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mutate(poi_name=case_when(is.na(poi_name) ~ "D7", TRUE ~ as.character(poi_name)))
## visitor location
vis_attributes <-arrow::read_parquet("data/reid_data/vis_loc_attributes.parquet")
vis_home_loc_df<-arrow::read_parquet("data/reid_data/vis_home_loc_df.parquet")
################
# zone_choices_ori = zone_choices
# zone_choices[which(zone_choices %in% "Tropicana Stadium")] = "Tropicana Field"
#
# zone_poi_choices = POI_df[["POI_ID"]]
#
# st_transform(gis_POI, 4326)
# gis_POI$lat = NA
# gis_POI$long = NA
# gis_POI$zoom = NA
#
# for(i in 1:10){
# gis_POI$lat[i] = mean(gis_POI[[4]][[i]][[1]][[1]][,2])
# gis_POI$long[i] = mean(gis_POI[[4]][[i]][[1]][[1]][,1])
# gis_POI$zoom[i] = 11
# }
### COVID
covid_wfh<-read_excel("data/reid_data/Tampa_Survey_Report_Storyboard_Mockup_v2.xlsx", sheet="covid_wfh")
covid_wfh2<-read_excel("data/reid_data/Tampa_Survey_Report_Storyboard_Mockup_v2.xlsx", sheet="covid_wfh2")
covid_pop<-read_excel("data/reid_data/Tampa_Survey_Report_Storyboard_Mockup_v2.xlsx", sheet="covid_pop")
covid_transit<-read_excel("data/reid_data/Tampa_Survey_Report_Storyboard_Mockup_v2.xlsx", sheet="covid_transit")
covid_vis<-read_excel("data/reid_data/Tampa_Survey_Report_Storyboard_Mockup_v2.xlsx", sheet="covid_vis_est")
### service population
serv_pop_lu <- read_parquet("data/reid_data/serv_pop_lu.parquet") %>%
mutate(POI=case_when(POI %in% c("Florida District 7","Florida") ~ "D7",
POI %in% c("Crystal River") ~ "Crystal River Wildlife Areas",
TRUE ~ as.character(POI)))
## land use
lu<- read_parquet("data/reid_data/land_use.parquet")
shiny::runApp()
shiny::runApp()
runApp()
shiny::runApp()
shiny::runApp()
rm(list=ls())
source(file.path("config_variables.R"))
gc()
rm(list=ls())
gc()
source(file.path("config_variables.R"))
#R package required
SYSTEM_PKGS <- c("tidyverse", "data.table", "leaflet", "plotly", "tigris", "sf", "sfarrow", "arrow" , 'shiny',
'shinyjs', 'shinyalert', 'shinycssloaders', 'shinydashboard', 'shinyWidgets', 'shinyFiles',
'htmltools', 'htmlwidgets', 'geojsonio', 'mapview', 'RColorBrewer', 'scales', 'Rfast', 'DT',
'BAMMtools', 'reactable', 'reactablefmtr', 'purrr', 'readxl', 'mltools', 'DT', 'formattable')
lapply(SYSTEM_PKGS, require, character.only = TRUE) # Load multiple packages
options(scipen=999)
options(digits = 6)
options(tigris_use_cache = TRUE)
# options(shiny.maxRequestSize=30*1024^2)
# TAMPA Mobility Dashboard. Data Import.R
# Developed by Kyeongsu Kim and Reid Haefer at RSG
rm(list=ls())
gc()
source(file.path("config_variables.R"))
#R package required
SYSTEM_PKGS <- c("tidyverse", "data.table", "leaflet", "plotly", "tigris", "sf", "sfarrow", "arrow" , 'shiny',
'shinyjs', 'shinyalert', 'shinycssloaders', 'shinydashboard', 'shinyWidgets', 'shinyFiles',
'htmltools', 'htmlwidgets', 'geojsonio', 'mapview', 'RColorBrewer', 'scales', 'Rfast', 'DT',
'BAMMtools', 'reactable', 'reactablefmtr', 'purrr', 'readxl', 'mltools', 'DT', 'formattable')
lapply(SYSTEM_PKGS, require, character.only = TRUE) # Load multiple packages
options(scipen=999)
options(digits = 6)
options(tigris_use_cache = TRUE)
# options(shiny.maxRequestSize=30*1024^2)
# 0. Read GIS County Place, TAZ and BG geospatial data ----------
gis_CPlace = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, "gis_CPlace.rds"))
st_crs(gis_CPlace) = 4326
gis_TAZ = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, "gis_TAZ.rds")) %>% st_transform(4326)
gis_TAZ = gis_TAZ[, c("ZONE", "COUNTY", "geom")]; names(gis_TAZ) = tolower(names(gis_TAZ))
gis_BG = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, "gis_BG.rds")) %>% st_transform(4326)
gis_POI = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, "gis_POI.rds")) %>% st_transform(4326)
gis_cengeo19 = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, APP_INPUT_OD_TOTAL_GIS_DATA_NAME_2019))
gis_cengeo22 = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, APP_INPUT_OD_TOTAL_GIS_DATA_NAME_2022))
st_crs(gis_cengeo19) = 4326
st_crs(gis_cengeo22) = 4326
# to get lat/lon for zoom-in set in 10.poi
lon = map_dbl(gis_POI$geom, ~st_point_on_surface(.x)[[1]])
lat = map_dbl(gis_POI$geom, ~st_point_on_surface(.x)[[2]])
# gis_POI$POI_Description[gis_POI$POI_Description == "Tropicana Stadium"] = "Tropicana Field"
poi_name = gis_POI$POI_Description
