Type:	Package
Title:	Easily Access US Census Bureau Survey and Geographic Data
Version:	0.1.5
Description:	The key function 'get_vintage_data()' returns a dataframe and is the window into the Census Bureau API requiring just a dataset name, vintage(year), and vector of variable names for survey estimates/percentages. Other functions assist in searching for available datasets, geographies, group/variable concepts of interest. Also provided are functions to access and layer (via standard piping) displayable geometries for the US, states, counties, blocks/tracts, roads, landmarks, places, and bodies of water. Joining survey data with many of the geometry functions is built-in to produce choropleth maps.
License:	MIT + file LICENSE
URL:	https://github.com/deandevl/RcensusPkg
BugReports:	https://github.com/deandevl/RcensusPkg/issues
Depends:	R (≥ 4.4.0)
Imports:	ggplot2 (≥ 3.5.1), data.table (≥ 1.16.4), httr2 (≥ 1.1.2), downloader (≥ 0.4.1), jsonlite (≥ 1.8.9), stringr (≥ 1.5.1), purrr (≥ 1.0.2), sf (≥ 1.0-19), ggplotify (≥ 0.1.2), gtable (≥ 0.3.6), RplotterPkg (≥ 0.1.3)
Suggests:	knitr (≥ 1.4.9), testthat (≥ 3.0.0), usethis (≥ 3.1.0), usmap (≥ 0.7.1), vdiffr (≥ 1.0.8), classInt (≥ 0.4-10), RColorBrewer (≥ 1.1-3), here (≥ 1.0.1), withr (≥ 3.0.2)
Encoding:	UTF-8
Language:	en-US
LazyData:	true
RoxygenNote:	7.3.2
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-04-10 13:55:56 UTC; deanr
Author:	Rick Dean [aut, cre, cph]
Maintainer:	Rick Dean <deanr3@bardstown.com>
Repository:	CRAN
Date/Publication:	2025-04-11 08:30:05 UTC

.get_url

Description

An internal function that creates the base url address to be submitted as a http GET request by .get_dt. It initializes with the dataset name and optionally the vintage.

Usage

.get_url(dataset, vintage)

Arguments

Value

Returns a url string.

.send_tiger_url

Description

An internal function for sending http requests to the US Census Bureau TIGER api via httr::GET() function.

Usage

.send_tiger_url(
  a_url = NULL,
  output_dir = NULL,
  delete_files = TRUE,
  set_crs = NULL,
  transform_crs = NULL,
  sf_info = FALSE,
  do_progress = FALSE,
  caller = NULL
)

Arguments

Value

A data frame object of class sf, data frame

get_category_strings

Description

For a Census Bureau categorical variable return a data.table with the variable's integer values and corresponding label strings.

The function will attempt to locate a Bureau's categorical variable by name and return a data.table with both the integer values and corresponding label strings. If the variable is not located then NULL is returned. Definitions for the variables were obtained from Population Estimates Categorical Variables and other Bureau sources.

Usage

get_category_strings(
  name = NULL,
  get_names = FALSE,
  start_idx = NULL,
  end_idx = NULL
)

Arguments

Value

A data.table with the variable's integer values and corresponding label strings.

Examples

library(data.table)
library(RcensusPkg)

# Get the names currently supported category variables
category_names_v <- RcensusPkg::get_category_strings(get_names = TRUE)

# Get the integer/string pairs for the category name "sex"
sex_category_dt <- RcensusPkg::get_category_strings(name = "sex")

get_dataset_names

Description

Get the acronym names and descriptions of the Census Bureau's datasets.

Function produces a data.table of the Census Bureau's dataset acronym names that can be used in other RcensusPkg:: functions calling for a dataset acronym name. See Census Bureau's publicly available datasets for descriptions. Also see All Surveys and Programs

Usage

get_dataset_names(
  vintage = NULL,
  filter_name_str = NULL,
  filter_title_str = NULL,
  ignore_case = TRUE,
  brief = TRUE
)

Arguments

Value

A list with a data.table of dataset names (named "data") and a vector of unique vintages (named "vintages").

Examples

## Not run: 
  # Requests for Census Bureau descriptions takes well over 10
  #  seconds in most cases.
  library(data.table)
  library(jsonlite)
  library(httr2)
  library(RcensusPkg)

  # Get descriptions/vintages for 2020 datasets with "acs5" in their name.
  acs5_datasets_ls <- RcensusPkg::get_dataset_names(
    vintage = 2020,
    filter_name_str = "acs5/"
  )

## End(Not run)

get_geography

Description

Get the list of geography entities available (state, county, tract, etc) for a specific dataset.

Function produces a data.table of all the geography "name" and "geoLevelDisplay" variables available for a specific dataset and optionally a vintage.

Usage

get_geography(dataset, vintage = NULL)

Arguments

Value

A data.table

Examples

library(jsonlite)
library(data.table)
library(httr2)
library(RcensusPkg)
# Get the geographies available for dataset "acs/acs1/profile" with vintage 2019
acs1_profile_geo_dt <- RcensusPkg::get_geography(
  dataset = "acs/acs1/profile",
  vintage = 2019
)

get_groups

Description

Get the names of Census Bureau variable groups and their descriptive parameters

Function produces a data.table of variable groups/tables and their descriptions.

Usage

get_groups(dataset = NULL, vintage = NULL)

Arguments

Value

A data.table

Examples

library(jsonlite)
library(data.table)
library(httr2)
library(RcensusPkg)

acs5_groups_dt <- RcensusPkg::get_groups(
  dataset = "acs/acs5",
  vintage = 2019)

get_idb_data

Description

Get data from the Census Bureau's Time Series International Database for a specific dataset: Single Year of Age and Sex("1year") or 5-Year Age Groups and Sex("5year"), year, variables, and country. See Census Bureau's publicly available International Database for dataset descriptions.

Usage

get_idb_data(
  dataset = NULL,
  years = 2023,
  vars = NULL,
  group = FALSE,
  wide_to_long = FALSE,
  countries = NULL,
  ages = 1:100,
  sex = c(0, 1, 2),
  key = Sys.getenv("CENSUS_KEY")
)

Arguments

Details

For the "1year", a data.table is returned with the following columns:

More details are available here.

For the "5year", see 5year and the 119 variable names and descriptions.

