R/parcel-data.R
In tiernanmartin/NeighborhoodChangeTypology: Project: Neighborhood Change Typology for King County, WA
#' @title Make A Tibble of The Project's Tax Assessor Variables
#' @description Return a `tibble` of all of the
#' @param model_table_inputs Tibble, the `model_table_inputs` object
#' @param acs_tables Tibble, the `acs_table` object
#' @param path Character, the path or connection to write to.
#' @param zip_path Character, the file path of the archive file
#' @param file_path Character, the file path of the file that is to be extracted
#' @param data_template Tibble, desc
#' @param metadata_template Tibble, desc
#' @return a `tibble`
#' @rdname parcel-data
#' @export
prepare_parcel_data <- function(model_table_inputs, acs_tables, zip_path){
# GET DATA ----------------------------------------------------------------
# DATA SOURCES:
# * 2018 data: https://info.kingcounty.gov/assessor/DataDownload/default.aspx
# * pre-2018 data: direct request fulfilled by the King County Assessor Office
sales <- readr::read_csv("extdata/source/Year2018/EXTR_RPSale.csv") %>%
janitor::clean_names(case = "screaming_snake")
val <- readr::read_csv("extdata/source/Year2018/EXTR_ValueHistory_V.csv") %>%
janitor::clean_names(case = "screaming_snake")
p_2018 <- readr::read_csv("extdata/source/Year2018/EXTR_Parcel.csv") %>%
janitor::clean_names(case = "screaming_snake")
condo_2018 <- readr::read_csv("extdata/source/Year2018/EXTR_CondoUnit2.csv") %>%
janitor::clean_names(case = "screaming_snake")
lut_2018 <- readr::read_csv("extdata/source/Year2018/EXTR_LookUp.csv") %>%
janitor::clean_names(case = "screaming_snake")
res_bldg_2018 <- readr::read_csv("extdata/source/Year2018/EXTR_ResBldg.csv") %>%
janitor::clean_names(case = "screaming_snake")
present_use_lut <- lut_2018 %>%
dplyr::filter(LU_TYPE %in% 102) %>%
dplyr::transmute(PRESENT_USE = LU_ITEM,
PRESENT_USE_DESC = LU_DESCRIPTION)
check_present_use <- function(x, n_min){
x %>%
dplyr::transmute(PIN = NeighborhoodChangeTypology::make_pin(MAJOR, MINOR),
PRESENT_USE) %>%
dplyr::left_join(present_use_lut, by = "PRESENT_USE") %>%
tidyr::drop_na() %>%
dplyr::count(PRESENT_USE_DESC, sort = TRUE) %>%
dplyr::filter(n > n_min) %>%
print(n=Inf)
}
# check_present_use(p, 100)
sales_reason_lut <- lut_2018 %>%
dplyr::filter(LU_TYPE %in% 5) %>%
dplyr::transmute(SALE_REASON = LU_ITEM,
SALE_REASON_DESC = LU_DESCRIPTION)
check_sale_reason <- function(x, n_min){
x %>%
dplyr::transmute(PIN = NeighborhoodChangeTypology::make_pin(MAJOR, MINOR),
SALE_REASON) %>%
dplyr::left_join(sales_reason_lut, by = "SALE_REASON") %>%
tidyr::drop_na() %>%
dplyr::count(SALE_REASON_DESC, sort = TRUE) %>%
dplyr::filter(n > n_min) %>%
print(n=Inf)
}
# check_sale_reason(sales, 100)
rgdal::ogrListLayers("extdata/source/Year2005.gdb")
rgdal::ogrListLayers("extdata/source/Year2010.gdb")
rgdal::ogrListLayers("extdata/source/Year2013.gdb")
rgdal::ogrListLayers("extdata/source/Year2014.gdb")
rgdal::ogrListLayers("extdata/source/Year2015.gdb")
rgdal::ogrListLayers("extdata/source/Year2016.gdb")
# rgdal::ogrListLayers("extdata/source/Year2017.gdb") # note: the contents of files in this geodatabase appear to be identical to those in Year2016.gdb
# 2005
p_2005 <- sf::st_read(dsn = "extdata/source/Year2005.gdb", layer = "parcel_extr")
condo_2005 <- sf::st_read(dsn = "extdata/source/Year2005.gdb", layer = "condounit_extr")
lut_2005 <- sf::st_read(dsn = "extdata/source/Year2005.gdb", layer = "lookup_extr")
res_bldg_2005 <- sf::st_read(dsn = "extdata/source/Year2005.gdb", layer = "resbldg_extr")
# 2010
p_2010 <- sf::st_read(dsn = "extdata/source/Year2010.gdb", layer = "parcel_extr")
condo_2010 <- sf::st_read(dsn = "extdata/source/Year2010.gdb", layer = "condounit_extr")
res_bldg_2010 <- sf::st_read(dsn = "extdata/source/Year2010.gdb", layer = "resbldg_extr")
# 2013
p_2013 <- sf::st_read(dsn = "extdata/source/Year2013.gdb", layer = "parcel_report_view")
condo_2013 <- sf::st_read(dsn = "extdata/source/Year2013.gdb", layer = "CONDOUNIT_EXTR")
