From 7d78aab6bf5464623877eb0579f34d355d0b4504 Mon Sep 17 00:00:00 2001
From: Dave Bosworth <David.Bosworth@water.ca.gov>
Date: Tue, 29 Aug 2023 11:08:20 -0700
Subject: [PATCH] Updating the processed daily average flow data to only
 include data within the NDFS period of interest

---
 .../processed/flow_daily_avg_2013-2019.csv    | 849 ++++--------------
 .../processed/flow_daily_avg_2013-2019.rds    | Bin 17211 -> 14850 bytes
 .../src/process_data_rtm_flow.R               | 153 ++--
 3 files changed, 240 insertions(+), 762 deletions(-)

diff --git a/manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.csv b/manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.csv
index 676cf9e..e1a4c13 100644
--- a/manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.csv
+++ b/manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.csv
@@ -1,4 +1,4 @@
-StationCode,Date,FlowAvg
+StationCode,Date,Flow
 LIB,2014-10-01,401.26975
 LIB,2014-10-02,-73.90033333333334
 LIB,2014-10-03,-352.5704166666667
@@ -35,18 +35,6 @@ LIB,2014-11-05,-900.0175
 LIB,2014-11-06,-900.5690416666666
 LIB,2014-11-07,-822.375625
 LIB,2014-11-08,-781.37575
-LIB,2014-11-09,-989.6840833333333
-LIB,2014-11-10,-1059.5687916666666
-LIB,2014-11-11,-736.6647083333334
-LIB,2014-11-12,-525.42125
-LIB,2014-11-13,-465.66179166666666
-LIB,2014-11-14,-346.2164583333333
-LIB,2014-11-15,-58.78695833333333
-LIB,2015-07-01,-1995
-LIB,2015-07-02,-2049.5833333333335
-LIB,2015-07-03,-2042.9166666666667
-LIB,2015-07-04,-2282.5
-LIB,2015-07-05,-2275.8333333333335
 LIB,2015-07-06,-1777.5
 LIB,2015-07-07,-1802.5
 LIB,2015-07-08,-2052.9166666666665
@@ -175,6 +163,30 @@ LIB,2015-11-12,-1169.5833333333333
 LIB,2015-11-13,-1260.4166666666667
 LIB,2015-11-14,-1582.9166666666667
 LIB,2015-11-15,-1293.0416666666667
+LIB,2015-11-16,-73.65166666666667
+LIB,2016-05-29,-1500.4166666666667
+LIB,2016-05-30,-1567.0833333333333
+LIB,2016-05-31,-1801.6666666666667
+LIB,2016-06-01,-1800
+LIB,2016-06-02,-1555.4166666666667
+LIB,2016-06-03,-1592.9166666666667
+LIB,2016-06-04,-1905.4166666666667
+LIB,2016-06-05,-2002
+LIB,2016-06-16,-1286.875
+LIB,2016-06-17,-1417.0833333333333
+LIB,2016-06-18,-1159.5833333333333
+LIB,2016-06-19,-1400
+LIB,2016-06-20,-1501.6666666666667
+LIB,2016-06-21,-1593.75
+LIB,2016-06-22,-1582.9166666666667
+LIB,2016-06-23,-1675
+LIB,2016-06-24,-1363.75
+LIB,2016-06-25,-1272.0833333333333
+LIB,2016-06-26,-1266.6666666666667
+LIB,2016-06-27,-1302.0833333333333
+LIB,2016-06-28,-1428.75
+LIB,2016-06-29,-1598.3333333333333
+LIB,2016-06-30,-1633.3333333333333
 LIB,2016-07-01,-1879.1666666666667
 LIB,2016-07-02,-1964.1666666666667
 LIB,2016-07-03,-1723.3333333333333
@@ -253,74 +265,6 @@ LIB,2016-09-13,-1186.2083333333333
 LIB,2016-09-14,-1004.6666666666666
 LIB,2016-09-15,-1270.8333333333333
 LIB,2016-09-16,-1369.1666666666667
