From 4b69a324e9b7a4206c163c983678ba290ba26e35 Mon Sep 17 00:00:00 2001
From: Dave Bosworth <David.Bosworth@water.ca.gov>
Date: Mon, 4 Dec 2023 12:34:02 -0800
Subject: [PATCH] Adding weekly average flow data for NDFS synthesis analyses;
 removing under-measured days from the daily average flow data

---
 .../processed/flow_daily_avg_2013-2019.csv    | 1676 ++++++++---------
 .../processed/flow_daily_avg_2013-2019.rds    |  Bin 14850 -> 14760 bytes
 .../processed/flow_week_avg_2013-2019.csv     |  543 ++++++
 .../processed/flow_week_avg_2013-2019.rds     |  Bin 0 -> 3488 bytes
 .../src/process_data_rtm_flow.R               |  113 +-
 5 files changed, 1421 insertions(+), 911 deletions(-)
 create mode 100644 manuscript_synthesis/data/processed/flow_week_avg_2013-2019.csv
 create mode 100644 manuscript_synthesis/data/processed/flow_week_avg_2013-2019.rds

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 e1a4c13..352f28f 100644
--- a/manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.csv
+++ b/manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.csv
@@ -28,7 +28,6 @@ LIB,2014-10-26,-208.74866666666668
 LIB,2014-10-27,-380.0002083333333
 LIB,2014-10-28,-488.27825
 LIB,2014-10-29,-554.2435833333333
-LIB,2014-10-30,-784.5495384615385
 LIB,2014-11-03,-457.8645
 LIB,2014-11-04,-670.8648333333333
 LIB,2014-11-05,-900.0175
@@ -111,8 +110,6 @@ LIB,2015-09-16,-1312.0833333333333
 LIB,2015-09-17,-1272.9166666666667
 LIB,2015-09-18,-1245.8333333333333
 LIB,2015-09-19,-1235.8333333333333
-LIB,2015-09-20,-1309.2857142857142
-LIB,2015-09-26,-1635
 LIB,2015-09-27,-1773.3333333333333
 LIB,2015-09-28,-1918.75
 LIB,2015-09-29,-1799.1666666666667
@@ -171,8 +168,6 @@ 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
@@ -335,12 +330,6 @@ LIB,2017-09-18,-1255.4166666666667
 LIB,2017-09-19,-1077.9166666666667
 LIB,2017-09-20,-1387.0833333333333
 LIB,2017-09-21,-1074.4782608695652
-LIB,2017-09-29,-921.1666666666666
-LIB,2017-09-30,-840
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-LIB,2017-10-09,17.62727272727273
-LIB,2017-10-13,-236.66666666666666
 LIB,2017-10-14,-315.5
 LIB,2017-10-15,-656.2916666666666
 LIB,2017-10-16,-726.75
@@ -360,8 +349,6 @@ LIB,2017-10-29,-646.25
 LIB,2017-10-30,-651
 LIB,2017-10-31,-526.5416666666666
 LIB,2017-11-01,-643.0416666666666
-LIB,2017-11-02,-651.6923076923077
-LIB,2018-07-14,-1110
 LIB,2018-07-15,-995.4583333333334
 LIB,2018-07-16,-691.6666666666666
 LIB,2018-07-17,-386.25
@@ -375,15 +362,9 @@ LIB,2018-07-24,-584.2916666666666
 LIB,2018-07-25,-618.4166666666666
 LIB,2018-07-26,-953.7083333333334
 LIB,2018-07-27,-1003.0833333333334
-LIB,2018-07-28,-765.8571428571429
 LIB,2018-08-03,-554.7826086956521
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-LIB,2018-08-21,-923.2
 LIB,2018-08-22,-842.25
 LIB,2018-08-23,-878.2916666666666
 LIB,2018-08-24,-652.4166666666666
@@ -401,22 +382,15 @@ LIB,2018-09-04,-663.0833333333334
 LIB,2018-09-05,-695.9166666666666
 LIB,2018-09-06,-456.5416666666667
 LIB,2018-09-07,-465.5833333333333
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 LIB,2018-09-30,-526.0833333333334
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 LIB,2018-10-02,-827.375
 LIB,2018-10-03,-649.5
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 LIB,2018-10-12,-477.3333333333333
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 LIB,2018-10-22,-666.5909090909091
-LIB,2018-10-31,-162
 LIB,2019-07-11,-890.375
 LIB,2019-07-12,-927.5416666666666
 LIB,2019-07-13,-931.0416666666666
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 LIB,2019-07-23,-769.25
 LIB,2019-07-24,-631.5833333333334
 LIB,2019-07-25,-670.625
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+LIS,2019-11-06,-59.78125
 RCS,2013-07-07,143.66666666666666
 RCS,2013-07-08,149.92708333333334
 RCS,2013-07-09,168.34375
@@ -1280,8 +1239,6 @@ RCS,2013-07-18,167.10416666666666