res_bldg_2013 <- sf::st_read(dsn = "extdata/source/Year2013.gdb", layer = "RESBLDG_EXTR")
# 2014
p_2014 <- sf::st_read(dsn = "extdata/source/Year2014.gdb", layer = "parcel_report_view")
condo_2014 <- sf::st_read(dsn = "extdata/source/Year2014.gdb", layer = "CONDOUNIT_EXTR")
res_bldg_2014 <- sf::st_read(dsn = "extdata/source/Year2014.gdb", layer = "RESBLDG_EXTR")
# 2015
p_2015 <- sf::st_read(dsn = "extdata/source/Year2015.gdb", layer = "PARCEL_EXTR")
condo_2015 <- sf::st_read(dsn = "extdata/source/Year2015.gdb", layer = "CONDOUNIT_EXTR")
res_bldg_2015 <- sf::st_read(dsn = "extdata/source/Year2015.gdb", layer = "RESBLDG_EXTR")
# 2016
p_2016 <- sf::st_read(dsn = "extdata/source/Year2016.gdb", layer = "PARCEL_EXTR")
condo_2016 <- sf::st_read(dsn = "extdata/source/Year2016.gdb", layer = "CONDOUNIT_EXTR")
res_bldg_2016 <- sf::st_read(dsn = "extdata/source/Year2016.gdb", layer = "RESBLDG_EXTR")
# 2017
p_2017 <- readr::read_csv("extdata/source/Year-2017/EXTR_Parcel_20171013.csv") %>%
janitor::clean_names(case = "screaming_snake")
condo_2017 <- readr::read_csv("extdata/source/Year-2017/EXTR_CondoUnit2.csv") %>%
janitor::clean_names(case = "screaming_snake")
res_bldg_2017 <- readr::read_csv("extdata/source/Year-2017/EXTR_ResBldg.csv") %>%
janitor::clean_names(case = "screaming_snake")
# WRITE DATA --------------------------------------------------------------
target_dir <- tools::file_path_sans_ext(zip_path)
suppressWarnings(suppressMessages(dir.create(target_dir)))
files_df <- tibble::tribble(
~x, ~path,
sales, "EXTR_RPSale.csv",
val, "EXTR_ValueHistory_V.csv",
p_2018, "EXTR_Parcel_2018.csv",
condo_2018, "EXTR_CondoUnit2_2018.csv",
lut_2018, "EXTR_LookUp_2018.csv",
res_bldg_2018, "EXTR_ResBldg_2018.csv",
p_2005, "EXTR_Parcel_2005.csv",
condo_2005, "EXTR_Condo_Unit_2005.csv",
lut_2005, "EXTR_LookUp_2005.csv",
res_bldg_2005, "EXTR_ResBldg_2005.csv",
p_2010, "EXTR_Parcel_2010.csv",
res_bldg_2010, "EXTR_ResBldg_2010.csv",
condo_2010, "EXTR_Condo_Unit_2010.csv",
p_2013, "EXTR_Parcel_2013.csv",
condo_2013, "EXTR_Condo_Unit_2013.csv",
res_bldg_2013, "EXTR_ResBldg_2013.csv",
p_2014, "EXTR_Parcel_2014.csv",
condo_2014, "EXTR_Condo_Unit_2014.csv",
res_bldg_2014, "EXTR_ResBldg_2014.csv",
p_2015, "EXTR_Parcel_2015.csv",
condo_2015, "EXTR_Condo_Unit_2015.csv",
res_bldg_2015, "EXTR_ResBldg_2015.csv",
p_2016, "EXTR_Parcel_2016.csv",
condo_2016, "EXTR_Condo_Unit_2016.csv",
res_bldg_2016, "EXTR_ResBldg_2016.csv",
p_2017, "EXTR_Parcel_2017.csv",
condo_2017, "EXTR_CondoUnit2_2017.csv",
res_bldg_2017, "EXTR_ResBldg_2017.csv"
) %>%
dplyr::mutate(path = stringr::str_c(target_dir, path, sep = "/"))
purrr::pwalk(files_df, readr::write_csv)
zip_subdirectory(zip_path = zip_path, dir_path = target_dir)
# RETURN ------------------------------------------------------------------
parcel_data_prep_status <- NeighborhoodChangeTypology::get_modified_time(zip_path)
return(parcel_data_prep_status)
}
#' @rdname parcel-data
#' @export
make_parcel_value <- function(data_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_value_filter <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::filter(TAX_STATUS %in% "T") %>% # The object takes up too much memory! Need to filter it down.
dplyr::filter(TAX_YR %in% c(2005, 2010, 2013, 2014, 2015, 2016, 2017, 2018))
parcel_value_long <- parcel_value_filter %>%
dplyr::mutate(DATE_BEGIN = as.character(get_date_end(TAX_YR)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(TAX_YR)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day",
VALUE_IMPROVEMENT = IMPS_VAL,
VALUE_TOTAL = LAND_VAL) %>%
dplyr::mutate_if(lubridate::is.Date, as.character) %>% # convert Date cols to character
dplyr::select(-dplyr::matches("VAL$")) %>% # remove the original value-related fields
tidyr::gather(VARIABLE, ESTIMATE, VALUE_IMPROVEMENT, VALUE_TOTAL) %>%
dplyr::rename_at(dplyr::vars(-dplyr::matches("VARIABLE|ESTIMATE|^DATE")), dplyr::funs(stringr::str_c("META_",.)))