-LIB,2016-09-17,-1482.9166666666667
-LIB,2016-09-18,-1511.25
-LIB,2016-09-19,-1700.8333333333333
-LIB,2016-09-20,-1876.6666666666667
-LIB,2016-09-21,-1855.8333333333333
-LIB,2016-09-22,-1265.4166666666667
-LIB,2016-09-23,-1146.25
-LIB,2016-09-24,-1350
-LIB,2016-09-25,-1411.6666666666667
-LIB,2016-09-26,-1462.0833333333333
-LIB,2016-09-27,-1673.75
-LIB,2016-09-28,-1707.0833333333333
-LIB,2016-09-29,-1852.5
-LIB,2016-09-30,-1805
-LIB,2016-10-01,-1345.8333333333333
-LIB,2016-10-02,-1337.5
-LIB,2016-10-03,-908.9166666666666
-LIB,2016-10-04,-1029.7083333333333
-LIB,2016-10-05,-1099.5833333333333
-LIB,2016-10-06,-1129.1666666666667
-LIB,2016-10-07,-1164.1666666666667
-LIB,2016-10-08,-1216.25
-LIB,2016-10-09,-1414.1666666666667
-LIB,2016-10-10,-1500
-LIB,2016-10-14,-1150.4347826086957
-LIB,2016-10-15,-1205.4166666666667
-LIB,2016-10-16,-1207.5
-LIB,2016-10-17,-1021.4583333333334
-LIB,2016-10-18,-746.3333333333334
-LIB,2016-10-19,-699.6666666666666
-LIB,2016-10-20,-849.2083333333334
-LIB,2016-10-21,-947
-LIB,2016-10-22,-948.6666666666666
-LIB,2016-10-23,-1048.625
-LIB,2016-10-24,-1035.0416666666667
-LIB,2016-10-25,-867.2083333333334
-LIB,2016-10-26,-882.875
-LIB,2016-10-27,-791.375
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-LIB,2016-10-29,-968.2083333333334
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-LIB,2016-10-31,-811.7083333333334
-LIB,2016-11-01,-693.9166666666666
-LIB,2016-11-02,-522.125
-LIB,2016-11-03,-629.5833333333334
-LIB,2016-11-04,-673.4166666666666
-LIB,2016-11-05,-697.1666666666666
-LIB,2016-11-06,-555.75
-LIB,2016-11-07,-453.9583333333333
-LIB,2016-11-08,-521.1666666666666
-LIB,2016-11-09,-614
-LIB,2016-11-10,-788.1666666666666
-LIB,2016-11-11,-897.875
-LIB,2016-11-12,-861.4166666666666
-LIB,2016-11-13,-838.3333333333334
-LIB,2016-11-14,-940.0416666666666
-LIB,2016-11-15,-982.4583333333334
-LIB,2017-07-03,-1019
-LIB,2017-07-04,-974.0416666666666
-LIB,2017-07-05,-910.4583333333334
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-LIB,2017-07-08,-976.2083333333334
-LIB,2017-07-09,-897.375
-LIB,2017-07-10,-973.625
-LIB,2017-07-11,-1037.875
-LIB,2017-07-12,-870.8333333333334
-LIB,2017-07-13,-729.5416666666666
 LIB,2017-07-14,-663.9583333333334
 LIB,2017-07-15,-564.875
 LIB,2017-07-16,-674
@@ -417,23 +361,6 @@ LIB,2017-10-30,-651
 LIB,2017-10-31,-526.5416666666666
 LIB,2017-11-01,-643.0416666666666
 LIB,2017-11-02,-651.6923076923077
-LIB,2017-11-05,-668
-LIB,2017-11-06,-501.5
-LIB,2017-11-07,-472.125
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-LIB,2017-11-09,-580.625
-LIB,2017-11-10,-404.5416666666667
-LIB,2017-11-11,-416.9583333333333
-LIB,2017-11-12,-442.6666666666667
-LIB,2017-11-13,-579.0588235294117
-LIB,2018-07-03,-623.8333333333334
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-LIB,2018-07-06,-116.675
-LIB,2018-07-07,-548.125
-LIB,2018-07-08,-894.2083333333334
