adjusted all functions and cleaned scripts incl rmd file
This commit is contained in:
Timon
parent
611bc17017
commit
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@ -2,8 +2,8 @@
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params:
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ref: "word-styles-reference-var1.docx"
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output_file: CI_report.docx
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report_date: "2024-04-18"
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data_dir: "Sony"
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report_date: "2024-08-28"
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data_dir: "Chemba"
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mail_day: "Wednesday"
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borders: TRUE
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output:
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@ -50,6 +50,9 @@ library(caret)
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library(randomForest)
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library(CAST)
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source(here("r_app/report_utils.R"))
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# source("report_utils.R")
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# Define the log file path
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log_file <- here("laravel_app/storage/app/rmd_log.txt")
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@ -202,197 +205,6 @@ AllPivots0 <- field_boundaries_sf
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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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create_CI_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend = F, legend_is_portrait = F, week, age, borders = FALSE){
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map <- 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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if (borders) {
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map <- map +
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tm_shape(pivot_shape) +
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tm_borders(lwd = 3) +
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tm_text("sub_field", size = 1/2) +
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tm_shape(pivot_spans) +
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tm_borders(lwd = 0.5, alpha = 0.5)
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}
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return(map)
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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, borders = TRUE){
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map <- 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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if (borders) {
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map <- map +
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tm_shape(pivot_shape) +
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tm_borders(lwd = 3) +
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tm_text("sub_field", size = 1/2) +
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tm_shape(pivot_spans) +
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tm_borders(lwd = 0.5, alpha = 0.5)
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}
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return(map)
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}
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ci_plot <- function(pivotName){
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# pivotName = "1.1"
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pivotShape <- AllPivots0 %>% terra::subset(field %in% pivotName) %>% st_transform(crs(CI))
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age <- harvesting_data %>% dplyr::filter(field %in% pivotName) %>% sort("year") %>% tail(., 1) %>% dplyr::select(age) %>% unique() %>% pull() %>% round()
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AllPivots2 <- AllPivots0 %>% dplyr::filter(field %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_three_week <- three_week_dif_raster_abs %>% crop(., pivotShape) %>% mask(., pivotShape)
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planting_date <- harvesting_data %>% dplyr::filter(field %in% pivotName) %>% ungroup() %>% dplyr::select(season_start) %>% unique()
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joined_spans2 <- AllPivots0 %>% st_transform(crs(pivotShape)) %>% dplyr::filter(field %in% pivotName) #%>% unique() %>% 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, borders = borders)
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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, borders = borders)
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CImap <- create_CI_map(singlePivot, AllPivots2, joined_spans2, show_legend= F, legend_is_portrait = F, week = week, age = age, borders = borders)
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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, borders = borders)
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CI_max_abs_three_week <- create_CI_diff_map(abs_CI_three_week, AllPivots2, joined_spans2, show_legend = T, legend_is_portrait = T, week_1 = week, week_2 = week_minus_3, age = age, borders = borders)
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tst <- tmap_arrange(CImap_m2, CImap_m1, CImap,CI_max_abs_last_week, CI_max_abs_three_week, nrow = 1)
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cat(paste("## Field", pivotName, "-", age, "weeks after planting/harvest", "\n"))
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# cat("\n")
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# cat('<h2> Pivot', pivotName, '- week', week, '-', age$Age, 'weeks after planting/harvest <h2>')
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# cat(paste("# Pivot",pivots$pivot[i],"\n"))
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print(tst)
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}
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cum_ci_plot <- function(pivotName){
