In SimonCoulombe/gwells_shiny: A shiny app for GWELLS QAQC
suppressPackageStartupMessages({
library(gwellsshiny)
library(bcmaps)
library(bcdata)
library(shiny)
library(leaflet)
library(DT)
library(RPostgres)
library(plotly)
library(pool)
library(DBI)
library(sf)
library(mapview)
library(dplyr)
library(stringr)
library(forcats)
library(hrbrthemes)
library(plotly)
library(tidyr)
library(gt)
library(glue)
})
hrbrthemes::import_roboto_condensed()
ggplot2::theme_set(hrbrthemes::theme_ipsum_rc()) # this ggplot2 theme uses roboto condensed font, which works well with the font used for the whole document.
options(ggplot2.discrete.fill = function() scale_fill_brewer(type = "qual", palette = "Set3")) # these scales are colorbliend friendly and start with "cooperators blue"
options(ggplot2.continuous.fill = function() scale_fill_viridis_c())
options(ggplot2.discrete.colour = function() scale_color_brewer(type = "qual", palette = "Set3"))
options(ggplot2.continuous.colour = function() scale_color_viridis_c())
start_date <- "2021-12-13"
end_date <- "2021-12-17"
# pool <- dbPool(
# drv = RPostgres::Postgres(),
# dbname = Sys.getenv("BCGOV_DB"),
# host = Sys.getenv("BCGOV_HOST"),
# port = 5432,
# user = Sys.getenv("BCGOV_USR"),
# password = Sys.getenv("BCGOV_PWD")
# )
# onStop(function() {
# poolClose(pool)
# })
con1 <- DBI::dbConnect(
#RPostgreSQL::PostgreSQL(),
RPostgres::Postgres(),
dbname = Sys.getenv("BCGOV_DB"),
host = Sys.getenv("BCGOV_HOST"),
user = Sys.getenv("BCGOV_USR"),
password=Sys.getenv("BCGOV_PWD")
)
TEST
qa <- dbGetQuery(con1, "select * from wells_qa ") # where well_tag_number > 124000
geocode <- dbGetQuery(con1, "select * from wells_geocoded ") # where well_tag_number > 124000
wells <- dbGetQuery(con1, "select * from wells") # where well_tag_number > 124000
drilling_method <- dbGetQuery(con1, "select well_tag_number,drilling_method_code from drilling_method ") #where well_tag_number > 124000
drilling_method_summary <- drilling_method %>%
group_by(well_tag_number) %>%
summarise(
has_air_rotary = max(str_detect(toupper(drilling_method_code), "AIR_ROTARY")),
has_auger = max(str_detect(toupper(drilling_method_code), "AUGER")),
has_cable_tool = max(str_detect(toupper(drilling_method_code), "CABLE_TOOL")),
has_driving = max(str_detect(toupper(drilling_method_code), "DRIVING")),
has_dugout = max(str_detect(toupper(drilling_method_code), "DUGOUT")),
has_duo_rotary = max(str_detect(toupper(drilling_method_code), "DUO_ROTARY")),
has_excavating = max(str_detect(toupper(drilling_method_code), "EXCAVATING")),
has_jetting = max(str_detect(toupper(drilling_method_code), "JETTING")),
has_mud_rotary = max(str_detect(toupper(drilling_method_code), "MUD_ROTARY")),
has_other = max(str_detect(toupper(drilling_method_code), "OTHER")),
has_unk = max(str_detect(toupper(drilling_method_code), "UNK"))
)
colnames_in_more_than_one_db <- c("identification_plate_number", "well_identification_plate_attached", "well_status_code", "well_class_code", "well_subclass", "licenced_status_code", "intended_water_use_code", "observation_well_number", "obs_well_status_code", "water_supply_system_name", "water_supply_system_well_name", "street_address", "city", "legal_lot", "legal_plan", "legal_district_lot", "legal_block", "legal_section", "legal_township", "legal_range", "land_district_code", "legal_pid", "well_location_description", "latitude_decdeg", "longitude_decdeg", "utm_zone_code", "utm_northing", "utm_easting", "coordinate_acquisition_code", "construction_start_date", "construction_end_date", "alteration_start_date", "alteration_end_date", "decommission_start_date", "decommission_end_date", "driller_name", "consultant_name", "consultant_company", "diameter_inches", "total_depth_drilled_ft_bgl", "finished_well_depth_ft_bgl", "final_casing_stick_up_inches", "bedrock_depth_ft_bgl", "ground_elevation_ft_asl", "ground_elevation_method_code", "static_water_level_ft_btoc", "well_yield_usgpm", "well_yield_unit_code", "artesian_flow_usgpm", "artesian_pressure_psi", "well_cap_type", "well_disinfected_code", "well_orientation_code", "alternative_specs_submitted", "surface_seal_material_code", "surface_seal_method_code", "surface_seal_depth_ft", "backfill_type", "backfill_depth_ft", "liner_material_code", "liner_diameter_inches", "liner_thickness_inches", "surface_seal_thickness_inches", "liner_from_ft_bgl", "liner_to_ft_bgl", "screen_intake_method_code", "screen_type_code", "screen_material_code", "other_screen_material", "screen_information", "screen_opening_code", "screen_bottom_code", "other_screen_bottom", "filter_pack_from_ft", "filter_pack_to_ft", "filter_pack_material_code", "filter_pack_thickness_inches", "filter_pack_material_size_code", "development_hours", "development_notes", "water_quality_colour", "water_quality_odour", "yield_estimation_method_code", "yield_estimation_rate_usgpm", "yield_estimation_duration_hours", "static_level_before_test_ft_btoc", "drawdown_ft_btoc", "hydro_fracturing_performed", "hydro_fracturing_yield_increase", "decommission_reason", "decommission_method_code", "decommission_details", "decommission_sealant_material", "decommission_backfill_material", "comments", "ems", "person_responsible", "company_of_person_responsible", "aquifer_id", "avi_years", "storativity", "transmissivity_m_2_s", "hydraulic_conductivity_m_s", "specific_storage_1_m", "specific_yield", "testing_method", "testing_duration_hours", "analytic_solution_type", "boundary_effect_code", "aquifer_lithology_code", "artesian_pressure_head_ft_agl", "artesian_conditions", "full_address", "civic_number", "civic_number_suffix", "street_name", "street_type", "is_street_type_prefix", "street_direction", "is_street_direction_prefix", "street_qualifier", "locality_name")
full_data <- wells %>%
left_join(geocode) %>%
left_join(qa %>% select(-all_of(colnames_in_more_than_one_db))) %>%
left_join(drilling_method_summary) %>%
filter(date_added >= start_date, date_added <= end_date)
interesting_columns <- c("well_tag_number", "identification_plate_number",
"date_added", "date_geocoded", "date_qa",
"construction_start_date", "construction_end_date",
"decommission_start_date", "decommission_end_date",
"alteration_start_date", "alteration_end_date",
"longitude_decdeg", "latitude_decdeg",
"well_class_code", "well_subclass",
"intended_water_use_code",
"finished_well_depth_ft_bgl" , "person_responsible", "company_of_person_responsible",
"artesian_conditions", "comments",
"nr_region_name")
z <- full_data %>%
mutate(
cross_referenced = replace_na(stringr::str_detect(comments, "xref|x-ref|cross ref|cross-ref"), FALSE),
max_date = pmax(coalesce(construction_start_date, construction_end_date, alteration_start_date, alteration_end_date, decommission_start_date, decommission_end_date)),
worktype = factor(case_when(
decommission_start_date == max_date | decommission_end_date == max_date ~ "decommission",
alteration_start_date == max_date | alteration_end_date == max_date ~ "alteration",
construction_start_date == max_date | construction_end_date == max_date ~ "construction",
TRUE ~ "no date"