parcel_value_transformed <- parcel_value_long %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID = make_pin(META_MAJOR, META_MINOR),
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_GROUP_ID,
DATE_BEGIN, # date of the last day of the calendar year
DATE_END, # also the date of the last day of the calendar year
DATE_RANGE,
DATE_RANGE_TYPE,
VARIABLE,
VARIABLE_SUBTOTAL = NA_character_,
VARIABLE_SUBTOTAL_DESC = NA_character_,
MEASURE_TYPE = "VALUE",
ESTIMATE,
MOE = 0
)
parcel_value_formatted <- data_template %>%
dplyr::full_join(parcel_value_transformed,
by = c("SOURCE",
"GEOGRAPHY_ID",
"GEOGRAPHY_ID_TYPE",
"GEOGRAPHY_NAME",
"GEOGRAPHY_TYPE",
"DATE_GROUP_ID",
"DATE_BEGIN",
"DATE_END",
"DATE_RANGE",
"DATE_RANGE_TYPE",
"VARIABLE",
"VARIABLE_SUBTOTAL",
"VARIABLE_SUBTOTAL_DESC",
"MEASURE_TYPE",
"ESTIMATE",
"MOE"))
parcel_value <- parcel_value_formatted
return(parcel_value)
}
#' @rdname parcel-data
#' @export
make_parcel_sales <- function(data_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_sales_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake")
parcel_sales_long <- parcel_sales_raw %>%
tidyr::gather(VARIABLE, ESTIMATE, SALE_PRICE) %>%
dplyr::rename_at(dplyr::vars(-dplyr::matches("VARIABLE|ESTIMATE")), dplyr::funs(stringr::str_c("META_",.)))
parcel_sales_transformed <- parcel_sales_long %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID = make_pin(META_MAJOR, META_MINOR),
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.Date(META_DOCUMENT_DATE, format = "%m/%d/%Y"), # date of the sale document
DATE_END = as.Date(META_DOCUMENT_DATE, format = "%m/%d/%Y"), # also the date of the sale document
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day",
VARIABLE,
VARIABLE_SUBTOTAL = NA_character_,
VARIABLE_SUBTOTAL_DESC = NA_character_,
MEASURE_TYPE = "VALUE",
ESTIMATE,
MOE = 0
) %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
parcel_sales_formatted <- data_template %>%
dplyr::full_join(parcel_sales_transformed,
by = c("SOURCE",
"GEOGRAPHY_ID",
"GEOGRAPHY_ID_TYPE",
"GEOGRAPHY_NAME",
"GEOGRAPHY_TYPE",
"DATE_GROUP_ID",
"DATE_BEGIN",
"DATE_END",
"DATE_RANGE",
"DATE_RANGE_TYPE",
"VARIABLE",
"VARIABLE_SUBTOTAL",
"VARIABLE_SUBTOTAL_DESC",
"MEASURE_TYPE",
"ESTIMATE",
"MOE"))
parcel_sales <- parcel_sales_formatted
return(parcel_sales)
}
#' @rdname parcel-data
#' @export
make_parcel_info_2005 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_info_2005_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
parcel_info_2005 <- metadata_template %>%
dplyr::full_join(parcel_info_2005_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2005L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2005L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(parcel_info_2005)
}
#' @rdname parcel-data
#' @export
make_parcel_info_2010 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_info_2010_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
parcel_info_2010 <- metadata_template %>%
dplyr::full_join(parcel_info_2010_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2010L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2010L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(parcel_info_2010)
}
#' @rdname parcel-data
#' @export
make_parcel_info_2013 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_info_2013_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
parcel_info_2013 <- metadata_template %>%
dplyr::full_join(parcel_info_2013_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2013L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2013L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(parcel_info_2013)
}
#' @rdname parcel-data
#' @export
make_parcel_info_2014 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_info_2014_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
parcel_info_2014 <- metadata_template %>%
dplyr::full_join(parcel_info_2014_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2014L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2014L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(parcel_info_2014)
}
#' @rdname parcel-data
#' @export
make_parcel_info_2015 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_info_2015_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
parcel_info_2015 <- metadata_template %>%
dplyr::full_join(parcel_info_2015_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2015L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2015L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(parcel_info_2015)
}
#' @rdname parcel-data
#' @export
make_parcel_info_2016 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_info_2016_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
parcel_info_2016 <- metadata_template %>%
dplyr::full_join(parcel_info_2016_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2016L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2016L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(parcel_info_2016)
}
#' @rdname parcel-data
#' @export
make_parcel_info_2017 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_info_2017_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(PRESENTUSE = PRESENT_USE,
SQFTLOT = SQ_FT_LOT) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.))) %>%
dplyr::mutate(META_PIN = make_pin(META_MAJOR, META_MINOR))
parcel_info_2017 <- metadata_template %>%
dplyr::full_join(parcel_info_2017_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2017L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2017L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(parcel_info_2017)
}
#' @rdname parcel-data
#' @export
make_parcel_info_2018 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_info_2018_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(PRESENTUSE = PRESENT_USE,
SQFTLOT = SQ_FT_LOT) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.))) %>%
dplyr::mutate(META_PIN = make_pin(META_MAJOR, META_MINOR))
parcel_info_2018 <- metadata_template %>%
dplyr::full_join(parcel_info_2018_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2018L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2018L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(parcel_info_2018)
}
#' @rdname parcel-data
#' @export
make_parcel_lut_2005 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_lut_2005 <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(LU_TYPE = LUTYPE,
LU_ITEM = LUITEM,
LU_DESCRIPTION = LUDESCRIPT) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
return(parcel_lut_2005)
}
#' @rdname parcel-data
#' @export
make_parcel_lut_2018 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_lut_2018 <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
return(parcel_lut_2018)
}
#' @rdname parcel-data
#' @export
make_condo_info_2005 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
condo_info_2005_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(FOOTAGE = FT,
UNIT_TYPE = UNITTYPE) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
condo_info_2005 <- metadata_template %>%
dplyr::full_join(condo_info_2005_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2005L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2005L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(condo_info_2005)
}
#' @rdname parcel-data
#' @export
make_condo_info_2010 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
condo_info_2010_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(FOOTAGE = FT,
UNIT_TYPE = UNITTYPE) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
condo_info_2010 <- metadata_template %>%
dplyr::full_join(condo_info_2010_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2010L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2010L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(condo_info_2010)
}
#' @rdname parcel-data
#' @export
make_condo_info_2013 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