-LIB,2018-07-09,-765.4583333333334
-LIB,2018-07-10,-671.8421052631579
 LIB,2018-07-14,-1110
 LIB,2018-07-15,-995.4583333333334
 LIB,2018-07-16,-691.6666666666666
@@ -490,19 +417,6 @@ LIB,2018-10-14,-361
 LIB,2018-10-21,-689
 LIB,2018-10-22,-666.5909090909091
 LIB,2018-10-31,-162
-LIB,2018-11-13,63.4804347826087
-LIB,2018-11-14,123.52916666666667
-LIB,2018-11-15,-10.0875
-LIB,2019-07-01,-935.9166666666666
-LIB,2019-07-02,-934.25
-LIB,2019-07-03,-909.1666666666666
-LIB,2019-07-04,-821.5833333333334
-LIB,2019-07-05,-778
-LIB,2019-07-06,-821.9166666666666
-LIB,2019-07-07,-1008.0833333333334
-LIB,2019-07-08,-917.4583333333334
-LIB,2019-07-09,-855
-LIB,2019-07-10,-894.75
 LIB,2019-07-11,-890.375
 LIB,2019-07-12,-927.5416666666666
 LIB,2019-07-13,-931.0416666666666
@@ -543,12 +457,6 @@ LIB,2019-09-21,162.285
 LIB,2019-09-22,-320.64285714285717
 LIB,2019-10-24,-543.1428571428571
 LIB,2019-10-25,-699.1428571428571
-LIS,2013-07-01,27.3
-LIS,2013-07-02,-6.96
-LIS,2013-07-03,-25.9
-LIS,2013-07-04,-42.9
-LIS,2013-07-05,-38.2
-LIS,2013-07-06,-21.8
 LIS,2013-07-07,-15.5
 LIS,2013-07-08,2.56
 LIS,2013-07-09,6.91
@@ -677,30 +585,8 @@ LIS,2013-11-12,-31
 LIS,2013-11-13,-37.2
 LIS,2013-11-14,-43.5
 LIS,2013-11-15,-50
-LIS,2014-07-01,-88
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+LIS,2013-11-17,-37.8
 LIS,2014-07-25,-103
 LIS,2014-07-26,-102
 LIS,2014-07-27,-96.1
@@ -804,18 +690,6 @@ LIS,2014-11-01,-35.4
 LIS,2014-11-02,-13.2
 LIS,2014-11-03,-22
 LIS,2014-11-04,-18.7
-LIS,2014-11-09,-36.6
-LIS,2014-11-10,-35.6
-LIS,2014-11-11,-13.9
-LIS,2014-11-12,10.2
-LIS,2014-11-13,20.1
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-LIS,2014-11-15,1.42
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-LIS,2015-07-05,-81.4
 LIS,2015-07-06,-60
 LIS,2015-07-07,-71.5
 LIS,2015-07-08,-95.8
@@ -944,6 +818,40 @@ LIS,2015-11-12,-76.4
 LIS,2015-11-13,-64.1
 LIS,2015-11-14,-84.2
 LIS,2015-11-15,-111
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 LIS,2016-07-01,-77.3
 LIS,2016-07-02,-81.2
 LIS,2016-07-03,-71.6
@@ -1019,79 +927,6 @@ LIS,2016-09-13,25.1
 LIS,2016-09-14,9.7
 LIS,2016-09-15,2
 LIS,2016-09-16,1.33
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 LIS,2017-07-14,4.53
 LIS,2017-07-15,-29.2
 LIS,2017-07-16,-35
@@ -1205,27 +1040,6 @@ LIS,2017-10-31,-27.8
 LIS,2017-11-01,-29.8
 LIS,2017-11-02,-29.7
 LIS,2017-11-03,-30.1
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 LIS,2018-07-13,-79.6
 LIS,2018-07-14,-88.9
 LIS,2018-07-15,-80.1
@@ -1344,16 +1158,6 @@ LIS,2018-11-08,-22.4
 LIS,2018-11-09,-48.8
 LIS,2018-11-10,-70.5
 LIS,2018-11-11,-76.3
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 LIS,2019-07-19,-37.2
 LIS,2019-07-20,-33.5
 LIS,2019-07-21,-19
@@ -1461,21 +1265,6 @@ LIS,2019-11-03,-66.5
 LIS,2019-11-04,-62.9
 LIS,2019-11-05,-57.1