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# pivotName = "1.1"
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data_ci <- CI_quadrant %>% filter(field == pivotName)
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if (nrow(data_ci) == 0) {
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return(cum_ci_plot2(pivotName)) # Return an empty data frame if no data is found
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}
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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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unique_seasons <- unique(date_preperation_perfect_pivot$season)
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g <- ggplot(data= data_ci2 %>% filter(season %in% unique_seasons)) +
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facet_wrap(~season, scales = "free_x") +
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geom_line( aes(Date, mean_rolling10, col = sub_field, group = sub_field)) +
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labs(title = paste("14 day rolling MEAN CI rate - Pivot ", pivotName))+
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theme_minimal() +
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theme(axis.text.x = element_text(angle = 60, hjust = 1),
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legend.justification=c(1,0), legend.position = c(1, 0),
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legend.title = element_text(size = 8),
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legend.text = element_text(size = 8)) +
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guides(color = guide_legend(nrow = 2, byrow = TRUE))
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subchunkify(g, 3.2, 10)
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}
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cum_ci_plot2 <- function(pivotName){
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end_date <- Sys.Date()
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start_date <- end_date %m-% months(11) # 11 months ago from end_date
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date_seq <- seq.Date(from = start_date, to = end_date, by = "month")
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midpoint_date <- start_date + (end_date - start_date) / 2
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g <- ggplot() +
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scale_x_date(limits = c(start_date, end_date), date_breaks = "1 month", date_labels = "%m-%Y") +
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scale_y_continuous(limits = c(0, 4)) +
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labs(title = paste("14 day rolling MEAN CI rate - Field ", pivotName),
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x = "Date", y = "CI Rate") +
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theme(axis.text.x = element_text(angle = 60, hjust = 1),
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legend.justification = c(1, 0), legend.position = c(1, 0),
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legend.title = element_text(size = 8),
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legend.text = element_text(size = 8)) +
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annotate("text", x = midpoint_date, y = 2, label = "No data available", size = 6, hjust = 0.5)
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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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@ -439,13 +251,6 @@ tm_shape(CI, unit = "m")+
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\newpage
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```{r plots_ci_estate, eval=TRUE, fig.height=3.8, fig.width=10, message=FALSE,echo=FALSE, warning=FALSE, include=TRUE, results='asis'}
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# # pivots <- AllPivots_merged %>% filter(pivot != c("1.1", "1.17"))
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# pivots_estate <- AllPivots_merged %>% filter(field %in% c("6.2")) #, "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")) %>% filter(pivot != "1.17")
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# pivots <- AllPivots_merged %>% filter(pivot != c("1.1", "1.17"))
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# pivots_estate <- AllPivots_merged %>% filter(pivot %in% c("1.1", "1.2", "1.7")) %>% filter(pivot != "1.17")
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AllPivots_merged <- AllPivots0 %>% dplyr::group_by(field) %>% summarise()
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walk(AllPivots_merged$field, ~ {
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@ -457,9 +262,7 @@ walk(AllPivots_merged$field, ~ {
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```
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```{r looping_over_sub_area, echo=FALSE, fig.height=3.8, fig.width=10, message=FALSE, warning=FALSE, results='asis', eval=FALSE}
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pivots_grouped <- AllPivots0 # %>%
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# group_by(sub_area) %>%
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# arrange(sub_area) # Optional: arrange the groups alphabetically by sub_area
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pivots_grouped <- AllPivots0
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# Iterate over each subgroup
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for (subgroup in unique(pivots_grouped$sub_area)) {
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@ -17,7 +17,7 @@ project_dir <- as.character(args[1])
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# Controleer of data_dir een geldige waarde is
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if (!is.character(project_dir)) {
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project_dir <- "sony"
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project_dir <- "chemba"
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}
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@ -133,8 +133,6 @@ CI_all <- CI_all %>% group_by(model) %>% mutate(CI_per_day = FitData - lag(FitDa
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cumulative_CI = cumsum(FitData))