), levels = c("construction", "alteration", "decommission", "no date")
),
old_or_unknown_date = max_date < lubridate::ymd("20160301") | is.na(max_date),
my_well_type = case_when(
artesian_conditions == "True" ~ "artesian",
well_class_code == "WATR_SPPLY" ~ "water supply",
well_class_code == "RECHARGE" ~ "recharge",
well_class_code == "INJECTION" ~ "injection",
well_class_code == "DEW_DRA" ~ "dewatering/discharge",
well_class_code == "CLS_LP_GEO" ~ "closed loop geo",
TRUE ~ NA_character_
),
table1_missing_lat_long_flag = case_when(
is.na(my_well_type) | old_or_unknown_date ~ FALSE,
is.na(latitude_decdeg) | is.na(longitude_decdeg) ~ TRUE,
!is.na(latitude_decdeg) | is.na(longitude_decdeg) ~ FALSE
),
table1_table1_missing__wdip_flag = case_when(
is.na(my_well_type) | old_or_unknown_date ~ FALSE,
is.na(well_identification_plate_attached) ~ TRUE,
!is.na(well_identification_plate_attached) ~ FALSE
),
table1_missing_finished_well_depth_flag = case_when(
is.na(my_well_type) | old_or_unknown_date ~ FALSE,
is.na(finished_well_depth_ft_bgl) ~ TRUE,
!is.na(finished_well_depth_ft_bgl) ~ FALSE
),
table1_missing_person_responsible_flag = case_when(
is.na(my_well_type) | old_or_unknown_date ~ FALSE,
is.na(person_responsible) ~ TRUE,
!is.na(person_responsible) ~ FALSE
),
table1_flag = table1_missing_lat_long_flag + table1_table1_missing__wdip_flag + table1_missing_finished_well_depth_flag + table1_missing_person_responsible_flag,
table2_flag = case_when(
distance_to_matching_pid >400 ~ TRUE,
0< score_address & score_address < 80 ~ TRUE,
0 <score_city ~ TRUE,
TRUE ~ FALSE
),
table3_missing_lat_long_flag = case_when(
is.na(my_well_type) | !old_or_unknown_date ~ FALSE,
is.na(latitude_decdeg) | is.na(longitude_decdeg) ~ TRUE,
!is.na(latitude_decdeg) | is.na(longitude_decdeg) ~ FALSE
),
table3_missing_finished_well_depth_flag = case_when(
is.na(my_well_type) | !old_or_unknown_date ~ FALSE,
is.na(finished_well_depth_ft_bgl) ~ TRUE,
!is.na(finished_well_depth_ft_bgl) ~ FALSE
),
table3_flag = table3_missing_lat_long_flag+ table3_missing_finished_well_depth_flag,
my_intended_water_use =
factor(if_else(well_class_code == "WATR_SPPLY", intended_water_use_code, "SPECIALIZED"),
levels = c("DOM", "UNK","DWS","COM","IRR","OTHER","TST","OBS","OP_LP_GEO", "SPECIALIZED")
)
)%>%
mutate(
fct_well_class_code = factor(well_class_code, levels = c("WATR_SPPLY", "UNK", "MONITOR", "DEW_DRA", "CLS_LP_GEO" ,"GEOTECH" ,"REMEDIATE" ,"INJECTION", "RECHARGE"))
)
table1 <- z %>% filter(table1_flag >0 ) %>% arrange(desc(table1_flag), desc(well_tag_number)) %>%
select(well_tag_number,table1_flag, my_well_type, table1_missing_lat_long_flag, table1_table1_missing__wdip_flag, table1_missing_finished_well_depth_flag, table1_missing_person_responsible_flag, company_of_person_responsible)
table1 %>%
datatable(caption =glue("table 1, from {start_date} to {end_date}"))
table2 <- z %>% filter(table2_flag) %>%
arrange(desc(score_address), desc(well_tag_number)) %>%
select(well_tag_number, distance_to_matching_pid, score_address, score_location_description, score_city, worktype, company_of_person_responsible)
table2 %>%
tail(1000) %>%
datatable(caption =glue("table 2, from {start_date} to {end_date} (showing only last 1000 records)"))
table3 <- z %>% filter(table3_flag >0 ) %>% arrange(desc(table3_flag), desc(well_tag_number)) %>%
select(well_tag_number,table3_flag, my_well_type, table3_missing_lat_long_flag,table3_missing_finished_well_depth_flag, company_of_person_responsible)
table3 %>%
datatable(caption =glue("table 3, from {start_date} to {end_date}"))
