R/0412-analyze_fish_morice.R
In NewGraphEnvironment/fish_passage_bulkley_2020_reporting: Does not matter.
source('R/functions.R')
source('R/packages.R')
source('R/private_info.R')
##will just run again for the Morice
#!!!!!!!!!all the width data is gone so we need to use our archived version!!!!!!!!!!!!Scroll down to the hashed out section
##get the road info from the database
conn <- DBI::dbConnect(
RPostgres::Postgres(),
dbname = dbname,
host = host,
port = port,
user = user,
password = password
)
# # ##list tables in a schema
dbGetQuery(conn,
"SELECT table_name
FROM information_schema.tables
WHERE table_schema='bcfishpass'")
# # # # #
# # # # # ##list column names in a table
dbGetQuery(conn,
"SELECT column_name,data_type
FROM information_schema.columns
WHERE table_schema = 'bcfishpass'
and table_name='streams'")
dbGetQuery(conn,
"SELECT column_name,data_type
FROM information_schema.columns
WHERE table_name='fiss_fish_obsrvtn_events_sp'")
##have a look at the channel width measured
query = 'SELECT * FROM bcfishpass.channel_width_measured'
channel_width_measured <- st_read(conn, query = query)
##try the calculation from davies_etal2007ModelingStream
channel_width_measured2 <- channel_width_measured %>%
mutate(cw_daviesetal2007 = 0.042*(upstream_area_ha/100)^0.48*(map/10)*0.74) %>%
mutate(diff = cw_daviesetal2007 - channel_width_measured)
##get some stats for
query = "SELECT
fish_observation_point_id,
s.gradient,
s.stream_order,
s.stream_magnitude,
s.upstream_area_ha,
s.channel_width,
e.species_code,
round((ST_Z((ST_Dump(ST_LocateAlong(s.geom, e.downstream_route_measure))).geom))::numeric) as elevation
FROM whse_fish.fiss_fish_obsrvtn_events_sp e
INNER JOIN bcfishpass.streams s
ON e.linear_feature_id = s.linear_feature_id
WHERE e.watershed_group_code = 'MORR';"
##e.species_code = 'WCT' AND
# unique(fiss_sum$species_code)
species_of_interest <- c('BT', 'CH', 'CM', 'CO', 'CT', 'DV', 'PK', 'RB','SK', 'ST')
fiss_sum <- st_read(conn, query = query) %>%
filter(species_code %in% species_of_interest)
#!!!!!!!!!all the width data is gone so we need to use our archived version!!!!!!!!!!!!
##burn it all to a file we can use later
# fiss_sum %>% readr::write_csv(file = paste0(getwd(), '/data/extracted_inputs/fiss_sum.csv'))
# fiss_sum <- readr::read_csv(file = paste0(getwd(), '/data/extracted_inputs/fiss_sum.csv'))
#
# ##lets put it in the sqlite for safekeeping
# conn <- rws_connect("data/bcfishpass.sqlite")
# rws_list_tables(conn)
# rws_write(fiss_sum, exists = F, delete = TRUE,
# conn = conn, x_name = "fiss_sum")
# rws_list_tables(conn)
# rws_disconnect(conn)
######################################################################################################################
######################################################################################################################
#########################################START HERE#########################################################
######################################################################################################################
######################################################################################################################
fiss_sum_grad_prep1 <- fiss_sum %>%
mutate(Gradient = case_when(
gradient < .03 ~ '0 - 3 %',
gradient >= .03 & gradient < .05 ~ '03 - 5 %',
gradient >= .05 & gradient < .08 ~ '05 - 8 %',
gradient >= .08 & gradient < .15 ~ '08 - 15 %',
gradient >= .15 & gradient < .22 ~ '15 - 22 %',
gradient >= .22 ~ '22+ %')) %>%
mutate(gradient_id = case_when(
gradient < .03 ~ 3,
gradient >= .03 & gradient < .05 ~ 5,
gradient >= .05 & gradient < .08 ~ 8,