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@@ -1565,30 +1354,8 @@ RCS,2013-11-12,45.14895833333333
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@@ -1690,13 +1457,6 @@ RCS,2014-11-05,74.509375
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@@ -1822,57 +1610,6 @@ RCS,2016-09-13,100.77083333333333
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 RCS,2017-07-14,13.334375
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@@ -1976,23 +1713,6 @@ RCS,2017-10-31,65.73958333333333
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 RCS,2018-07-13,80.42708333333333
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 RCS,2018-07-15,106.125
@@ -2115,20 +1835,6 @@ RCS,2018-11-08,14.205208333333333
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 RCS,2019-07-13,-0.4263157894736842
@@ -2248,15 +1954,6 @@ RCS,2019-11-03,24.83606557377049
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@@ -2297,18 +1994,6 @@ RD22,2014-11-05,56.33229166666666
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@@ -2442,6 +2127,40 @@ RD22,2015-11-12,9.2715625
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@@ -2520,79 +2239,6 @@ RD22,2016-09-13,38.9
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@@ -2706,30 +2352,6 @@ RD22,2017-10-31,6.055416666666667
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@@ -2852,20 +2474,6 @@ RD22,2018-11-08,6.2446875
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@@ -2985,21 +2593,6 @@ RD22,2019-11-03,10.685416666666667
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@@ -3132,30 +2725,8 @@ RVB,2013-11-12,6025.833333333333
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@@ -3263,18 +2834,6 @@ RVB,2014-11-05,5422.916666666667
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@@ -3408,6 +2967,40 @@ RVB,2015-11-12,3274.5833333333335
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diff --git a/manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.rds b/manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.rds
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diff --git a/manuscript_synthesis/src/process_data_rtm_flow.R b/manuscript_synthesis/src/process_data_rtm_flow.R
index e212959..043d18d 100644
--- a/manuscript_synthesis/src/process_data_rtm_flow.R
+++ b/manuscript_synthesis/src/process_data_rtm_flow.R
@@ -1,8 +1,7 @@
 # NDFS Synthesis Manuscript
 # Purpose: Process and integrate the continuous flow data collected from the
-  # Yolo Bypass and downstream from July 1 - Nov 15 during years 2013-2019.
-  # Calculate daily averages which are used in figures and analysis for the NDFS
-  # synthesis manuscript.
+  # Yolo Bypass and downstream during years 2013-2019. Calculate daily averages
+  # which are used in figures and analysis for the NDFS synthesis manuscript.
 # Author: Dave Bosworth
 # Contacts: David.Bosworth@water.ca.gov
 
