Added SQL version of AWRR table generator. Fixed scientific notation …

OWS/AnnualReporting/AWRR_data.R

@@ -50,6 +50,7 @@ for (y in year.range) {
   colnames(data) <- c('HydroID', 'Hydrocode', 'Source_Type',
                       'MP_Name', 'Facility', 'Use_Type', 'Year',
                       'mgy', 'mgd', 'lat', 'lon', 'locality')
+
   #make use type values lowecase
   data$Use_Type <- str_to_lower(data$Use_Type)
   #change 'Well' and 'Surface Water Intake' values in source_type column to match report headers
@@ -67,6 +68,7 @@ for (y in year.range) {
     mgd = sum(mgd),
     mgy = sum(mgy)
   )
+  catsourcesum$mgd = round(catsourcesum$mgy / 365.0,2)
   catsourcesum <- arrange(catsourcesum, Source_Type, Use_Type)
 
 

OWS/AnnualReporting/AWRR_data_sql.R

@@ -0,0 +1,134 @@
+#library("readxl")
+library("dplyr")
+library('httr')
+library("sqldf")
+options(timeout=24000)
+a <- c('agricultural', 'municipal', 'mining', 'commercial', 'manufacturing', 'irrigation')
+b <- c('Surface Water Intake', 'Well', 'Total (GW + SW)')
+cat_table <- expand.grid(a,b)
+syear = 2016
+eyear = 2018
+s_cols = FALSE
+
+
+for (y in year.range) {
+
+  print(y)
+  startdate <- paste(y, "-01-01",sep='')
+  enddate <- paste(y, "-12-31", sep='')
+
+  localpath <- tempdir()
+  filename <- paste("data.all_",y,".csv",sep="")
+  destfile <- paste(localpath,filename,sep="\\")  
+  download.file(paste("http://deq2.bse.vt.edu/d.dh/ows-awrr-map-export/wd_mgy?ftype_op=not&ftype=power&tstime_op=between&tstime%5Bvalue%5D=&tstime%5Bmin%5D=",startdate,"&tstime%5Bmax%5D=",enddate,"&bundle%5B0%5D=well&bundle%5B1%5D=intake&dh_link_admin_reg_issuer_target_id%5B0%5D=65668&dh_link_admin_reg_issuer_target_id%5B1%5D=91200&dh_link_admin_reg_issuer_target_id%5B2%5D=77498",sep=""), destfile = destfile, method = "libcurl")  
+  data.all <- read.csv(file=paste(localpath , filename,sep="\\"), header=TRUE, sep=",")
+
+  #has 3 issuing authorities, does not include power
+  #  data.all <- read.csv(file=paste("http://deq2.bse.vt.edu/d.dh/ows-awrr-map-export/wd_mgy?ftype_op=not&ftype=power&tstime_op=between&tstime%5Bvalue%5D=&tstime%5Bmin%5D=",startdate,"&tstime%5Bmax%5D=",enddate,"&bundle%5B0%5D=well&bundle%5B1%5D=intake&dh_link_admin_reg_issuer_target_id%5B0%5D=65668&dh_link_admin_reg_issuer_target_id%5B1%5D=91200&dh_link_admin_reg_issuer_target_id%5B2%5D=77498",sep=""), header=TRUE, sep=",")
+
+  data <- data.all
+
+  #remove duplicates (keeps one row)
+  data <- distinct(data, HydroID, .keep_all = TRUE)
+  #exclude dalecarlia
+  data <- data[-which(data$Facility=='DALECARLIA WTP'),]
+
+  if (length(which(data$Use.Type=='facility')) > 0) {
+    data <- data[-which(data$Use.Type=='facility'),]
+  }
+  assign(paste("yd", y, sep=''), data)
+
+  #rename columns
+  colnames(data) <- c('HydroID', 'Hydrocode', 'Source_Type',
+                      'MP_Name', 'Facility', 'Use_Type', 'Year',
+                      'mgy', 'mgd', 'lat', 'lon', 'locality')
+  #make use type values lowecase
+  data$Use_Type <- str_to_lower(data$Use_Type)
+  #change 'Well' and 'Surface Water Intake' values in source_type column to match report headers
+  levels(data$Source_Type) <- c(levels(data$Source_Type), "Groundwater", "Surface Water")
+  data$Source_Type[data$Source_Type == 'Well'] <- 'Groundwater'
+  data$Source_Type[data$Source_Type == 'Surface Water Intake'] <- 'Surface Water'
+
+
+  data$Use_Type[data$Use_Type == 'industrial'] <- 'manufacturing'
+
+
+  catsourcesum <- data %>% group_by(Use_Type, Source_Type)
+
+  catsourcesum <- catsourcesum %>% summarise(
+    mgd = sum(mgd),
+    mgy = sum(mgy)
+  )
+  catsourcesum$mgd = round(catsourcesum$mgy / 365.0,2)
+  catsourcesum <- arrange(catsourcesum, Source_Type, Use_Type)
+
+
+  catsum <- catsourcesum
+  catsum$Source_Type <- "Total (GW + SW)"
+  catsum <- catsum %>% group_by(Use_Type, Source_Type)
+
+  catsum <- catsum %>% summarise(
+    mgd = sum(mgd),
+    mgy = sum(mgy)
+  )
+  catsum <- arrange(catsum, Source_Type, Use_Type)
+
+
+  year_table <- rbind(catsourcesum, catsum)
+  year_table <- arrange(year_table, Source_Type, Use_Type)
+  assign(paste("y", y, sep=''), year_table)
+
+  tname = paste("tbl", y, sep='')
+  assign(tname, year_table)
+  if (is.logical(s_cols)) {
+    s_cols = paste(
+      "select ", tname, ".Use_Type, ", 
+      tname, ".Source_Type, ", 
+      tname, ".mgd as wd", y,
+      sep=''
+    )
+    t_names = paste("FROM ", tname)
+    btname = tname
+  } else {
+    s_cols <- paste(s_cols, ", ", tname, ".mgd as wd", y, sep='')
+    t_names <- paste(
+      t_names, 
+      " left outer join ", 
+      tname, " on (", btname, ".Use_Type", " = ",
+      tname, ".Use_Type",
+      " AND ", btname, ".Source_Type", " = ",
+      tname, ".Source_Type",
+      " )",
+      sep = ''
+    )
+  }
+
+}
+
+q = paste(s_cols, t_names)
+print(q)
+sqldf(q)
+
+#sqldf("select a.Use_Type, a.Source_Type, a.mgd, b.mgd from y2017 as a left outer join y2018 as b on (a.Use_Type = b.Use_Type and a.Source_type = b.Source_Type)")
+#names(cat_table) <- c('cat', 'use_type', 'y1', 'y2', 'y3')
+
+#year_frame <- arrange(year_table, Source_Type, Use_Type)
+
+
+
+
+#ardp <- 'U:\\OWS\\Report Development\\Annual Water Resources Report\\October 2019 Report\\Water Use Exports\\All Withdrawals By Use Type\\Water Use Exports By Type and Permit_Use For AWRR\\'
+
+# Merge these 3
+# read vwuds tab, add 'ptype' <= 'vwuds' 
+# read VWP tab, add 'ptype' <= 'vwp' 
+# Read GWP file, add 'ptype' <= 'gwp' 
+
+
+#source <- "ALL SW VWP AND VWUDS SEPARATED CLEANED.xlsx"
+#folder <- ardp
+
+#sheet <- "VWUDS"
+#rawdata <- read_excel(paste(folder,source,sep=''),sheet)
+#data <- rawdata
+