161 lines
8.1 KiB
R
161 lines
8.1 KiB
R
# utils for report
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#functions for rmarkdown file
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create_RGB_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend = TRUE, legend_is_portrait = FALSE, week, age, red = TRUE) {
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r <- if (red) 1 else 4 # Set r based on the value of red
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title <- if (red) paste0("RGB image of the fields") else paste0("False colour image of the fields")
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tm_shape(pivot_raster, unit = "m") + tm_rgb(r = r, g = 2, b = 3, max.value = 255) +
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tm_layout(main.title = title,
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main.title.size = 1) +
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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(pivot_shape) + tm_borders(col = "gray") +
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tm_text("sub_field", size = 0.6, col = "gray") +
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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_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend = F, legend_is_portrait = F, week, age, legend_only = F){
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tm_shape(pivot_raster, unit = "m")+
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tm_raster(breaks = CI_breaks, palette = "RdYlGn",legend.is.portrait = legend_is_portrait ,midpoint = NA) +
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tm_layout(main.title = paste0("Max CI week ", week,"\n", age, " weeks old"),
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main.title.size = 1, legend.show = show_legend, legend.only = legend_only) +
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tm_shape(pivot_shape) +
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tm_borders(lwd = 3) + tm_text("sub_field", size = 1/2) +
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tm_shape(pivot_spans) + tm_borders(lwd = 0.5, alpha=0.5) +tmap_options(check.and.fix = TRUE)
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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 = CI_diff_breaks, palette = "PRGn",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 = 1, legend.show = show_legend) +
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tm_shape(pivot_shape) +
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tm_borders(lwd = 3) + tm_text("sub_field", 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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ci_plot <- function(pivotName){
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# pivotName = "MV2B09"
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# pivotName = "1.1"
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pivotShape <- AllPivots_merged %>% terra::subset(field %in% pivotName) %>% st_transform(crs(CI))
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# age <- AllPivots %>% dplyr::filter(field %in% pivotName) %>% st_drop_geometry() %>% dplyr::select(Age) %>% unique() %>%
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# mutate(Age = round(Age))
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age <- AllPivots %>%
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group_by(field) %>%
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filter(Season == max(Season, na.rm = TRUE), field %in% pivotName) %>%
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dplyr::select(Age)%>% st_drop_geometry() %>% unique()
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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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singlePivot_RGB <- RGB_raster %>% crop(., pivotShape) %>% mask(., pivotShape)
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singlePivot_false <- RGB_raster_stretch %>% 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(planting_date) %>% unique()
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joined_spans2 <- joined_spans %>% st_transform(crs(pivotShape)) %>% dplyr::filter(field %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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Legend_map <- create_CI_map(singlePivot_m1, AllPivots2, joined_spans2, show_legend= T, legend_is_portrait =T, week = week_minus_1, age = age -1, legend_only = T)
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CImap_m1 <- create_CI_map(singlePivot_m1, AllPivots2, joined_spans2, show_legend= T, legend_is_portrait =T, week = week_minus_1, age = age -1)
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CImap <- create_CI_map(singlePivot, AllPivots2, joined_spans2, show_legend= F, legend_is_portrait = T, week = week, age = age )
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RGBmap <- create_RGB_map(singlePivot_false, AllPivots2, joined_spans2, show_legend= F, week = week, age = age, red =T )
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Falsemap <- create_RGB_map(singlePivot_false, AllPivots2, joined_spans2, show_legend= F, week = week, age = age, red =F )
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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_three_week <- create_CI_diff_map(abs_CI_three_week, AllPivots2, joined_spans2, show_legend = F, legend_is_portrait = T, week_1 = week, week_2 = week_minus_3, age = age)
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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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tst <- tmap_arrange(RGBmap,Falsemap,
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CImap_m1, CImap,
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CI_max_abs_last_week, CI_max_abs_three_week,
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ncol = 2)
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cat(paste("## field", pivotName, "-", age$Age[1], "weeks after planting/harvest", "\n"))
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# cat("\n")
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# cat('<h2> Pivot', pivotName, '- week', week, '-', age$Age, 'weeks after planting/harvest <h2>')
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# cat(paste("# Pivot",pivots$pivot[i],"\n"))
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print(tst)
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}
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subchunkify <- function(g, fig_height=7, fig_width=5) {
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g_deparsed <- paste0(deparse(
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function() {g}
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), collapse = '')
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sub_chunk <- paste0("```{r sub_chunk_", floor(runif(1) * 10000), ", fig.height=", fig_height, ", fig.width=", fig_width, ", echo=FALSE}",
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"\n(",
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g_deparsed
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, ")()",
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"\n```
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")
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cat(knitr::knit(text = knitr::knit_expand(text = sub_chunk), quiet = TRUE))
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}
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cum_ci_plot <- function(pivotName){
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# pivotName = "2.1"
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# Check if pivotName exists in the data
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if (!pivotName %in% unique(CI_quadrant$field)) {
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# message("PivotName '", pivotName, "' not found. Plotting empty graph.")
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g <- ggplot() + labs(title = "Empty Graph - Yield dates missing")
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return(
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subchunkify(g, fig_height=2, fig_width = 10)
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)
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} else {
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# message("PivotName '", pivotName, "' found. Plotting normal graph.")
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data_ci <- CI_quadrant %>% filter(field %in% pivotName)
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data_ci2 <- data_ci %>% mutate(CI_rate = cumulative_CI/DOY,
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week = week(Date))%>% group_by(sub_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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# Identify unique seasons
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filtered_data <- data_ci2 %>%
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group_by(season) %>%
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mutate(rank = dense_rank(desc(season))) %>%
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filter(rank <= 2) %>%
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ungroup() %>%
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dplyr::select(-rank)
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# g <- ggplot(data= data_ci2 %>% filter(season %in% unique_seasons)) +
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g <- ggplot(data= filtered_data ) +
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# geom_line(aes(Date, mean_rolling10, col = sub_field)) +
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geom_line(aes(Date, CI_rate, col = sub_field)) +
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facet_wrap(~season, scales = "free_x") +
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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("CI rate - field", pivotName),
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y = "CI rate (cumulative CI / Age)")+
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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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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, fig_height=6, fig_width = 10)
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}
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} |