Value

A data.table

Examples

## Not run: 
  # Requires Census Bureau API key
  library(data.table)
  library(httr2)
  library(jsonlite)
  library(RcensusPkg)

  # 1year wide form, default variables, 2 countries
  # (Botswana, Norway)
  one_year_dt <- RcensusPkg::get_idb_data(
    dataset = "1year",
    years = c(2023, 2024),
    countries = c("BW", "NO")
  )

  # 5year long form, all variables, 2023, country = US
  five_year_US_long_group_dt <- RcensusPkg::get_idb_data(
    dataset = "5year",
    years = 2023,
    group = TRUE,
    countries = "US",
    wide_to_long = TRUE
  )

## End(Not run)

get_multi_vintage_data

Description

Get Census Bureau data for a specific dataset, variables, and region in the form of a data.table for multiple vintages. The function requires a Census Bureau access key.

Usage

get_multi_vintage_data(
  dataset = NULL,
  vintage_v = NULL,
  vars = NULL,
  region = NULL,
  regionin = NULL,
  key = Sys.getenv("CENSUS_KEY")
)

Arguments

Value

A data.table

Examples

## Not run: 
  # Requires Census Bureau API key
  # Obtain the median home value (“B25077_001E”) for Deschutes County, Oregon
  #   back to 2005 through 2019.
  library(jsonlite)
  library(data.table)
  library(httr2)
  library(usmap)
  library(RcensusPkg)

  # Get the fips codes for state and county
  deschutes_fips <- usmap::fips("OR","Deschutes")
  state <- substr(deschutes_fips,1,2)
  county <- substr(deschutes_fips,3,5)

  RcensusPkg::get_multi_vintage_data(
    dataset = "acs/acs1",
    vintage_v = 2005:2019,
    vars = c("B25077_001E", "B25077_001M"),
    region = paste0("county:", county),
    regionin = paste0("state:", state)
  )

## End(Not run)

get_variable_names

Description

Get Census Bureau variable acronym names and their label descriptions.

Function produces a data.table of variable acronym names and their descriptions. The function returns 4 columns:

Note that a variable with a required character value "true" must be included in your data requests (i.e. RcensusPkg::get_vintage_data()) or the API will return an error.

Usage

get_variable_names(
  dataset = NULL,
  category = NULL,
  vintage = 2020,
  vars = NULL,
  group = NULL,
  filter_group_est = FALSE,
  filter_name_str = NULL,
  filter_label_str = NULL,
  filter_concept_str = NULL,
  ignore_case = TRUE,
  fixed = FALSE
)

Arguments

Details

The function's search for variable names depends on either specifying the parameters 'dataset' name or a dataset 'category'. Entering an available 'vintage' also influences obtaining a full dataframe of variable names and descriptions. To assist in using the function the user should consult the Census Bureau's publicly available datasets descriptions. Also of help is Rcensus::get_dataset_names() for available dataset acronym names and their available years.

Value

A data.table

Examples

# Get available variables that have the phrase "educational attainment"
# in "label" column of the resultant data.table.

library(data.table)
library(httr2)
library(RcensusPkg)

educational_attainment_2019_dt <- RcensusPkg::get_variable_names(
  dataset = "acs/acs1/profile",
  vintage = 2019,
  filter_label_str = "educational attainment"
)

get_vintage_data

Description

Get Census Bureau data for a specific dataset, variables, and region in the form of a data.table.

Function produces a data.table with selected Census Bureau variables as columns. The function requires an access key issued from the Bureau. Variables of interest can be specified individually or by group/table name. Predicate phrases can be specified for filtering the results.

Usage

get_vintage_data(
  dataset = NULL,
  vintage = NULL,
  vars = NULL,
  NAME_GEOID = TRUE,
  predicates = NULL,
  group = NULL,
  wide_to_long = FALSE,
  region = NULL,
  regionin = NULL,
  na_cols = NULL,
  key = Sys.getenv("CENSUS_KEY")
)

Arguments

Details

See Census Bureau's publicly available datasets for dataset descriptions.

Some of the more common Census Bureau datasets include:

• American Community Survey 1-year Data

• American Community Survey 1-year Supplemental Data

• American Community Survey 5-Year Data

• Decennial Census

• Economic Census

• Population Estimates and Projections

• Annual Business Survey (ABS)

Value

A data.table

Examples

## Not run: 
  # Requires Census Bureau API key
  # ------American Community Survey 5-Year Data (2009-2021)-----------
  # Description: https://www.census.gov/data/developers/data-sets/acs-5year.html

  library(data.table)
  library(httr2)
  library(jsonlite)
  library(stringr)
  library(RcensusPkg)

  # Get the data for the total number of white females ("B01001A_017E") and
  # total number of white individuals ("B01001A_001E") across the US

  white_females_dt <- RcensusPkg::get_vintage_data(
    dataset = "acs/acs5",
    vars = c("B01001A_017E", "B01001A_001E"),
    vintage = 2021,
    region = "state:*"
  )

## End(Not run)

join_it

Description

Outer join two dataframes that have a common column variable.

Function uses fast data.table techniques to join two data.tables by their common key values. Examples might include using the "GEOID" variable as a key to join data from RcensusPkg::get_vintage_data() with a simple feature with its geometries for counties, states, countries for example from RcensusPkg::tiger_*_sf(). The resulting dataframe could then display the geometries (with RplotterPkg::create_sf_plot()) with an aesthetic mapping (e.g. fill/color/size) with a joined data column. Joining could also take place between two simple features (created by RcensusPkg::tiger_*_sf()) or between two dataframes (created by RcensusPkg::get_vintage_data()).

The important thing to remember is that all the rows in 'df_2' will be present in the resultant data.table.

Usage

join_it(
  df_1 = NULL,
  df_2 = NULL,
  key_1 = NULL,
  key_2 = NULL,
  negate = FALSE,
  match = FALSE,
  return_sf = FALSE,
  na_rm = FALSE
)

Arguments

Value

A data.table or simple feature object if 'return_sf' is TRUE.

Examples

## Not run: 
  # Requires Census Bureau API key
  # Get the median household income by tract for Washington DC and join
  # this data with DC tract boundaries.

  library(data.table)
  library(httr2)
  library(jsonlite)
  library(sf)
  library(usmap)
  library(withr)
  library(ggplot2)
  library(RcensusPkg)

  # Get the 2020 median household income data by tract for DC
  dc_fips <- usmap::fips(state = "dc")
  dc_B19013_dt <- RcensusPkg::get_vintage_data(
    dataset = "acs/acs5",
    vintage = 2020,
    vars = "B19013_001E",
    region = "tract",
    regionin = paste0("state:", dc_fips)
  )
  # Get the simple feature DC tract geometries and join the data dataframe "dc_B19013_dt"
  output_dir <- withr::local_tempdir()
  if(!dir.exists(output_dir)){
    dir.create(output_dir)
  }
  dc_tracts_sf <- RcensusPkg::tiger_tracts_sf(
    state = dc_fips,
    output_dir = output_dir,
    general = TRUE,
    delete_files = FALSE
  )
  # Join the data with simple feature object
  dc_joined_sf <- RcensusPkg::join_it(
    df_1 = dc_B19013_dt,
    df_2 = dc_tracts_sf,
    key_1 = "GEOID",
    key_2 = "GEOID",
    return_sf = TRUE
  )

## End(Not run)

long_to_wide

Description

Reshape a data.table from a "long" format to a "wide" format.