 RCS,2013-07-19,166.375
 RCS,2013-07-20,154.44791666666666
 RCS,2013-07-21,118.11458333333333
-RCS,2013-07-22,130.1
-RCS,2013-08-08,128.0344827586207
 RCS,2013-08-09,168.13541666666666
 RCS,2013-08-10,127.875
 RCS,2013-08-11,143.85416666666666
@@ -1332,7 +1289,6 @@ RCS,2013-09-24,298.5416666666667
 RCS,2013-09-25,278.875
 RCS,2013-09-26,227.58333333333334
 RCS,2013-09-27,189.53125
-RCS,2013-09-28,157.94117647058823
 RCS,2013-10-26,146.0215053763441
 RCS,2013-10-27,104.38541666666667
 RCS,2013-10-28,69.64583333333333
@@ -1442,8 +1398,6 @@ RCS,2014-10-15,51.895833333333336
 RCS,2014-10-16,54.697916666666664
 RCS,2014-10-17,47.135416666666664
 RCS,2014-10-18,30.290625
-RCS,2014-10-19,35.424358974358974
-RCS,2014-10-26,101.88135593220339
 RCS,2014-10-27,115.73958333333333
 RCS,2014-10-28,96.55208333333333
 RCS,2014-10-29,81.35416666666667
@@ -1457,7 +1411,6 @@ RCS,2014-11-05,74.509375
 RCS,2014-11-06,53.697916666666664
 RCS,2014-11-07,36.395833333333336
 RCS,2014-11-08,55.302083333333336
-RCS,2015-08-07,263.859375
 RCS,2015-08-08,217.14583333333334
 RCS,2015-08-09,175.52083333333334
 RCS,2015-08-10,161.71875
@@ -1509,8 +1462,6 @@ RCS,2015-09-24,175.29166666666666
 RCS,2015-09-25,148.10416666666666
 RCS,2015-09-26,122.08333333333333
 RCS,2015-09-27,109.83333333333333
-RCS,2015-09-28,99.39285714285714
-RCS,2015-11-11,47.869565217391305
 RCS,2015-11-12,32.572916666666664
 RCS,2015-11-13,-0.9042105263157895
 RCS,2015-11-14,-6.759340659340659
@@ -1518,9 +1469,6 @@ RCS,2015-11-15,23.728125
 RCS,2015-11-16,31.661458333333332
 RCS,2016-05-29,103.83333333333333
 RCS,2016-05-30,83.94565217391305
-RCS,2016-05-31,52.64383561643836
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-RCS,2016-06-03,109.14893617021276
 RCS,2016-06-04,146.23958333333334
 RCS,2016-06-05,204.94791666666666
 RCS,2016-06-06,143.75
@@ -1531,9 +1479,6 @@ RCS,2016-06-10,139.90625
 RCS,2016-06-11,149.34375
 RCS,2016-06-12,124.02083333333333
 RCS,2016-06-13,98.83333333333333
-RCS,2016-06-14,75.52941176470588
-RCS,2016-06-15,44.45
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 RCS,2016-06-20,137.69791666666666
 RCS,2016-06-21,115.33333333333333
 RCS,2016-06-22,143.55208333333334
@@ -1541,9 +1486,6 @@ RCS,2016-06-23,126.23958333333333
 RCS,2016-06-24,129.88541666666666
 RCS,2016-06-25,133.78125
 RCS,2016-06-26,99.58333333333333
-RCS,2016-06-27,67.0047619047619
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-RCS,2016-07-12,182
 RCS,2016-07-13,266.6041666666667
 RCS,2016-07-14,378.7395833333333
 RCS,2016-07-15,438.5520833333333
@@ -1624,7 +1566,6 @@ RCS,2017-07-24,24.372527472527473
 RCS,2017-07-25,29.201041666666665
 RCS,2017-07-26,20.579166666666666
 RCS,2017-07-27,34.05833333333333
-RCS,2017-07-28,16.064285714285713
 RCS,2017-07-29,36.98229166666667
 RCS,2017-07-30,25.779166666666665
 RCS,2017-07-31,52.78125
@@ -1692,21 +1633,12 @@ RCS,2017-09-30,23.124468085106383
 RCS,2017-10-01,21.708333333333332
 RCS,2017-10-02,11.621875
 RCS,2017-10-03,21.672916666666666
-RCS,2017-10-04,5.328235294117647
 RCS,2017-10-05,10.707291666666666
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 RCS,2017-10-07,6.491578947368421
 RCS,2017-10-08,19.820833333333333
 RCS,2017-10-09,8.9375
 RCS,2017-10-10,0.45730337078651684
-RCS,2017-10-11,4.023529411764706
-RCS,2017-10-12,4.0875
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-RCS,2017-10-25,46.542857142857144
 RCS,2017-10-26,24.657777777777778
-RCS,2017-10-27,13.234177215189874
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 RCS,2017-10-29,51
 RCS,2017-10-30,73.95833333333333
 RCS,2017-10-31,65.73958333333333
@@ -1809,7 +1741,6 @@ RCS,2018-10-13,8.528125
 RCS,2018-10-14,25.322916666666668
 RCS,2018-10-15,10.509473684210526
 RCS,2018-10-16,2.6021978021978023
-RCS,2018-10-17,5.144262295081967
 RCS,2018-10-18,5.695744680851064
 RCS,2018-10-19,4.935416666666667