poi_latlon = data.frame(poi_name, lon, lat); rm(lon, lat, poi_name)
## POI flows to be merged with o_and_d_tatals tables -----------
gis_d7_edge19 = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, APP_INPUT_NETWORK_D7_EDGES_DATA_NAME_2019))
st_crs(gis_d7_edge19) = 4326
gis_d7_edge22 = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, APP_INPUT_NETWORK_D7_EDGES_DATA_NAME_2022))
st_crs(gis_d7_edge22) = 4326
APP_INPUT_DATA_DIR_NAME = "data"
APP_INPUT_DATA_PATH = file.path(SYSTEM_APP_PATH, APP_INPUT_DATA_DIR_NAME)
APP_INPUT_GIS_DATA_DIR_NAME = "data/gis"
APP_INPUT_GIS_DATA_PATH = file.path(SYSTEM_APP_PATH, APP_INPUT_GIS_DATA_DIR_NAME)
APP_INPUT_SPEND_DATA_DIR_NAME = "data/spending"
APP_INPUT_SPEND_DATA_PATH = file.path(SYSTEM_APP_PATH, APP_INPUT_SPEND_DATA_DIR_NAME)
APP_INPUT_TRIP_CHAR_DATA_DIR_NAME = "data/trip_characteristics"
APP_INPUT_TRIP_CHAR_DATA_PATH = file.path(SYSTEM_APP_PATH, APP_INPUT_TRIP_CHAR_DATA_DIR_NAME)
# 1. Read Input Data ---------
## 1.2 for O-D Comparison Tab: od_tables (from Replica data) -------
# APP_INPUT_OD_TOTAL_DATA = readRDS(file.path(APP_INPUT_OD_REPLICA_DATA_PATH, APP_INPUT_OD_REPLICA_DATA_NAME))
APP_INPUT_REPLICA_OD_DATA_2022 = readRDS(file.path(APP_INPUT_REPLICA_OD_DATA_PATH, APP_INPUT_REPLICA_OD_DATA_NAME_2022))
APP_INPUT_REPLICA_OD_COLORPAL_2022 = readRDS(file.path(APP_INPUT_REPLICA_OD_DATA_PATH, APP_INPUT_REPLICA_OD_COLORPAL_NAME_2022))
APP_INPUT_RMERGE_OD_DATA_2019 = readRDS(file.path(APP_INPUT_RMERGE_OD_DATA_PATH, APP_INPUT_RMERGE_OD_DATA_NAME_2019))
# year_choices = parse_number(names(APP_INPUT_OD_REPLICA_DATA_LIST))
# od_market_type = unique(APP_INPUT_OD_REPLICA_DATA_2019[["market"]])
## POI OD rds files processing ---------
APP_POI_OD_TABLE_2019 = lapply(file.path(APP_INPUT_OD_TOTAL_DATA_PATH, APP_INPUT_OD_TOTAL_DATA_NAME_2019), readRDS)[[1]]
APP_POI_OD_TABLE_2022 = lapply(file.path(APP_INPUT_OD_TOTAL_DATA_PATH, APP_INPUT_OD_TOTAL_DATA_NAME_2022), readRDS)[[1]]
# gis_APP_POI_OD_TABLE_2019 = merge(gis_cengeo19, APP_POI_OD_TABLE_2019[[1]][[1]], by = 'GEOID')
# names(APP_POI_OD_TABLE_2019); names(APP_POI_OD_TABLE_2022)
## POI LINK FLOWS rds files into list ---------
APP_POI_OD_LINK_FLOWS_2019 <- lapply(file.path(APP_INPUT_LINK_FLOWS_DATA_PATH, APP_INPUT_LINK_FLOWS_DATA_NAME_2019), readRDS)[[1]]
APP_POI_OD_LINK_FLOWS_2022 <- lapply(file.path(APP_INPUT_LINK_FLOWS_DATA_PATH, APP_INPUT_LINK_FLOWS_DATA_NAME_2022), readRDS)[[1]]
APP_POI_OD_LINK_FLOWS_BINS_LIST <- readRDS(file.path(APP_INPUT_LINK_FLOWS_DATA_PATH, APP_INPUT_LINK_FLOWS_BIN_LIST_NAME))
# read trip characteristics rds files into list ---------
APP_INPUT_TRIP_CATEGORY_LIST <- list.files(APP_INPUT_TRIP_CHAR_DATA_PATH, pattern='trip_category')
APP_INPUT_TRIP_CATEGORY_FILELIST <- lapply(file.path(APP_INPUT_TRIP_CHAR_DATA_PATH, APP_INPUT_TRIP_CATEGORY_LIST), readRDS)
names(APP_INPUT_TRIP_CATEGORY_FILELIST) <- gsub(".rds","",APP_INPUT_TRIP_CATEGORY_LIST)
APP_INPUT_TRIP_DISTANCE_LIST <- list.files(APP_INPUT_TRIP_CHAR_DATA_PATH, pattern='trip_distance')
APP_INPUT_TRIP_DISTANCE_FILELIST <- lapply(file.path(APP_INPUT_TRIP_CHAR_DATA_PATH, APP_INPUT_TRIP_DISTANCE_LIST), readRDS)
names(APP_INPUT_TRIP_DISTANCE_FILELIST) <- gsub(".rds","",APP_INPUT_TRIP_DISTANCE_LIST)
APP_INPUT_TRIP_InOut_D7_LIST <- list.files(APP_INPUT_TRIP_CHAR_DATA_PATH, pattern='trip_in_out_d7')
APP_INPUT_TRIP_InOut_D7_FILELIST <- lapply(file.path(APP_INPUT_TRIP_CHAR_DATA_PATH, APP_INPUT_TRIP_InOut_D7_LIST), readRDS)
names(APP_INPUT_TRIP_InOut_D7_FILELIST) <- gsub(".rds","",APP_INPUT_TRIP_InOut_D7_LIST)
APP_INPUT_TRIP_PURPOSE_LIST <- list.files(APP_INPUT_TRIP_CHAR_DATA_PATH, pattern='trip_purpose')
APP_INPUT_TRIP_PURPOSE_FILELIST <- lapply(file.path(APP_INPUT_TRIP_CHAR_DATA_PATH, APP_INPUT_TRIP_PURPOSE_LIST), readRDS)
names(APP_INPUT_TRIP_PURPOSE_FILELIST) <- gsub(".rds","",APP_INPUT_TRIP_PURPOSE_LIST)
APP_INPUT_TRIP_TOD_LIST <- list.files(APP_INPUT_TRIP_CHAR_DATA_PATH, pattern='trip_tod')
APP_INPUT_TRIP_TOD_FILELIST <- lapply(file.path(APP_INPUT_TRIP_CHAR_DATA_PATH, APP_INPUT_TRIP_TOD_LIST), readRDS)
names(APP_INPUT_TRIP_TOD_FILELIST) <- gsub(".rds","",APP_INPUT_TRIP_TOD_LIST)
APP_INPUT_TRIP_CHARACTERISTIC_FILELIST <- readRDS(file.path(APP_INPUT_TRIP_CHAR_DATA_PATH, APP_INPUT_TRIP_CHAR_DATA_NAME1))
