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velocity.R
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velocity.R
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#merge all station data for pub
library(readr)
library(dplyr)
library(lubridate)
library(DroughtData)
library(glue)
#import 4 data sets
Cache <- read_rds("dv_cache.rds")
Jersey <- read_rds("dv_jersey.rds")
Middle <- read_rds("dv_middle.rds")
Old <- read_rds("dv_old.rds")
#round dates down to week
Cache_weekly <- Cache
Cache_weekly$week <- floor_date(Cache_weekly$Date, "week")
Jersey_weekly <- Jersey
Jersey_weekly$week <- floor_date(Jersey_weekly$Date, "week")
Middle_weekly <- Middle
Middle_weekly$week <- floor_date(Middle_weekly$Date, "week")
Old_weekly <- Old
Old_weekly$week <- floor_date(Old_weekly$Date, "week")
#downstep to week
Cache_week <- Cache_weekly%>%
group_by(week) %>%
summarize(max_tidal_vel = max(max_tidal),
min_tidal_vel = min(min_tidal),
max_net_vel = max(max_net),
min_net_vel = min(min_net),
max_abs_tidal = max(max_abs_tidal),
mean_net_vel = mean(mean_net))
Jersey_week <- Jersey_weekly%>%
group_by(week) %>%
summarize(max_tidal_vel = max(max_tidal),
min_tidal_vel = min(min_tidal),
max_net_vel = max(max_net),
min_net_vel = min(min_net),
max_abs_tidal = max(maxabs_tidal),
mean_net_vel = mean(mean_net))
Middle_week <- Middle_weekly%>%
group_by(week) %>%
summarize(max_tidal_vel = max(max_tidal),
min_tidal_vel = min(min_tidal),
max_net_vel = max(max_net),
min_net_vel = min(min_net),
max_abs_tidal = max(maxabs_tidal),
mean_net_vel = mean(mean_net))
Old_week <- Old_weekly%>%
group_by(week) %>%
summarize(max_tidal_vel = max(max_tidal),
min_tidal_vel = min(min_tidal),
max_net_vel = max(max_net),
min_net_vel = min(min_net),
max_abs_tidal = max(maxabs_tidal),
mean_net_vel = mean(mean_net))
#add station column and sign column back to dfs
Cache_week <- Cache_week %>%
mutate(net_sign=case_when(abs(min_net_vel) > max_net_vel ~ "-", abs(min_net_vel) < max_net_vel ~ "+"),
tide_sign=case_when(abs(min_tidal_vel) > max_tidal_vel ~ "-", abs(min_tidal_vel) < max_tidal_vel ~ "+"))
Cache_week$station <- "Cache"
Jersey_week <- Jersey_week %>%
mutate(net_sign=case_when(abs(min_net_vel) > max_net_vel ~ "-", abs(min_net_vel) < max_net_vel ~ "+"),
tide_sign=case_when(abs(min_tidal_vel) > max_tidal_vel ~ "-", abs(min_tidal_vel) < max_tidal_vel ~ "+"))
Jersey_week$station <- "Jersey"
Middle_week <- Middle_week %>%
mutate(net_sign=case_when(abs(min_net_vel) > max_net_vel ~ "-", abs(min_net_vel) < max_net_vel ~ "+"),
tide_sign=case_when(abs(min_tidal_vel) > max_tidal_vel ~ "-", abs(min_tidal_vel) < max_tidal_vel ~ "+"))
Middle_week$station <- "Middle"
Old_week <- Old_week %>%
mutate(net_sign=case_when(abs(min_net_vel) > max_net_vel ~ "-", abs(min_net_vel) < max_net_vel ~ "+"),
tide_sign=case_when(abs(min_tidal_vel) > max_tidal_vel ~ "-", abs(min_tidal_vel) < max_tidal_vel ~ "+"))
Old_week$station <- "Old"
#bind all dfs to one weekly df
vel_weekly <- rbind(Jersey_week, Old_week, Middle_week, Cache_week) %>%
mutate(Year = year(week), Month = month(week), YearAdj = case_when(Month == 12 ~ Year +1,
TRUE ~ Year))
#For the white paper we don't need the outflow and X2 stuff