 RCS,2018-10-20,2.9791666666666665
@@ -1938,11 +1869,8 @@ RCS,2019-10-18,-4.324719101123596
 RCS,2019-10-19,7.014736842105263
 RCS,2019-10-20,22.23263157894737
 RCS,2019-10-21,25.810526315789474
-RCS,2019-10-22,8.228
 RCS,2019-10-23,7.801052631578948
 RCS,2019-10-24,18.946875
-RCS,2019-10-25,12.082432432432432
-RCS,2019-10-26,28.41219512195122
 RCS,2019-10-27,23.194791666666667
 RCS,2019-10-28,90.05208333333333
 RCS,2019-10-29,48.427083333333336
@@ -1950,11 +1878,9 @@ RCS,2019-10-30,15.940625
 RCS,2019-10-31,25.259375
 RCS,2019-11-01,19.94891304347826
 RCS,2019-11-02,25.79021739130435
-RCS,2019-11-03,24.83606557377049
 RCS,2019-11-04,19.433695652173913
 RCS,2019-11-05,39.297916666666666
 RCS,2019-11-06,29.829166666666666
-RD22,2014-09-30,45.4
 RD22,2014-10-01,44.965625
 RD22,2014-10-02,47.348958333333336
 RD22,2014-10-03,49.43958333333333
@@ -2295,18 +2221,6 @@ RD22,2017-09-04,14.572916666666666
 RD22,2017-09-05,16.438541666666666
 RD22,2017-09-06,17.305208333333333
 RD22,2017-09-07,15.998958333333334
-RD22,2017-09-08,16.258695652173913
-RD22,2017-09-09,19.4
-RD22,2017-09-10,20.45
-RD22,2017-09-11,21
-RD22,2017-09-12,20.4
-RD22,2017-09-13,20.700000000000003
-RD22,2017-09-14,20.975
-RD22,2017-09-15,15.636986301369863
-RD22,2017-09-16,13.161764705882353
-RD22,2017-09-17,17.4125
-RD22,2017-09-18,18.7375
-RD22,2017-09-19,14.673239436619719
 RD22,2017-09-20,13.095833333333333
 RD22,2017-09-21,13.121875
 RD22,2017-09-22,14.921875
@@ -2510,7 +2424,6 @@ RD22,2019-08-12,16.365625
 RD22,2019-08-13,18.140625
 RD22,2019-08-14,19.960416666666667
 RD22,2019-08-15,18.426041666666666
-RD22,2019-08-16,16.349333333333334
 RD22,2019-08-17,16.077173913043477
 RD22,2019-08-18,16.33125
 RD22,2019-08-19,15.897916666666667
@@ -3326,8 +3239,6 @@ RVB,2019-08-03,8890.416666666666
 RVB,2019-08-04,9094.583333333334
 RVB,2019-08-05,9051.666666666666
 RVB,2019-08-06,8452.083333333334
-RVB,2019-08-07,8847.5
-RVB,2019-08-15,8425
 RVB,2019-08-16,7677.5
 RVB,2019-08-17,8949.166666666666
 RVB,2019-08-18,11733.333333333334
@@ -3388,7 +3299,6 @@ RVB,2019-10-11,6105.416666666667
 RVB,2019-10-12,5169.166666666667
 RVB,2019-10-13,4818.333333333333
 RVB,2019-10-14,5382.5
-RVB,2019-10-15,5704.444444444444
 RVB,2019-10-26,5332.608695652174
 RVB,2019-10-27,5072.083333333333
 RVB,2019-10-28,3034.5833333333335
diff --git a/manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.rds b/manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.rds
index 9c18cf9fca7a96920abfb2cec63185d8bb807b62..7425c8354a56897faee3a6288fd0c20490cb25c7 100644
GIT binary patch
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diff --git a/manuscript_synthesis/data/processed/flow_week_avg_2013-2019.csv b/manuscript_synthesis/data/processed/flow_week_avg_2013-2019.csv
new file mode 100644
index 0000000..43c9b38
--- /dev/null
+++ b/manuscript_synthesis/data/processed/flow_week_avg_2013-2019.csv
@@ -0,0 +1,543 @@
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diff --git a/manuscript_synthesis/data/processed/flow_week_avg_2013-2019.rds b/manuscript_synthesis/data/processed/flow_week_avg_2013-2019.rds
new file mode 100644
index 0000000000000000000000000000000000000000..c6b818efd538906dd334ac76625169d761d0e7da
GIT binary patch
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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 b0fc57b..da1976d 100644
--- a/manuscript_synthesis/src/process_data_rtm_flow.R
+++ b/manuscript_synthesis/src/process_data_rtm_flow.R
@@ -1,13 +1,15 @@
 # NDFS Synthesis Manuscript
 # Purpose: Process and integrate the continuous flow data collected from the
-  # Yolo Bypass and downstream 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 and weekly
+  # averages which are used in figures and analysis for the NDFS synthesis
+  # manuscript.
 # Author: Dave Bosworth
 # Contacts: David.Bosworth@water.ca.gov
 
