winnipegr: winnipeg open data aggregator

#' Mode of transportation characteristics organized by neighbourhoods,
#' neighbourhood clusters, wards, community areas, and custom areas
#' including downtown and the entire city for the City of Winnipeg.
#'
#' @source Statistics Canada via (Winnipeg Open Data)[https://data.winnipeg.ca/].
#' \url{https://data.winnipeg.ca/Census/Census-Mode-Of-Transportation/ijxa-tybv}
#' @format A data frame with 31 columns:
#' \describe{
#'  \item{bicycle_female}{Females who travel via bicycle.}
#'  \item{bicycle_male}{Males who travel via bicycle.}
#'  \item{bicycle_total}{Total who travel via bicycle.}
#'  \item{boundary_name}{...}
#'  \item{boundary_number}{...}
#'  \item{boundary_type}{...}
#'  \item{car_female}{Females who travel via car.}
#'  \item{car_male}{...}
#'  \item{car_total}{...}
#'  \item{census_year}{...}
#'  \item{motorcycle_female}{Females who travel via motorcycle.}
#'  \item{motorcycle_male}{Males who travel via motorcycle.}
#'  \item{motorcycle_total}{Total motorcycle travellers.}
#'  \item{other_female}{Females who travel via other means.}
#'  \item{other_male}{Males who travel via other means.}
#'  \item{other_total}{Total indivuduals traveling via other means.}
#'  \item{passenger_female}{...}
#'  \item{passenger_male}{...}
#'  \item{passenger_total}{...}
#'  \item{public_female}{Females who travel via public transport.}
#'  \item{public_male}{...}
#'  \item{public_total}{...}
#'  \item{taxi_female}{Females who travel via taxi.}
#'  \item{taxi_male}{Males who travel via taxi.}
#'  \item{taxi_total}{Total who travel via taxi.}
#'  \item{total}{...}
#'  \item{total_female}{Total in group female.}
#'  \item{total_male}{Total in group male.}
#'  \item{walk_female}{Females who walk as means of transportation.}
#'  \item{walk_male}{Males who walk as means of transportation.}
#'  \item{walk_total}{Total who walk as means of transportation.}
#' }
#' @examples
#' \dontrun{
#'  transport_mode
#' }
"transport_mode"


#' Census commute data for Winnipeg CMA 2019 at the DA level.
#'
#' @source Statistics Canada via (cancensus)[https://mountainmath.github.io/cancensus/articles/cancensus.html] package.
#' \url{https://cran.r-project.org/web/packages/cancensus/index.html}
#' @format A data frame with 27 columns:
#' \describe{
#'  \item{geo_uid}{DA identifier}
#'  \item{region}{Region name}
#'  \item{leaving_time_total}{v_CA16_5831: Total - Time leaving for work for the employed labour force aged 15 years and over in private households with a usual place of work or no fixed workplace address - 25% sample data}
#'  \item{leaving_time_5}{v_CA16_5834: Between 5 a.m. and 5:59 a.m.}
#'  \item{leaving_time_6}{v_CA16_5837: Between 6 a.m. and 6:59 a.m.}
#'  \item{leaving_time_7}{v_CA16_5840: Between 7 a.m. and 7:59 a.m.}
#'  \item{leaving_time_8}{v_CA16_5843: Between 8 a.m. and 8:59 a.m.}
#'  \item{leaving_time_9to1159}{v_CA16_5846: Between 9 a.m. and 11:59 a.m.}
#'  \item{leaving_time_rest}{v_CA16_5849: Between 12 p.m. and 4:59 a.m.}
#'  \item{commute_mode_total}{v_CA16_5792: Total - Main mode of commuting for the employed labour force aged 15 years and over in private households with a usual place of work or no fixed workplace address - 25% sample data}
#'  \item{commute_mode_driver}{v_CA16_5795: Car, truck, van - as a driver}
#'  \item{commute_mode_passenger}{v_CA16_5798: Car, truck, van - as a passenger}
#'  \item{commute_mode_walked}{v_CA16_5804: Walked}
#'  \item{commute_mode_bicycle}{v_CA16_5807: Bicycle}
#'  \item{commute_mode_public_transit}{v_CA16_5801: Public transit}
#'  \item{commute_mode_other}{v_CA16_5810: Other method}
#'  \item{duration_total}{v_CA16_5813: Total - Commuting duration for the employed labour force aged 15 years and over in private households with a usual place of work or no fixed workplace address - 25% sample data}
#'  \item{duration_lt_15}{v_CA16_5816: Less than 15 minutes}
#'  \item{duration_15to29}{v_CA16_5819: 15 to 29 minutes}
#'  \item{duration_30to44}{v_CA16_5822: 30 to 44 minutes}
#'  \item{duration_45to59}{v_CA16_5825: 45 to 59 minutes}
#'  \item{duration_60plus}{v_CA16_5828: 60 minutes and over}
#'  \item{dist_total}{v_CA16_5777: Total - Commuting destination for the employed labour force aged 15 years and over in private households with a usual place of work - 25% sample data}
#'  \item{dist_short_inCSD}{v_CA16_5780: Commute within census subdivision (CSD) of residence}
#'  \item{dist_med_inCD}{v_CA16_5783: Commute to a different census subdivision (CSD) within census division (CD) of residence}
#'  \item{dist_long_inProv}{v_CA16_5786: Commute to a different census subdivision (CSD) and census division (CD) within province or territory of residence}
#'  \item{dist_extreme_outProv}{v_CA16_5789: Commute to a different province or territory}
#'  }
#' @examples
#' \dontrun{
#'  census_commute_2019
#' }
"census_commute_2019"


