---format: html---# Blackrock Waterfowl Management Area```{r read_csvs, echo=FALSE, message=FALSE, warning=FALSE}library(dplyr)library(readr)library(stringr)library(lubridate)# 1a. Point to the folder containing your flood_report CSVscsv_dir <-"flood_reports/csv_output"# 1b. List all matching CSV files (e.g. "flood_report_data_2024-09-22_0.21.csv")csv_files <-list.files(path = csv_dir,pattern ="^flood_report_data_[0-9]{4}-[0-9]{2}-[0-9]{2}_.*\\.csv$",full.names =TRUE)# 1c. Read each CSV, parse the date from its filename, and bind them togethermonthly_raw <-lapply(csv_files, function(path) { fname <-basename(path) date_part <-str_match( fname,"^flood_report_data_([0-9]{4}-[0-9]{2}-[0-9]{2})_.*\\.csv$" )[,2] report_dt <-as.Date(date_part, format ="%Y-%m-%d")# Read the CSV (must contain at least columns "Flood_Unit" and "acres_flooded") df <-read_csv(path, show_col_types =FALSE) %>%mutate(report_date = report_dt)return(df)}) %>%bind_rows() %>%# Keep only the columns we needselect(Flood_Unit, acres_flooded, report_date)# (Optional) Quick check:# glimpse(monthly_raw)``````{r monthly_raw, echo=FALSE, message=FALSE, warning=FALSE}library(dplyr)library(lubridate)library(tidyr)library(knitr)library(stringr)# 4a. Point to your folder of report HTMLs for scanning# Use docs/reports for scanning during render, but links use "reports" for GitHub Pagesreport_scan_dir <-"docs/reports"report_link_dir <-"reports"# 4b. Grab all filenames matching "bwma_flood_report_YYYY-MM-DD_*.html"html_files <-list.files(path = report_scan_dir,pattern ="^bwma_flood_report_[0-9]{4}-[0-9]{2}-[0-9]{2}_.*\\.html$",full.names =FALSE)# 4c. Build a tibble with columns:# - file (the filename itself)# - date = as.Date("YYYY-MM-DD") parsed from the filename# - year = year(date)# - month_num = month(date)# - month_name = ordered factor (Jan, Feb, …, Dec)# - fy_num = if month >= 7 then year + 1 else year# - fy_label = "2025-26", etc.# - day_only = format(date, "%d")# - full_link = "<a href='...'>day_only</a>"df_links <-tibble(file = html_files) %>%mutate(# Extract "YYYY-MM-DD" via regexdate =as.Date(str_extract(file, "[0-9]{4}-[0-9]{2}-[0-9]{2}")),year =year(date),month_num =month(date),month_name =month(date, label =TRUE, abbr =TRUE),# Fiscal year runs July→June: if month >= 7, belong to (year+1); else to (year)fy_num =if_else(month_num >=7, year, year -1L),# fy_num = if_else(month_num >= 7, year + 1L, year),# Build label "2025-26", "2026-27", etc.fy_label =paste0( fy_num, "-",str_sub(as.character(fy_num +1L), 3, 4) ),# Only show the day number (e.g. "22") as link textday_only =format(date, "%d"),# Build the clickable HTML anchor (use report_link_dir for GitHub Pages compatibility)full_link =paste0("<a href='", report_link_dir, "/", file,"' target='_blank'>", day_only, "</a>" ) )# 4d. For each (fy_label × month_name), pick the **earliest** datedf_one_per_month <- df_links %>%group_by(fy_label, month_name) %>%slice_min(date, n =1, with_ties =FALSE) %>%ungroup()# 4e. Make an ordered factor of all 12 calendar months (Jan → Dec)all_months_ord <-ordered(month.abb, levels = month.abb)# 4f. Use complete() so that every (fy_label × month) combination exists (fill missing with "")wide_tbl <- df_one_per_month %>%select(fy_label, month_name, full_link) %>%complete( fy_label,month_name = all_months_ord,fill =list(full_link ="") ) %>%# 4g. Reorder month_name into new factor "Jul→Jun"mutate(month_for_fy =factor(as.character(month_name),levels =c("Jul", "Aug", "Sep", "Oct", "Nov", "Dec","Jan", "Feb", "Mar", "Apr", "May", "Jun" ) ) ) %>%select(fy_label, month_for_fy, full_link) %>%pivot_wider(names_from = month_for_fy,values_from = full_link ) %>%arrange(fy_label) %>%# 4h. Explicitly choose Jul→Jun order for the columns:select( fy_label, Jul, Aug, Sep, Oct, Nov, Dec, Jan, Feb, Mar, Apr, May, Jun )# 4i. Render as an HTML table (escape=FALSE so the <a> HTML works)knitr::kable( wide_tbl,format ="html",escape =FALSE,caption ="Monthly Reports",table.attr ="class='table table-striped'")``````{r monthly_flooded_acres, echo=FALSE, message=FALSE, warning=FALSE, results='hide'}library(dplyr)library(lubridate)library(ggplot2)# 3a. Fix any "Waggonner" → "Waggoner" typo and floor each report_date to the 1st of that monthmonthly_fixed <- monthly_raw %>%mutate(Flood_Unit =recode(Flood_Unit,"Waggonner"="Waggoner",.default = Flood_Unit),month_date =floor_date(report_date, unit ="month") )# 3b. Compute latest flooded acres per (Flood_Unit × month_date)# Uses the MOST RECENT date within each month (not the first date)# This ensures the plot shows the most current data available for each month# Filter to start from September 2024monthly_avg <- monthly_fixed %>%filter(Flood_Unit !="Total") %>%filter(month_date >=as.Date("2024-09-01")) %>%group_by(Flood_Unit, month_date) %>%slice_max(report_date, n =1, with_ties =FALSE) %>%# Gets most recent date per monthungroup() %>%select(Flood_Unit, month_date, acres_flooded) %>%rename(avg_acres = acres_flooded)# 3c. Plot stacked bars + a dashed "500 ac target" line from Nov 1 → Mar 1monthly_avg %>%ggplot(aes(x = month_date, y = avg_acres, fill = Flood_Unit)) +geom_col() +geom_segment(aes(x =as.Date("2024-11-01"),xend =as.Date("2025-10-01"), #update monthlyy =500,yend =500 ),color ="black",linetype ="dashed",size =0.7 ) +annotate("text",x =as.Date("2025-01-01"),y =530, # slight offset above 500label ="500 ac target (Nov 1 → Mar 1)",size =3.5,hjust =0.5 ) +scale_x_date(limits =c(min(monthly_avg$month_date) -15, max(monthly_avg$month_date) +15), # dynamic limitsdate_breaks ="1 month",date_labels ="%b '%y"# e.g. "Sep '24", "Oct '24", … "Sep '25" ) +labs(title ="Monthly Flooded Acreage by Unit", # update on new calendar yearx ="Month",y ="Acres Flooded" ) +theme_minimal() +theme(axis.text.x =element_text(angle =45, hjust =1),plot.title =element_text(size =16, face ="bold", hjust =0.5) )```## Most Recent Report<!-- HTML iframe to embed a specific report --><iframe src="reports/bwma_flood_report_2026-03-16_0.14.html" width="100%" height="1000px" style="border: none;" id="report-frame"></iframe>