 # Load packages
 library(tidyverse)
 library(fs)
+library(readxl)
 library(here)
 library(conflicted)
 
@@ -24,11 +26,11 @@ i_am("manuscript_synthesis/src/process_data_rtm_flow.R")
 # Import Continuous Flow Data -----------------------------------------------
 
 # 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")
+fp_rtm_flow_sp <- 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"),
+  file = path(fp_rtm_flow_sp, "RTM_INPUT_all_2021-04-20.csv"),
   col_types = cols_only(
     StationCode = "c",
     DateTime = "c",
@@ -37,61 +39,116 @@ df_rtm_flow <- read_csv(
   )
 )
 
-# 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"))
+# Import instantaneous and tidally-filtered flow data from LIS collected from
+  # 2013-2022
+df_rtm_flow_lis <- read_excel(
+  here("Water_Quality/Continuous_WQ/data-raw/LIS_FlowData_2013_2022.xlsx"),
+  sheet = "15 Min Net Flow",
+  skip = 10
+)
 
 
 # Clean and Integrate Continuous Data -------------------------------------
 
-# 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 %>% 
+# Prepare tidally-filtered flow data collected at LIS to be combined to all
+  # other data
+df_rtm_flow_lis_c <- df_rtm_flow_lis %>% 
   transmute(
     StationCode = "LIS",
-    Date, 
-    Flow = LIS_DailyAvgNetFlow
+    DateTime = round_date(force_tz(`Date & Time (PST)`, tzone = "Etc/GMT+8"), unit = "15 minute"),
+    Date = date(DateTime),
+    Flow = `Net Flow (cfs)`
   ) %>% 
   drop_na(Flow) %>% 
   # only include data within the NDFS period of interest
+  filter(year(Date) %in% 2013:2019) %>% 
   ndfa_action_periods() %>% 
   drop_na(FlowActionPeriod) %>% 
   select(-c(Year, FlowActionPeriod))
 