condo_info_2013_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(FOOTAGE = FT,
UNIT_TYPE = UNITTYPE) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
condo_info_2013 <- metadata_template %>%
dplyr::full_join(condo_info_2013_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2013L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2013L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(condo_info_2013)
}
#' @rdname parcel-data
#' @export
make_condo_info_2014 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
condo_info_2014_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(FOOTAGE = FT,
UNIT_TYPE = UNITTYPE) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
condo_info_2014 <- metadata_template %>%
dplyr::full_join(condo_info_2014_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2014L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2014L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(condo_info_2014)
}
#' @rdname parcel-data
#' @export
make_condo_info_2015 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
condo_info_2015_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(FOOTAGE = FT,
UNIT_TYPE = UNITTYPE) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
condo_info_2015 <- metadata_template %>%
dplyr::full_join(condo_info_2015_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2015L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2015L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(condo_info_2015)
}
#' @rdname parcel-data
#' @export
make_condo_info_2016 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
condo_info_2016_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(FOOTAGE = FT,
UNIT_TYPE = UNITTYPE) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.))) %>%
dplyr::mutate(META_PIN = make_pin(META_MAJOR, META_MINOR))
condo_info_2016 <- metadata_template %>%
dplyr::full_join(condo_info_2016_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2016L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2016L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(condo_info_2016)
}
#' @rdname parcel-data
#' @export
make_condo_info_2017 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
condo_info_2017_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.))) %>%
dplyr::mutate(META_PIN = make_pin(META_MAJOR, META_MINOR))
condo_info_2017 <- metadata_template %>%
dplyr::full_join(condo_info_2017_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2017L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2017L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(condo_info_2017)
}
#' @rdname parcel-data
#' @export
make_condo_info_2018 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
condo_info_2018_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.))) %>%
dplyr::mutate(META_PIN = make_pin(META_MAJOR, META_MINOR))
condo_info_2018 <- metadata_template %>%
dplyr::full_join(condo_info_2018_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2018L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2018L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(condo_info_2018)
}
#' @rdname parcel-data
#' @export
make_res_bldg_2005 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
res_bldg_2005_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(SQ_FT_TOT_LIVING = SQFTTOTLIV,
BLDG_NBR = BLDGNBR,
YR_BUILT = YRBUILT) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
res_bldg_2005 <- metadata_template %>%
dplyr::full_join(res_bldg_2005_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2005L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2005L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(res_bldg_2005)
}
#' @rdname parcel-data
#' @export
make_res_bldg_2010 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
res_bldg_2010_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(SQ_FT_TOT_LIVING = SQFTTOTLIVING,
BLDG_NBR = BLDGNBR,
YR_BUILT = YRBUILT) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
res_bldg_2010 <- metadata_template %>%
dplyr::full_join(res_bldg_2010_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2010L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2010L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(res_bldg_2010)
}
#' @rdname parcel-data
#' @export
make_res_bldg_2013 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
res_bldg_2013_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(SQ_FT_TOT_LIVING = SQFTTOTLIVING,
BLDG_NBR = BLDGNBR,
YR_BUILT = YRBUILT) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
res_bldg_2013 <- metadata_template %>%
dplyr::full_join(res_bldg_2013_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2013L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2013L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(res_bldg_2013)
}
#' @rdname parcel-data
#' @export
make_res_bldg_2014 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
res_bldg_2014_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(SQ_FT_TOT_LIVING = SQFTTOTLIVING,
BLDG_NBR = BLDGNBR,
YR_BUILT = YRBUILT) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
res_bldg_2014 <- metadata_template %>%
dplyr::full_join(res_bldg_2014_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2014L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2014L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(res_bldg_2014)
}
#' @rdname parcel-data
#' @export
make_res_bldg_2015 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
res_bldg_2015_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(SQ_FT_TOT_LIVING = SQFTTOTLIVING,
BLDG_NBR = BLDGNBR,
YR_BUILT = YRBUILT) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
res_bldg_2015 <- metadata_template %>%
dplyr::full_join(res_bldg_2015_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2015L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2015L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(res_bldg_2015)
}
#' @rdname parcel-data
#' @export
make_res_bldg_2016 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
res_bldg_2016_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(SQ_FT_TOT_LIVING = SQFTTOTLIVING,
BLDG_NBR = BLDGNBR,
YR_BUILT = YRBUILT) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
res_bldg_2016 <- metadata_template %>%
dplyr::full_join(res_bldg_2016_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2016L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2016L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(res_bldg_2016)
}
#' @rdname parcel-data
#' @export
make_res_bldg_2017 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
res_bldg_2017_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.))) %>%
dplyr::mutate(META_PIN = make_pin(META_MAJOR, META_MINOR))
res_bldg_2017 <- metadata_template %>%
dplyr::full_join(res_bldg_2017_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2017L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2017L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(res_bldg_2017)
}
#' @rdname parcel-data
#' @export
make_res_bldg_2018 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
res_bldg_2018_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.))) %>%
dplyr::mutate(META_PIN = make_pin(META_MAJOR, META_MINOR))
res_bldg_2018 <- metadata_template %>%
dplyr::full_join(res_bldg_2018_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2018L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2018L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(res_bldg_2018)
}
tiernanmartin/NeighborhoodChangeTypology documentation built on May 17, 2019, 1:02 p.m.
R Package Documentation
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#' @title Make A Tibble of The Project's Tax Assessor Variables
#' @description Return a `tibble` of all of the
#' @param model_table_inputs Tibble, the `model_table_inputs` object
#' @param acs_tables Tibble, the `acs_table` object
#' @param path Character, the path or connection to write to.