Function calls data.table::dcast() to reshape a long single column and its values to multiple columns.

Usage

long_to_wide(
  dt = NULL,
  id_v = c("NAME", "GEOID"),
  parameter_col = NULL,
  value_col = NULL
)

Arguments

Value

A reshaped data.table in the "wide" format.

Examples

## Not run: 
  # Requires Census Bureau API key
  library(data.table)
  library(httr2)
  library(jsonlite)
  library(stringr)
  library(RcensusPkg)

  # Request for data from Census Bureau in "long" form
  B19001_1yr_long_dt <- RcensusPkg::get_vintage_data(
    dataset = "acs/acs1",
    vintage = 2016,
    group = "B19001",
    region = "state",
    wide_to_long = TRUE
  )

  # Resulting data.table is in the "long" form. Convert it back to
  # to the wide form.
  B19001_1yr_wide_dt <- RcensusPkg::long_to_wide(
    dt = B19001_1yr_long_dt,
    parameter_col = "variable",
    value_col = c("estimate", "moe")
  )

## End(Not run)

plot_us_data

Description

This function produces a ggplot2 based choropleth map of a discrete/continuous variable across all/selected US states including placement of Alaska, Hawaii, and Puerto Rico. The function accepts a data frame with a column of state names and a column with their respective values. If the data frame (parameter 'df') is submitted as NULL then only the simple feature state geometries are returned for mapping. The function offers several options for control/selection of state geographies and variable scaling.

This function depends extensively on RcensusPkg::tiger_states_sf() for obtaining state geometries, so many of that function's parameters are repeated in this function. Also RplotterPkg::create_sf_plot() is called upon for displaying the shapefile geometries, so this package should be installed.

Usage

plot_us_data(
  df = NULL,
  states_col = NULL,
  value_col = NULL,
  title = NULL,
  title_fontsz = 18,
  text_col = NULL,
  text_size = 3,
  text_color = "black",
  text_fontface = "plain",
  output_dir = tempdir(),
  delete_files = TRUE,
  vintage = 2020,
  general = FALSE,
  resol = "500k",
  na_rm = FALSE,
  scale_breaks = NULL,
  scale_values = NULL,
  scale_limits = NULL,
  scale_labels = waiver(),
  scale_colors = grDevices::heat.colors(8),
  scale_na_value = "gray50",
  own_scale = FALSE,
  sf_color = "black",
  sf_fill = "gray",
  sf_linewidth = 0.1,
  sf_alpha = 1,
  display_plot = TRUE,
  show_legend = TRUE,
  legend_pos = "right",
  legend_key_width = 0.5,
  legend_key_height = 0.7,
  legend_key_backgrd = "white"
)

Arguments

Value

A list of ggplot2 objects if 'display_plot' is FALSE. Included in the list is the plot of all the states ("us_states") along with the original ggplot2 ggplot2::geom_sf plots of the lower 48 ("lower_48"), Alaska ("alaska"), Hawaii ("hawaii") and Puerto Rico ("puerto_rico").

Examples

## Not run: 
library(sf)
library(grid)
library(ggplot2)
library(ggplotify)
library(data.table)
library(gtable)
library(httr2)
library(withr)
library(RplotterPkg)

# Plot just the states without joining data (the default case)
# Define a temporary output folder for the downloaded shapefiles
output_dir <- withr::local_tempdir()
if(!dir.exists(output_dir)){
  dir.create(output_dir)
}
us_without_data_plot <- RcensusPkg::plot_us_data(
 title = "A Default Mapping of US States",
 output_dir = output_dir,
 delete_files = FALSE
)

# Requires US Census Bureau API key
# Plot of US map with discrete 2020 presidential results
output_dir <- withr::local_tempdir()
if(!dir.exists(output_dir)){
  dir.create(output_dir)
}
a_plot <- RcensusPkg::plot_us_data(
  df = RcensusPkg::vote2020,
  title = "US Presidential Vote 2020",
  states_col = "State",
  value_col = "Party",
  output_dir = output_dir,
  delete_files = FALSE,
  scale_breaks = c("R","D"),
  scale_limits = c("R","D"),
  scale_values = c("red","blue"),
  scale_labels = c("Republican","Democrat"),
  sf_color = "white"
)

## End(Not run)

remove_area_water

Description

Function removes water area geometries from downloaded Census Bureau shapefiles

Usage

remove_area_water(
  x,
  vintage = 2020,
  output_dir = tempdir(),
  delete_files = TRUE
)

Arguments

Value

An sf object with the water area geometries removed.

Examples

## Not run: 
  # Request for Census Bureau data plus CPU processing can exceed well over 10
  #  seconds in some cases.
  library(downloader)
  library(sf)
  library(usmap)
  library(withr)
  library(data.table)
  library(RcensusPkg)

  # Remove water areas from New York, New York sf object
  ny_state_county_fips <- usmap::fips(state = "New York", county = "New York")
  ny_state_fips <- substr(ny_state_county_fips, 1,2)
  ny_county_fips <- substr(ny_state_county_fips, 3, 5)

  # Define a temporary output folder for the downloaded shapefiles
  output_dir <- withr::local_tempdir()
  if(!dir.exists(output_dir)){
    dir.create(output_dir)
  }

  # Define an expression for filtering just the New York county
  express <- parse(text = paste0("COUNTYFP == ", '"', ny_county_fips, '"'))

  # Get the New York,New York county tract sf geometries object
  ny_tracts_sf <- RcensusPkg::tiger_tracts_sf(
    state = ny_state_fips,
    vintage = 2020,
    general = FALSE,
    transform_crs = 6538,
    express = express,
    output_dir = output_dir,
    delete_files = FALSE
  )

  # Remove the area waters from New York, New York sf object
    ny_without_water_sf <- RcensusPkg::remove_area_water(
    ny_tracts_sf,
    output_dir = output_dir,
    delete_files = FALSE
  )

## End(Not run)

tiger_block_groups_sf

Description

This function performs three tasks:

1. Download to an output directory a zip file from the TIGER/Line Shapefiles database.

2. Unzip the zip file and locate the shape file of interest.

3. Read and convert the shape file to a simple feature object.

Usage

tiger_block_groups_sf(
  state = NULL,
  output_dir = tempdir(),
  delete_files = TRUE,
  vintage = 2020,
  general = FALSE,
  sf_info = FALSE,
  do_progress = FALSE,
  set_crs = NULL,
  transform_crs = NULL,
  shapefile = NULL,
  datafile = NULL,
  datafile_key = NULL,
  sf_key = "GEOID",
  express = NULL,
  check_na = FALSE
)

Arguments

Details

Returns simple feature (sf) of US Census block group boundary related geometric polygons, provided by the US Census Bureau's TIGER/Line Shapefiles database. See Simple Features for R for more information on simple features. Along with the geometries, additional block group related variables are provided. See Appendix G-2. Record Layout: Block Group State-based Shapefile) for a description of block group related variables of the sf file. For further information on the Census Bureau's shape files see About the 2021 TIGER/Line Shapefiles. From Chapter 4.3 Block Groups – "Standard block groups are clusters of blocks within the same census tract that have the same first digit of their 4-character census block number (e.g., Blocks 3001, 3002, 3003 to 3999 in census tract 1210.02 belong to block group 3). Current block groups do not always maintain these same block number to block group relationships due to boundary and feature changes that occur throughout the decade. A block group usually covers a contiguous area. Each census tract contains one or more block groups and block groups have unique numbers within census tract."

A more generalized, recognizable version of the block group geometries that has less download size is also available. For more information on cartographic boundary files see Cartographic Boundary File Description. These files are available for vintages greater than 2013 with resolution 1:500k meters.

Some earlier vintages may have NA for the crs so you may need to specify the crs_transform to 3426. Also you may be interested in using a state level crs. See epsg.io to search worldwide for crs.

The function returns the simple feature object which can easily be mapped (see RplotterPkg::create_sf_plot()) or joined with US Census Bureau demographic data via the GEOID value.

Value

A data frame object of class sf

Examples

library(sf)
library(downloader)
library(data.table)
library(usmap)
library(withr)
library(RcensusPkg)

# Get block groups for Washington, DC
# Get the DC fips code
dc_fips <- usmap::fips(state = "dc")

# Define a temporary output folder for the downloaded shapefiles
output_dir <- withr::local_tempdir()
if(!dir.exists(output_dir)){
  dir.create(output_dir)
}

dc_block_groups_sf <- RcensusPkg::tiger_block_groups_sf(
  state = dc_fips,
  output_dir = output_dir,
  delete_files = FALSE
)

tiger_blocks_sf

Description

This function performs three tasks:

1. Download to an output directory a zip file from the TIGER/Line Shapefiles database.

2. Unzip the zip file and locate the shape file of interest.

3. Read and convert the shape file to a simple feature object.

Usage

tiger_blocks_sf(
  state = NULL,
  output_dir = tempdir(),
  delete_files = TRUE,
  vintage = 2020,
  set_crs = NULL,
  transform_crs = NULL,
  sf_info = FALSE,
  do_progress = FALSE,
  shapefile = NULL,
  express = NULL,
  check_na = FALSE
)

Arguments

Details

Returns simple feature (sf) of US Census block boundary related geometric polygons, provided by the US Census Bureau's TIGER/Line Shapefiles database. See Simple Features for R for more information on simple features. Along with the geometries, additional block related variables are provided. See Appendix G-1. Record Layout: Block State-based Shapefile) for a description of block related variables of the sf file. For further information on the Census Bureau's shape files see About the 2021 TIGER/Line Shapefiles. From Chapter 4.2 Blocks – "Census blocks are statistical areas bounded on all sides by visible features (e.g., streets, roads, streams, and railroad tracks), and by non-visible boundaries (e.g., city, town, township, county limits, and short line-of-sight extensions of streets and roads)."

The function returns the simple feature object which can easily be mapped (see RplotterPkg::create_sf_plot()) or joined with US Census Bureau demographic data via the GEOID value. Be aware that shapefiles for blocks can be very large and time consuming to download for some states.

Some earlier vintages may have NA for the crs so you may need to specify the crs_transform to 3426. Also you may be interested in using a state level crs. See epsg.io to search worldwide for crs.

Value

A data frame object of class sf

Examples

## Not run: 
  # Beware that downloading and processing state blocks can be time consuming
  library(sf)
  library(downloader)
  library(usmap)
  library(withr)
  library(data.table)
  library(RcensusPkg)

  vt_fips <- usmap::fips(state="vermont")
  output_dir <- withr::local_tempdir()
  if(!dir.exists(output_dir)){
    dir.create(output_dir)
  }
  vt_blocks_sf <- RcensusPkg::tiger_blocks_sf(
    state = vt_fips,
    output_dir = output_dir,
    delete_files = FALSE
  )

## End(Not run)

tiger_cbsa_sf

Description

This function performs three tasks:

1. Download to an output directory a zip file from the TIGER/Line Shapefiles database.

2. Unzip the zip file and locate the shape file of interest.

3. Read and convert the shape file to a simple feature object.

Usage

tiger_cbsa_sf(
  output_dir = tempdir(),
  delete_files = TRUE,
  vintage = 2020,
  general = FALSE,
  resol = "500k",
  set_crs = NULL,
  transform_crs = NULL,
  sf_info = FALSE,
  do_progress = FALSE,
  shapefile = NULL,
  datafile = NULL,
  datafile_key = NULL,
  sf_key = "GEOID",
  state_filter = NULL,
  city_filter = NULL,
  check_na = FALSE
)

Arguments

Details

Returns simple feature (sf) of core based statistical area (CBSA) boundary related geometric polygons, provided by the US Census Bureau's TIGER/Line Shapefiles database. See Simple Features for R for more information on simple features.

A more generalized, recognizable version of the CBSA geometries that has less download size is also available. For more information on cartographic boundary files see Cartographic Boundary File Description. These files are available for vintages greater than 2009 with resolution 1:500k, 1:5m, 1:20m meters. For descriptive information on CBSA see About.

The function returns the simple feature object which can easily be mapped (see RplotterPkg::create_sf_plot()) or joined with US Census Bureau demographic data. To help incorporate data files, this function has a 'datafile' parameter which will be joined with the resultant simple feature object. The only requirement is that a common "key" for joining exist between the data dataframe and the simple feature dataframe.