# names(APP_INPUT_TRIP_CHARACTERISTIC_FILELIST)
vmt_per_capita_2019 = APP_INPUT_TRIP_CHARACTERISTIC_FILELIST[names(APP_INPUT_TRIP_CHARACTERISTIC_FILELIST) %in% "vmt_per_capita_2019"][[1]]
vmt_per_capita_2022 = APP_INPUT_TRIP_CHARACTERISTIC_FILELIST[names(APP_INPUT_TRIP_CHARACTERISTIC_FILELIST) %in% "vmt_per_capita_2022"][[1]]
# zone_choices[which(zone_choices %in% "Tropicana Stadium")] = "Tropicana Field"
# spend data ----------
spend_tract = readRDS(file.path("data/reid_data/spending/spend_tract.rds"))
spend_county = readRDS(file.path("data/reid_data/spending/spend_county.rds"))
SYSTEM_PKGS <- c("tidyverse", "data.table", "leaflet", "plotly", "tigris", "sf", "sfarrow", "arrow" , 'shiny',
'shinyjs', 'shinyalert', 'shinycssloaders', 'shinydashboard', 'shinyWidgets', 'shinyFiles',
'htmltools', 'htmlwidgets', 'geojsonio', 'mapview', 'RColorBrewer', 'scales', 'Rfast', 'DT',
'BAMMtools', 'reactable', 'reactablefmtr', 'purrr', 'readxl', 'mltools', 'DT', 'formattable',
'geojsonsf')
lapply(SYSTEM_PKGS, require, character.only = TRUE) # Load multiple packages
SYSTEM_PKGS <- c("tidyverse", "data.table", "leaflet", "plotly", "tigris", "sf", "sfarrow", "arrow" , 'shiny',
'shinyjs', 'shinyalert', 'shinycssloaders', 'shinydashboard', 'shinyWidgets', 'shinyFiles',
'htmltools', 'htmlwidgets', 'geojsonio', 'mapview', 'RColorBrewer', 'scales', 'Rfast', 'DT',
'BAMMtools', 'reactable', 'reactablefmtr', 'purrr', 'readxl', 'mltools', 'DT', 'formattable',
'geojsonsf')
SYSTEM_PKGS
lapply(SYSTEM_PKGS, require, character.only = TRUE) # Load multiple packages
county_point = geojson_sf("data/gis/county_point.geojson") %>% st_as_sf(crs=4326)
states_point = geojson_sf("data/gis/states_point.geojson") %>% st_as_sf(crs=4326)
tracts_point = geojson_sf("data/gis/tracts_point.geojson") %>% st_as_sf(crs=4326)
APP_INPUT_GIS_DATA_PATH
APP_INPUT_GIS_DATA_PATH
# TAMPA Mobility Dashboard. Data Import.R
# Developed by Kyeongsu Kim and Reid Haefer at RSG
rm(list=ls())
gc()
source(file.path("config_variables.R"))
#R package required
SYSTEM_PKGS <- c("tidyverse", "data.table", "leaflet", "plotly", "tigris", "sf", "sfarrow", "arrow" , 'shiny',
'shinyjs', 'shinyalert', 'shinycssloaders', 'shinydashboard', 'shinyWidgets', 'shinyFiles',
'htmltools', 'htmlwidgets', 'geojsonio', 'mapview', 'RColorBrewer', 'scales', 'Rfast', 'DT',
'BAMMtools', 'reactable', 'reactablefmtr', 'purrr', 'readxl', 'mltools', 'DT', 'formattable',
'geojsonsf')
lapply(SYSTEM_PKGS, require, character.only = TRUE) # Load multiple packages
options(scipen=999)
options(digits = 6)
options(tigris_use_cache = TRUE)
# options(shiny.maxRequestSize=30*1024^2)
# 0. Read GIS County Place, TAZ and BG geospatial data ----------
gis_CPlace = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, "gis_CPlace.rds"))
st_crs(gis_CPlace) = 4326
gis_TAZ = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, "gis_TAZ.rds")) %>% st_transform(4326)
gis_TAZ = gis_TAZ[, c("ZONE", "COUNTY", "geom")]; names(gis_TAZ) = tolower(names(gis_TAZ))
gis_BG = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, "gis_BG.rds")) %>% st_transform(4326)
gis_POI = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, "gis_POI.rds")) %>% st_transform(4326)
gis_cengeo19 = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, APP_INPUT_OD_TOTAL_GIS_DATA_NAME_2019))
gis_cengeo22 = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, APP_INPUT_OD_TOTAL_GIS_DATA_NAME_2022))
st_crs(gis_cengeo19) = 4326
st_crs(gis_cengeo22) = 4326
# to get lat/lon for zoom-in set in 10.poi
lon = map_dbl(gis_POI$geom, ~st_point_on_surface(.x)[[1]])
lat = map_dbl(gis_POI$geom, ~st_point_on_surface(.x)[[2]])
# gis_POI$POI_Description[gis_POI$POI_Description == "Tropicana Stadium"] = "Tropicana Field"
poi_name = gis_POI$POI_Description
poi_latlon = data.frame(poi_name, lon, lat); rm(lon, lat, poi_name)
## POI flows to be merged with o_and_d_tatals tables -----------
gis_d7_edge19 = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, APP_INPUT_NETWORK_D7_EDGES_DATA_NAME_2019))
st_crs(gis_d7_edge19) = 4326
gis_d7_edge22 = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, APP_INPUT_NETWORK_D7_EDGES_DATA_NAME_2022))
st_crs(gis_d7_edge22) = 4326
APP_INPUT_DATA_DIR_NAME = "data"
