524 lines
24 KiB
Plaintext
524 lines
24 KiB
Plaintext
---
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# title: paste0("CI report week ", week, " - all pivots from ", last_tuesday, " to ", today)
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params:
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ref: word-styles-reference-03.docx
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output:
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word_document:
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reference_docx: !expr file.path("word-styles-reference-03.docx")
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# toc: true
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editor_options:
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chunk_output_type: console
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---
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```{r setup, include=FALSE}
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# Activeer de renv omgeving
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renv::activate()
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# Optioneel: Herstel de omgeving als dat nodig is
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# Je kunt dit commentaar geven als je het normaal niet wilt uitvoeren
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# renv::restore()
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```
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```{r libraries, message=FALSE, warning=FALSE, include=FALSE}
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library(here)
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library(sf)
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library(tidyverse)
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library(tmap)
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library(lubridate)
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library(exactextractr)
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library(zoo)
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library(raster)
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library(rsample)
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library(caret)
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library(CAST)
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```
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```{r directories, message=FALSE, warning=FALSE, include=FALSE}
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laravel_storage_dir <- here("../laravel_app/storage/app")
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data_dir <- here(laravel_storage_dir, "Data")
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extracted_CI_dir <- here(data_dir, "extracted_ci")
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daily_CI_vals_dir <- here(extracted_CI_dir, "daily_vals")
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cumulative_CI_vals_dir <- here(extracted_CI_dir, "cumulative_vals")
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harvest_dir <- here(data_dir, "HarvestData")
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weekly_CI_mosaic <- here(data_dir, "weekly_mosaic")
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s2_dir <- "C:/Users/timon/Resilience BV/4002 CMD App - General/4002 CMD Team/4002 TechnicalData/04 WP2 technical/python/chemba_S2/"
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```
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```{r week, message=FALSE, warning=FALSE, include=FALSE}
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# week <- 5
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# today = "2023-10-08"
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# week <- week(today)
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#today = "2022-08-18"
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today = as.character(Sys.Date())
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week = lubridate::week(Sys.time())
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## week = 26
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title_var <- paste0("CI dashboard week ", week, " - all pivots dashboard using 3x3 meter resolution")
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```
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---
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title: `r title_var`
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---
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This PDF-dashboard shows the status of your fields on a weekly basis. We will show this in different ways:
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1) The first way is with a general overview of field heterogeneity using ‘variation’ – a higher number indicates more differences between plants in the same field.
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2) The second map shows a normal image of the latest week in colour, of the demo fields.