Value

A data frame object of class sf

Examples

library(downloader)
library(sf)
library(data.table)
library(withr)
library(RcensusPkg)

# Define a temporary, self deleting output folder for the downloaded shapefiles
output_dir <- withr::local_tempdir()
if(!dir.exists(output_dir)){
  dir.create(output_dir)
}

# Get shapefile geometries for the Core-based statistical areas
#   of Texas with vintage 2020.
# Note that there is also a city filter parameter (city_filter).
cbsa_tx_sf <- RcensusPkg::tiger_cbsa_sf(
  vintage = 2020,
  resol = "20m",
  # city_filter = "Kansas City",
  state_filter = "TX",
  general = TRUE,
  output_dir = output_dir,
  delete_files = FALSE
)

tiger_counties_sf

Description

This function performs three tasks:

1. Download to an output directory a zip file from the TIGER/Line Shapefiles database.

2. Unzip the zip file and locate the shape file of interest.

3. Read and convert the shape file to a simple feature object.

Usage

tiger_counties_sf(
  output_dir = tempdir(),
  delete_files = TRUE,
  vintage = 2020,
  general = FALSE,
  resol = "500k",
  set_crs = NULL,
  transform_crs = NULL,
  sf_info = FALSE,
  do_progress = FALSE,
  shapefile = NULL,
  datafile = NULL,
  datafile_key = NULL,
  sf_key = "COUNTYFP",
  express = NULL,
  check_na = FALSE
)

Arguments

Details

Returns simple feature (sf) of the entire US county boundary related geometric polygons, provided by the US Census Bureau's TIGER/Line Shapefiles database. See Simple Features for R for more information on simple features. Along with the geometries, additional county related variables are provided. See Appendix I-3. Record Layout: County Subdivision State-based Shapefile) for a description of county related variables of the sf file. For further information on the Census Bureau's shape files see About the 2021 TIGER/Line Shapefiles. From Chapter 4.7 Counties and Equivalent Enties – "Counties and equivalent entities are primary legal divisions of states. In most states, these entities are termed 'counties.'"

A more generalized, recognizable version of the county geometries that has less download size is also available. For more information on cartographic boundary files see Cartographic Boundary File Description. These files are available for vintages greater than 2013 with resolution 1:500k, 1:5m, 1:20m meters.

Some earlier vintages may have NA for the crs so you may need to specify the 'crs_transform' to 3426. Also you may be interested in using a state level crs. See epsg.io to search worldwide for crs.

The function returns the simple feature object which can easily be mapped (see RplotterPkg::create_sf_plot()) or joined with US Census Bureau demographic data via the GEOID value.

Value

An object of class sf

Examples

library(downloader)
library(sf)
library(data.table)
library(usmap)
library(withr)
library(RcensusPkg)

# Get the geometries for Ohio counties
# Determine the fips code for Ohio (returns "39")
ohio_fips <- usmap::fips(state = "ohio")
# Create an expression to filter out just Ohio counties
#   from the simple feature dataframe
express <- parse(text = paste0("STATEFP == ", '"', ohio_fips, '"'))
# Define a temporary output folder for the downloaded shapefiles
output_dir <- withr::local_tempdir()
if(!dir.exists(output_dir)){
  dir.create(output_dir)
}
# Get the Ohio county's generalized geometries
ohio_counties_sf <- RcensusPkg::tiger_counties_sf(
  general = TRUE,
  express = express,
  output_dir = output_dir,
  delete_files = FALSE
)

tiger_county_subsection_sf

Description

This function performs three tasks:

1. Download to an output directory a zip file from the TIGER/Line Shapefiles database.

2. Unzip the zip file and locate the shape file of interest.

3. Read and convert the shape file to a simple feature object.

Usage

tiger_county_subsection_sf(
  state = NULL,
  output_dir = tempdir(),
  delete_files = TRUE,
  vintage = 2020,
  general = FALSE,
  set_crs = NULL,
  transform_crs = NULL,
  sf_info = FALSE,
  do_progress = FALSE,
  shapefile = NULL,
  datafile = NULL,
  datafile_key = NULL,
  sf_key = "GEOID",
  express = NULL,
  check_na = FALSE
)

Arguments

Details

Returns simple feature (sf) of US Census county subsection boundary related geometric polygons, provided by the US Census Bureau's TIGER/Line Shapefiles database. See Simple Features for R for more information on simple features. Along with the geometries, additional county subsection related variables are provided. See Appendix I-3. Record Layout: County Subdivision State-based Shapefile) for a description of county subsection related variables of the sf file. For further information on the Census Bureau's shape files see About the 2021 TIGER/Line Shapefiles. See Chapter 4.8 County Subdivisions for a description of the term.

A more generalized, recognizable version of the tract geometries that has less download size is also available. For more information on cartographic boundary files see Cartographic Boundary File Description. These files are available for vintages greater than 2013 with resolution 1:500k meters.

The function returns the simple feature object which can easily be mapped (see RplotterPkg::create_sf_plot()) or joined with US Census Bureau demographic data. To help incorporate data files, this function has a 'datafile' parameter which will be joined with the resultant simple feature object. The only requirement is that a common "key" for joining exist between the data dataframe and the simple feature dataframe.

Some earlier vintages may have NA for the crs so you may need to specify the 'crs_transform' to 3426. Also you may be interested in using a state level crs. See epsg.io to search worldwide for crs.

Value

A data frame object of class sf

Examples

library(downloader)
library(sf)
library(data.table)
library(withr)
library(usmap)
library(RcensusPkg)

# Find the subsections for a county in Ohio
# Define a temporary, self deleting output folder for the downloaded shapefiles
output_dir <- withr::local_tempdir()
if(!dir.exists(output_dir)){
  dir.create(output_dir)
}

# Define the fips values for state and county
ohio_hc_fips <- usmap::fips(state = "ohio", county = "holmes")
ohio_fips <- substr(ohio_hc_fips,1,2)
hc_fips <- substr(ohio_hc_fips,3,5)

# Define a filtering expression for the county
express <- parse(text = paste0("COUNTYFP == ", '"', hc_fips, '"'))

# Get the subsection sf object for the Ohio county
hc_ctysub_sf <- RcensusPkg::tiger_county_subsection_sf(
  state = ohio_fips,
  vintage = 2020,
  general = TRUE,
  express = express,
  output_dir = output_dir,
  delete_files = FALSE
)

tiger_landmarks_sf

Description

This function performs three tasks:

1. Download to an output directory a zip file from the TIGER/Line Shapefiles database.

2. Unzip the zip file and locate the shape file of interest.

3. Read and convert the shape file to a simple feature object.

Usage

tiger_landmarks_sf(
  state = NULL,
  output_dir = tempdir(),
  delete_files = TRUE,
  vintage = 2020,
  entity = "point",
  set_crs = NULL,
  transform_crs = NULL,
  sf_info = FALSE,
  do_progress = FALSE,
  shapefile = NULL,
  express = NULL,
  check_na = FALSE
)

Arguments

Details

Returns simple feature (sf) landmark related geometric polygons provided by the US Census Bureau's TIGER/Line Shapefiles database. See Simple Features for R for more information on simple features. Along with the geometries, additional landmark related variables are provided. See Appendix K-1.K-2, Record Layout: Area/Point Landmark Shapefile for a description of landmark related area and point variables of the sf file. For further information on the Census Bureau's shape files see About the 2021 TIGER/Line Shapefiles. From Chapter 4.11 Landmark (Area and Point – "The Census Bureau includes landmarks in the MAF/TIGER System to locate special features and help enumerators during field operations. Some of the more common landmark types include area landmarks (e.g., airports, cemeteries, parks, and educational facilities) and point landmarks (e.g., schools and churches)"

The function returns the simple feature object which can easily be mapped (see RplotterPkg::create_sf_plot()) or joined with US Census Bureau demographic data via the GEOID value.