#' @param zip_path Character, the file path of the archive file
#' @param file_path Character, the file path of the file that is to be extracted
#' @param data_template Tibble, desc
#' @param metadata_template Tibble, desc
#' @return a `tibble`
#' @rdname parcel-data
#' @export
prepare_parcel_data <- function(model_table_inputs, acs_tables, zip_path){
# GET DATA ----------------------------------------------------------------
# DATA SOURCES:
# * 2018 data: https://info.kingcounty.gov/assessor/DataDownload/default.aspx
# * pre-2018 data: direct request fulfilled by the King County Assessor Office
sales <- readr::read_csv("extdata/source/Year2018/EXTR_RPSale.csv") %>%
janitor::clean_names(case = "screaming_snake")
val <- readr::read_csv("extdata/source/Year2018/EXTR_ValueHistory_V.csv") %>%
janitor::clean_names(case = "screaming_snake")
p_2018 <- readr::read_csv("extdata/source/Year2018/EXTR_Parcel.csv") %>%
janitor::clean_names(case = "screaming_snake")
condo_2018 <- readr::read_csv("extdata/source/Year2018/EXTR_CondoUnit2.csv") %>%
janitor::clean_names(case = "screaming_snake")
lut_2018 <- readr::read_csv("extdata/source/Year2018/EXTR_LookUp.csv") %>%
janitor::clean_names(case = "screaming_snake")
res_bldg_2018 <- readr::read_csv("extdata/source/Year2018/EXTR_ResBldg.csv") %>%
janitor::clean_names(case = "screaming_snake")
present_use_lut <- lut_2018 %>%
dplyr::filter(LU_TYPE %in% 102) %>%
dplyr::transmute(PRESENT_USE = LU_ITEM,
PRESENT_USE_DESC = LU_DESCRIPTION)
check_present_use <- function(x, n_min){
x %>%
dplyr::transmute(PIN = NeighborhoodChangeTypology::make_pin(MAJOR, MINOR),
PRESENT_USE) %>%
dplyr::left_join(present_use_lut, by = "PRESENT_USE") %>%
tidyr::drop_na() %>%
dplyr::count(PRESENT_USE_DESC, sort = TRUE) %>%
dplyr::filter(n > n_min) %>%
print(n=Inf)
}
# check_present_use(p, 100)
sales_reason_lut <- lut_2018 %>%
dplyr::filter(LU_TYPE %in% 5) %>%
dplyr::transmute(SALE_REASON = LU_ITEM,
SALE_REASON_DESC = LU_DESCRIPTION)
check_sale_reason <- function(x, n_min){
x %>%
dplyr::transmute(PIN = NeighborhoodChangeTypology::make_pin(MAJOR, MINOR),
SALE_REASON) %>%
dplyr::left_join(sales_reason_lut, by = "SALE_REASON") %>%
tidyr::drop_na() %>%
dplyr::count(SALE_REASON_DESC, sort = TRUE) %>%
dplyr::filter(n > n_min) %>%
print(n=Inf)
}
# check_sale_reason(sales, 100)
rgdal::ogrListLayers("extdata/source/Year2005.gdb")
rgdal::ogrListLayers("extdata/source/Year2010.gdb")
rgdal::ogrListLayers("extdata/source/Year2013.gdb")
rgdal::ogrListLayers("extdata/source/Year2014.gdb")
rgdal::ogrListLayers("extdata/source/Year2015.gdb")
rgdal::ogrListLayers("extdata/source/Year2016.gdb")
# rgdal::ogrListLayers("extdata/source/Year2017.gdb") # note: the contents of files in this geodatabase appear to be identical to those in Year2016.gdb
# 2005
p_2005 <- sf::st_read(dsn = "extdata/source/Year2005.gdb", layer = "parcel_extr")
condo_2005 <- sf::st_read(dsn = "extdata/source/Year2005.gdb", layer = "condounit_extr")
lut_2005 <- sf::st_read(dsn = "extdata/source/Year2005.gdb", layer = "lookup_extr")
res_bldg_2005 <- sf::st_read(dsn = "extdata/source/Year2005.gdb", layer = "resbldg_extr")
# 2010
p_2010 <- sf::st_read(dsn = "extdata/source/Year2010.gdb", layer = "parcel_extr")
condo_2010 <- sf::st_read(dsn = "extdata/source/Year2010.gdb", layer = "condounit_extr")
res_bldg_2010 <- sf::st_read(dsn = "extdata/source/Year2010.gdb", layer = "resbldg_extr")
# 2013
p_2013 <- sf::st_read(dsn = "extdata/source/Year2013.gdb", layer = "parcel_report_view")
condo_2013 <- sf::st_read(dsn = "extdata/source/Year2013.gdb", layer = "CONDOUNIT_EXTR")
res_bldg_2013 <- sf::st_read(dsn = "extdata/source/Year2013.gdb", layer = "RESBLDG_EXTR")
# 2014
p_2014 <- sf::st_read(dsn = "extdata/source/Year2014.gdb", layer = "parcel_report_view")
condo_2014 <- sf::st_read(dsn = "extdata/source/Year2014.gdb", layer = "CONDOUNIT_EXTR")
res_bldg_2014 <- sf::st_read(dsn = "extdata/source/Year2014.gdb", layer = "RESBLDG_EXTR")
# 2015
p_2015 <- sf::st_read(dsn = "extdata/source/Year2015.gdb", layer = "PARCEL_EXTR")
condo_2015 <- sf::st_read(dsn = "extdata/source/Year2015.gdb", layer = "CONDOUNIT_EXTR")
res_bldg_2015 <- sf::st_read(dsn = "extdata/source/Year2015.gdb", layer = "RESBLDG_EXTR")
# 2016
p_2016 <- sf::st_read(dsn = "extdata/source/Year2016.gdb", layer = "PARCEL_EXTR")
condo_2016 <- sf::st_read(dsn = "extdata/source/Year2016.gdb", layer = "CONDOUNIT_EXTR")