APP_INPUT_DATA_PATH = file.path(SYSTEM_APP_PATH, APP_INPUT_DATA_DIR_NAME)
APP_INPUT_GIS_DATA_DIR_NAME = "data/gis"
APP_INPUT_GIS_DATA_PATH = file.path(SYSTEM_APP_PATH, APP_INPUT_GIS_DATA_DIR_NAME)
APP_INPUT_SPEND_DATA_DIR_NAME = "data/spending"
APP_INPUT_SPEND_DATA_PATH = file.path(SYSTEM_APP_PATH, APP_INPUT_SPEND_DATA_DIR_NAME)
APP_INPUT_TRIP_CHAR_DATA_DIR_NAME = "data/trip_characteristics"
APP_INPUT_TRIP_CHAR_DATA_PATH = file.path(SYSTEM_APP_PATH, APP_INPUT_TRIP_CHAR_DATA_DIR_NAME)
# 1. Read Input Data ---------
## 1.2 for O-D Comparison Tab: od_tables (from Replica data) -------
# APP_INPUT_OD_TOTAL_DATA = readRDS(file.path(APP_INPUT_OD_REPLICA_DATA_PATH, APP_INPUT_OD_REPLICA_DATA_NAME))
APP_INPUT_REPLICA_OD_DATA_2022 = readRDS(file.path(APP_INPUT_REPLICA_OD_DATA_PATH, APP_INPUT_REPLICA_OD_DATA_NAME_2022))
APP_INPUT_REPLICA_OD_COLORPAL_2022 = readRDS(file.path(APP_INPUT_REPLICA_OD_DATA_PATH, APP_INPUT_REPLICA_OD_COLORPAL_NAME_2022))
APP_INPUT_RMERGE_OD_DATA_2019 = readRDS(file.path(APP_INPUT_RMERGE_OD_DATA_PATH, APP_INPUT_RMERGE_OD_DATA_NAME_2019))
# year_choices = parse_number(names(APP_INPUT_OD_REPLICA_DATA_LIST))
# od_market_type = unique(APP_INPUT_OD_REPLICA_DATA_2019[["market"]])
## POI OD rds files processing ---------
APP_POI_OD_TABLE_2019 = lapply(file.path(APP_INPUT_OD_TOTAL_DATA_PATH, APP_INPUT_OD_TOTAL_DATA_NAME_2019), readRDS)[[1]]
APP_POI_OD_TABLE_2022 = lapply(file.path(APP_INPUT_OD_TOTAL_DATA_PATH, APP_INPUT_OD_TOTAL_DATA_NAME_2022), readRDS)[[1]]
# gis_APP_POI_OD_TABLE_2019 = merge(gis_cengeo19, APP_POI_OD_TABLE_2019[[1]][[1]], by = 'GEOID')
# names(APP_POI_OD_TABLE_2019); names(APP_POI_OD_TABLE_2022)
## POI LINK FLOWS rds files into list ---------
APP_POI_OD_LINK_FLOWS_2019 <- lapply(file.path(APP_INPUT_LINK_FLOWS_DATA_PATH, APP_INPUT_LINK_FLOWS_DATA_NAME_2019), readRDS)[[1]]
APP_POI_OD_LINK_FLOWS_2022 <- lapply(file.path(APP_INPUT_LINK_FLOWS_DATA_PATH, APP_INPUT_LINK_FLOWS_DATA_NAME_2022), readRDS)[[1]]
APP_POI_OD_LINK_FLOWS_BINS_LIST <- readRDS(file.path(APP_INPUT_LINK_FLOWS_DATA_PATH, APP_INPUT_LINK_FLOWS_BIN_LIST_NAME))
# read trip characteristics rds files into list ---------
APP_INPUT_TRIP_CATEGORY_LIST <- list.files(APP_INPUT_TRIP_CHAR_DATA_PATH, pattern='trip_category')
APP_INPUT_TRIP_CATEGORY_FILELIST <- lapply(file.path(APP_INPUT_TRIP_CHAR_DATA_PATH, APP_INPUT_TRIP_CATEGORY_LIST), readRDS)
names(APP_INPUT_TRIP_CATEGORY_FILELIST) <- gsub(".rds","",APP_INPUT_TRIP_CATEGORY_LIST)
APP_INPUT_TRIP_DISTANCE_LIST <- list.files(APP_INPUT_TRIP_CHAR_DATA_PATH, pattern='trip_distance')
APP_INPUT_TRIP_DISTANCE_FILELIST <- lapply(file.path(APP_INPUT_TRIP_CHAR_DATA_PATH, APP_INPUT_TRIP_DISTANCE_LIST), readRDS)
names(APP_INPUT_TRIP_DISTANCE_FILELIST) <- gsub(".rds","",APP_INPUT_TRIP_DISTANCE_LIST)
APP_INPUT_TRIP_InOut_D7_LIST <- list.files(APP_INPUT_TRIP_CHAR_DATA_PATH, pattern='trip_in_out_d7')
APP_INPUT_TRIP_InOut_D7_FILELIST <- lapply(file.path(APP_INPUT_TRIP_CHAR_DATA_PATH, APP_INPUT_TRIP_InOut_D7_LIST), readRDS)
names(APP_INPUT_TRIP_InOut_D7_FILELIST) <- gsub(".rds","",APP_INPUT_TRIP_InOut_D7_LIST)
APP_INPUT_TRIP_PURPOSE_LIST <- list.files(APP_INPUT_TRIP_CHAR_DATA_PATH, pattern='trip_purpose')
APP_INPUT_TRIP_PURPOSE_FILELIST <- lapply(file.path(APP_INPUT_TRIP_CHAR_DATA_PATH, APP_INPUT_TRIP_PURPOSE_LIST), readRDS)
names(APP_INPUT_TRIP_PURPOSE_FILELIST) <- gsub(".rds","",APP_INPUT_TRIP_PURPOSE_LIST)
APP_INPUT_TRIP_TOD_LIST <- list.files(APP_INPUT_TRIP_CHAR_DATA_PATH, pattern='trip_tod')
APP_INPUT_TRIP_TOD_FILELIST <- lapply(file.path(APP_INPUT_TRIP_CHAR_DATA_PATH, APP_INPUT_TRIP_TOD_LIST), readRDS)
names(APP_INPUT_TRIP_TOD_FILELIST) <- gsub(".rds","",APP_INPUT_TRIP_TOD_LIST)
APP_INPUT_TRIP_CHARACTERISTIC_FILELIST <- readRDS(file.path(APP_INPUT_TRIP_CHAR_DATA_PATH, APP_INPUT_TRIP_CHAR_DATA_NAME1))
# names(APP_INPUT_TRIP_CHARACTERISTIC_FILELIST)
vmt_per_capita_2019 = APP_INPUT_TRIP_CHARACTERISTIC_FILELIST[names(APP_INPUT_TRIP_CHARACTERISTIC_FILELIST) %in% "vmt_per_capita_2019"][[1]]
vmt_per_capita_2022 = APP_INPUT_TRIP_CHARACTERISTIC_FILELIST[names(APP_INPUT_TRIP_CHARACTERISTIC_FILELIST) %in% "vmt_per_capita_2022"][[1]]
# zone_choices[which(zone_choices %in% "Tropicana Stadium")] = "Tropicana Field"
# #### Reid import ####
# spend data ----------
spend_tract = readRDS(file.path("data/reid_data/spending/spend_tract.rds"))