Some earlier vintages may have NA for the crs so you may need to specify the 'transform_crs' to 3426. Also you may be interested in using a state level crs. See epsg.io to search worldwide for crs.

Value

A data frame object of class sf

Examples

library(downloader)
library(sf)
library(data.table)
library(withr)
library(usmap)
library(RcensusPkg)

# Get the sf object for landmarks in the state of Kentucky
# Define a temporary, self deleting output folder for the downloaded shapefiles
output_dir <- withr::local_tempdir()
if(!dir.exists(output_dir)){
  dir.create(output_dir)
}

# Get the fips code for the state
kentucky_fips <- usmap::fips(state = "kentucky")

# Get the "point" landmarks for Kentucky
kentucky_landmarks_sf <- RcensusPkg::tiger_landmarks_sf(
  state = kentucky_fips,
  entity = "point",
  check_na = TRUE,
  output_dir = output_dir,
  delete_files = FALSE
)

tiger_places_sf

Description

This function performs three tasks:

1. Download to an output directory a zip file from the TIGER/Line Shapefiles database.

2. Unzip the zip file and locate the shape file of interest.

3. Read and convert the shape file to a simple feature object.

Usage

tiger_places_sf(
  state = NULL,
  output_dir = tempdir(),
  delete_files = TRUE,
  vintage = 2020,
  general = FALSE,
  set_crs = NULL,
  transform_crs = NULL,
  sf_info = FALSE,
  do_progress = FALSE,
  shapefile = NULL,
  express = NULL,
  check_na = FALSE
)

Arguments

Details

Returns simple feature (sf) places related geometric polygons provided by the US Census Bureau's TIGER/Line Shapefiles database. See Simple Features for R for more information on simple features. Along with the geometries, additional places related variables are provided. See Appendix I-5. Record Layout: Place State-based Shapefile for a description of place related variables of the sf file. For further information on the Census Bureau's shape files see About the 2021 TIGER/Line Shapefiles. From 4.14 Places – Incorporated Places and Census Designated Places –

"Incorporated Places: An incorporated place provides governmental functions for a concentration of people. Incorporated places may extend across county and county subdivision boundaries, but never across state boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions."

"Census Designated Places (CDPs): CDPs are the statistical counterparts of incorporated places. CDPs are settled concentrations of population that are identifiable by name but not legally incorporated under the laws of the state in which the CDPs are located."

The function returns the simple feature object which can easily be mapped (see RplotterPkg::create_sf_plot()) or joined with US Census Bureau demographic data via the GEOID value.

Some earlier vintages may have NA for the crs so you may need to specify the 'crs_transform' to 3426. Also you may be interested in using a state level crs. See epsg.io to search worldwide for crs.

Value

A data frame object of class sf

Examples

library(downloader)
library(sf)
library(data.table)
library(withr)
library(usmap)
library(RcensusPkg)

# Get the major places locations in the state of Kentucky
# Define a temporary, self deleting output folder for the downloaded shapefiles
output_dir <- withr::local_tempdir()
if(!dir.exists(output_dir)){
  dir.create(output_dir)
}

# Get the fips code for the state
kentucky_fips <- usmap::fips(state = "kentucky")

# Create a filter expression to get select places
major_places_express <- expression(NAME %in% c(
  "Bardstown",
  "Bowling Green",
  "Louisville",
  "Versailles",
  "Owensboro",
  "Frankfort",
  "Elizabethtown",
  "Danville"
))

# Get the sf object and the geometric locations of the selected places
kentucky_places_sf <- RcensusPkg::tiger_places_sf(
  state = kentucky_fips,
  express = major_places_express,
  general = TRUE,
  output_dir = output_dir,
  delete_files = FALSE
)

tiger_roads_sf

Description

This function performs three tasks:

1. Download to an output directory a zip file from the TIGER/Line Shapefiles database.

2. Unzip the zip file and locate the shape file of interest.

3. Read and convert the shape file to a simple feature object.

Usage

tiger_roads_sf(
  state = NULL,
  county = NULL,
  output_dir = tempdir(),
  delete_files = TRUE,
  vintage = 2020,
  entity = "us_roads",
  set_crs = NULL,
  transform_crs = NULL,
  sf_info = FALSE,
  do_progress = FALSE,
  shapefile = NULL,
  datafile = NULL,
  datafile_key = NULL,
  sf_key = "GEOID",
  express = NULL,
  check_na = FALSE
)

Arguments

Details

Returns simple feature (sf) of US Census roads boundary related geometric polygons, provided by the US Census Bureau's TIGER/Line Shapefiles database. See Simple Features for R for more information on simple features. Along with the geometries, additional roads related variables are provided. See Appendix L-3. Record Layouts: Roads Shapefile) for a description of road related variables of the sf file. For further information on the Census Bureau's shape files see About the 2021 TIGER/Line Shapefiles. From Chapter 4.12.3 Roads – Primary, Secondary and All Roads – "The primary and secondary roads shapefile contains all linear street features with MTFCCs of primary roads (S1100) or secondary roads (S1200) in the MAF/TIGER System. Secondary roads are main arteries, usually in the U.S. highway, state highway, or county highway system. These roads have one or more lanes of traffic in each direction, may or may not be divided, and usually have at-grade intersections with many other roads and driveways. These roads often have both a local name and a route number."

The function returns the simple feature object which can easily be mapped (see RplotterPkg::create_sf_plot()) or joined with US Census Bureau demographic data via the GEOID value.

Some earlier vintages may have NA for the crs so you may need to specify the 'crs_transform' to 3426. Also you may be interested in using a state level crs. See epsg.io to search worldwide for crs.

