data to the table.")
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filtered_files <- map(dates$days_filter, ~ raster_files_NEW[grepl(pattern = .x, x = raster_files_NEW)]) %>%
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compact() %>%
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flatten_chr()
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walk(filtered_files, extract_rasters_daily, field_geojson= pivot_sf_q, quadrants = TRUE, daily_CI_vals_dir)
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extracted_values <- list.files(daily_CI_vals_dir, full.names = TRUE)
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extracted_values <- map(dates$days_filter, ~ extracted_values[grepl(pattern = .x, x = extracted_values)]) %>%
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compact() %>%
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flatten_chr()
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pivot_stats <- extracted_values %>%
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map(readRDS) %>% list_rbind() %>%
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group_by(pivot_quadrant) %>%
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summarise(across(everything(), ~ first(na.omit(.))))
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combined_CI_data <- readRDS(here(cumulative_CI_vals_dir,"combined_CI_data.rds")) %>% drop_na(pivot_quadrant)
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pivot_stats2 <- bind_rows(pivot_stats, combined_CI_data)
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print("All CI values extracted from latest 7 images.")
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}
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#extracted_values <- map(dates$days_filter, ~ extracted_values[grepl(pattern = .x, x = extracted_values)]) %>%
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# compact() %>%
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# flatten_chr()
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#combine them into one df
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pivot_stats <- extracted_values %>%
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map(readRDS) %>% list_rbind() %>%
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group_by(pivot_quadrant) %>%
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summarise(across(everything(), ~ first(na.omit(.))))
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#saveRDS(pivot_stats, here(cumulative_CI_vals_dir,"combined_CI_data.rds")) #used to save the rest of the data into one file
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#load historic CI data and update it with last week of CI data
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combined_CI_data <- readRDS(here(cumulative_CI_vals_dir,"combined_CI_data.rds")) %>% drop_na(pivot_quadrant)
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pivot_stats2 <- bind_rows(pivot_stats, combined_CI_data)
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# combined_CI_data <- readRDS(here(cumulative_CI_vals_dir,"combined_CI_data.rds")) %>% drop_na(pivot_quadrant)
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# pivot_stats2 <- bind_rows(pivot_stats, combined_CI_data)
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# pivot_stats2 <- purrr::map(list.files(here(daily_CI_vals_dir), full.names = TRUE, pattern = "quadrant"), readRDS) %>% list_rbind() %>% group_by(pivot_quadrant) %>%
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# summarise(across(everything(), ~ first(na.omit(.))))
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pivots_data_present <- unique(pivots_dates0$pivot_quadrant)
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quadrant_list <- pivots_data_present
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# gather data into long format for easier calculation and visualisation
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pivot_stats_long <- pivot_stats2 %>%
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@ -321,7 +355,8 @@ pivot_stats_long <- pivot_stats2 %>%
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drop_na(c("value","Date")) %>%
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mutate(value = as.numeric(value))%>%
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filter_all(all_vars(!is.infinite(.)))%>%
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rename(Field = pivot_quadrant)
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rename(Field = pivot_quadrant) %>%
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unique()
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# #2021
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pivots_dates_Data_2021 <- pivots_dates0 %>% filter(!is.na(season_start_2021))
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