spend_county = readRDS(file.path("data/reid_data/spending/spend_county.rds"))
county_point = geojson_sf("data/gis/county_point.geojson") %>% st_as_sf(crs=4326)
states_point = geojson_sf("data/gis/states_point.geojson") %>% st_as_sf(crs=4326)
tracts_point = geojson_sf("data/gis/tracts_point.geojson") %>% st_as_sf(crs=4326)
#county= geojson_sf(file.path(APP_INPUT_GIS_DATA_PATH, "county.geojson")) %>% st_as_sf(crs=4326)
# county_point= geojson_sf(file.path(APP_INPUT_GIS_DATA_PATH, "county_point.geojson")) %>% st_as_sf(crs=4326)
gis_state = geojson_sf(file.path(APP_INPUT_GIS_DATA_PATH, "states.geojson")) %>% st_as_sf(crs=4326)
tract_spend_point = geojson_sf(file.path(APP_INPUT_GIS_DATA_PATH, "tract_spend_point.geojson")) %>% st_as_sf(crs=4326)
# sf::sf_use_s2(FALSE)
# gis_d7 = geojson_sf(file.path("data/gis/d7_study_boundary.geojson")) %>% st_as_sf(crs=4326) %>%
# summarise() %>% mutate(d7="yes") %>%
# mutate(POI_ID="D7", POI_Description="D7") %>%
# select(POI_ID,POI_Description)
gis_d7 = geojson_sf(file.path("data/gis/d7_study_boundary.geojson")) %>% st_as_sf(crs=4326) %>%
mutate(d7="yes") %>%
mutate(POI_ID="D7", POI_Description="D7") %>%
select(POI_ID,POI_Description)
gis_POI_D7 <- bind_rows(
gis_POI %>% mutate(POI_ID=as.character(POI_ID)) %>% select(POI_ID,POI_Description),
gis_d7 %>% rename(geom=geometry)
)
#tracts = geojson_sf(file.path(APP_INPUT_GIS_DATA_PATH, "tracts_FL.geojson")) %>% st_as_sf(crs=4326)
vis_lbs_home_d7_state_df <- arrow::read_parquet("data/reid_data/vis_lbs_home_d7_state_df.parquet")
vis_lbs_home_state<- readRDS("data/reid_data/vis_lbs_home_state.rds")
#lu<- arrow::read_parquet("data/reid_data/land_use.parquet")
#vis_bg_sf<- readRDS("data/reid_data/vis_bg_sf.rds")
#vis_replica_clean<- arrow::read_parquet("data/reid_data/vis_replica_clean.parquet")
#service_pop<- arrow::read_parquet("data/reid_data/service_pop.parquet")
#commercial vehicles
tod_all<- read_parquet("data/reid_data/tod_all.parquet")
trip_all<- read_parquet("data/reid_data/trip_all.parquet")
#geo data
com_veh_geo<- bind_rows(
county_point %>% select(GEOID) %>% mutate(type='county') %>% filter(GEOID %in% unique(trip_all$geo)),
states_point %>% select(GEOID) %>% mutate(type='state') %>% filter(GEOID %in% unique(trip_all$geo)),
tracts_point %>% select(GEOID) %>% mutate(type='tract') %>% filter(GEOID %in% unique(trip_all$geo))
)
## resident home location
res_data <- read_parquet("data/reid_data/res_loc_attributes.parquet")%>%
mutate(poi_name=case_when(is.na(poi_name) ~ "D7", TRUE ~ as.character(poi_name)))
res_work_loc<- read_parquet("data/reid_data/res_work_locations.parquet") %>%
mutate(poi_name=case_when(is.na(poi_name) ~ "D7", TRUE ~ as.character(poi_name)))
############
### employee location
worker_data <- read_parquet("data/reid_data/worker_loc_attributes.parquet")%>%
mutate(poi_name=case_when(is.na(poi_name) ~ "D7", TRUE ~ as.character(poi_name)))
worker_home_loc<-read_parquet("data/reid_data/worker_home_locations.parquet")%>%
mutate(poi_name=case_when(is.na(poi_name) ~ "D7", TRUE ~ as.character(poi_name)))
## visitor location
vis_attributes <-arrow::read_parquet("data/reid_data/vis_loc_attributes.parquet")
vis_home_loc_df<-arrow::read_parquet("data/reid_data/vis_home_loc_df.parquet")
################
# zone_choices_ori = zone_choices
# zone_choices[which(zone_choices %in% "Tropicana Stadium")] = "Tropicana Field"
#
# zone_poi_choices = POI_df[["POI_ID"]]
#
# st_transform(gis_POI, 4326)
# gis_POI$lat = NA
# gis_POI$long = NA
# gis_POI$zoom = NA
#
# for(i in 1:10){
# gis_POI$lat[i] = mean(gis_POI[[4]][[i]][[1]][[1]][,2])
# gis_POI$long[i] = mean(gis_POI[[4]][[i]][[1]][[1]][,1])
# gis_POI$zoom[i] = 11
# }
### COVID
covid_wfh<-read_excel("data/reid_data/Tampa_Survey_Report_Storyboard_Mockup_v2.xlsx", sheet="covid_wfh")
covid_wfh2<-read_excel("data/reid_data/Tampa_Survey_Report_Storyboard_Mockup_v2.xlsx", sheet="covid_wfh2")
covid_pop<-read_excel("data/reid_data/Tampa_Survey_Report_Storyboard_Mockup_v2.xlsx", sheet="covid_pop")
covid_transit<-read_excel("data/reid_data/Tampa_Survey_Report_Storyboard_Mockup_v2.xlsx", sheet="covid_transit")
covid_vis<-read_excel("data/reid_data/Tampa_Survey_Report_Storyboard_Mockup_v2.xlsx", sheet="covid_vis_est")