gradient >= .08 & gradient < .15 ~ 15,
gradient >= .15 & gradient < .22 ~ 22,
gradient >= .22 ~ 99))
fiss_sum_grad_prep2 <- fiss_sum_grad_prep1 %>%
group_by(species_code) %>%
summarise(total_spp = n())
fiss_sum_grad_prep3 <- fiss_sum_grad_prep1 %>%
group_by(species_code, Gradient, gradient_id) %>%
summarise(Count = n())
fiss_sum_grad <- left_join(
fiss_sum_grad_prep3,
fiss_sum_grad_prep2,
by = 'species_code'
) %>%
mutate(Percent = round(Count/total_spp * 100, 0))
##save this for the report
##burn it all to a file we can use later
# fiss_sum_grad %>% readr::write_csv(file = paste0(getwd(), '/data/extracted_inputs/fiss_sum_grad.csv'))
plot_grad <- fiss_sum_grad %>%
filter(gradient_id != 99) %>%
ggplot(aes(x = Gradient, y = Percent)) +
geom_bar(stat = "identity")+
facet_wrap(~species_code, ncol = 2)+
theme_bw(base_size = 11)+
labs(x = "Average Stream Gradient", y = "Occurrences (%)")
plot_grad
#############################CHANNEL WIDTH######################################
fiss_sum_width_prep1 <- fiss_sum %>%
mutate(Width = case_when(
channel_width < 2 ~ '0 - 2m',
channel_width >= 2 & channel_width < 4 ~ '02 - 04m',
channel_width >= 4 & channel_width < 6 ~ '04 - 06m',
channel_width >= 6 & channel_width < 10 ~ '06 - 10m',
channel_width >= 10 & channel_width < 15 ~ '10 - 15m',
# channel_width >= 15 & channel_width < 20 ~ '15 - 20m',
channel_width >= 15 ~ '15m+')) %>%
mutate(width_id = case_when(
channel_width < 2 ~ 2,
channel_width >= 2 & channel_width < 4 ~ 4,
channel_width >= 4 & channel_width < 6 ~ 6,
channel_width >= 6 & channel_width < 10 ~ 10,
channel_width >= 10 & channel_width < 15 ~ 15,
# channel_width >= 15 & channel_width < 20 ~ 20,
channel_width >= 15 ~ 99))
fiss_sum_width_prep2 <- fiss_sum_width_prep1 %>%
group_by(species_code) %>%
summarise(total_spp = n())
fiss_sum_width_prep3 <- fiss_sum_width_prep1 %>%
group_by(species_code, Width, width_id) %>%
summarise(Count = n())
fiss_sum_width <- left_join(
fiss_sum_width_prep3,
fiss_sum_width_prep2,
by = 'species_code'
) %>%
mutate(Percent = round(Count/total_spp * 100, 0))
##save this for the report
##burn it all to a file we can use later
# fiss_sum_width %>% readr::write_csv(file = paste0(getwd(), '/data/extracted_inputs/fiss_sum_width.csv'))
plot_width <- fiss_sum_width %>%
filter(!is.na(width_id)) %>%
ggplot(aes(x = Width, y = Percent)) +
geom_bar(stat = "identity")+
facet_wrap(~species_code, ncol = 2)+
ggdark::dark_theme_bw(base_size = 11)+
labs(x = "Channel Width", y = "Occurrences (%)")
plot_width
#############WATERSHED SIZE#################################################
# bin_1 <- min(fiss_sum$upstream_area_ha, na.rm = TRUE)
fiss_sum_wshed_filter <- fiss_sum %>%
filter(upstream_area_ha < 10000)
max(fiss_sum_wshed_filter$upstream_area_ha, na.rm = TRUE)
bin_1 <- 0
# bin_1 <- floor(min(fiss_sum_wshed_filter$upstream_area_ha, na.rm = TRUE)/5)*5
bin_n <- ceiling(max(fiss_sum_wshed_filter$upstream_area_ha, na.rm = TRUE)/5)*5
bins <- seq(bin_1,bin_n, by = 1000)
plot_wshed_hist <- ggplot(fiss_sum_wshed_filter, aes(x=upstream_area_ha
# fill=alias_local_name
# color = alias_local_name
)) +
geom_histogram(breaks = bins,
position="identity", size = 0.75)+
labs(x = "Upstream Watershed Area (ha)", y = "Count Fish (#)") +
facet_wrap(~species_code, ncol = 2)+
# scale_color_grey() +
# scale_fill_grey() +
theme_bw(base_size = 11)+
scale_x_continuous(breaks = bins[seq(1, length(bins), by = 2)])+
# scale_color_manual(values=c("grey90", "grey60", "grey30", "grey0"))+
# theme(axis.text.x = element_text(angle = 45, hjust = 1))+
geom_histogram(aes(y=..density..), breaks = bins, alpha=0.5,
position="identity", size = 0.75)
plot_wshed_hist