z %>%
count(fct_well_class_code, my_intended_water_use) %>%
ggplot()+
geom_col(aes(x=fct_well_class_code, y= n, fill = forcats::fct_rev(my_intended_water_use)))+
scale_y_continuous(expand = expansion(mult = c(0, 0.05))) +
labs(
title = "Figure 1 - Intended Use by Well Class Code",
subtitle = glue("from {start_date} to {end_date}"),
x= "Well Class Code",
y = "Count",
fill = "Intended Use"
)
#bc_landmass <- bcmaps::bc_neighbours(ask = FALSE) %>% filter(name == "British Columbia") %>%
bc_landmass <- bcmaps::bc_bound(ask = FALSE) %>%
mutate(dummy = 1) %>%
group_by(dummy) %>%
summarise(dummy2 = 1)
regions_landmass <- nr_regions(ask=FALSE) %>% st_intersection(bc_landmass)
mymap <- ggplot() +
#geom_sf(data = bcmaps::bc_neighbours(ask = FALSE))+
geom_sf(data = regions_landmass %>% st_cast("MULTIPOLYGON"), fill = NA) +
# geom_sf(data = z %>%
# filter(!is.na(longitude_decdeg), !is.na(latitude_decdeg)) %>%
# st_as_sf(coords= c("longitude_decdeg", "latitude_decdeg"), crs = 4326, remove = FALSE) %>%
# tail(1000),
# color = "white",
# size = 1
# )+
geom_sf(data = z %>%
filter(!is.na(longitude_decdeg), !is.na(latitude_decdeg)) %>%
st_as_sf(coords= c("longitude_decdeg", "latitude_decdeg"), crs = 4326, remove = FALSE) %>%
tail(1000),
aes(color = well_class_code, text = paste0("well_tag_number: ",well_tag_number, ", well_class_code:", well_class_code, ", date_added to gwells:", date_added)),
size = 1, shape = 1
) +
labs(
title = glue("Figure 2: Map of New Wells from {start_date} to {end_date} (last 1000 wells)"),
subtitle = "We may have to go non-interactive in the shiny if this takes too long to render"
) +
theme_void() +
theme(legend.position = c(0.85, 0.7))
ggplotly(mymap, tooltip ="text")
region_intended_use_counts <-
z %>%
group_by(nr_region_name) %>%
count(my_intended_water_use) %>%
spread(key=my_intended_water_use, value = n, fill = 0)
z %>%
group_by(nr_region_name) %>%
summarise(
Total = n()
) %>%
left_join(region_intended_use_counts)%>%
gt(rowname_col = "nr_region_name") %>%
tab_header(
title = "Summary Table 1 - Count of intended use code by region ",
subtitle = glue("{start_date} to {end_date}")
) %>%
fmt_number(columns = everything(), decimals = 0) %>%
summary_rows(
# columns = is.numeric(),
fns = list(Total = "sum"),
decimals = 0
)
z %>%
group_by(nr_region_name) %>%
count(fct_well_class_code) %>%
spread(key=fct_well_class_code, value = n, fill = 0) %>%
ungroup() %>%
left_join (z %>% count(nr_region_name)) %>%
rename(Total = "n") %>%
gt(rowname_col = "nr_region_name") %>%
tab_header(
title = "Summary Table 2 - Count of well_class_code by region",
subtitle = glue("{start_date} to {end_date}")
) %>%
fmt_number(columns = everything(), decimals = 0) %>%
summary_rows(
# columns = is.numeric(),
fns = list(Total = "sum"),
decimals = 0
)
z %>%
group_by(nr_region_name) %>%
summarise(Total = n(),
before_feb2016_or_unknown_date = sum(old_or_unknown_date),
after_feb2016 = sum(!old_or_unknown_date),
cross_referenced = sum(cross_referenced == TRUE),
artesian = sum(artesian_conditions == "True"),
in_table1 = sum(table1_flag>0),
in_table2 = sum(table2_flag),
in_table3 = sum(table3_flag>0)
) %>%
gt(rowname_col = "nr_region_name") %>%
tab_header(
title = "Summary Table 3 - Count of 'interesting' wells by region",
subtitle = glue("{start_date} to {end_date}")
) %>%
fmt_number(columns = everything(), decimals = 0) %>%
summary_rows(
# columns = is.numeric(),
fns = list(Total = "sum"),
decimals = 0
)
SimonCoulombe/gwells_shiny documentation built on Jan. 14, 2022, 9:33 p.m.