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CI_all <- CI_all %>% group_by(model) %>% mutate(CI_per_day = FitData - lag(FitData),
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cumulative_CI = cumsum(FitData))
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message('CI_all cumulative')
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head(CI_all)
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145
r_app/report_utils.R
Normal file
145
r_app/report_utils.R
Normal file
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@ -0,0 +1,145 @@
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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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create_CI_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend = F, legend_is_portrait = F, week, age, borders = FALSE){
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map <- 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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if (borders) {
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map <- map +
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tm_shape(pivot_shape) +
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tm_borders(lwd = 3) +
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tm_text("sub_field", size = 1/2) +
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tm_shape(pivot_spans) +
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tm_borders(lwd = 0.5, alpha = 0.5)
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}
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return(map)
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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, borders = TRUE){
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map <- 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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if (borders) {
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map <- map +
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tm_shape(pivot_shape) +
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tm_borders(lwd = 3) +
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tm_text("sub_field", size = 1/2) +
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tm_shape(pivot_spans) +
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tm_borders(lwd = 0.5, alpha = 0.5)
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}
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return(map)
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}
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ci_plot <- function(pivotName){
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# pivotName = "1.1"
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pivotShape <- AllPivots0 %>% terra::subset(field %in% pivotName) %>% st_transform(crs(CI))
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age <- harvesting_data %>% dplyr::filter(field %in% pivotName) %>% sort("year") %>% tail(., 1) %>% dplyr::select(age) %>% unique() %>% pull() %>% round()
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AllPivots2 <- AllPivots0 %>% dplyr::filter(field %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_three_week <- three_week_dif_raster_abs %>% crop(., pivotShape) %>% mask(., pivotShape)
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planting_date <- harvesting_data %>% dplyr::filter(field %in% pivotName) %>% ungroup() %>% dplyr::select(season_start) %>% unique()
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joined_spans2 <- AllPivots0 %>% st_transform(crs(pivotShape)) %>% dplyr::filter(field %in% pivotName) #%>% unique() %>% 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, borders = borders)
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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, borders = borders)
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CImap <- create_CI_map(singlePivot, AllPivots2, joined_spans2, show_legend= F, legend_is_portrait = F, week = week, age = age, borders = borders)
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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, borders = borders)
|
||||
CI_max_abs_three_week <- create_CI_diff_map(abs_CI_three_week, AllPivots2, joined_spans2, show_legend = T, legend_is_portrait = T, week_1 = week, week_2 = week_minus_3, age = age, borders = borders)
|
||||
|
||||
tst <- tmap_arrange(CImap_m2, CImap_m1, CImap,CI_max_abs_last_week, CI_max_abs_three_week, nrow = 1)
|
||||
|
||||
cat(paste("## Field", pivotName, "-", age, "weeks after planting/harvest", "\n"))
|
||||
# cat("\n")
|
||||
# cat('<h2> Pivot', pivotName, '- week', week, '-', age$Age, 'weeks after planting/harvest <h2>')
|
||||
# cat(paste("# Pivot",pivots$pivot[i],"\n"))
|
||||
print(tst)
|
||||
|
||||
}
|
||||
|
||||
cum_ci_plot <- function(pivotName){
|
||||
|
||||
# pivotName = "1.1"
|
||||
data_ci <- CI_quadrant %>% filter(field == pivotName)
|
||||
|
||||
if (nrow(data_ci) == 0) {
|
||||
return(cum_ci_plot2(pivotName)) # Return an empty data frame if no data is found
|
||||
}
|
||||
|
||||
data_ci2 <- data_ci %>% mutate(CI_rate = cumulative_CI/DOY,
|
||||
week = week(Date))%>% group_by(field) %>%
|
||||
mutate(mean_rolling10 = rollapplyr(CI_rate , width = 10, FUN = mean, partial = TRUE))
|
||||
|
||||
date_preperation_perfect_pivot <- data_ci2 %>% group_by(season) %>% summarise(min_date = min(Date),
|
||||
max_date = max(Date),
|
||||
days = max_date - min_date)
|
||||
|
||||
|
||||
unique_seasons <- unique(date_preperation_perfect_pivot$season)
|
||||
|
||||
g <- ggplot(data= data_ci2 %>% filter(season %in% unique_seasons)) +
|
||||
facet_wrap(~season, scales = "free_x") +
|
||||
geom_line( aes(Date, mean_rolling10, col = sub_field, group = sub_field)) +
|
||||
labs(title = paste("14 day rolling MEAN CI rate - Pivot ", pivotName),
|
||||
color = "Field name")+
|
||||
scale_x_date(date_breaks = "1 month", date_labels = "%m-%Y") +
|
||||
theme_minimal() +
|
||||
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))
|
||||
|
||||
subchunkify(g, 3.2, 10)
|
||||
|
||||
}
|
||||
|
||||
cum_ci_plot2 <- function(pivotName){
|
||||
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
|
||||
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)
|
||||
}
|
||||
Loading…
Reference in a new issue