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index.Rmd
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index.Rmd
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---
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
source("src/00_analysis_setup.R")
```
# Grab data
```{r}
#discrete sample data and location metadata from most recent pub
DOI = "10372690"
data_version = "v12.13.23b"
source("src/analysis_fxns/grab_zenodo_data.R")
all_params <- chem_units%>%
pull(simple)
# sensor data
source("src/analysis_fxns/grab_sensor_data.R")
```
## Tidy discrete
```{r}
tidy_chem <- most_recent_chem%>%
filter(Year > 2020)%>%
mutate(Date = as.Date(Date,format = "%m/%d/%y"),
distance_upstream_km = case_when(site_code == "PFAL" ~ 73.9, TRUE ~ distance_upstream_km))%>%
left_join(order_table, by = "site_code")
#site order now added to tidy chem and thus not needed
rm(order_table)
```
## Run above to get data loaded
```{r}
print("Data Successfully loaded for analysis")
```
# Map
Things in map
WQ sensor sites
JWC+BS res
Beav Sites
Mainstem
Things to include in the site
- what part of the study
_ # of samples per year
- if sensor, POR for sensor deployment and # of match ups between sonde and grab
- if reservoir, # of RS match ups
## Create data frames
### All sites samples
```{r}
sample_nums <- tidy_chem %>%
# mutate(site_code = case_when(
# site_code == "SLEP" ~"PFAL",
# TRUE ~ site_code
# ))%>%
group_by(site_code, Year) %>%
count(name = "samples") %>%
pivot_wider(names_from = Year, values_from = samples, values_fill = 0)%>%
mutate(`# of samples (2021 & 2022)` = `2021` +`2022`)%>%
select(site_code, `# of samples (2023)` = `2023`, `# of samples (2021 & 2022)`)
```
### sensors
```{r}
PBR <- tibble(site_code = "PBR",
min_date = as.Date("2023-4-20"),
max_date = as.Date("2023-11-13"),
POR = "2023-4-20 : 2023-11-13",
sensor_measurements = 13364,
unique_parameters = "pH, Turbidity, Specific Conductivity, DO, Temperature")
sensor_POR <- sensor_data %>%
filter(parameter %nin% c("% Saturation O₂","Resistivity", "ORP", "Actual Conductivity" ))%>%
group_by(site)%>%
dplyr::summarise(min_date = as.Date(min(timestamp)),
max_date = as.Date(max(timestamp)),
sensor_measurements = n_distinct(timestamp),
unique_parameters = toString(unique(parameter))
)%>%
ungroup()%>%
mutate(POR = paste0(min_date, " : ", max_date),
site_code = toupper(site))%>%
plyr::rbind.fill(PBR)
matching_counts <- tidy_chem %>%
inner_join(sensor_POR, by = "site_code") %>%
filter(between(x = Date, left = min_date, right = max_date)) %>%
group_by(site_code) %>%
count(name = "Sensor and Grab Matches (2023)")
sensor_sites <- sensor_POR%>%
left_join(matching_counts, by = "site_code")%>%
left_join(most_recent_meta, by = "site_code")%>%
left_join(sample_nums, by = "site_code")%>%
st_as_sf(coords = c("Long", "Lat"), crs = 4326)%>%
select(Site_Name, POR, `# of samples (2023)`,`Sensor and Grab Matches (2023)`,sensor_measurements, unique_parameters,watershed, `# of samples (2021 & 2022)`)
mapview::mapview(sensor_sites, layer.name = "Water Quality Sensor Sites",col.regions = "#34b1eb", cex = 8)
```
### reservoirs
```{r}
CLP_RS <- read_feather("data/RS/CLP_point_DSWE1_for_analysis_2023-12-08.feather")%>%
filter(year(date)%in% c(2021, 2022, 2023)& GNIS_Name %in% c("Chambers Lake", "Comanche Reservoir",
"Barnes Meadow Reservoir",
"Joe Wright Reservoir", "Long Draw Reservoir",
"Hourglass Reservoir", "Peterson Lake" ))%>%
select(site_name = GNIS_Name, date)%>%
mutate(site_code = case_when(
site_name == "Chambers Lake" ~ "CBRR",
site_name == "Barnes Meadow Reservoir" ~ "BRNR",
site_name == "Joe Wright Reservoir" ~ "JOER",
site_name == "Long Draw Reservoir" ~ "LNGR",
site_name == "Peterson Lake" ~ "PTRR",
site_name == "Comanche Reservoir" ~ "COMR",
site_name == "Hourglass Reservoir" ~ "HORR"))%>%
rename(RS_date = date)%>%
distinct()
# for each RS image, see if there is a grab sample at the same site and the grab date is within +-3 days of RS image
RS_match_ups <- tibble()
for(i in 1:nrow(CLP_RS)){
rs_loop <- tidy_chem%>%
filter(site_code == CLP_RS$site_code[i] & Date >= (CLP_RS$RS_date[i] - days(4)) & Date <= (CLP_RS$RS_date[i] + days(4)))%>%
mutate(RS_date = CLP_RS$RS_date[i])%>%
rename(grab_date = Date)
RS_match_ups <- rbind(RS_match_ups, rs_loop)
rm(rs_loop, i)
}
RS_matches_summary <- RS_match_ups%>%
group_by(site_code, Year)%>%
summarise(`Grab and RS Matches (within 4 days)` = n())
images_overall <- CLP_RS%>%