# fiss_sum_wshed_prep1 <- fiss_sum %>%
# mutate(Watershed = case_when(
# upstream_area_ha < 5 ~ '0 - 5m',
# upstream_area_ha >= 5 & upstream_area_ha < 10 ~ '05 - 10m',
# upstream_area_ha >= 10 & upstream_area_ha < 15 ~ '10 - 15m',
# upstream_area_ha >= 15 & upstream_area_ha < 20 ~ '15 - 20m',
# upstream_area_ha >= 20 ~ '20m+')) %>%
# mutate(watershed_id = case_when(
# upstream_area_ha < 5 ~ 5,
# upstream_area_ha >= 5 & upstream_area_ha < 10 ~ 10,
# upstream_area_ha >= 10 & upstream_area_ha < 15 ~ 15,
# upstream_area_ha >= 15 & upstream_area_ha < 20 ~ 20,
# upstream_area_ha >= 20 ~ 99))
#
# fiss_sum_width_prep2 <- fiss_sum_width_prep1 %>%
# group_by(species_code) %>%
# summarise(total_spp = n())
#
# fiss_sum_width_prep3 <- fiss_sum_width_prep1 %>%
# group_by(species_code, Width, width_id) %>%
# summarise(Count = n())
#
# fiss_sum_width <- left_join(
# fiss_sum_width_prep3,
# fiss_sum_width_prep2,
# by = 'species_code'
# ) %>%
# mutate(Percent = round(Count/total_spp * 100, 0))
#
# ##save this for the report
# ##burn it all to a file we can use later
# fiss_sum_width %>% readr::write_csv(file = paste0(getwd(), '/data/extracted_inputs/fiss_sum_width.csv'))
#
# plot_width <- fiss_sum_width %>%
# filter(!is.na(width_id)) %>%
# ggplot(aes(x = Width, y = Percent)) +
# geom_bar(stat = "identity")+
# facet_wrap(~species_code, ncol = 2)+
# theme_bw(base_size = 11)+
# labs(x = "Channel Width", y = "Occurrences (%)")
# plot_width
NewGraphEnvironment/fish_passage_bulkley_2020_reporting documentation built on July 9, 2024, 4:10 a.m.
R Package Documentation
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source('R/functions.R')
source('R/packages.R')
source('R/private_info.R')
##will just run again for the Morice
#!!!!!!!!!all the width data is gone so we need to use our archived version!!!!!!!!!!!!Scroll down to the hashed out section
##get the road info from the database
conn <- DBI::dbConnect(
RPostgres::Postgres(),
dbname = dbname,
host = host,
port = port,
user = user,
password = password
)
# # ##list tables in a schema
dbGetQuery(conn,
"SELECT table_name
FROM information_schema.tables
WHERE table_schema='bcfishpass'")
# # # # #
# # # # # ##list column names in a table
dbGetQuery(conn,
"SELECT column_name,data_type
FROM information_schema.columns
WHERE table_schema = 'bcfishpass'
and table_name='streams'")
dbGetQuery(conn,
"SELECT column_name,data_type
FROM information_schema.columns
WHERE table_name='fiss_fish_obsrvtn_events_sp'")
##have a look at the channel width measured
query = 'SELECT * FROM bcfishpass.channel_width_measured'
channel_width_measured <- st_read(conn, query = query)
##try the calculation from davies_etal2007ModelingStream
channel_width_measured2 <- channel_width_measured %>%
mutate(cw_daviesetal2007 = 0.042*(upstream_area_ha/100)^0.48*(map/10)*0.74) %>%
mutate(diff = cw_daviesetal2007 - channel_width_measured)
##get some stats for
query = "SELECT
fish_observation_point_id,
s.gradient,
s.stream_order,
s.stream_magnitude,
s.upstream_area_ha,
s.channel_width,
e.species_code,
round((ST_Z((ST_Dump(ST_LocateAlong(s.geom, e.downstream_route_measure))).geom))::numeric) as elevation
FROM whse_fish.fiss_fish_obsrvtn_events_sp e
INNER JOIN bcfishpass.streams s
ON e.linear_feature_id = s.linear_feature_id
WHERE e.watershed_group_code = 'MORR';"
##e.species_code = 'WCT' AND
# unique(fiss_sum$species_code)
species_of_interest <- c('BT', 'CH', 'CM', 'CO', 'CT', 'DV', 'PK', 'RB','SK', 'ST')
fiss_sum <- st_read(conn, query = query) %>%
filter(species_code %in% species_of_interest)
#!!!!!!!!!all the width data is gone so we need to use our archived version!!!!!!!!!!!!
