working on verna data prep

tbep-tech · Jun 26, 2024 · e1f4600 · e1f4600
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diff --git a/.Rbuildignore b/.Rbuildignore
@@ -11,3 +11,4 @@
 ^data-raw/dbasing_0407\.sas7bdat$
 ^data-raw/*$
 ^R/helpers\.R$
+^data-raw$
diff --git a/R/ad_load_calc.R b/R/ad_load_calc.R
@@ -151,7 +151,7 @@ rain <- db2 %>%
                  source = "Atmospheric Deposition")
 
 #Acquire and read-in Verna NTN atmospheric deposition concentration data over the period of interest from: https://nadp.slh.wisc.edu/sites/ntn-FL41/
-verna <- read.csv(file = "./data-raw/NADP/NTN-fl41-i-mgl.csv") %>%
+verna <- read.csv(file = "./data-raw/NADP/NTN-fl41-i-mgl_2017-2022.csv") %>%
           mutate(mo = seas+ 0) %>%
           mutate(nh4 = case_when(yr == 2022 & mo == 12 ~ mean(c(0.046, 0.063, 0.09, 0.105, 0.173)), #Dec. NH4 mean from 2017-2021 to fill in missing data
                                  TRUE ~ NH4)) %>%

diff --git a/R/util_ad_prepverna.R b/R/util_ad_prepverna.R
@@ -0,0 +1,28 @@
+util_ad_prepverna <- function(fl, fillmis = T){
+
+  dat <- read.csv(fl, header = T, stringsAsFactors = F) |>
+    dplyr::mutate(Month = seas + 0) |>
+    dplyr::select(
+      Year = yr,
+      Month = mo,
+      nh4 = NH4,
+      no3 = NO3
+    )
+
+  # fill missing annual data by monthly means from other years
+  if(fillmis)
+    dat <- dat #|>
+      # dplyr::mutate(
+      #   nh4
+      # )
+
+  dat <- dat |>
+    mutate(
+      nh4 = nh4 * 0.78, # NADP data are reported as mg NO3 and mg NH4, this corrects for % of ions that is N;
+      no3 = no3 * 0.23,
+      TNConc = nh4 + no3,
+      TPConc = 0.01262 * TNConc + 0.00110 # from regression relationship between TBADS TN and TP, applied to Verna;
+    ) %>%
+    select(yr, mo, TNConc, TPConc)
+
+}
diff --git a/data-raw/verna.R b/data-raw/verna.R
@@ -0,0 +1,26 @@
+# Verna well field NADP NTN atmospheric deposition concentration data
+# https://nadp.slh.wisc.edu/sites/ntn-FL41/
+# 2017 to 2022
+
+library(here)
+library(dplyr)
+
+verna <- read.csv(file = here("data-raw/NTN-fl41-i-mgl_2017-2022.csv")) %>%
+  mutate(mo = seas + 0) %>%
+  mutate(
+    nh4 = case_when(
+      yr == 2022 & mo == 12 ~ mean(c(0.046, 0.063, 0.09, 0.105, 0.173)), # Dec. NH4 mean from 2017-2021 to fill in missing data
+      TRUE ~ NH4
+    ),
+    no3 = case_when(
+      yr == 2022 & mo == 12 ~ mean(c(0.194, 0.257, 0.364, 0.327, 1.41)), # Dec. NO3 mean from 2017-2021 to fill in missing data
+    TRUE ~ NO3
+    ),
+    nh4 = nh4 * 0.78, # NADP data are reported as mg NO3 and mg NH4, this corrects for % of ions that is N;
+    no3 = no3 * 0.23,
+    TNConc = nh4 + no3,
+    TPConc = 0.01262 * TNConc + 0.00110 # from regression relationship between TBADS TN and TP, applied to Verna;
+  ) %>%
+  select(yr, mo, TNConc, TPConc)
+
+usethis::use_data(verna, overwrite = TRUE)
diff --git a/inst/extdata/verna-raw.csv b/inst/extdata/verna-raw.csv
@@ -0,0 +1,73 @@
+siteID,seas,yr,Criteria1,Criteria2,Criteria3,Ca,Mg,K,Na,NH4,NO3,Cl,SO4,pH,conduc,svol,ppt,fullChemLab,daysSample,startDate,endDate
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+FL41,9,2020,93,93,100,.103,.023,.011,.122,.058,.341,.205,.261,5.361,4.245,16380.600,25.222,4,30,"Sep  1 2020 12:00AM","Sep 29 2020 12:00AM"
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+FL41,2,2021,100,100,100,.095,.102,.048,.828,.099,.349,1.567,.691,5.126,10.501,3551.600,5.664,3,28,"Feb  2 2021 10:00AM","Mar  2 2021 10:00AM"
+FL41,3,2021,100,100,100,.233,.066,.042,.482,.275,.842,.849,.771,5.230,9.911,6770.100,9.982,3,28,"Mar  2 2021 10:00AM","Mar 30 2021  9:00AM"
+FL41,4,2021,100,100,100,.232,.080,.050,.676,.279,.683,1.220,.987,5.185,11.210,7856.400,11.836,4,35,"Mar 30 2021  9:00AM","May  4 2021  9:00AM"
+FL41,5,2021,100,100,93,.218,.065,.047,.588,.372,1.234,1.080,.727,5.220,10.900,300.400,.475,1,28,"May  4 2021  9:00AM","Jun  1 2021  8:55AM"
+FL41,6,2021,100,100,100,.092,.037,.017,.299,.117,.513,.516,.307,5.324,5.626,15524.400,22.798,4,27,"Jun  1 2021  8:55AM","Jun 28 2021  9:20PM"
+FL41,7,2021,100,100,100,.045,.024,.027,.184,.051,.296,.328,.200,5.339,4.193,18926.300,28.575,4,36,"Jun 28 2021  9:20PM","Aug  3 2021  9:00AM"
+FL41,8,2021,66,100,73,.074,.037,.017,.239,.050,.505,.413,.318,5.076,6.390,14807.500,30.232,3,28,"Aug  3 2021  9:00AM","Aug 31 2021  9:00AM"
+FL41,9,2021,100,100,100,.138,.036,.020,.220,.073,.462,.379,.345,5.294,5.267,10096.900,15.113,4,29,"Aug 31 2021  9:00AM","Sep 29 2021  9:00AM"
+FL41,1,2022,60,100,95,.264,.371,.128,3.128,.216,.737,5.567,2.083,4.661,34.221,2026.200,3.378,2,35,"Dec 28 2021 11:50AM","Feb  1 2022  9:55AM"
+FL41,10,2022,75,100,57,.183,.050,.024,.317,.146,.738,.540,.664,5.110,8.300,1069.500,2.794,1,28,"Oct  4 2022 12:45PM","Nov  1 2022  9:00AM"
+FL41,11,2022,74,100,98,.054,.067,.025,.552,.031,.167,1.024,.285,5.237,7.002,6304.000,9.677,3,28,"Nov  1 2022  9:00AM","Nov 29 2022 10:00AM"
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+FL41,6,2022,100,100,100,.206,.043,.025,.264,.209,.951,.445,.494,5.336,7.443,13724.200,20.447,4,28,"May 31 2022  9:00AM","Jun 28 2022 11:00AM"
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