#' Winnipeg 2018 Electoral Wards
#'
#' @source (Winnipeg Open Data)[https://data.winnipeg.ca/].
#' \url{https://data.winnipeg.ca/Council-Services/Electoral-Ward/ede3-teb8}
#' @format A data frame with 6 columns:
#' \describe{
#'  \item{councillor}{Self-explanatory.}
#'  \item{name}{Ward name.}
#'  \item{number}{Ward number.}
#'  \item{phone}{Councillor phone number.}
#'  \item{website}{Councillor website.}
#'  \item{geometry}{Shapefile geometry.}
#' }
#' @examples
#' \dontrun{
#'  wpg_electoral_wards_2018
#' }
"wpg_electoral_wards_2018"


#' Boundaries of neighbourhood characterization areas (aka NCA)
#' as polygons. Neighbourhood characterization areas are used
#' to delineate neighbourhoods for city planning purposes.
#'
#' @source (Winnipeg Open Data)[https://data.winnipeg.ca/].
#' \url{https://data.winnipeg.ca/City-Planning/Neighbourhood/fen6-iygi}
#' @format A data frame with 3 columns:
#' \describe{
#'  \item{id}{Neighbourhood id number.}
#'  \item{name}{Neighbourhood name.}
#'  \item{geometry}{Neighbourhood shapefile geometry.}
#' }
#' @examples
#' \dontrun{
#'  neighbourhoods_2017
#' }
"neighbourhoods_2017"


#' Winnipeg active transportation cycling network data and shapefiles
#' @source (Winnipeg Open Data)[https://data.winnipeg.ca/].
#' \url{https://data.winnipeg.ca/Transportation-Planning-Traffic-Management/Active-Transportation-Network/wm39-dswz}
#' @format A data frame with 9 columns:
#' \describe{
#' \item{city_area}{Area of the city the path is in.}
#'  \item{id}{Network id number.}
#'  \item{location}{Off road, on road, etc.}
#'  \item{nbhd}{Neighbourhood.}
#'  \item{spine_ntwk}{Whether it is a spine network. 'Yes' or 'No'.}
#'  \item{st_name}{Street name.}
#'  \item{type}{Type of network.}
#'  \item{ward}{Ward name.}
#'  \item{geometry}{Shapefile geometry for network path.}
#' }
#' @examples
#' \dontrun{
#'  active_trans_ntwk_2019
#' }
"active_trans_ntwk_2019"