##burn it all to a file we can use later
# fiss_sum %>% readr::write_csv(file = paste0(getwd(), '/data/extracted_inputs/fiss_sum.csv'))
# fiss_sum <- readr::read_csv(file = paste0(getwd(), '/data/extracted_inputs/fiss_sum.csv'))
#
# ##lets put it in the sqlite for safekeeping
# conn <- rws_connect("data/bcfishpass.sqlite")
# rws_list_tables(conn)
# rws_write(fiss_sum, exists = F, delete = TRUE,
# conn = conn, x_name = "fiss_sum")
# rws_list_tables(conn)
# rws_disconnect(conn)
######################################################################################################################
######################################################################################################################
#########################################START HERE#########################################################
######################################################################################################################
######################################################################################################################
fiss_sum_grad_prep1 <- fiss_sum %>%
mutate(Gradient = case_when(
gradient < .03 ~ '0 - 3 %',
gradient >= .03 & gradient < .05 ~ '03 - 5 %',
gradient >= .05 & gradient < .08 ~ '05 - 8 %',
gradient >= .08 & gradient < .15 ~ '08 - 15 %',
gradient >= .15 & gradient < .22 ~ '15 - 22 %',
gradient >= .22 ~ '22+ %')) %>%
mutate(gradient_id = case_when(
gradient < .03 ~ 3,
gradient >= .03 & gradient < .05 ~ 5,
gradient >= .05 & gradient < .08 ~ 8,
gradient >= .08 & gradient < .15 ~ 15,
gradient >= .15 & gradient < .22 ~ 22,
gradient >= .22 ~ 99))
fiss_sum_grad_prep2 <- fiss_sum_grad_prep1 %>%
group_by(species_code) %>%
summarise(total_spp = n())
fiss_sum_grad_prep3 <- fiss_sum_grad_prep1 %>%
group_by(species_code, Gradient, gradient_id) %>%
summarise(Count = n())
fiss_sum_grad <- left_join(
fiss_sum_grad_prep3,
fiss_sum_grad_prep2,
by = 'species_code'
) %>%
mutate(Percent = round(Count/total_spp * 100, 0))
##save this for the report
##burn it all to a file we can use later
# fiss_sum_grad %>% readr::write_csv(file = paste0(getwd(), '/data/extracted_inputs/fiss_sum_grad.csv'))
plot_grad <- fiss_sum_grad %>%
filter(gradient_id != 99) %>%
ggplot(aes(x = Gradient, y = Percent)) +
geom_bar(stat = "identity")+
facet_wrap(~species_code, ncol = 2)+
theme_bw(base_size = 11)+
labs(x = "Average Stream Gradient", y = "Occurrences (%)")
plot_grad
#############################CHANNEL WIDTH######################################
fiss_sum_width_prep1 <- fiss_sum %>%
mutate(Width = case_when(
channel_width < 2 ~ '0 - 2m',
channel_width >= 2 & channel_width < 4 ~ '02 - 04m',
channel_width >= 4 & channel_width < 6 ~ '04 - 06m',
channel_width >= 6 & channel_width < 10 ~ '06 - 10m',
channel_width >= 10 & channel_width < 15 ~ '10 - 15m',
# channel_width >= 15 & channel_width < 20 ~ '15 - 20m',
channel_width >= 15 ~ '15m+')) %>%
mutate(width_id = case_when(
channel_width < 2 ~ 2,
channel_width >= 2 & channel_width < 4 ~ 4,
channel_width >= 4 & channel_width < 6 ~ 6,
channel_width >= 6 & channel_width < 10 ~ 10,
channel_width >= 10 & channel_width < 15 ~ 15,
# channel_width >= 15 & channel_width < 20 ~ 20,
channel_width >= 15 ~ 99))
fiss_sum_width_prep2 <- fiss_sum_width_prep1 %>%
group_by(species_code) %>%
summarise(total_spp = n())