Value

A data frame object of class sf

Examples

library(downloader)
library(sf)
library(data.table)
library(withr)
library(usmap)
library(RcensusPkg)

# Get the sf geometries of roads in Ohio
# Get the fips for Ohio
oh_fips <- usmap::fips(state = "ohio")

# Define a temporary, self deleting output folder for the downloaded shapefiles
output_dir <- withr::local_tempdir()
if(!dir.exists(output_dir)){
  dir.create(output_dir)
}

# Get the sf object
ohio_roads_sf <- RcensusPkg::tiger_roads_sf(
  state = oh_fips,
  entity = "state_roads",
  output_dir = output_dir,
  delete_files = FALSE
)

tiger_states_sf

Description

This function performs three tasks:

1. Download to an output directory a zip file from the TIGER/Line Shapefiles database.

2. Unzip the zip file and locate the shape file of interest.

3. Read and convert the shape file to a simple feature object.

Usage

tiger_states_sf(
  output_dir = tempdir(),
  delete_files = TRUE,
  vintage = 2020,
  general = FALSE,
  resol = "500k",
  set_crs = NULL,
  transform_crs = NULL,
  sf_info = FALSE,
  do_progress = FALSE,
  shapefile = NULL,
  datafile = NULL,
  datafile_key = NULL,
  sf_key = "GEOID",
  express = NULL,
  check_na = FALSE
)

Arguments

Details

Returns simple feature (sf) of state boundary related geometric polygons, provided by the US Census Bureau's TIGER/Line Shapefiles database. See Simple Features for R for more information on simple features. Along with the geometries, additional state related variables are provided. See Appendix P-5. Record Layout: State and Equivalent Entity National Shapefile) for a description of state related variables of the sf file. For further information on the Census Bureau's shape files see About the 2021 TIGER/Line Shapefiles. From Chapter 4.17 States and State Equivalent Entities – "States and equivalent entities are the primary governmental divisions of the United States. In addition to the fifty states, the Census Bureau treats the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands) as statistical equivalents of states for the purpose of data presentation."

A more generalized, recognizable version of the state geometries that has less download size is also available. For more information on cartographic boundary files see Cartographic Boundary File Description. These files are available for vintages greater than 2013 with resolution 1:500k, 1:5m, 1:20m meters.

The function returns the simple feature object which can easily be mapped (see RplotterPkg::create_sf_plot()) or joined with US Census Bureau demographic data. To help incorporate data files, this function has a 'datafile' parameter which will be joined with the resultant simple feature object. The only requirement is that a common "key" for joining exist between the data dataframe and the simple feature dataframe.

Some earlier vintages may have NA for the crs so you may need to specify the 'crs_transform' to 3426. Also you may be interested in using a state level crs. See epsg.io to search worldwide for crs.

Value

A data frame object of class sf (simple feature)

Examples

library(usmap)
library(sf)
library(data.table)
library(downloader)
library(withr)
library(RcensusPkg)

# Get the fips code for Florida
florida_fips <- usmap::fips(state = "florida")
#
# Define an expression that will filter out Florida from
#   the simple feature obtained from the downloaded/converted
#   tiger states shapefile.
express <- parse(text = paste0("STATEFP == ", '"', florida_fips, '"'))
#
# Define a temporary output folder for the downloaded shapefiles
output_dir <- withr::local_tempdir()
if(!dir.exists(output_dir)){
  dir.create(output_dir)
}

# Download, convert, and filter the states shapefile for Florida
#   using the expression.
florida_general_sf <- RcensusPkg::tiger_states_sf(
  general = TRUE,
  express = express,
  output_dir = output_dir,
  delete_files = FALSE
)

tiger_tracts_sf

Description

This function performs three tasks:

1. Download to an output directory a zip file from the TIGER/Line Shapefiles database.

2. Unzip the zip file and locate the shape file of interest.

3. Read and convert the shape file to a simple feature object.

Usage

tiger_tracts_sf(
  state = NULL,
  output_dir = tempdir(),
  delete_files = TRUE,
  vintage = 2020,
  general = FALSE,
  set_crs = NULL,
  transform_crs = NULL,
  sf_info = FALSE,
  do_progress = FALSE,
  shapefile = NULL,
  datafile = NULL,
  datafile_key = NULL,
  sf_key = "GEOID",
  express = NULL,
  check_na = FALSE
)

Arguments

Details

Returns simple feature (sf) of US Census tract boundary related geometric polygons, provided by the US Census Bureau's TIGER/Line Shapefiles database. See Simple Features for R for more information on simple features. Along with the geometries, additional tract related variables are provided. See Appendix G-3. Record Layout: Census Tract State-based Shapefile) for a description of tract related variables of the sf file. For further information on the Census Bureau's shape files see About the 2021 TIGER/Line Shapefiles. From Chapter 4.4 Census Tracts – "Census tracts are small and relatively permanent statistical subdivisions of a county or equivalent entity. Local participants review and update census tracts prior to each decennial census as part of the Census Bureau’s PSAP."

A more generalized, recognizable version of the tract geometries that has less download size is also available. For more information on cartographic boundary files see Cartographic Boundary File Description. These files are available for vintages greater than 2013 with resolution 1:500k meters.

The function returns the simple feature object which can easily be mapped (see RplotterPkg::create_sf_plot()) or joined with US Census Bureau demographic data. To help incorporate data files, this function has a datafile parameter which will be joined with the resultant simple feature object. The only requirement is that a common "key" for joining exist between the data dataframe and the simple feature dataframe.

Some earlier vintages may have NA for the crs so you may need to specify the 'crs_transform' to 3426. Also you may be interested in using a state level crs. See epsg.io to search worldwide for crs.

Value

A data frame object of class sf (simple feature)

Examples

library(downloader)
library(sf)
library(data.table)
library(usmap)
library(withr)
library(RcensusPkg)

# Get the generalized tract geometries for Los Alamos, New Mexico
# Determine the fips codes for New Mexico and Los Alamos
nm_los_alamos_fips <- usmap::fips(state = "new mexico", county = "los alamos")
nm_fips <- substr(nm_los_alamos_fips, 1, 2)
los_alamos_fips <- substr(nm_los_alamos_fips, 3, 5)

# Create an expression to filter just the Los Alamos geometries
express <- parse(text = paste0("COUNTYFP == ", '"', los_alamos_fips, '"'))

# Define a temporary output folder for the downloaded shapefiles
output_dir <- withr::local_tempdir()
if(!dir.exists(output_dir)){
  dir.create(output_dir)
}

# Get the tract geometries for just Los Alamos
losalamos_tracts_sf <- RcensusPkg::tiger_tracts_sf(
  state = nm_fips,
  general = TRUE,
  express = express,
  output_dir = output_dir,
  delete_files = FALSE
)

tiger_urban_area_sf

Description

This function performs three tasks:

1. Download to an output directory a zip file from the TIGER/Line Shapefiles database.

2. Unzip the zip file and locate the shape file of interest.

3. Read and convert the shape file to a simple feature object.

The function returns geometries for the Census Bureau's defined "urban areas" across the entire US

Usage

tiger_urban_area_sf(
  output_dir = tempdir(),
  delete_files = TRUE,
  vintage = 2020,
  general = FALSE,
  resol = "500k",
  set_crs = NULL,
  transform_crs = NULL,
  sf_info = FALSE,
  do_progress = FALSE,
  shapefile = NULL,
  datafile = NULL,
  datafile_key = NULL,
  sf_key = "GEOID",
  check_na = FALSE
)

Arguments

Details

Returns simple feature(sf) of the Census Bureau's defined "urban area" boundary related geometric polygons, provided by the US Census Bureau's TIGER/Line Shapefiles database. See Simple Features for R for more information on simple features.