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3) Then come the maps per field, which show the status for three weeks ago, two weeks ago, last week, and this week, as well as a percentage difference map between last week and this week. The percentage difference maps shows the relative difference in growth over the last week, with positive numbers showing growth, and negative numbers showing decline.
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4) Below the maps are graphs that show how each pivot quadrant is doing, measured through the chlorophyll index.
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```{r data, message=FALSE, warning=FALSE, include=FALSE}
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# get latest CI index
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week_minus_1 <- as.numeric(week) -1
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week_minus_2 <- as.numeric(week) -2
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week_minus_3 <- as.numeric(week) -3
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today_minus_1 <- as.character(ymd(today) - 7)
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today_minus_2 <- as.character(ymd(today) - 14)
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today_minus_3 <- as.character(ymd(today) - 21)
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# remove_pivots <- c("1.1", "1.12", "1.8", "1.9", "1.11", "1.14")
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CI_quadrant <- readRDS(here(cumulative_CI_vals_dir,"All_pivots_Cumulative_CI_quadrant_year_v2.rds"))# %>%
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# rename(pivot_quadrant = Field)
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message("STOP - check ci name in layer")
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CI <- brick(here(weekly_CI_mosaic, paste0("week_",week, "_2023.tif"))) %>% subset("CI")
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CI_m1 <- brick(here(weekly_CI_mosaic, paste0("week_",week_minus_1, "_2023.tif"))) %>% subset("CI") #%>% subset("CI")
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CI_m2 <- brick(here(weekly_CI_mosaic, paste0("week_",week_minus_2, "_2023.tif"))) %>% subset("CI") #%>% subset("CI")
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CI_m3 <- brick(here(weekly_CI_mosaic, paste0("week_",week_minus_3, "_2023.tif"))) %>% subset("CI") #%>% subset("CI")
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last_week_dif_raster <- ((CI - CI_m1) / CI_m1) * 100
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last_week_dif_raster_abs <- (CI - CI_m1)
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two_week_dif_raster_abs <- (CI - CI_m2)
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AllPivots0 <-st_read(here(data_dir, "pivot_20210625.geojson"))
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joined_spans <-st_read(here(data_dir, "spans2.geojson")) %>% st_transform(crs(AllPivots0))
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pivots_dates <- readRDS(here(harvest_dir, "harvest_data_new")) %>% filter(
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pivot %in% c("1.1", "1.2", "1.3", "1.4", "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") #without 1.6
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)
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AllPivots <- merge(AllPivots0, pivots_dates, by = "pivot_quadrant") %>%
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rename(pivot = pivot.x) #%>% select(-pivot.y)
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AllPivots_merged <- AllPivots %>%
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group_by(pivot) %>% summarise()
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AllPivots_merged <- st_transform(AllPivots_merged, crs = proj4string(CI))
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AllPivots_merged$pivot <- as.factor(AllPivots_merged$pivot)
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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"))
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pivot_names <- unique(CI_quadrant$pivot)
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```
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```{r eval=FALSE, include=FALSE}
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pivot_stats_q <- cbind(AllPivots0, round(exact_extract(CI, AllPivots0, c("coefficient_of_variation", "mean"), default_value = -9999),2)) %>%
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st_drop_geometry() %>% as_tibble()
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hetero_pivots0 <- merge(AllPivots, pivot_stats_q %>% dplyr::select(-hectares, -radius, -pivot), by = "pivot_quadrant")
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hetero_pivots <- hetero_pivots0 %>% #dplyr::filter(variable %in% "CV") %>%
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mutate(class = case_when(coefficient_of_variation <= 0.001 ~ "Missing data",
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coefficient_of_variation >= 0.002 & coefficient_of_variation < 0.1 ~ "Homogeneous",