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suppressPackageStartupMessages({ library(gwellsshiny) library(bcmaps) library(bcdata) library(shiny) library(leaflet) library(DT) library(RPostgres) library(plotly) library(pool) library(DBI) library(sf) library(mapview) library(dplyr) library(stringr) library(forcats) library(hrbrthemes) library(plotly) library(tidyr) library(gt) library(glue) }) hrbrthemes::import_roboto_condensed() ggplot2::theme_set(hrbrthemes::theme_ipsum_rc()) # this ggplot2 theme uses roboto condensed font, which works well with the font used for the whole document. options(ggplot2.discrete.fill = function() scale_fill_brewer(type = "qual", palette = "Set3")) # these scales are colorbliend friendly and start with "cooperators blue" options(ggplot2.continuous.fill = function() scale_fill_viridis_c()) options(ggplot2.discrete.colour = function() scale_color_brewer(type = "qual", palette = "Set3")) options(ggplot2.continuous.colour = function() scale_color_viridis_c()) start_date <- "2021-12-13" end_date <- "2021-12-17"
# pool <- dbPool( # drv = RPostgres::Postgres(), # dbname = Sys.getenv("BCGOV_DB"), # host = Sys.getenv("BCGOV_HOST"), # port = 5432, # user = Sys.getenv("BCGOV_USR"), # password = Sys.getenv("BCGOV_PWD") # ) # onStop(function() { # poolClose(pool) # })
con1 <- DBI::dbConnect( #RPostgreSQL::PostgreSQL(), RPostgres::Postgres(), dbname = Sys.getenv("BCGOV_DB"), host = Sys.getenv("BCGOV_HOST"), user = Sys.getenv("BCGOV_USR"), password=Sys.getenv("BCGOV_PWD") )
TEST
qa <- dbGetQuery(con1, "select * from wells_qa ") # where well_tag_number > 124000 geocode <- dbGetQuery(con1, "select * from wells_geocoded ") # where well_tag_number > 124000 wells <- dbGetQuery(con1, "select * from wells") # where well_tag_number > 124000 drilling_method <- dbGetQuery(con1, "select well_tag_number,drilling_method_code from drilling_method ") #where well_tag_number > 124000 drilling_method_summary <- drilling_method %>% group_by(well_tag_number) %>% summarise( has_air_rotary = max(str_detect(toupper(drilling_method_code), "AIR_ROTARY")), has_auger = max(str_detect(toupper(drilling_method_code), "AUGER")), has_cable_tool = max(str_detect(toupper(drilling_method_code), "CABLE_TOOL")), has_driving = max(str_detect(toupper(drilling_method_code), "DRIVING")), has_dugout = max(str_detect(toupper(drilling_method_code), "DUGOUT")), has_duo_rotary = max(str_detect(toupper(drilling_method_code), "DUO_ROTARY")), has_excavating = max(str_detect(toupper(drilling_method_code), "EXCAVATING")), has_jetting = max(str_detect(toupper(drilling_method_code), "JETTING")), has_mud_rotary = max(str_detect(toupper(drilling_method_code), "MUD_ROTARY")), has_other = max(str_detect(toupper(drilling_method_code), "OTHER")), has_unk = max(str_detect(toupper(drilling_method_code), "UNK")) ) colnames_in_more_than_one_db <- c("identification_plate_number", "well_identification_plate_attached", "well_status_code", "well_class_code", "well_subclass", "licenced_status_code", "intended_water_use_code", "observation_well_number", "obs_well_status_code", "water_supply_system_name", "water_supply_system_well_name", "street_address", "city", "legal_lot", "legal_plan", "legal_district_lot", "legal_block", "legal_section", "legal_township", "legal_range", "land_district_code", "legal_pid", "well_location_description", "latitude_decdeg", "longitude_decdeg", "utm_zone_code", "utm_northing", "utm_easting", "coordinate_acquisition_code", "construction_start_date", "construction_end_date", "alteration_start_date", "alteration_end_date", "decommission_start_date", "decommission_end_date", "driller_name", "consultant_name", "consultant_company", "diameter_inches", "total_depth_drilled_ft_bgl", "finished_well_depth_ft_bgl", "final_casing_stick_up_inches", "bedrock_depth_ft_bgl", "ground_elevation_ft_asl", "ground_elevation_method_code", "static_water_level_ft_btoc", "well_yield_usgpm", "well_yield_unit_code", "artesian_flow_usgpm", "artesian_pressure_psi", "well_cap_type", "well_disinfected_code", "well_orientation_code", "alternative_specs_submitted", "surface_seal_material_code", "surface_seal_method_code", "surface_seal_depth_ft", "backfill_type", "backfill_depth_ft", "liner_material_code", "liner_diameter_inches", "liner_thickness_inches", "surface_seal_thickness_inches", "liner_from_ft_bgl", "liner_to_ft_bgl", "screen_intake_method_code", "screen_type_code", "screen_material_code", "other_screen_material", "screen_information", "screen_opening_code", "screen_bottom_code", "other_screen_bottom", "filter_pack_from_ft", "filter_pack_to_ft", "filter_pack_material_code", "filter_pack_thickness_inches", "filter_pack_material_size_code", "development_hours", "development_notes", "water_quality_colour", "water_quality_odour", "yield_estimation_method_code", "yield_estimation_rate_usgpm", "yield_estimation_duration_hours", "static_level_before_test_ft_btoc", "drawdown_ft_btoc", "hydro_fracturing_performed", "hydro_fracturing_yield_increase", "decommission_reason", "decommission_method_code", "decommission_details", "decommission_sealant_material", "decommission_backfill_material", "comments", "ems", "person_responsible", "company_of_person_responsible", "aquifer_id", "avi_years", "storativity", "transmissivity_m_2_s", "hydraulic_conductivity_m_s", "specific_storage_1_m", "specific_yield", "testing_method", "testing_duration_hours", "analytic_solution_type", "boundary_effect_code", "aquifer_lithology_code", "artesian_pressure_head_ft_agl", "artesian_conditions", "full_address", "civic_number", "civic_number_suffix", "street_name", "street_type", "is_street_type_prefix", "street_direction", "is_street_direction_prefix", "street_qualifier", "locality_name") full_data <- wells %>% left_join(geocode) %>% left_join(qa %>% select(-all_of(colnames_in_more_than_one_db))) %>% left_join(drilling_method_summary) %>% filter(date_added >= start_date, date_added <= end_date)
interesting_columns <- c("well_tag_number", "identification_plate_number", "date_added", "date_geocoded", "date_qa", "construction_start_date", "construction_end_date", "decommission_start_date", "decommission_end_date", "alteration_start_date", "alteration_end_date", "longitude_decdeg", "latitude_decdeg", "well_class_code", "well_subclass", "intended_water_use_code", "finished_well_depth_ft_bgl" , "person_responsible", "company_of_person_responsible", "artesian_conditions", "comments", "nr_region_name")
z <- full_data %>% mutate( cross_referenced = replace_na(stringr::str_detect(comments, "xref|x-ref|cross ref|cross-ref"), FALSE), max_date = pmax(coalesce(construction_start_date, construction_end_date, alteration_start_date, alteration_end_date, decommission_start_date, decommission_end_date)), worktype = factor(case_when( decommission_start_date == max_date | decommission_end_date == max_date ~ "decommission", alteration_start_date == max_date | alteration_end_date == max_date ~ "alteration", construction_start_date == max_date | construction_end_date == max_date ~ "construction", TRUE ~ "no