filter(month(RS_date)>=5 & month(RS_date)<=11)%>%
mutate(Year = year(RS_date))%>%
group_by(Year, site_code)%>%
summarise(`RS Images` = n())%>%
left_join(RS_matches_summary, by = c("Year", "site_code"))
images_2021_2022 <- filter(images_overall, Year %in% c(2021, 2022))%>%
group_by(site_code)%>%
summarise(`RS Images (2021 and 2022)` = sum(`RS Images`),
`Grab and RS Matches (2021 and 2022)` = sum(`Grab and RS Matches (within 4 days)`))
reservoir_sites <- filter(images_overall, Year == 2023)%>%
left_join(images_2021_2022, by = "site_code")%>%
left_join(most_recent_meta, by = "site_code")%>%
left_join(sample_nums, by = "site_code")%>%
st_as_sf(coords = c("Long", "Lat"), crs = 4326)%>%
select(Site_Name, `2023 Grab and RS Matches (within 4 days)` = `Grab and RS Matches (within 4 days)`,
`2023 RS Images` = `RS Images`, `# of samples (2023)`, `Grab and RS Matches (2021 and 2022)`,`RS Images (2021 and 2022)`, `# of samples (2021 & 2022)`)
mapview::mapview(reservoir_sites, layer.name = "Reservoir Sites",col.regions = "#009E73", cex = 8)+
mapview::mapview(sensor_sites, layer.name = "Water Quality Sensor Sites",col.regions = "#34b1eb", cex = 8)
```
### Additional Grab locations
all the sites we sample with no sensor and no RS
```{r}
add_sampling <- filter(tidy_chem, Campaign %in% c("Mainstem","South Fork","Chambers Complex","Longdraw" )& site_code %nin% unique(CLP_RS$site_code) & site_code %nin% c("JOEI", "CBRI", "PBR", "CHD","LRT", "SFM", "PBD", "SLEP")& watershed != "CLP Mainstem-Fort Collins" )%>%
left_join(sample_nums, by = "site_code")%>%
st_as_sf(coords = c("Long", "Lat"), crs = 4326)%>%
select(Site, `# of samples (2023)`, `# of samples (2021 & 2022)`, watershed)
Manners_bridge <- tibble(Site_Name = "Manner's Bridge WQ",
POR = "2023-3-23 : 2023-11-13",
sensor_measurements = 28712,
unique_parameters = "pH, Turbidity, Specific Conductivity, DO, Temperature",
Long = -105.268816,
Lat = 40.696629 )%>%
st_as_sf(coords = c("Long", "Lat"), crs = 4326)
mapview::mapview(reservoir_sites, layer.name = "Sampled Reservoir Sites",col.regions = "#009E73", cex = 8)+
mapview::mapview(sensor_sites, layer.name = "Sampled Water Quality Sensor Sites",col.regions = "#34b1eb", cex = 8)+
mapview::mapview(add_sampling, layer.name = "Additional Water Sampling",col.regions = "#D55E00", cex = 4)+
mapview::mapview(Manners_bridge, layer.name = "Manner's Bridge WQ: FC Monitoring", col.regions = "#CC79A7", cex= 6 )
```
# Map for funder report
```{r}
sites <- most_recent_meta%>%
select(site_code, Lat, Long)%>%
filter(site_code %in% c(
#Reservoirs
"JOEI", "JOER", "CBRI", "CBRR", "CHD", "BRNR", "PTRR", "LNGR", "COMR", "HORR",
#Mainstem
"JWC", "PJW", "PFAL", "PBR", "SFM", "PSF", "PNF", "PBD",
# Trib
"NJOE", "ROAR", "LRT", "SHEP", "BLAK", "FISH", "BEAV", "BENN", "PENN")
)%>%
mutate( map_2021_2022 = case_when(
site_code %in% c("JOEI", "JOER", "CBRI", "CBRR", "CHD", "BRNR", "PTRR", "LNGR") ~ "weekly",
site_code %in% c("JWC", "PJW", "PFAL", "PBR", "SFM", "PSF", "PNF", "PBD") ~ "bi-weekly",
site_code %in% c("COMR", "HORR") ~ "monthly",
site_code %in% c("NJOE", "ROAR", "LRT", "SHEP", "BLAK", "FISH", "BEAV", "BENN", "PENN") ~ "trib"
),
map_2023 = case_when(
site_code %in% c( "JOER", "BRNR", "PTRR", "LNGR", "JWC", "PJW", "PBR", "PSF", "PNF", "COMR", "HORR", "BEAV") ~ "bi and monthly",
site_code %in% c("JOEI", "CBRI", "CHD","PFAL", "SFM", "PBD" ) ~ "sensor",
site_code %in% c("NJOE", "ROAR", "LRT", "SHEP", "BLAK", "FISH", "BENN", "PENN") ~ "trib"
))%>%
st_as_sf(coords = c("Long", "Lat"), crs = 4326)
map <- mapview::mapview(filter(sites, map_2021_2022 == "trib"), layer.name = "trib",col.regions = "#999999", cex = 6, alpha =1)+ mapview::mapview(filter(sites, map_2021_2022 == "weekly"), layer.name = "weekly",col.regions = "#ff0303", cex = 10, alpha = 1 )+
mapview::mapview(filter(sites, map_2021_2022 == "bi-weekly" ), layer.name = "biweek",col.regions = "#03ffff", cex = 10,alpha = 1)+
mapview::mapview(filter(sites, map_2021_2022 == "monthly"), layer.name = "comr",col.regions = "#184dff", cex = 10,alpha = 1)
map
map <- mapview::mapview(filter(sites, map_2023 == "trib"), layer.name = "trib",col.regions = "#999999", cex = 5, alpha =1)+
mapview::mapview(filter(sites, map_2023 == "bi and monthly" ), layer.name = "biweek",col.regions = "#BE398D", cex = 6,alpha = 1)+ mapview::mapview(filter(sites, map_2023 == "sensor"), layer.name = "weekly",col.regions = "#000000", cex = 6, alpha = 1 )
map
```