@@ -24,7 +23,7 @@ i_am("manuscript_synthesis/src/process_data_rtm_flow.R")
 # Define absolute file path for NDFS SharePoint
 # Function creates a filepath to the NDFA SharePoint site
 # The optional fp_rel argument allows for more specific file paths beyond the
-# NDFA SharePoint root directory
+  # NDFA SharePoint root directory
 ndfa_abs_sp_path <- function(fp_rel = NULL) {
   fp_fastr <- "California Department of Water Resources/North Delta Flow Action - Documents"
   
@@ -37,48 +36,51 @@ ndfa_abs_sp_path <- function(fp_rel = NULL) {
   return(fp_abs)
 }
 
+# Add Flow Action Periods - Before, During, After flow action
+ndfa_action_periods <- function(df) {
+  # Import dates for flow action periods
+  fp_act_dates <- ndfa_abs_sp_path("2011-2019 Synthesis Study-FASTR/Data Management/FlowDatesDesignations_45days.csv")
+  df_act_dates_orig <- read_csv(fp_act_dates)
+  
+  # Convert date columns in df_act_dates_orig to date type
+  df_act_dates_clean <- df_act_dates_orig %>% mutate(across(starts_with(c("Pre", "Post")), mdy))
+  
+  # Add a Year variable to df if it doesn't already exist
+  if (!"Year" %in% names(df)) {
+    df <- df %>% mutate(Year = year(Date))
+    message("Adding a Year variable to the dataframe.")
+  }
+  
+  # Join flow action dates to df and add FlowActionPeriod variable
+  df %>%  
+    left_join(df_act_dates_clean, by  = "Year") %>% 
+    mutate(
+      FlowActionPeriod = case_when(
+        Date >= PreFlowStart & Date <= PreFlowEnd ~ "Before",
+        Date > PreFlowEnd & Date < PostFlowStart ~ "During",
+        Date >= PostFlowStart & Date <= PostFlowEnd ~ "After"
+      )
+    ) %>% 
+    # Remove some variables from df_act_dates_clean
+    select(-c(WYType, FlowPulseType, NetFlowDays, starts_with(c("PreFlow", "PostFlow"))))
+}
+
 
 # Import Continuous Flow Data -----------------------------------------------
 
-# Define root directory for the continuous WQ data on the NDFA SharePoint
-fp_rtm_wq_root <- ndfa_abs_sp_path("2011-2019 Synthesis Study-FASTR/WQ_Subteam/Processed_Data/Continuous/All_Dates")
-
-# Create vectors of all relevant file paths for the processed flow data
-# Not tidally-filtered
-fp_flow_std <- dir_ls(path = path(fp_rtm_wq_root), regexp = "RTM_OUTPUT_(RCS|RD22)_formatted_all\\.csv$")
-
-# Tidally-filtered
-fp_flow_tf <- dir_ls(path = path(fp_rtm_wq_root), regexp = "RTM_OUTPUT_(LIB|RVB)_formatted_all\\.csv$")
-
-# Import processed continuous flow data for RCS and RD22 (not tidally-filtered)
-df_flow_std <- 
-  map(
-    fp_flow_std,
-    ~ read_csv(
-      file = .x, 
-      col_types = cols_only(
-        StationCode = "c",
-        DateTime = "c",
-        Flow = "d"
-      )
-    )
-  ) %>% 
-  list_rbind()
-
-# Import processed continuous flow data for LIB and RVB (Tidally-filtered)
-df_flow_tf <- 
-  map(
-    fp_flow_tf,
-    ~ read_csv(
-      file = .x, 
-      col_types = cols_only(
-        StationCode = "c",
-        DateTime = "c",
-        FlowTF = "d"
-      )
-    )
-  ) %>% 
-  list_rbind()
+# Define directory for the continuous flow data on the NDFA SharePoint
+fp_rtm_flow <- ndfa_abs_sp_path("2011-2019 Synthesis Study-FASTR/WQ_Subteam/Processed_Data/Continuous")
+
+# Import QA'ed and cleaned continuous flow data for the NDFS period of interest
+df_rtm_flow <- read_csv(
+  file = path(fp_rtm_flow, "RTM_INPUT_all_2021-04-20.csv"),
+  col_types = cols_only(
+    StationCode = "c",
+    DateTime = "c",
+    Flow = "d",
+    FlowTF = "d"
+  )
+)
 