date" ), levels = c("construction", "alteration", "decommission", "no date") ), old_or_unknown_date = max_date < lubridate::ymd("20160301") | is.na(max_date), my_well_type = case_when( artesian_conditions == "True" ~ "artesian", well_class_code == "WATR_SPPLY" ~ "water supply", well_class_code == "RECHARGE" ~ "recharge", well_class_code == "INJECTION" ~ "injection", well_class_code == "DEW_DRA" ~ "dewatering/discharge", well_class_code == "CLS_LP_GEO" ~ "closed loop geo", TRUE ~ NA_character_ ), table1_missing_lat_long_flag = case_when( is.na(my_well_type) | old_or_unknown_date ~ FALSE, is.na(latitude_decdeg) | is.na(longitude_decdeg) ~ TRUE, !is.na(latitude_decdeg) | is.na(longitude_decdeg) ~ FALSE ), table1_table1_missing__wdip_flag = case_when( is.na(my_well_type) | old_or_unknown_date ~ FALSE, is.na(well_identification_plate_attached) ~ TRUE, !is.na(well_identification_plate_attached) ~ FALSE ), table1_missing_finished_well_depth_flag = case_when( is.na(my_well_type) | old_or_unknown_date ~ FALSE, is.na(finished_well_depth_ft_bgl) ~ TRUE, !is.na(finished_well_depth_ft_bgl) ~ FALSE ), table1_missing_person_responsible_flag = case_when( is.na(my_well_type) | old_or_unknown_date ~ FALSE, is.na(person_responsible) ~ TRUE, !is.na(person_responsible) ~ FALSE ), table1_flag = table1_missing_lat_long_flag + table1_table1_missing__wdip_flag + table1_missing_finished_well_depth_flag + table1_missing_person_responsible_flag, table2_flag = case_when( distance_to_matching_pid >400 ~ TRUE, 0< score_address & score_address < 80 ~ TRUE, 0 <score_city ~ TRUE, TRUE ~ FALSE ), table3_missing_lat_long_flag = case_when( is.na(my_well_type) | !old_or_unknown_date ~ FALSE, is.na(latitude_decdeg) | is.na(longitude_decdeg) ~ TRUE, !is.na(latitude_decdeg) | is.na(longitude_decdeg) ~ FALSE ), table3_missing_finished_well_depth_flag = case_when( is.na(my_well_type) | !old_or_unknown_date ~ FALSE, is.na(finished_well_depth_ft_bgl) ~ TRUE, !is.na(finished_well_depth_ft_bgl) ~ FALSE ), table3_flag = table3_missing_lat_long_flag+ table3_missing_finished_well_depth_flag, my_intended_water_use = factor(if_else(well_class_code == "WATR_SPPLY", intended_water_use_code, "SPECIALIZED"), levels = c("DOM", "UNK","DWS","COM","IRR","OTHER","TST","OBS","OP_LP_GEO", "SPECIALIZED") ) )%>% mutate( fct_well_class_code = factor(well_class_code, levels = c("WATR_SPPLY", "UNK", "MONITOR", "DEW_DRA", "CLS_LP_GEO" ,"GEOTECH" ,"REMEDIATE" ,"INJECTION", "RECHARGE")) )
table1 <- z %>% filter(table1_flag >0 ) %>% arrange(desc(table1_flag), desc(well_tag_number)) %>% select(well_tag_number,table1_flag, my_well_type, table1_missing_lat_long_flag, table1_table1_missing__wdip_flag, table1_missing_finished_well_depth_flag, table1_missing_person_responsible_flag, company_of_person_responsible) table1 %>% datatable(caption =glue("table 1, from {start_date} to {end_date}"))
table2 <- z %>% filter(table2_flag) %>% arrange(desc(score_address), desc(well_tag_number)) %>% select(well_tag_number, distance_to_matching_pid, score_address, score_location_description, score_city, worktype, company_of_person_responsible) table2 %>% tail(1000) %>% datatable(caption =glue("table 2, from {start_date} to {end_date} (showing only last 1000 records)"))
table3 <- z %>% filter(table3_flag >0 ) %>% arrange(desc(table3_flag), desc(well_tag_number)) %>% select(well_tag_number,table3_flag, my_well_type, table3_missing_lat_long_flag,table3_missing_finished_well_depth_flag, company_of_person_responsible) table3 %>% datatable(caption =glue("table 3, from {start_date} to {end_date}"))