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coefficient_of_variation >= 0.1 & coefficient_of_variation < 0.2~ "Somewhat Homogeneous",
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coefficient_of_variation >= 0.2 & coefficient_of_variation < 0.4 ~ "Somewhat Heterogeneous",
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coefficient_of_variation >= 0.4 ~ "Heterogeneous")) %>%
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mutate(class = as.factor(class))
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# hetero_pivots %>% filter(pivot == "1.3")
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hetero_pivots$class <- factor(hetero_pivots$class, levels = c("Missing data", "Homogeneous","Somewhat Homogeneous", "Somewhat Heterogeneous", "Heterogeneous" ))
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# hetero_pivots %>% select(pivot_quadrant, class, Age, coefficient_of_variation , mean) %>% view()
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# hetero_pivots %>% filter(class == "Somewhat Heterogeneous" ) %>% select(pivot_quadrant, class, Age, coefficient_of_variation , mean)
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Mypal <- c('#dcdcdc', '#008000','#8db600','#FFC300','#F22222')
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hetero_plot <- function(data){
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# map <-
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tm_shape(data) + tm_polygons(col = "class", palette=Mypal) +
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tm_text("pivot_quadrant", size = 1/2) +
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tm_layout(main.title=paste0("Homogeneity of pivot quadrants, week ", week, " 2022"),main.title.position = "center")+
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tm_compass(position = c("center", "top")) +
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tm_scale_bar(position = c("center", "top"))
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# print(map, width = 20, units = "cm")
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}
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```
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\newpage
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```{r eval=FALSE, fig.height=7, fig.width=10, message=FALSE, warning=FALSE, include=FALSE}
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hetero_plot(hetero_pivots)
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```
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```{r function, message=FALSE, warning=FALSE, include=FALSE}
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subchunkify <- function(g, fig_height=7, fig_width=5) {
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g_deparsed <- paste0(deparse(
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function() {g}
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), collapse = '')
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sub_chunk <- paste0("
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`","``{r sub_chunk_", floor(runif(1) * 10000), ", fig.height=", fig_height, ", fig.width=", fig_width, ", echo=FALSE}",
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"\n(",
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g_deparsed
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, ")()",
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"\n`","``
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")
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cat(knitr::knit(text = knitr::knit_expand(text = sub_chunk), quiet = TRUE))
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}
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ci_plot <- function(pivotName){
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# pivotName = "2.1"
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pivotShape <- AllPivots_merged %>% terra::subset(pivot %in% pivotName) %>% st_transform(crs(CI))
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age <- AllPivots %>% dplyr::filter(pivot %in% pivotName) %>% st_drop_geometry() %>% dplyr::select(Age) %>% unique()
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AllPivots2 <- AllPivots %>% dplyr::filter(pivot %in% pivotName)
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singlePivot <- CI %>% crop(., pivotShape) %>% mask(., pivotShape)
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singlePivot_m1 <- CI_m1 %>% crop(., pivotShape) %>% mask(., pivotShape)
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singlePivot_m2 <- CI_m2 %>% crop(., pivotShape) %>% mask(., pivotShape)
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singlePivot_m3 <- CI_m3 %>% crop(., pivotShape) %>% mask(., pivotShape)
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abs_CI_last_week <- last_week_dif_raster_abs %>% crop(., pivotShape) %>% mask(., pivotShape)
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abs_CI_two_week <- two_week_dif_raster_abs %>% crop(., pivotShape) %>% mask(., pivotShape)