yrs = read_csv("data/yearassignments.csv") %>%
rename(YearAdj = Year)
vel_weekly_WP = left_join(vel_weekly, yrs) %>%
filter(YearAdj != 2023) %>%
mutate(Whitepaper = factor(Whitepaper, levels = c("Critical", "Dry", "Below Normal", "Above Normal", "Wet", "2020", "2021", "2022")))
save(vel_weekly_WP, file = "data/vel_weekly_WP.RData")
###############################################################
#Now the stuff for the other data package
#make raw_hydro into weekly mean
#
#merge dayflow params and WY type
week_hydro <- subset(raw_hydro_1975_2021, Date > as.Date('2007-09-30'))
week_hydro$week <- floor_date(week_hydro$Date, "week")
week_hydro <- week_hydro%>%
group_by(week) %>%
summarize(X2 = mean(X2),
Export = mean(Export),
Outflow = mean(Outflow),
Inflow = mean(InflowTotal))
vel_weekly_pub <- left_join(vel_weekly, week_hydro, by = 'week')
lt_seas <- lt_avg_hydro[c(1, 4:5)]
lt_seas <- unique(lt_seas)
#vel_daily_pub <- merge(vel_daily_pub, lt_seas, by = "YearAdj")
vel_weekly_pub <- vel_weekly_pub %>%
# Add variables for adjusted calendar year and season
# Adjusted calendar year: December-November, with December of the previous calendar year
# included with the following year
mutate(
Month = month(week),
YearAdj = if_else(Month == 12, year(week) + 1, year(week)),
Season = case_when(
Month %in% 3:5 ~ "Spring",
Month %in% 6:8 ~ "Summer",
Month %in% 9:11 ~ "Fall",
Month %in% c(12, 1, 2) ~ "Winter"))
vel_weekly_pub <- merge(vel_weekly_pub, lt_seas, by = "YearAdj")
vel_weekly_pub$Log_Outflow <- log(vel_weekly_pub$Outflow)
#add wateryear to weekly data frame
wtr_yr <- function(Date, start_month=10) {
# Convert dates into POSIXlt
dates.posix = as.POSIXlt(Date)
# Year offset
offset = ifelse(dates.posix$mon >= start_month - 1, 1, 0)
# Water year
adj.year = dates.posix$year + 1900 + offset
# Return the water year
adj.year
}
vel_weekly_WY_pub <- vel_weekly_pub %>%
mutate(wtr_yr = wtr_yr(week))
vel_weekly_WY_pub <- vel_weekly_WY_pub %>%
group_by(wtr_yr) %>%
mutate(wtr_day = (as.integer(difftime(week,ymd(paste0(wtr_yr - 1 ,'-09-30')), units = "days"))))
vel_weekly <- vel_weekly_WY_pub%>%
#filter(!is.na(mean_vel))%>%
mutate(across(c(Drought, YearType),
list(`20_21`=~case_when(YearAdj==2021 ~ "2021",YearAdj==2020 ~ "2020",TRUE ~ as.character(.x)))),
across(c(YearType, YearType_20_21), ~factor(.x, levels=c("2020", "2021", "Critical", "Dry", "Below Normal", "Above Normal", "Wet"))),
Season=factor(Season, levels=c("Winter", "Spring", "Summer", "Fall")))
vel_metric <- vel_weekly %>%
#select(c(1,2,6,7,9,11:24)) %>%
mutate(max_tidal_vel_m = max_tidal_vel/3.2808399,
min_tidal_vel_m = min_tidal_vel/3.2808399,
max_net_vel_m = max_net_vel/3.2808399,
min_net_vel_m = min_net_vel/3.2808399,
max_abs_tidal_vel_m = max_abs_tidal/3.2808399,
mean_net_vel_m = mean_net_vel/3.2808399,
Export_m = Export/35.315,
Outflow_m = Outflow/35.315,
Inflow_m = Inflow/35.315,
Log_Outflow_m = log(Outflow_m),
Log_Inflow_m = log(Inflow_m),
Export_ft = Export,
Outflow_ft = Outflow,
Inflow_ft = Inflow,
Log_Outflow_ft = Log_Outflow,
Log_Inflow_ft = log(Inflow))
vel_metric <- vel_metric %>%
select(-c(3:8, 13:15, 20, 36:40))
write_rds(vel_metric, glue("data/velocity.rds"))