res_bldg_2016 <- sf::st_read(dsn = "extdata/source/Year2016.gdb", layer = "RESBLDG_EXTR")
# 2017
p_2017 <- readr::read_csv("extdata/source/Year-2017/EXTR_Parcel_20171013.csv") %>%
janitor::clean_names(case = "screaming_snake")
condo_2017 <- readr::read_csv("extdata/source/Year-2017/EXTR_CondoUnit2.csv") %>%
janitor::clean_names(case = "screaming_snake")
res_bldg_2017 <- readr::read_csv("extdata/source/Year-2017/EXTR_ResBldg.csv") %>%
janitor::clean_names(case = "screaming_snake")
# WRITE DATA --------------------------------------------------------------
target_dir <- tools::file_path_sans_ext(zip_path)
suppressWarnings(suppressMessages(dir.create(target_dir)))
files_df <- tibble::tribble(
~x, ~path,
sales, "EXTR_RPSale.csv",
val, "EXTR_ValueHistory_V.csv",
p_2018, "EXTR_Parcel_2018.csv",
condo_2018, "EXTR_CondoUnit2_2018.csv",
lut_2018, "EXTR_LookUp_2018.csv",
res_bldg_2018, "EXTR_ResBldg_2018.csv",
p_2005, "EXTR_Parcel_2005.csv",
condo_2005, "EXTR_Condo_Unit_2005.csv",
lut_2005, "EXTR_LookUp_2005.csv",
res_bldg_2005, "EXTR_ResBldg_2005.csv",
p_2010, "EXTR_Parcel_2010.csv",
res_bldg_2010, "EXTR_ResBldg_2010.csv",
condo_2010, "EXTR_Condo_Unit_2010.csv",
p_2013, "EXTR_Parcel_2013.csv",
condo_2013, "EXTR_Condo_Unit_2013.csv",
res_bldg_2013, "EXTR_ResBldg_2013.csv",
p_2014, "EXTR_Parcel_2014.csv",
condo_2014, "EXTR_Condo_Unit_2014.csv",
res_bldg_2014, "EXTR_ResBldg_2014.csv",
p_2015, "EXTR_Parcel_2015.csv",
condo_2015, "EXTR_Condo_Unit_2015.csv",
res_bldg_2015, "EXTR_ResBldg_2015.csv",
p_2016, "EXTR_Parcel_2016.csv",
condo_2016, "EXTR_Condo_Unit_2016.csv",
res_bldg_2016, "EXTR_ResBldg_2016.csv",
p_2017, "EXTR_Parcel_2017.csv",
condo_2017, "EXTR_CondoUnit2_2017.csv",
res_bldg_2017, "EXTR_ResBldg_2017.csv"
) %>%
dplyr::mutate(path = stringr::str_c(target_dir, path, sep = "/"))
purrr::pwalk(files_df, readr::write_csv)
zip_subdirectory(zip_path = zip_path, dir_path = target_dir)
# RETURN ------------------------------------------------------------------
parcel_data_prep_status <- NeighborhoodChangeTypology::get_modified_time(zip_path)
return(parcel_data_prep_status)
}
#' @rdname parcel-data
#' @export
make_parcel_value <- function(data_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_value_filter <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::filter(TAX_STATUS %in% "T") %>% # The object takes up too much memory! Need to filter it down.
dplyr::filter(TAX_YR %in% c(2005, 2010, 2013, 2014, 2015, 2016, 2017, 2018))
parcel_value_long <- parcel_value_filter %>%
dplyr::mutate(DATE_BEGIN = as.character(get_date_end(TAX_YR)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(TAX_YR)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day",
VALUE_IMPROVEMENT = IMPS_VAL,
VALUE_TOTAL = LAND_VAL) %>%
dplyr::mutate_if(lubridate::is.Date, as.character) %>% # convert Date cols to character
dplyr::select(-dplyr::matches("VAL$")) %>% # remove the original value-related fields
tidyr::gather(VARIABLE, ESTIMATE, VALUE_IMPROVEMENT, VALUE_TOTAL) %>%
dplyr::rename_at(dplyr::vars(-dplyr::matches("VARIABLE|ESTIMATE|^DATE")), dplyr::funs(stringr::str_c("META_",.)))
parcel_value_transformed <- parcel_value_long %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID = make_pin(META_MAJOR, META_MINOR),
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_GROUP_ID,
DATE_BEGIN, # date of the last day of the calendar year
DATE_END, # also the date of the last day of the calendar year
DATE_RANGE,
DATE_RANGE_TYPE,
VARIABLE,
VARIABLE_SUBTOTAL = NA_character_,
VARIABLE_SUBTOTAL_DESC = NA_character_,
MEASURE_TYPE = "VALUE",
ESTIMATE,
MOE = 0
)
parcel_value_formatted <- data_template %>%
dplyr::full_join(parcel_value_transformed,
by = c("SOURCE",
"GEOGRAPHY_ID",
"GEOGRAPHY_ID_TYPE",
"GEOGRAPHY_NAME",
"GEOGRAPHY_TYPE",
"DATE_GROUP_ID",
"DATE_BEGIN",
"DATE_END",
"DATE_RANGE",
"DATE_RANGE_TYPE",
"VARIABLE",
"VARIABLE_SUBTOTAL",
"VARIABLE_SUBTOTAL_DESC",
"MEASURE_TYPE",
"ESTIMATE",
"MOE"))
parcel_value <- parcel_value_formatted
return(parcel_value)
}
#' @rdname parcel-data
#' @export
make_parcel_sales <- function(data_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_sales_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake")
parcel_sales_long <- parcel_sales_raw %>%
tidyr::gather(VARIABLE, ESTIMATE, SALE_PRICE) %>%
dplyr::rename_at(dplyr::vars(-dplyr::matches("VARIABLE|ESTIMATE")), dplyr::funs(stringr::str_c("META_",.)))