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planting_date <- pivots_dates %>% dplyr::filter(pivot %in% pivotName) %>% ungroup() %>% dplyr::select(planting_date) %>% unique()
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joined_spans2 <- joined_spans %>% st_transform(crs(pivotShape)) %>% dplyr::filter(pivot %in% pivotName) %>% st_crop(., pivotShape)
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CImap_m2 <- create_CI_map(singlePivot_m2, AllPivots2, joined_spans2, show_legend= T, legend_is_portrait = T, week = week_minus_2, age = age -2)
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CImap_m1 <- create_CI_map(singlePivot_m1, AllPivots2, joined_spans2, show_legend= F, legend_is_portrait = F, week = week_minus_1, age = age -1)
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CImap <- create_CI_map(singlePivot_m1, AllPivots2, joined_spans2, show_legend= F, legend_is_portrait = F, week = week_minus_1, age = age )
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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)
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CI_max_abs_two_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_2, age = age)
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tst <- tmap_arrange(CImap_m2, CImap_m1, CImap,CI_max_abs_last_week, CI_max_abs_two_week, nrow = 1)
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cat('<h1> Pivot', pivotName, '- week', week, '-', age$Age, 'weeks after planting/harvest <h1>')
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print(tst)
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}
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create_CI_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend = F, legend_is_portrait = F, week, age){
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tm_shape(pivot_raster, unit = "m")+
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tm_raster(breaks = c(0,0.5,1,2,3,4,5,6,7,Inf), palette = "RdYlGn",legend.is.portrait = legend_is_portrait ,midpoint = NA) +
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tm_layout(main.title = paste0("\nMax CI week ", week,"\n", age, " weeks old"),
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main.title.size = 0.7, legend.show = show_legend) +
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tm_shape(pivot_shape) +
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tm_borders(lwd = 3) + tm_text("pivot_quadrant", size = 1/2) +
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tm_shape(pivot_spans) + tm_borders(lwd = 0.5, alpha=0.5)
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}
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create_CI_diff_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend = F, legend_is_portrait = F, week_1, week_2, age){
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tm_shape(pivot_raster, unit = "m")+
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tm_raster(breaks = c(-3,-2,-1,0,1,2, 3), palette = "RdYlGn",legend.is.portrait = legend_is_portrait ,midpoint = 0, title = "CI difference") +
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tm_layout(main.title = paste0("CI change week ", week_1, "- week ",week_2, "\n", age," weeks old"),
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main.title.size = 0.7, legend.show = show_legend) +
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tm_shape(pivot_shape) +
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tm_borders(lwd = 3) + tm_text("pivot_quadrant", size = 1/2) +
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tm_shape(pivot_spans) + tm_borders(lwd = 0.5, alpha=0.5)
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}
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cum_ci_plot <- function(pivotName){
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# pivotName = "1.17"
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data_ci <- CI_quadrant %>% filter(pivot == pivotName)
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data_ci2 <- data_ci %>% mutate(CI_rate = cumulative_CI/DOY,
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week = week(Date))%>% group_by(Field) %>%
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mutate(mean_rolling10 = rollapplyr(CI_rate , width = 10, FUN = mean, partial = TRUE)) #%>%
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date_preperation_perfect_pivot <- data_ci2 %>% group_by(season) %>% summarise(min_date = min(Date),
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max_date = max(Date),
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days = max_date - min_date)
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perfect_pivot_raw <- CI_quadrant %>% group_by(pivot) %>% filter(pivot == "5.1" & season == "2022") %>%
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group_by(DOY) %>% summarise(cumulative_CI = mean(cumulative_CI)) %>% mutate(CI_rate = cumulative_CI/DOY) %>%
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mutate(mean_rolling10 = rollapplyr(CI_rate , width = 10, FUN = mean, partial = TRUE))