z %>% count(fct_well_class_code, my_intended_water_use) %>% ggplot()+ geom_col(aes(x=fct_well_class_code, y= n, fill = forcats::fct_rev(my_intended_water_use)))+ scale_y_continuous(expand = expansion(mult = c(0, 0.05))) + labs( title = "Figure 1 - Intended Use by Well Class Code", subtitle = glue("from {start_date} to {end_date}"), x= "Well Class Code", y = "Count", fill = "Intended Use" )
#bc_landmass <- bcmaps::bc_neighbours(ask = FALSE) %>% filter(name == "British Columbia") %>% bc_landmass <- bcmaps::bc_bound(ask = FALSE) %>% mutate(dummy = 1) %>% group_by(dummy) %>% summarise(dummy2 = 1) regions_landmass <- nr_regions(ask=FALSE) %>% st_intersection(bc_landmass)
mymap <- ggplot() + #geom_sf(data = bcmaps::bc_neighbours(ask = FALSE))+ geom_sf(data = regions_landmass %>% st_cast("MULTIPOLYGON"), fill = NA) + # geom_sf(data = z %>% # filter(!is.na(longitude_decdeg), !is.na(latitude_decdeg)) %>% # st_as_sf(coords= c("longitude_decdeg", "latitude_decdeg"), crs = 4326, remove = FALSE) %>% # tail(1000), # color = "white", # size = 1 # )+ geom_sf(data = z %>% filter(!is.na(longitude_decdeg), !is.na(latitude_decdeg)) %>% st_as_sf(coords= c("longitude_decdeg", "latitude_decdeg"), crs = 4326, remove = FALSE) %>% tail(1000), aes(color = well_class_code, text = paste0("well_tag_number: ",well_tag_number, ", well_class_code:", well_class_code, ", date_added to gwells:", date_added)), size = 1, shape = 1 ) + labs( title = glue("Figure 2: Map of New Wells from {start_date} to {end_date} (last 1000 wells)"), subtitle = "We may have to go non-interactive in the shiny if this takes too long to render" ) + theme_void() + theme(legend.position = c(0.85, 0.7)) ggplotly(mymap, tooltip ="text")
region_intended_use_counts <- z %>% group_by(nr_region_name) %>% count(my_intended_water_use) %>% spread(key=my_intended_water_use, value = n, fill = 0) z %>% group_by(nr_region_name) %>% summarise( Total = n() ) %>% left_join(region_intended_use_counts)%>% gt(rowname_col = "nr_region_name") %>% tab_header( title = "Summary Table 1 - Count of intended use code by region ", subtitle = glue("{start_date} to {end_date}") ) %>% fmt_number(columns = everything(), decimals = 0) %>% summary_rows( # columns = is.numeric(), fns = list(Total = "sum"), decimals = 0 )
z %>% group_by(nr_region_name) %>% count(fct_well_class_code) %>% spread(key=fct_well_class_code, value = n, fill = 0) %>% ungroup() %>% left_join (z %>% count(nr_region_name)) %>% rename(Total = "n") %>% gt(rowname_col = "nr_region_name") %>% tab_header( title = "Summary Table 2 - Count of well_class_code by region", subtitle = glue("{start_date} to {end_date}") ) %>% fmt_number(columns = everything(), decimals = 0) %>% summary_rows( # columns = is.numeric(), fns = list(Total = "sum"), decimals = 0 )
z %>% group_by(nr_region_name) %>% summarise(Total = n(), before_feb2016_or_unknown_date = sum(old_or_unknown_date), after_feb2016 = sum(!old_or_unknown_date), cross_referenced = sum(cross_referenced == TRUE), artesian = sum(artesian_conditions == "True"), in_table1 = sum(table1_flag>0), in_table2 = sum(table2_flag), in_table3 = sum(table3_flag>0) ) %>% gt(rowname_col = "nr_region_name") %>% tab_header( title = "Summary Table 3 - Count of 'interesting' wells by region", subtitle = glue("{start_date} to {end_date}") ) %>% fmt_number(columns = everything(), decimals = 0) %>% summary_rows( # columns = is.numeric(), fns = list(Total = "sum"), decimals = 0 )
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