### service population
serv_pop_lu <- read_parquet("data/reid_data/serv_pop_lu.parquet") %>%
mutate(POI=case_when(POI %in% c("Florida District 7","Florida") ~ "D7",
POI %in% c("Crystal River") ~ "Crystal River Wildlife Areas",
TRUE ~ as.character(POI)))
## land use
lu<- read_parquet("data/reid_data/land_use.parquet")
runApp()
gis_d7_edge19 = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, APP_INPUT_NETWORK_D7_EDGES_DATA_NAME_2019))
# TAMPA Mobility Dashboard. Data Import.R
# Developed by Kyeongsu Kim and Reid Haefer at RSG
rm(list=ls())
gc()
source(file.path("config_variables.R"))
#R package required
SYSTEM_PKGS <- c("tidyverse", "data.table", "leaflet", "plotly", "tigris", "sf", "sfarrow", "arrow" , 'shiny',
'shinyjs', 'shinyalert', 'shinycssloaders', 'shinydashboard', 'shinyWidgets', 'shinyFiles',
'htmltools', 'htmlwidgets', 'geojsonio', 'mapview', 'RColorBrewer', 'scales', 'Rfast', 'DT',
'BAMMtools', 'reactable', 'reactablefmtr', 'purrr', 'readxl', 'mltools', 'DT', 'formattable',
'geojsonsf')
lapply(SYSTEM_PKGS, require, character.only = TRUE) # Load multiple packages
options(scipen=999)
options(digits = 6)
options(tigris_use_cache = TRUE)
# options(shiny.maxRequestSize=30*1024^2)
# 0. Read GIS County Place, TAZ and BG geospatial data ----------
gis_CPlace = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, "gis_CPlace.rds"))
st_crs(gis_CPlace) = 4326
gis_TAZ = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, "gis_TAZ.rds")) %>% st_transform(4326)
gis_TAZ = gis_TAZ[, c("ZONE", "COUNTY", "geom")]; names(gis_TAZ) = tolower(names(gis_TAZ))
gis_BG = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, "gis_BG.rds")) %>% st_transform(4326)
gis_POI = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, "gis_POI.rds")) %>% st_transform(4326)
gis_cengeo19 = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, APP_INPUT_OD_TOTAL_GIS_DATA_NAME_2019))
gis_cengeo22 = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, APP_INPUT_OD_TOTAL_GIS_DATA_NAME_2022))
st_crs(gis_cengeo19) = 4326
st_crs(gis_cengeo22) = 4326
# to get lat/lon for zoom-in set in 10.poi
lon = map_dbl(gis_POI$geom, ~st_point_on_surface(.x)[[1]])
lat = map_dbl(gis_POI$geom, ~st_point_on_surface(.x)[[2]])
# gis_POI$POI_Description[gis_POI$POI_Description == "Tropicana Stadium"] = "Tropicana Field"
poi_name = gis_POI$POI_Description
poi_latlon = data.frame(poi_name, lon, lat); rm(lon, lat, poi_name)
## POI flows to be merged with o_and_d_tatals tables -----------
gis_d7_edge19 = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, APP_INPUT_NETWORK_D7_EDGES_DATA_NAME_2019))
st_crs(gis_d7_edge19)
st_crs(gis_d7_edge19) = 4326
gis_d7_edge22 = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, APP_INPUT_NETWORK_D7_EDGES_DATA_NAME_2022))
st_crs(gis_d7_edge22) = 4326
spend_tract = readRDS(file.path("data/reid_data/spending/spend_tract.rds"))
spend_county = readRDS(file.path("data/reid_data/spending/spend_county.rds"))
st_crs(spend_tract)
spend_tract = readRDS(file.path("data/reid_data/spending/spend_tract.rds"))
spend_county = readRDS(file.path("data/reid_data/spending/spend_county.rds"))
st_crs(spend_tract) = 4326; st_crs(spend_county) = 4326
spend_tract = readRDS(file.path("data/reid_data/spending/spend_tract.rds"))
spend_county = readRDS(file.path("data/reid_data/spending/spend_county.rds"))
st_crs(spend_tract) = 4326; st_crs(spend_county) = 4326
st_crs(spend_tract)
st_crs(spend_tract) = 4326
st_crs(gis_d7_edge19) = 4326
st_crs(spend_tract
)
county_point = geojson_sf("data/gis/county_point.geojson") %>% st_as_sf(crs=4326)
states_point = geojson_sf("data/gis/states_point.geojson") %>% st_as_sf(crs=4326)
tracts_point = geojson_sf("data/gis/tracts_point.geojson") %>% st_as_sf(crs=4326)
county_point = geojson_sf("data/gis/county_point.geojson")
st_crs(spend_tract)
county_point = geojson_sf("data/gis/county_point.geojson") %>% st_as_sf(crs=4326)
st_crs(spend_tract)
st_crs(spend_tract) = 4326
county_point = geojson_sf("data/gis/county_point.geojson") %>% st_as_sf(crs=4326)
st_crs(county_point)
st_crs(county_point) = 4326
st_crs(county_point)
st_crs(county_point) = 4326; st_crs(states_point) = 4326; st_crs(tracts_point) = 4326
gis_state = geojson_sf(file.path(APP_INPUT_GIS_DATA_PATH, "states.geojson"))
st_crs(gis_state)
gis_state = geojson_sf(file.path(APP_INPUT_GIS_DATA_PATH, "states.geojson"))
tract_spend_point = geojson_sf(file.path(APP_INPUT_GIS_DATA_PATH, "tract_spend_point.geojson"))
tract_spend_point
gis_state = geojson_sf(file.path(APP_INPUT_GIS_DATA_PATH, "states.geojson"))
st_crs(gis_state)
st_crs(tract_spend_point)
st_crs(gis_state) = 4326; st_crs(tract_spend_point) = 4326;
st_crs(gis_state)
st_crs(tract_spend_point)
gis_state = geojson_sf(file.path(APP_INPUT_GIS_DATA_PATH, "states.geojson")) %>% st_as_sf(crs=4326)
tract_spend_point = geojson_sf(file.path(APP_INPUT_GIS_DATA_PATH, "tract_spend_point.geojson")) %>% st_as_sf(crs=4326)
st_crs(gis_state) = 4326; st_crs(tract_spend_point) = 4326;
st_crs(gis_state)
gis_d7 = geojson_sf(file.path("data/gis/d7_study_boundary.geojson")) %>% st_as_sf(crs=4326) %>%
mutate(d7="yes") %>%
mutate(POI_ID="D7", POI_Description="D7") %>%
select(POI_ID,POI_Description)
st_crs(gis_d7)
st_crs(gis_d7) = 4326;
st_crs(gis_d7)
# TAMPA Mobility Dashboard. Data Import.R
# Developed by Kyeongsu Kim and Reid Haefer at RSG
rm(list=ls())
gc()
source(file.path("config_variables.R"))
#R package required
SYSTEM_PKGS <- c("tidyverse", "data.table", "leaflet", "plotly", "tigris", "sf", "sfarrow", "arrow" , 'shiny',
'shinyjs', 'shinyalert', 'shinycssloaders', 'shinydashboard', 'shinyWidgets', 'shinyFiles',
'htmltools', 'htmlwidgets', 'geojsonio', 'mapview', 'RColorBrewer', 'scales', 'Rfast', 'DT',
'BAMMtools', 'reactable', 'reactablefmtr', 'purrr', 'readxl', 'mltools', 'formattable', 'geojson_sf')
invisible(lapply(SYSTEM_PKGS, require, character.only = TRUE)) # Load multiple packages
options(scipen=999)
options(digits = 6)
options(tigris_use_cache = TRUE)
# options(shiny.maxRequestSize=30*1024^2)
# 0. Read GIS County Place, TAZ and BG geospatial data ----------
gis_CPlace = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, "gis_CPlace.rds"))
st_crs(gis_CPlace) = 4326
gis_TAZ = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, "gis_TAZ.rds")) %>% st_transform(4326)
gis_TAZ = gis_TAZ[, c("ZONE", "COUNTY", "geom")]; names(gis_TAZ) = tolower(names(gis_TAZ))
gis_BG = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, "gis_BG.rds")) %>% st_transform(4326)
gis_POI = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, "gis_POI.rds")) %>% st_transform(4326)
gis_cengeo19 = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, APP_INPUT_OD_TOTAL_GIS_DATA_NAME_2019))
gis_cengeo22 = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, APP_INPUT_OD_TOTAL_GIS_DATA_NAME_2022))
st_crs(gis_cengeo19) = 4326; st_crs(gis_cengeo22) = 4326
st_crs(gis_BG) = 4326; st_crs(gis_TAZ) = 4326; st_crs(gis_POI) = 4326
# to get lat/lon for zoom-in set in 10.poi
lon = map_dbl(gis_POI$geom, ~st_point_on_surface(.x)[[1]])
lat = map_dbl(gis_POI$geom, ~st_point_on_surface(.x)[[2]])
# gis_POI$POI_Description[gis_POI$POI_Description == "Tropicana Stadium"] = "Tropicana Field"
poi_name = gis_POI$POI_Description
poi_latlon = data.frame(poi_name, lon, lat); rm(lon, lat, poi_name)
## POI flows to be merged with o_and_d_tatals tables -----------
gis_d7_edge19 = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, APP_INPUT_NETWORK_D7_EDGES_DATA_NAME_2019))
gis_d7_edge22 = readRDS(file.path(APP_INPUT_GIS_DATA_PATH, APP_INPUT_NETWORK_D7_EDGES_DATA_NAME_2022))
APP_INPUT_DATA_DIR_NAME = "data"
APP_INPUT_DATA_PATH = file.path(SYSTEM_APP_PATH, APP_INPUT_DATA_DIR_NAME)
APP_INPUT_GIS_DATA_DIR_NAME = "data/gis"
APP_INPUT_GIS_DATA_PATH = file.path(SYSTEM_APP_PATH, APP_INPUT_GIS_DATA_DIR_NAME)
APP_INPUT_SPEND_DATA_DIR_NAME = "data/spending"
APP_INPUT_SPEND_DATA_PATH = file.path(SYSTEM_APP_PATH, APP_INPUT_SPEND_DATA_DIR_NAME)
APP_INPUT_TRIP_CHAR_DATA_DIR_NAME = "data/trip_characteristics"
APP_INPUT_TRIP_CHAR_DATA_PATH = file.path(SYSTEM_APP_PATH, APP_INPUT_TRIP_CHAR_DATA_DIR_NAME)
# 1. Read Input Data ---------
## 1.2 for O-D Comparison Tab: od_tables (from Replica data) -------
# APP_INPUT_OD_TOTAL_DATA = readRDS(file.path(APP_INPUT_OD_REPLICA_DATA_PATH, APP_INPUT_OD_REPLICA_DATA_NAME))
APP_INPUT_REPLICA_OD_DATA_2022 = readRDS(file.path(APP_INPUT_REPLICA_OD_DATA_PATH, APP_INPUT_REPLICA_OD_DATA_NAME_2022))
APP_INPUT_REPLICA_OD_COLORPAL_2022 = readRDS(file.path(APP_INPUT_REPLICA_OD_DATA_PATH, APP_INPUT_REPLICA_OD_COLORPAL_NAME_2022))