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unique_seasons <- unique(date_preperation_perfect_pivot$season)
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if(length(unique_seasons) == 3) {
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unique_seasons <- unique_seasons[c(2,3)]
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} else {
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unique_seasons <- unique_seasons
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}
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perfect_pivot <- perfect_pivot_raw
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for (s in unique_seasons) {
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season_dates <- seq(from = date_preperation_perfect_pivot$min_date[date_preperation_perfect_pivot$season == s],
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to = date_preperation_perfect_pivot$min_date[date_preperation_perfect_pivot$season == s] + nrow(perfect_pivot_raw) - 1,
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by = "1 day")
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col_name <- as.character(s)
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perfect_pivot <- dplyr::bind_cols(perfect_pivot, tibble(!!col_name := season_dates))
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}
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perfect_pivot <- perfect_pivot %>%
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pivot_longer(cols = -c("DOY", "cumulative_CI", "CI_rate", "mean_rolling10"),
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names_to = "season", values_to = "Date")
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g <- ggplot() +
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facet_wrap(~season, scales = "free_x") +
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geom_line(data= data_ci2 %>% filter(season %in% unique_seasons), aes(Date, mean_rolling10, col = Field)) +
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geom_line(data= perfect_pivot, aes(Date , mean_rolling10, col = "Model CI (p5.1 Data 2022, \n date x axis is fictive)"), lty="11",size=1) +
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labs(title = paste("14 day rolling MEAN CI rate - Pivot ", pivotName))+
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# scale_y_continuous(limits=c(0.5,3), breaks = seq(0.5, 3, 0.5))+
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scale_x_date(date_breaks = "1 month", date_labels = "%m-%Y") +
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theme(axis.text.x = element_text(angle = 60, hjust = 1))
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# options(repr.plot.width = 2, repr.plot.height = 2)
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subchunkify(g, 3.2, 10)
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}
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```
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```{r eval=FALSE, fig.height=7.2, fig.width=10, message=FALSE, warning=FALSE, include=FALSE}
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RGB_raster <- list.files(paste0(s2_dir,week),full.names = T, pattern = ".tiff", recursive = TRUE)[1] #use pattern = '.tif$' or something else if you have multiple files in this folder
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RGB_raster <- brick(RGB_raster)
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# RGB_raster <- brick(here("planet", paste0("week_",week, ".tif")))
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tm_shape(RGB_raster, unit = "m") + tm_rgb(r=1, g=2, b=3, max.value = 255) +
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tm_layout(main.title = paste0("RGB image of the fields - week ", week),
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main.title.position = 'center') +
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tm_scale_bar(position = c("right", "top"), text.color = "white") +
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tm_compass(position = c("right", "top"), text.color = "white") +
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tm_shape(AllPivots)+ tm_borders( col = "white") +
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tm_text("pivot_quadrant", size = .6, col = "white")
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```
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\newpage
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```{r eval=FALSE, fig.height=7.2, fig.width=10, message=FALSE, warning=FALSE, include=FALSE}
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tm_shape(CI, unit = "m")+
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tm_raster(breaks = c(0,0.5,1,2,3,4,5,6,7,Inf), palette = "RdYlGn", midpoint = NA,legend.is.portrait = F) +
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tm_layout(legend.outside = TRUE,legend.outside.position = "bottom",legend.show = T, main.title = "Overview all fields (CI)")+
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tm_scale_bar(position = c("right", "top"), text.color = "black") +