#' Key Winnipeg demographics information for 100% data
#' and total data. Data is at the finest geographical
#' resolution (Dissemination Area---DA) available. Admittedly not
#' complete, and based on variables of interest in my work.
#'
#' @source Statistics Canada via (cancensus)[https://mountainmath.github.io/cancensus/articles/cancensus.html] package.
#' \url{https://cran.r-project.org/web/packages/cancensus/index.html}
#' @format A data frame with 71 columns:
#' \describe{
#' \item{geo_uid}{}
#' \item{population_2016}{}
#' \item{total_private_dwellings}{}
#' \item{private_dwellings_occupied_by_usual_residents}{}
#' \item{land_area_in_square_kilometres}{}
#' \item{occupied_private_dwellings_by_structural_type_of_dwelling_data}{}
#' \item{apartment_in_a_building_that_has_five_or_more_storeys}{}
#' \item{semi_detached_house}{}
#' \item{row_house}{}
#' \item{apartment_or_flat_in_a_duplex}{}
#' \item{apartment_in_a_building_that_has_fewer_than_five_storeys}{}
#' \item{other_single_attached_house}{}
#' \item{movable_dwelling}{}
#' \item{private_households_by_household_size}{}
#' \item{x1_person}{}
#' \item{x2_persons}{}
#' \item{x3_persons}{}
#' \item{x4_persons}{}
#' \item{x5_or_more_persons}{}
#' \item{age_stats}{}
#' \item{x0_to_4_years}{}
#' \item{x5_to_9_years}{}
#' \item{x10_to_14_years}{}
#' \item{x15_to_19_years}{}
#' \item{x20_to_24_years}{}
#' \item{x25_to_29_years}{}
#' \item{x30_to_34_years}{}
#' \item{x35_to_39_years}{}
#' \item{x40_to_44_years}{}
#' \item{x45_to_49_years}{}
#' \item{x50_to_54_years}{}
#' \item{x55_to_59_years}{}
#' \item{x60_to_64_years}{}
#' \item{x65_years_and_over}{}
#' \item{total_marital_status_for_the_population_aged_15_years_and_over_100_percent_data}{}
#' \item{married_or_living_common_law}{}
#' \item{not_married_and_not_living_common_law}{}
#' \item{total_number_of_census_families_in_private_households_100_percent_data}{}
#' \item{total_couple_census_families_in_private_households_100_percent_data}{}
#' \item{couples_without_children}{}
#' \item{couples_with_children}{}
#' \item{x1_child}{}
#' \item{x2_children}{}
#' \item{x3_or_more_children}{}
#' \item{total_lone_parent_census_families_in_private_households_100_percent_data}{}
#' \item{x1_child_2}{}
#' \item{x2_children_2}{}
#' \item{x3_or_more_children_2}{}
#' \item{total_persons_not_in_census_families_in_private_households_100_percent_data}{}
#' \item{official_languages}{}
#' \item{non_official_languages}{}
#' \item{total_total_income_groups_in_2015_for_the_population_aged_15_years_and_over_in_private_households_100_percent_data}{}
#' \item{without_total_income}{}
#' \item{under_10_000_including_loss}{}
#' \item{x10_000_to_19_999}{}
#' \item{x20_000_to_29_999}{}
#' \item{x30_000_to_39_999}{}
#' \item{x40_000_to_49_999}{}
#' \item{x50_000_to_59_999}{}
#' \item{x60_000_to_69_999}{}
#' \item{x70_000_to_79_999}{}
#' \item{x80_000_to_89_999}{}
#' \item{x90_000_to_99_999}{}
#' \item{x100_000_to_149_999}{}
#' \item{x150_000_and_over}{}
#' \item{total_income_statistics_in_2015_for_private_households_by_household_size_100_percent_data}{}
#' \item{median_total_income_of_households_in_2015}{}
#' \item{total_low_income_status_in_2015_for_the_population_in_private_households_to_whom_low_income_concepts_are_applicable_100_percent_data}{}
#' \item{x0_to_17_years}{}
#' \item{x18_to_64_years}{}
#' \item{x65_years_and_over_2}{}
#' }
#' @examples
#' \dontrun{
#'  census_demo_2019
#' }
"census_demo_2019"

#' Key Winnipeg demographics information for DAs with shapefiles.
#' Data is at the finest geographical resolution
#' (Dissemination Area---DA) available.
#'
#' @source Statistics Canada via [cancensus](https://mountainmath.github.io/cancensus/articles/cancensus.html) package.
#' \url{https://cran.r-project.org/web/packages/cancensus/index.html}
#' @format A data frame with 13 columns:
#' \describe{
#' \item{shape_area}{Area in kms^2.}
#' \item{type}{Geography type/granularity. Dissemination area.}
#' \item{dwellings}{Dwellings.}
#' \item{households}{Households.}
#' \item{geo_uid}{Unique DA indentifier.}
#' \item{population}{Population.}
#' \item{cd_uid}{CD identifier.}
#' \item{csd_uid}{DSG identifier.}
#' \item{ct_uid}{CT identifier.}
#' \item{cma_uid}{CMA (Winnipeg) identifier.}
#' \item{region_name}{Region name.}
#' \item{area_sq_km}{Area_sq_km.}
#' \item{geometry}{Geometrical boudary in sfc multipolygon format.}
#' \item{}{}
#' }
#' @examples
#' \dontrun{
#'  census_DA_2019
#' }
"census_DA_2019"