parcel_sales_transformed <- parcel_sales_long %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID = make_pin(META_MAJOR, META_MINOR),
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.Date(META_DOCUMENT_DATE, format = "%m/%d/%Y"), # date of the sale document
DATE_END = as.Date(META_DOCUMENT_DATE, format = "%m/%d/%Y"), # also the date of the sale document
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day",
VARIABLE,
VARIABLE_SUBTOTAL = NA_character_,
VARIABLE_SUBTOTAL_DESC = NA_character_,
MEASURE_TYPE = "VALUE",
ESTIMATE,
MOE = 0
) %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
parcel_sales_formatted <- data_template %>%
dplyr::full_join(parcel_sales_transformed,
by = c("SOURCE",
"GEOGRAPHY_ID",
"GEOGRAPHY_ID_TYPE",
"GEOGRAPHY_NAME",
"GEOGRAPHY_TYPE",
"DATE_GROUP_ID",
"DATE_BEGIN",
"DATE_END",
"DATE_RANGE",
"DATE_RANGE_TYPE",
"VARIABLE",
"VARIABLE_SUBTOTAL",
"VARIABLE_SUBTOTAL_DESC",
"MEASURE_TYPE",
"ESTIMATE",
"MOE"))
parcel_sales <- parcel_sales_formatted
return(parcel_sales)
}
#' @rdname parcel-data
#' @export
make_parcel_info_2005 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_info_2005_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
parcel_info_2005 <- metadata_template %>%
dplyr::full_join(parcel_info_2005_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2005L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2005L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(parcel_info_2005)
}
#' @rdname parcel-data
#' @export
make_parcel_info_2010 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_info_2010_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
parcel_info_2010 <- metadata_template %>%
dplyr::full_join(parcel_info_2010_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2010L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2010L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(parcel_info_2010)
}
#' @rdname parcel-data
#' @export
make_parcel_info_2013 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_info_2013_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
parcel_info_2013 <- metadata_template %>%
dplyr::full_join(parcel_info_2013_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2013L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2013L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(parcel_info_2013)
}
#' @rdname parcel-data
#' @export
make_parcel_info_2014 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_info_2014_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
parcel_info_2014 <- metadata_template %>%
dplyr::full_join(parcel_info_2014_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2014L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2014L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(parcel_info_2014)
}
#' @rdname parcel-data
#' @export
make_parcel_info_2015 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_info_2015_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
parcel_info_2015 <- metadata_template %>%
dplyr::full_join(parcel_info_2015_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2015L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2015L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(parcel_info_2015)
}
#' @rdname parcel-data
#' @export
make_parcel_info_2016 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_info_2016_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
parcel_info_2016 <- metadata_template %>%
dplyr::full_join(parcel_info_2016_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2016L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2016L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(parcel_info_2016)
}
#' @rdname parcel-data
#' @export
make_parcel_info_2017 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_info_2017_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(PRESENTUSE = PRESENT_USE,
SQFTLOT = SQ_FT_LOT) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.))) %>%
dplyr::mutate(META_PIN = make_pin(META_MAJOR, META_MINOR))
parcel_info_2017 <- metadata_template %>%
dplyr::full_join(parcel_info_2017_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2017L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2017L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(parcel_info_2017)
}
#' @rdname parcel-data
#' @export
make_parcel_info_2018 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_info_2018_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(PRESENTUSE = PRESENT_USE,
SQFTLOT = SQ_FT_LOT) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.))) %>%
dplyr::mutate(META_PIN = make_pin(META_MAJOR, META_MINOR))
parcel_info_2018 <- metadata_template %>%
dplyr::full_join(parcel_info_2018_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2018L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2018L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(parcel_info_2018)
}
#' @rdname parcel-data
#' @export
make_parcel_lut_2005 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_lut_2005 <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(LU_TYPE = LUTYPE,
LU_ITEM = LUITEM,
LU_DESCRIPTION = LUDESCRIPT) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
return(parcel_lut_2005)
}
#' @rdname parcel-data
#' @export
make_parcel_lut_2018 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
parcel_lut_2018 <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
return(parcel_lut_2018)
}
#' @rdname parcel-data
#' @export
make_condo_info_2005 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
condo_info_2005_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(FOOTAGE = FT,
UNIT_TYPE = UNITTYPE) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
condo_info_2005 <- metadata_template %>%
dplyr::full_join(condo_info_2005_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2005L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2005L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(condo_info_2005)
}
#' @rdname parcel-data
#' @export
make_condo_info_2010 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
condo_info_2010_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(FOOTAGE = FT,
UNIT_TYPE = UNITTYPE) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
condo_info_2010 <- metadata_template %>%
dplyr::full_join(condo_info_2010_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2010L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2010L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(condo_info_2010)
}
#' @rdname parcel-data
#' @export
make_condo_info_2013 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
condo_info_2013_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(FOOTAGE = FT,
UNIT_TYPE = UNITTYPE) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
condo_info_2013 <- metadata_template %>%
dplyr::full_join(condo_info_2013_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2013L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2013L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(condo_info_2013)
}
#' @rdname parcel-data
#' @export
make_condo_info_2014 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
condo_info_2014_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(FOOTAGE = FT,
UNIT_TYPE = UNITTYPE) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
condo_info_2014 <- metadata_template %>%