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tm_compass(position = c("right", "top"), text.color = "black") +
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tm_shape(AllPivots)+ tm_borders( col = "black") +
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tm_text("pivot_quadrant", size = .6, col = "black")
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tm_shape(last_week_dif_raster_abs, unit = "m")+
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tm_raster(breaks = c(-3,-2,-1,0,1,2, 3), palette = "RdYlGn", midpoint = NA,legend.is.portrait = F) +
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tm_layout(legend.outside = TRUE,legend.outside.position = "bottom",legend.show = F, main.title = "Overview all fields - CI difference")+
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tm_scale_bar(position = c("right", "top"), text.color = "black") +
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tm_compass(position = c("right", "top"), text.color = "black") +
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tm_shape(AllPivots)+ tm_borders( col = "black") +
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tm_text("pivot_quadrant", size = .6, col = "black")
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tm_shape(last_week_dif_raster, unit = "m")+
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tm_raster(breaks = c(-Inf,-50,-25,-5,5,25, Inf), palette = "RdYlGn", midpoint = NA,legend.is.portrait = T) +
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tm_layout(legend.outside = TRUE,legend.outside.position = "right",legend.show = F, main.title = "Overview all fields - CI difference %")+
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tm_scale_bar(position = c("right", "top"), text.color = "black") +
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tm_compass(position = c("right", "top"), text.color = "black") +
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tm_shape(AllPivots)+ tm_borders( col = "black") +
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tm_text("pivot_quadrant", size = .6, col = "black")
|
||
```
|
||
|
||
|
||
```{r plots_ci, echo=FALSE, fig.height=3.7, fig.width=10, message=FALSE, warning=FALSE, results='asis'}
|
||
# ci_plot("1.17")
|
||
# cum_ci_plot("1.17")
|
||
# x = 1
|
||
# for(j in x){
|
||
# coops <- Chemba_pivot_owners %>% filter(`OWNER update 18/6/2022` %in% c("chapo", "Lambane", "Canhinbe" ))
|
||
pivots <- AllPivots_merged %>% filter(pivot != "1.17")
|
||
|
||
#%>% filter(pivot %in% c( "2.1", "2.2", "2.3", "2.4", "3.1", "3.2", "3.3", "4.4", "4.6" , "4.3", "4.5", "4.2", "4.1", "5.1", "5.2", "5.3", "5.4", "7.1", "7.2", "7.3" , "7.4", "7.5", "7.6" ))
|
||
|
||
for(i in pivots$pivot) {
|
||
ci_plot(i)
|
||
cum_ci_plot(i)
|
||
}
|
||
|
||
# lapply(pivots, function(pivot) {
|
||
# ci_plot(pivot)
|
||
# cum_ci_plot(pivot)
|
||
# })
|
||
|
||
#cat("\\newpage")
|
||
# }
|
||
```
|
||
|
||
```{r eval=FALSE, fig.height=10, fig.width=14, include=FALSE}
|
||
CI_all2 <- readRDS(here(cumulative_CI_vals_dir, "All_pivots_Cumulative_CI_whole_pivot_year.rds")) %>%
|
||
mutate(#line = substr(pivot, 1 , 1),
|
||
season = as.factor(season))
|
||
|
||
pivots_dates <- readRDS(here(harvest_dir, "harvest_data_new")) #%>%
|
||
|
||
pvt_age <- pivots_dates %>% ungroup() %>% select(pivot, Age) %>% unique()
|
||
CI_all2 <- left_join(CI_all2, pvt_age , by = "pivot") %>% mutate(month = plyr::round_any((Age/4),2)) %>%
|
||
mutate(month = case_when(month > 16 ~ 18,
|
||
TRUE ~month)) %>%
|
||
group_by(pivot) %>% filter(Age == max(Age)) %>% ungroup()
|
||
|
||
CI_all2$season <- ordered(CI_all2$season, levels = c("Data_2021", "Data_2022"))
|
||
# CI_all2 <- CI_all2 %>% mutate(season_order = as.integer(season))
|
||
latest_model <- CI_all2 %>% group_by(pivot) %>% filter(season =="Data_2022")
|
||
# latest_model <- CI_all2 %>% group_by(pivot) %>% arrange(season, desc(DOY)) %>% slice(1)
|
||
|
||
# CI_all2 <- CI_all %>% group_by(pivot, DOY ) %>% mutate(pivot_cumulative_CI = mean(cumulative_CI))
|
||
# label_data <- CI_all2 %>% group_by(pivot) %>% arrange(season, desc(cumulative_CI)) %>% slice(1) %>% mutate(label = paste(pivot, " - ", round(cumulative_CI)))
|
||
label_data <- latest_model %>% arrange(season, desc(cumulative_CI)) %>% slice(1) %>% mutate(label = paste(pivot, " - ", round(cumulative_CI)))
|
||
|
||
max_day <- label_data %>% group_by(pivot) %>% summarise(max_day = max(DOY))
|
||
|
||
ggplot(data= CI_all2%>% filter(season =="Data_2022"), aes(DOY, cumulative_CI, col = pivot)) +
|
||
facet_wrap(~month) +
|
||
geom_line() +
|
||
# scale_y_continuous(breaks = seq(0, max(label_data$cumulative_CI) + 100, by = 100)) +
|
||
labs(title = "Cumulative CI values over the year per pivot, split per 2-month age (rounded down)", x = "Days after harvest/planting (scale is per 2 weeks)", y = "Cumulative CI value",
|
||
color = "Line") +
|
||
geom_label_repel(data = label_data %>% filter(model %in% latest_model$model), aes(DOY, cumulative_CI, label = label), box.padding = 1,
|
||
# ylim = c(1300, NA),
|
||
max.overlaps = Inf
|
||
# segment.linetype = 4,
|