fiss_sum_width_prep3 <- fiss_sum_width_prep1 %>%
group_by(species_code, Width, width_id) %>%
summarise(Count = n())
fiss_sum_width <- left_join(
fiss_sum_width_prep3,
fiss_sum_width_prep2,
by = 'species_code'
) %>%
mutate(Percent = round(Count/total_spp * 100, 0))
##save this for the report
##burn it all to a file we can use later
# fiss_sum_width %>% readr::write_csv(file = paste0(getwd(), '/data/extracted_inputs/fiss_sum_width.csv'))
plot_width <- fiss_sum_width %>%
filter(!is.na(width_id)) %>%
ggplot(aes(x = Width, y = Percent)) +
geom_bar(stat = "identity")+
facet_wrap(~species_code, ncol = 2)+
ggdark::dark_theme_bw(base_size = 11)+
labs(x = "Channel Width", y = "Occurrences (%)")
plot_width
#############WATERSHED SIZE#################################################
# bin_1 <- min(fiss_sum$upstream_area_ha, na.rm = TRUE)
fiss_sum_wshed_filter <- fiss_sum %>%
filter(upstream_area_ha < 10000)
max(fiss_sum_wshed_filter$upstream_area_ha, na.rm = TRUE)
bin_1 <- 0
# bin_1 <- floor(min(fiss_sum_wshed_filter$upstream_area_ha, na.rm = TRUE)/5)*5
bin_n <- ceiling(max(fiss_sum_wshed_filter$upstream_area_ha, na.rm = TRUE)/5)*5
bins <- seq(bin_1,bin_n, by = 1000)
plot_wshed_hist <- ggplot(fiss_sum_wshed_filter, aes(x=upstream_area_ha
# fill=alias_local_name
# color = alias_local_name
)) +
geom_histogram(breaks = bins,
position="identity", size = 0.75)+
labs(x = "Upstream Watershed Area (ha)", y = "Count Fish (#)") +
facet_wrap(~species_code, ncol = 2)+
# scale_color_grey() +
# scale_fill_grey() +
theme_bw(base_size = 11)+
scale_x_continuous(breaks = bins[seq(1, length(bins), by = 2)])+
# scale_color_manual(values=c("grey90", "grey60", "grey30", "grey0"))+
# theme(axis.text.x = element_text(angle = 45, hjust = 1))+
geom_histogram(aes(y=..density..), breaks = bins, alpha=0.5,
position="identity", size = 0.75)
plot_wshed_hist
# fiss_sum_wshed_prep1 <- fiss_sum %>%
# mutate(Watershed = case_when(
# upstream_area_ha < 5 ~ '0 - 5m',
# upstream_area_ha >= 5 & upstream_area_ha < 10 ~ '05 - 10m',
# upstream_area_ha >= 10 & upstream_area_ha < 15 ~ '10 - 15m',
# upstream_area_ha >= 15 & upstream_area_ha < 20 ~ '15 - 20m',
# upstream_area_ha >= 20 ~ '20m+')) %>%
# mutate(watershed_id = case_when(
# upstream_area_ha < 5 ~ 5,
# upstream_area_ha >= 5 & upstream_area_ha < 10 ~ 10,
# upstream_area_ha >= 10 & upstream_area_ha < 15 ~ 15,
# upstream_area_ha >= 15 & upstream_area_ha < 20 ~ 20,
# upstream_area_ha >= 20 ~ 99))
#
# fiss_sum_width_prep2 <- fiss_sum_width_prep1 %>%
# group_by(species_code) %>%
# summarise(total_spp = n())
#
# fiss_sum_width_prep3 <- fiss_sum_width_prep1 %>%
# group_by(species_code, Width, width_id) %>%
# summarise(Count = n())
#
# fiss_sum_width <- left_join(
# fiss_sum_width_prep3,
# fiss_sum_width_prep2,
# by = 'species_code'
# ) %>%
# mutate(Percent = round(Count/total_spp * 100, 0))
#
# ##save this for the report
# ##burn it all to a file we can use later
# fiss_sum_width %>% readr::write_csv(file = paste0(getwd(), '/data/extracted_inputs/fiss_sum_width.csv'))
#
# plot_width <- fiss_sum_width %>%
# filter(!is.na(width_id)) %>%
# ggplot(aes(x = Width, y = Percent)) +
# geom_bar(stat = "identity")+
# facet_wrap(~species_code, ncol = 2)+
# theme_bw(base_size = 11)+
# labs(x = "Channel Width", y = "Occurrences (%)")
# plot_width
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