+# Look for duplicated time stamps in the LIS flow data
+df_rtm_flow_lis_c %>%
+  count(DateTime) %>%
+  filter(n > 1)
+# No duplicated time stamps present in data set
+
 # Prepare all other continuous flow data for averaging
 df_rtm_flow_c <- df_rtm_flow %>% 
+  # only keep RCS, RD22, LIB, and RVB stations
+  filter(StationCode %in% c("RCS", "RD22", "LIB", "RVB")) %>% 
   mutate(
-    # parse date-time variable and define tz as PST; add date variable
+    # parse date-time variable and define tz as PST; add date variables
     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(
+    Flow = case_when(
       StationCode %in% c("RCS", "RD22") ~ Flow,
-      StationCode %in% c("LIB", "RVB") ~ FlowTF,
-      TRUE ~ NA_real_
+      StationCode %in% c("LIB", "RVB") ~ FlowTF
     )
   ) %>% 
-  # remove rows with NA values in FlowCombined, which only keeps RCS, RD22, LIB,
-    # and RVB
-  drop_na(FlowCombined)
+  # remove rows with NA values in Flow
+  drop_na(Flow) %>% 
+  # remove FlowTF variable because its no longer needed
+  select(-FlowTF) %>% 
+  # filter to years 2013-2019
+  filter(year(Date) %in% 2013:2019)
+
+# Combine LIS flow data to all other flow data
+df_rtm_flow_all <- 
+  bind_rows(df_rtm_flow_lis_c, df_rtm_flow_c) %>% 
+  # Add Year and Week variables
+  mutate(
+    Year = year(Date),
+    Week = week(Date)
+  )
 
 
 # Calculate Daily Averages ------------------------------------------------
 
-df_flow_dv <- df_rtm_flow_c %>% 
+# Inspect number of measurements per day for each station
+df_rtm_flow_all %>% 
+  count(StationCode, Date, name = "NumMeas") %>% 
+  count(StationCode, NumMeas, name = "NumDays") %>% 
+  arrange(StationCode, desc(NumMeas)) %>% 
+  print(n = 90)
+
+# Every station has at least a few days with large numbers of missing values.
+  # We'll only calculate daily averages for days that have 10% or fewer missing
+  # timestamps, which is 86-96 timestamps for LIS, RCS, and RD22 (collected at
+  # 15-min intervals) and 24-22 timestamps for LIB and RVB (collected at 1-hour
+  # intervals).
+
+df_flow_daily_avg <- df_rtm_flow_all %>% 
   # calculate daily average flow values
-  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) %>% 
+  summarize(
+    Flow = mean(Flow),
+    NumMeas = n(),
+    .by = c(StationCode, Date)
+  ) %>% 
+  # Remove days with >10% missing timestamps
+  filter(
+    StationCode %in% c("LIS", "RCS", "RD22") & NumMeas >= 86 | 
+      StationCode %in% c("LIB", "RVB") & NumMeas >= 22
+  ) %>% 
+  select(-NumMeas) %>% 
   arrange(StationCode, Date)
 
 
+# Calculate Weekly Averages -----------------------------------------------
+
+df_flow_week_avg <- df_rtm_flow_all %>% 
+  # calculate weekly average flow values
+  summarize(Flow = mean(Flow), .by = c(StationCode, Year, Week)) %>% 
+  arrange(StationCode, Year, Week)
+
+
 # Save and Export Data ----------------------------------------------------
 
-# 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"))
+# Save daily and weekly average flow data as csv and rds files
+df_flow_daily_avg %>% write_csv(here("manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.csv"))
+df_flow_daily_avg %>% saveRDS(here("manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.rds"))
+
+df_flow_week_avg %>% write_csv(here("manuscript_synthesis/data/processed/flow_week_avg_2013-2019.csv"))
+df_flow_week_avg %>% saveRDS(here("manuscript_synthesis/data/processed/flow_week_avg_2013-2019.rds"))