||
|
||
# segment.curvature = -1e-20,
|
||
# arrow = arrow(length = unit(0.015, "npc"))
|
||
) +
|
||
theme(legend.position="right", legend.text = element_text(size=8), legend.title = element_text(size = 8),
|
||
plot.title = element_text(size=19)) +
|
||
# geom_smooth()+
|
||
guides(fill = guide_legend(nrow=2,byrow=TRUE)) +
|
||
scale_y_continuous(breaks=seq(0,max(label_data$cumulative_CI),100)) +
|
||
scale_x_continuous(breaks=seq(0,max(max_day$max_day),30)) + theme(axis.text.x = element_text(angle = 90)) +
|
||
labs(x = "Weeks")+
|
||
theme(legend.position = "none")
|
||
|
||
```
|
||
|
||
The below table shows estimates of the biomass if you would harvest them now.
|
||
```{r eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
|
||
CI_quadrant <- readRDS(here(cumulative_CI_vals_dir,"All_pivots_Cumulative_CI_quadrant_year_v2.rds")) %>%
|
||
rename( pivot_quadrant = Field)#All_pivots_Cumulative_CI.rds
|
||
|
||
pivots_dates0 <- readRDS(here(harvest_dir, "harvest_data_new")) %>% ungroup() %>% unique() %>%
|
||
dplyr::select(pivot, pivot_quadrant, Tcha_2021, Tcha_2022 ) %>%
|
||
pivot_longer(cols = c("Tcha_2021", "Tcha_2022"), names_to = "Tcha_Year", values_to = "Tcha") %>%
|
||
filter(Tcha > 50)
|
||
|
||
CI_and_yield <- left_join(CI_quadrant , pivots_dates0, by = c("pivot", "pivot_quadrant")) %>% filter(!is.na(Tcha)) %>%
|
||
group_by(pivot_quadrant) %>% slice(which.max(DOY)) %>%
|
||
dplyr::select(pivot, pivot_quadrant, Tcha_Year, Tcha, cumulative_CI, DOY) %>%
|
||
mutate(CI_per_day = cumulative_CI/ DOY)
|
||
|
||
set.seed(20)
|
||
CI_and_yield_split <- initial_split(CI_and_yield, prop = 0.75, strata = pivot_quadrant)
|
||
CI_and_yield_test <- training(CI_and_yield_split)
|
||
CI_and_yield_validation <- testing(CI_and_yield_split)
|
||
|
||
|
||
predictors <- c( "cumulative_CI" , "DOY" ,"CI_per_day" )
|
||
response <- "Tcha"
|
||
CI_and_yield_test <- as.data.frame(CI_and_yield_test)
|
||
|
||
ctrl <- trainControl(method="cv",
|
||
savePredictions = TRUE,
|
||
allowParallel= TRUE,
|
||
number = 5,
|
||
verboseIter = TRUE)
|
||
|
||
set.seed(202)
|
||
model_ffs_rf <- ffs( CI_and_yield_test[,predictors],
|
||
CI_and_yield_test[,response],
|
||
method="rf" ,
|
||
trControl=ctrl,
|
||
importance=TRUE,
|
||
withinSE = TRUE,
|
||
tuneLength = 5,
|
||
na.rm = TRUE
|
||
)
|
||
|
||
pred_ffs_rf <-
|
||
predict(model_ffs_rf, newdata = CI_and_yield_validation) %>% as.data.frame() %>% rename(predicted_Tcha = ".") %>% mutate(
|
||
pivot_quadrant = CI_and_yield_validation$pivot_quadrant,
|
||
pivot = CI_and_yield_validation$pivot,
|
||
Age_days = CI_and_yield_validation$DOY,
|
||
total_CI = round(CI_and_yield_validation$cumulative_CI, 0),
|
||
predicted_Tcha = round(predicted_Tcha, 0)
|
||
) %>% dplyr::select(pivot , pivot_quadrant, Age_days, total_CI, predicted_Tcha) %>%
|
||
left_join(., CI_and_yield_validation, by = c("pivot", "pivot_quadrant")) %>%
|
||
filter(Age_days > 250)
|
||
|
||
ggplot(pred_ffs_rf, aes(y = predicted_Tcha , x = Tcha , col = pivot )) +
|
||
geom_point() +geom_abline() +
|
||
scale_x_continuous(limits = c(50, 160))+
|
||
scale_y_continuous(limits = c(50, 160)) +
|
||
labs(title = "Model trained and tested on historical results - RF")
|
||
|
||
prediction_2023 <- CI_quadrant %>% filter(season == "Data_2023") %>% group_by(pivot_quadrant) %>% slice(which.max(DOY))%>%
|
||
mutate(CI_per_day = cumulative_CI/ DOY)
|
||
|
||
pred_rf_2023 <- predict(model_ffs_rf, newdata=prediction_2023) %>%
|
||
as.data.frame() %>% rename(predicted_Tcha_2023 = ".") %>% mutate(pivot_quadrant = prediction_2023$pivot_quadrant,
|
||
pivot = prediction_2023$pivot,
|
||
Age_days = prediction_2023$DOY,
|
||
total_CI = round(prediction_2023$cumulative_CI,0),
|
||
predicted_Tcha_2023 = round(predicted_Tcha_2023, 0)) %>%
|
||
filter(Age_days > 300) %>%
|
||
dplyr::select(pivot ,pivot_quadrant, Age_days, total_CI, predicted_Tcha_2023)%>%
|
||
mutate(CI_per_day = round(total_CI/ Age_days, 1))
|
||
|
||
ggplot(pred_rf_2023, aes(total_CI , predicted_Tcha_2023 , col = pivot )) +
|
||
geom_point() + labs(title = "2023 data (still to be harvested) - Fields over 300 days old")
|
||
knitr::kable(pred_rf_2023)
|
||
|
||
```
|
||
|
||
```{r eval=FALSE, include=FALSE}
|
||
|
||
model_CI <-lm(
|
||
formula = cumulative_CI ~ DOY ,
|
||
data = CI_and_yield_test
|
||
)
|
||
pivot_ = "4.4"
|
||
df4 = data.frame(pivot_, 365, NA)
|
||
names(df4)=c("pivot", "DOY", "cumulative_CI")
|
||
a <- CI_all %>% filter(season == "Data_2022", pivot == pivot_) %>% ungroup() %>% select(pivot, DOY, cumulative_CI) %>%
|
||
complete(DOY = seq.int(max(DOY), 365, 1), pivot = pivot_) %>% arrange(DOY) # complete(DOY = seq.int(max(DOY), 365, 1)) # rbind(.,df4)
|
||
|
||
b <- predict(model_CI, a) %>%
|
||
as.data.frame() %>% slice(which.max(.)) %>% rename(cumulative_CI = ".") %>% mutate(DOY = 365)
|
||
|
||
pred_CI_2022 <- predict(model, newdata=b ) %>%
|
||
as.data.frame() %>% rename(predicted_Tcha_365 = ".") %>% mutate(pivot = df4$pivot,
|
||
predicted_Tcha_365 = round(predicted_Tcha_365, 0),
|
||
Age_days = df4$DOY)
|
||
|
||
pred_CI_2022
|
||
```
|
||
|