## ----set-options, echo=FALSE---------------------------------------------
knitr::opts_chunk$set(fig.width = 6, fig.height = 4)
## ---- eval=FALSE---------------------------------------------------------
# install.packages("remotes")
## ---- eval=FALSE---------------------------------------------------------
# library("remotes")
# install_github("bcgov/bcgroundwater")
## ------------------------------------------------------------------------
library(bcgroundwater)
## ---- message=FALSE, warning=FALSE---------------------------------------
daily_data <- get_gwl(wells = 309, which = "daily")
head(daily_data)
## ---- message=FALSE, warning=FALSE---------------------------------------
recent_data <- get_gwl(wells = 309, which = "recent")
head(recent_data)
## ---- message=FALSE, warning=FALSE---------------------------------------
data <- get_gwl(wells = 309)
head(data)
## ---- message=FALSE, warning=FALSE---------------------------------------
monthly_data <- monthly_values(data)
head(monthly_data)
## ---- message=FALSE, warning=FALSE, fig.align='center'-------------------
monthlyplot <- gwl_monthly_plot(monthly_data, last12 = TRUE)
plot(monthlyplot)
## ---- fig.width=6, fig.height=4, message=FALSE, warning=FALSE------------
full_monthly_data <- make_well_ts(monthly_data)
head(full_monthly_data)
## ---- message=FALSE, warning=FALSE---------------------------------------
library(dplyr)
time_series_attr <- full_monthly_data %>%
group_by(Well_Num) %>%
summarise(dataStart = as.Date(min(Date)),
dataEnd = as.Date(max(Date)),
dataYears = as.numeric(dataEnd - dataStart) / 365,
nObs = n(),
nMissing = length(med_GWL[nReadings == 0]),
percent_missing = round(nMissing/nObs*100, 1)) %>%
select(Well_Num, dataYears, nMissing, percent_missing)
time_series_attr
## ---- message=FALSE, warning=FALSE---------------------------------------
library(dplyr)
annual_data <- full_monthly_data %>%
select(-yearmonth) %>%
group_by(EMS_ID, Well_Num, Year) %>%
summarize(nReadings = n(),
mean_GWL = mean(med_GWL),
SD = sd(med_GWL),
med_GWL = median(med_GWL),
q95_GWL = quantile(med_GWL, 0.95),
n_months = n()) %>%
filter(n_months == 12)
head(annual_data)
## ---- message=FALSE, warning=FALSE---------------------------------------
trends <- gwl_zyp_test(annual_data, byID = "Well_Num", col = "mean_GWL")
trends
## ---- message=FALSE, warning=FALSE---------------------------------------
result <- trends %>%
filter(test_type == "yuepilon") %>%
mutate(trend_category = case_when(trend >= 0.1 & sig < 0.05 ~ "Large Rate of Decline",
trend >= 0.03 & trend < 0.1 & sig < 0.05 ~ "Moderate Rate of Decline",
trend <= -0.03 & sig < 0.05 ~ "Increasing",
TRUE ~ "Stable"))
result
## ---- message=FALSE, warning=FALSE, fig.align='center'-------------------
trend_plot <- gwl_area_plot(full_monthly_data, trend = result$trend,
intercept = result$intercept, sig = result$sig,
trend_category = result$trend_category,
mkperiod = "annual", showInterpolated = TRUE,
show_stable_line = TRUE)
plot(trend_plot)
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