 # Import daily average tidally-filtered flow data for LIS
 df_flow_tf_lis <- readRDS(here("Water_Quality/Data_Processed/LIS_SR_DailyAvgFlow_2013-2022.rds"))
@@ -86,52 +88,55 @@ df_flow_tf_lis <- readRDS(here("Water_Quality/Data_Processed/LIS_SR_DailyAvgFlow
 
 # Clean and Integrate Continuous Data -------------------------------------
 
-# Combine continuous flow data and prepare for averaging
-df_flow_rtm_all <- 
-  bind_rows(
-    df_flow_std,
-    df_flow_tf %>% rename(Flow = FlowTF)
-  ) %>% 
-  # parse date-time variable and define tz as PST
-  mutate(DateTime = ymd_hms(DateTime, tz = "Etc/GMT+8"))
-
 # Prepare daily average tidally-filtered flow data collected at LIS to be
   # combined to all other data
 df_flow_tf_lis_c <- df_flow_tf_lis %>% 
   transmute(
     StationCode = "LIS",
     Date, 
-    FlowAvg = LIS_DailyAvgNetFlow
+    Flow = LIS_DailyAvgNetFlow
   ) %>% 
-  drop_na(FlowAvg)
+  drop_na(Flow) %>% 
+  # only include data within the NDFS period of interest
+  ndfa_action_periods() %>% 
+  drop_na(FlowActionPeriod) %>% 
+  select(-c(Year, FlowActionPeriod))
+
+# Prepare all other continuous flow data for averaging
+df_rtm_flow_c <- df_rtm_flow %>% 
+  mutate(
+    # parse date-time variable and define tz as PST; add date variable
+    DateTime = ymd_hms(DateTime, tz = "Etc/GMT+8"),
+    Date = date(DateTime),
+    # use standard flow data for RCS and RD22 and tidally-filtered flow data for
+      # LIB and RVB
+    FlowCombined = case_when(
+      StationCode %in% c("RCS", "RD22") ~ Flow,
+      StationCode %in% c("LIB", "RVB") ~ FlowTF,
+      TRUE ~ NA_real_
+    )
+  ) %>% 
+  # remove rows with NA values in FlowCombined, which only keeps RCS, RD22, LIB,
+    # and RVB
+  drop_na(FlowCombined)
 
 
 # Calculate Daily Averages ------------------------------------------------
 
-df_flow_dv <- df_flow_rtm_all %>% 
-  # remove NA Flow values
-  drop_na(Flow) %>% 
-  mutate(Date = date(DateTime)) %>% 
+df_flow_dv <- df_rtm_flow_c %>% 
   # calculate daily average flow values
-  summarize(FlowAvg = mean(Flow), .by = c(StationCode, Date))
-  
-# Add daily average tidally-filtered flow data collected at LIS to all other
-  # daily average flow data
-df_flow_dv_all <- bind_rows(df_flow_dv, df_flow_tf_lis_c)
-
-# filter flow data to July 1 - Nov 15 for years 2013-2019
-df_flow_dv_all_f <- df_flow_dv_all %>% 
-  filter(
-    month(Date) %in% 7:11,
-    !(month(Date) == 11 & day(Date) > 15),
-    year(Date) %in% 2013:2019
-  ) %>% 
+  summarize(Flow = mean(FlowCombined), .by = c(StationCode, Date)) %>% 
+  # add daily average tidally-filtered flow data collected at LIS to all other
+    # daily average flow data
+  bind_rows(df_flow_tf_lis_c) %>% 
+  # filter to years 2013-2019
+  filter(year(Date) %in% 2013:2019) %>% 
   arrange(StationCode, Date)
 
 
 # Save and Export Data ----------------------------------------------------
 
-# Save daily average chlorophyll data as csv and rds files
-df_flow_dv_all_f %>% write_csv(here("manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.csv"))
-df_flow_dv_all_f %>% saveRDS(here("manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.rds"))
+# Save daily average flow data as csv and rds files
+df_flow_dv %>% write_csv(here("manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.csv"))
+df_flow_dv %>% saveRDS(here("manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.rds"))