#' Winnipeg police crime historical crime statistics up to August 2019
#' pulled on 2019-12-02. Original csv names standardized with  the
#' \code{janitor::clean_names} function. Descriptions are my best guess.
#'
#' @source Winnipeg police crime map for public use
#' \url{https://www.winnipeg.ca/police/crimestat/viewMap.stm}
#' @format A data frame with 9 columns:
#' \describe{
#' \item{community}{Community.}
#' \item{count}{Number of crimes.}
#' \item{level_3_not_displayed}{Description of crime.}
#' \item{level_4}{Description of drug offence where applicable.}
#' \item{neighbourhoods}{Neigbourhood of crime's occurance.}
#' \item{number_of_records}{Number of records, note can be distinct from count.}
#' \item{offence_category}{Offence category.}
#' \item{offence}{Specific offence.}
#' \item{report_date}{Character string report date in m/d/yyyy format.}
#' }
#' @examples
#' \dontrun{
#'  crime_aug_2019
#' }
"crime_aug_2019"


#' Yearly incident based crime statistics with detailed violations
#' for Winnipeg, Manitoba, municipal code 46064 collected by Statistics Canada.
#' Data ranges from 1998 to 2018, as pulled on 2019-12-02.
#' Original names standardized with the \code{janitor::clean_names}
#' function.
#'
#' @source Statistics Canada. Table 35-10-0181-01
#' Incident-based crime statistics, by detailed violations,
#' police services in Winnipeg, Manitoba.
#' \url{https://doi.org/10.25318/3510018101-eng}
#' @format A data frame with 12 columns:
#' \describe{
#' \item{ref_date}{Year for which the statistics are compiled.}
#' \item{dguid}{Statistics Canada region code for Winnipeg municipal region.}
#' \item{violations}{Type of violation.}
#' \item{statistics}{Description of incident content.}
#' \item{uom}{Interpretation of number---number, rate or percent.}
#' \item{uom_id}{Internal uom id.}
#' \item{vector}{Statistics Canada vector data identifier.}
#' \item{coordinate}{Coordinate string.}
#' \item{value}{Value in question---number rate or percent.}
#' \item{status}{Status, either NA or "..".}
#' \item{terminated}{Either NA or TRUE.}
#' \item{decimals}{Whether the value is recorded to zero or two decimals.}
#' }
#' @examples
#' \dontrun{
#'  yrly_crime_2018
#' }
"yrly_crime_2018"



#' Municipal Benchmarking Network Canada (MBNC) dataset. MBNC collects and
#' distributes efficiency statistics on participating canadian municipalities.
#' Data pulled on 2019-12-27.
#' Original names standardized with the \code{janitor::clean_names}
#' function.
#'
#' @source Municipal Benchmarking Network Canada. Public measures.
#' \url{http://mbncanada.ca/}
#' @format A data frame with 21 columns:
#' \describe{
#' \item{measure_type_cd}{MBNC classification of measurement.}
#' \item{municipality_id}{MBNC unique municipality identifier.}
#' \item{fiscal_year}{Fiscal year of measurement.}
#' \item{program_area}{MBNC classification of measurement area.}
#' \item{municipality}{Name of municipality.}
#' \item{section_desc}{MBNC desciption of of measurement area.}
#' \item{measure_number}{Unique identifier of measure.}
#' \item{measure_name}{Name of measure (non-unique).}
#' \item{numerator}{Numerator of measure (where applicable).}
#' \item{denominator}{Denominator of measure (where applicable).}
#' \item{result}{Either the measured result or numerator/denominator (where applicable).}
#' \item{numerator_definition}{Definition of the numerator.}
#' \item{denominator_definition}{Definition of the denominator.}
#' \item{notes}{Further notes on the measure.}
#' \item{mpmp_measure}{Whether the measure is used in mpmp.}
#' \item{cao_measure}{Whether the measure is used in cao.}
#' \item{measure_category}{Broad measure category.}
#' \item{year_created}{First Year measure was instituted.}
#' \item{other}{Other notes and peculiarities in the measure.}
#' \item{influencing_factor}{Things contributing to the measure result.}
#' \item{measure}{Verbose name of measure with ID. Paste of measure_name and measure_number.}
#' }
#' @examples
#' \dontrun{
#'  data(mbnc)
#' }
#' \dontrun{
#' # To tidy the dataset & remove notes
#' tidy_mbnc <- winnipegr::mbnc %>%
#'     select(municipality, fiscal_year, measure, result) %>%
#'     pivot_wider(names_from = measure, values_from = result)
#' }
"mbnc"