APP_INPUT_RMERGE_OD_DATA_2019 = readRDS(file.path(APP_INPUT_RMERGE_OD_DATA_PATH, APP_INPUT_RMERGE_OD_DATA_NAME_2019))
# year_choices = parse_number(names(APP_INPUT_OD_REPLICA_DATA_LIST))
# od_market_type = unique(APP_INPUT_OD_REPLICA_DATA_2019[["market"]])
## POI OD rds files processing ---------
APP_POI_OD_TABLE_2019 = lapply(file.path(APP_INPUT_OD_TOTAL_DATA_PATH, APP_INPUT_OD_TOTAL_DATA_NAME_2019), readRDS)[[1]]
APP_POI_OD_TABLE_2022 = lapply(file.path(APP_INPUT_OD_TOTAL_DATA_PATH, APP_INPUT_OD_TOTAL_DATA_NAME_2022), readRDS)[[1]]
# gis_APP_POI_OD_TABLE_2019 = merge(gis_cengeo19, APP_POI_OD_TABLE_2019[[1]][[1]], by = 'GEOID')
# names(APP_POI_OD_TABLE_2019); names(APP_POI_OD_TABLE_2022)
## POI LINK FLOWS rds files into list ---------
APP_POI_OD_LINK_FLOWS_2019 <- lapply(file.path(APP_INPUT_LINK_FLOWS_DATA_PATH, APP_INPUT_LINK_FLOWS_DATA_NAME_2019), readRDS)[[1]]
APP_POI_OD_LINK_FLOWS_2022 <- lapply(file.path(APP_INPUT_LINK_FLOWS_DATA_PATH, APP_INPUT_LINK_FLOWS_DATA_NAME_2022), readRDS)[[1]]
APP_POI_OD_LINK_FLOWS_BINS_LIST <- readRDS(file.path(APP_INPUT_LINK_FLOWS_DATA_PATH, APP_INPUT_LINK_FLOWS_BIN_LIST_NAME))
# read trip characteristics rds files into list ---------
APP_INPUT_TRIP_CATEGORY_LIST <- list.files(APP_INPUT_TRIP_CHAR_DATA_PATH, pattern='trip_category')
APP_INPUT_TRIP_CATEGORY_FILELIST <- lapply(file.path(APP_INPUT_TRIP_CHAR_DATA_PATH, APP_INPUT_TRIP_CATEGORY_LIST), readRDS)
names(APP_INPUT_TRIP_CATEGORY_FILELIST) <- gsub(".rds","",APP_INPUT_TRIP_CATEGORY_LIST)
APP_INPUT_TRIP_DISTANCE_LIST <- list.files(APP_INPUT_TRIP_CHAR_DATA_PATH, pattern='trip_distance')
APP_INPUT_TRIP_DISTANCE_FILELIST <- lapply(file.path(APP_INPUT_TRIP_CHAR_DATA_PATH, APP_INPUT_TRIP_DISTANCE_LIST), readRDS)
names(APP_INPUT_TRIP_DISTANCE_FILELIST) <- gsub(".rds","",APP_INPUT_TRIP_DISTANCE_LIST)
APP_INPUT_TRIP_InOut_D7_LIST <- list.files(APP_INPUT_TRIP_CHAR_DATA_PATH, pattern='trip_in_out_d7')
APP_INPUT_TRIP_InOut_D7_FILELIST <- lapply(file.path(APP_INPUT_TRIP_CHAR_DATA_PATH, APP_INPUT_TRIP_InOut_D7_LIST), readRDS)
names(APP_INPUT_TRIP_InOut_D7_FILELIST) <- gsub(".rds","",APP_INPUT_TRIP_InOut_D7_LIST)
APP_INPUT_TRIP_PURPOSE_LIST <- list.files(APP_INPUT_TRIP_CHAR_DATA_PATH, pattern='trip_purpose')
APP_INPUT_TRIP_PURPOSE_FILELIST <- lapply(file.path(APP_INPUT_TRIP_CHAR_DATA_PATH, APP_INPUT_TRIP_PURPOSE_LIST), readRDS)
names(APP_INPUT_TRIP_PURPOSE_FILELIST) <- gsub(".rds","",APP_INPUT_TRIP_PURPOSE_LIST)
APP_INPUT_TRIP_TOD_LIST <- list.files(APP_INPUT_TRIP_CHAR_DATA_PATH, pattern='trip_tod')
APP_INPUT_TRIP_TOD_FILELIST <- lapply(file.path(APP_INPUT_TRIP_CHAR_DATA_PATH, APP_INPUT_TRIP_TOD_LIST), readRDS)
names(APP_INPUT_TRIP_TOD_FILELIST) <- gsub(".rds","",APP_INPUT_TRIP_TOD_LIST)
APP_INPUT_TRIP_CHARACTERISTIC_FILELIST <- readRDS(file.path(APP_INPUT_TRIP_CHAR_DATA_PATH, APP_INPUT_TRIP_CHAR_DATA_NAME1))
# names(APP_INPUT_TRIP_CHARACTERISTIC_FILELIST)
vmt_per_capita_2019 = APP_INPUT_TRIP_CHARACTERISTIC_FILELIST[names(APP_INPUT_TRIP_CHARACTERISTIC_FILELIST) %in% "vmt_per_capita_2019"][[1]]
vmt_per_capita_2022 = APP_INPUT_TRIP_CHARACTERISTIC_FILELIST[names(APP_INPUT_TRIP_CHARACTERISTIC_FILELIST) %in% "vmt_per_capita_2022"][[1]]
# zone_choices[which(zone_choices %in% "Tropicana Stadium")] = "Tropicana Field"
# #### Reid import ####
# spend data ----------
spend_tract = readRDS(file.path("data/reid_data/spending/spend_tract.rds"))
spend_county = readRDS(file.path("data/reid_data/spending/spend_county.rds"))
county_point = geojson_sf("data/gis/county_point.geojson")
states_point = geojson_sf("data/gis/states_point.geojson")
tracts_point = geojson_sf("data/gis/tracts_point.geojson")
st_crs(county_point) = 4326; st_crs(states_point) = 4326; st_crs(tracts_point) = 4326
#county= geojson_sf(file.path(APP_INPUT_GIS_DATA_PATH, "county.geojson")) %>% st_as_sf(crs=4326)
# county_point= geojson_sf(file.path(APP_INPUT_GIS_DATA_PATH, "county_point.geojson")) %>% st_as_sf(crs=4326)
gis_state = geojson_sf(file.path(APP_INPUT_GIS_DATA_PATH, "states.geojson")) %>% st_as_sf(crs=4326)
tract_spend_point = geojson_sf(file.path(APP_INPUT_GIS_DATA_PATH, "tract_spend_point.geojson")) %>% st_as_sf(crs=4326)
# st_crs(gis_state) = 4326; st_crs(tract_spend_point) = 4326;
st_crs(gis_state)
runApp()
runApp()