dplyr::full_join(condo_info_2014_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2014L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2014L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(condo_info_2014)
}
#' @rdname parcel-data
#' @export
make_condo_info_2015 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
condo_info_2015_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(FOOTAGE = FT,
UNIT_TYPE = UNITTYPE) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
condo_info_2015 <- metadata_template %>%
dplyr::full_join(condo_info_2015_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2015L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2015L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(condo_info_2015)
}
#' @rdname parcel-data
#' @export
make_condo_info_2016 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
condo_info_2016_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(FOOTAGE = FT,
UNIT_TYPE = UNITTYPE) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.))) %>%
dplyr::mutate(META_PIN = make_pin(META_MAJOR, META_MINOR))
condo_info_2016 <- metadata_template %>%
dplyr::full_join(condo_info_2016_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2016L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2016L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(condo_info_2016)
}
#' @rdname parcel-data
#' @export
make_condo_info_2017 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
condo_info_2017_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.))) %>%
dplyr::mutate(META_PIN = make_pin(META_MAJOR, META_MINOR))
condo_info_2017 <- metadata_template %>%
dplyr::full_join(condo_info_2017_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2017L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2017L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(condo_info_2017)
}
#' @rdname parcel-data
#' @export
make_condo_info_2018 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
condo_info_2018_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.))) %>%
dplyr::mutate(META_PIN = make_pin(META_MAJOR, META_MINOR))
condo_info_2018 <- metadata_template %>%
dplyr::full_join(condo_info_2018_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2018L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2018L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(condo_info_2018)
}
#' @rdname parcel-data
#' @export
make_res_bldg_2005 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
res_bldg_2005_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(SQ_FT_TOT_LIVING = SQFTTOTLIV,
BLDG_NBR = BLDGNBR,
YR_BUILT = YRBUILT) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
res_bldg_2005 <- metadata_template %>%
dplyr::full_join(res_bldg_2005_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2005L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2005L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(res_bldg_2005)
}
#' @rdname parcel-data
#' @export
make_res_bldg_2010 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
res_bldg_2010_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(SQ_FT_TOT_LIVING = SQFTTOTLIVING,
BLDG_NBR = BLDGNBR,
YR_BUILT = YRBUILT) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
res_bldg_2010 <- metadata_template %>%
dplyr::full_join(res_bldg_2010_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2010L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2010L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(res_bldg_2010)
}
#' @rdname parcel-data
#' @export
make_res_bldg_2013 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
res_bldg_2013_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(SQ_FT_TOT_LIVING = SQFTTOTLIVING,
BLDG_NBR = BLDGNBR,
YR_BUILT = YRBUILT) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
res_bldg_2013 <- metadata_template %>%
dplyr::full_join(res_bldg_2013_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2013L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2013L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(res_bldg_2013)
}
#' @rdname parcel-data
#' @export
make_res_bldg_2014 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
res_bldg_2014_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(SQ_FT_TOT_LIVING = SQFTTOTLIVING,
BLDG_NBR = BLDGNBR,
YR_BUILT = YRBUILT) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
res_bldg_2014 <- metadata_template %>%
dplyr::full_join(res_bldg_2014_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2014L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2014L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(res_bldg_2014)
}
#' @rdname parcel-data
#' @export
make_res_bldg_2015 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
res_bldg_2015_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(SQ_FT_TOT_LIVING = SQFTTOTLIVING,
BLDG_NBR = BLDGNBR,
YR_BUILT = YRBUILT) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
res_bldg_2015 <- metadata_template %>%
dplyr::full_join(res_bldg_2015_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2015L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2015L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(res_bldg_2015)
}
#' @rdname parcel-data
#' @export
make_res_bldg_2016 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
res_bldg_2016_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename(SQ_FT_TOT_LIVING = SQFTTOTLIVING,
BLDG_NBR = BLDGNBR,
YR_BUILT = YRBUILT) %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.)))
res_bldg_2016 <- metadata_template %>%
dplyr::full_join(res_bldg_2016_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2016L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2016L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(res_bldg_2016)
}
#' @rdname parcel-data
#' @export
make_res_bldg_2017 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
res_bldg_2017_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.))) %>%
dplyr::mutate(META_PIN = make_pin(META_MAJOR, META_MINOR))
res_bldg_2017 <- metadata_template %>%
dplyr::full_join(res_bldg_2017_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2017L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2017L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(res_bldg_2017)
}
#' @rdname parcel-data
#' @export
make_res_bldg_2018 <- function(metadata_template, zip_path, file_path){
NeighborhoodChangeTypology::extract_file(zip_path, file_path)
res_bldg_2018_raw <- suppressWarnings(suppressMessages(readr::read_csv(file_path))) %>%
janitor::clean_names(case = "screaming_snake") %>%
dplyr::rename_all(dplyr::funs(stringr::str_c("META_",.))) %>%
dplyr::mutate(META_PIN = make_pin(META_MAJOR, META_MINOR))
res_bldg_2018 <- metadata_template %>%
dplyr::full_join(res_bldg_2018_raw, by = c(GEOGRAPHY_ID = "META_PIN")) %>%
dplyr::mutate(SOURCE = "ASSESSOR",
GEOGRAPHY_ID_TYPE = "PIN",
GEOGRAPHY_NAME = NA_character_,
GEOGRAPHY_TYPE = "parcel",
DATE_BEGIN = as.character(get_date_end(2018L)), # date of the last day of the calendar year
DATE_END = as.character(get_date_end(2018L)), # also the date of the last day of the calendar year
DATE_GROUP_ID = create_range_year(DATE_BEGIN, DATE_END),
DATE_RANGE = create_range_date(DATE_BEGIN, DATE_END),
DATE_RANGE_TYPE = "one day") %>%
dplyr::mutate_if(lubridate::is.Date, as.character)
return(res_bldg_2018)
}
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