A more generalized, recognizable version of the CBSA geometries that has less download size is also available. For more information on cartographic boundary files see Cartographic Boundary File Description. These files are available for vintages greater than 2009 with resolution 1:500k, 1:5m, 1:20m meters. For descriptive information on CBSA see About.

The function returns the simple feature object which can easily be mapped (see RplotterPkg::create_sf_plot()) or joined with US Census Bureau demographic data. To help incorporate data files, this function has a 'datafile' parameter which will be joined with the resultant simple feature object. The only requirement is that a common "key" for joining exist between the data dataframe and the simple feature dataframe.

Value

A data frame object of class sf

Examples

library(downloader)
library(sf)
library(data.table)
library(withr)
library(RcensusPkg)

# Get the defined urban areas for 2019
# Define a temporary, self deleting output folder for the downloaded shapefiles
output_dir <- withr::local_tempdir()
if(!dir.exists(output_dir)){
  dir.create(output_dir)
}

us_urban_areas_sf <- RcensusPkg::tiger_urban_area_sf(
  output_dir = output_dir,
  vintage = 2019,
  general = TRUE,
  delete_files = FALSE
)

tiger_water_sf

Description

This function performs three tasks:

1. Download to an output directory a zip file from the TIGER/Line Shapefiles database.

2. Unzip the zip file and locate the shape file of interest.

3. Read and convert the shape file to a simple feature object.

Usage

tiger_water_sf(
  state = NULL,
  county = NULL,
  output_dir = tempdir(),
  delete_files = TRUE,
  vintage = 2020,
  entity = "area",
  set_crs = NULL,
  transform_crs = NULL,
  sf_info = FALSE,
  do_progress = FALSE,
  shapefile = NULL,
  datafile = NULL,
  datafile_key = NULL,
  sf_key = "GEOID",
  express = NULL,
  check_na = FALSE
)

Arguments

Details

Returns simple feature (sf) water related geometric polygons provided by the US Census Bureau's TIGER/Line Shapefiles database. See Simple Features for R for more information on simple features. Along with the geometries, additional water related variables are provided. See Appendix J. Record Layouts: Hydrography (Area and Line) for a description of water related area and line variables of the sf file. See Appendix L-2. Record Layout: Coastlines Shapefile for a description of coastline related variables of the sf file. For further information on the Census Bureau's shape files see About the 2021 TIGER/Line Shapefiles. From Chapter 4.10 Hydrography (Area and Linear and 4.12.2 Coastline – "The area hydrography shapefile contains the geometry and attributes of both perennial and intermittent area hydrography features (e.g., ponds, lakes, oceans, swamps, glaciers, and the area covered by large streams represented as double-line drainage). The linear hydrography shapefile includes streams/rivers, braided streams, canals, ditches, artificial paths, and aqueducts. A linear hydrography feature may include edges with both perennial and intermittent persistence"

The function returns the simple feature object which can easily be mapped (see RplotterPkg::create_sf_plot()) or joined with US Census Bureau demographic data via the GEOID value.

Some earlier vintages may have NA for the crs so you may need to specify the 'crs_transform' to 3426. Also you may be interested in using a state level crs. See epsg.io to search worldwide for crs.

Value

A data frame object of class sf

Examples

library(sf)
library(data.table)
library(downloader)
library(usmap)
library(withr)
library(RcensusPkg)

# Get the water areas for a county in Ohio
state_county_fips <- usmap::fips(state = "Ohio", county = "Geauga")
state_fips <- substr(state_county_fips,1,2)
county_fips <- substr(state_county_fips,3,5)
# Define a temporary output folder for the downloaded shapefiles
output_dir <- withr::local_tempdir()
if(!dir.exists(output_dir)){
  dir.create(output_dir)
}
geauga_area_water_sf <- RcensusPkg::tiger_water_sf(
  state = state_fips,
  county = county_fips,
  output_dir = output_dir,
  delete_files = FALSE
)

tiger_zctas_sf

Description

This function performs three tasks:

1. Download to an output directory a zip file from the TIGER/Line Shapefiles database.

2. Unzip the zip file and locate the shape file of interest.

3. Read and convert the shape file to a simple feature object.

Usage

tiger_zctas_sf(
  output_dir = tempdir(),
  delete_files = TRUE,
  vintage = 2020,
  general = FALSE,
  sf_info = FALSE,
  do_progress = FALSE,
  datafile = NULL,
  datafile_key = NULL,
  sf_key = NULL,
  express = NULL
)

Arguments

Details

Returns simple feature (sf) shapefile Zip Code Tabulation Areas(ZCTA) shapefile of the entire US.

Value

A data frame object of class sf

Examples

## Not run: 
  # Beware that downloading and processing Zip Code Tabulation Areas(ZCTA)
  #   shapefiles of the entire US can be time consuming
  library(downloader)
  library(sf)
  library(data.table)
  library(withr)
  library(RcensusPkg)

  # Get the sf geometries for the Zip Code Tabulation Area in the county of
  #   Middlesex, MA near Boston
  # Define a temporary, self deleting output folder for the downloaded shapefiles
  output_dir <- withr::local_tempdir()
  if(!dir.exists(output_dir)){
    dir.create(output_dir)
  }

  # Define the codes of interest in a dataframe
  mun_zcta_df <- data.frame(
    name = c(
      "Carlisle", "Concord", "Bedford",
      "Lexington", "Lexington", "Lincoln",
      "Lincoln", "Sudbury", "Wayland",
      "Weston","Waltham"
    ),
    zcta = c(
      "01741","01742","01730",
      "02420","02421","01773",
      "01731","01776","01778",
      "02493","02451"
    )
  )

  # Define an expression to filter the downloaded shapefile
  express <- expression(ZCTA5CE20 %in% mun_zcta_df$zcta)

  # Get the sf geometries
  mun_zcta_sf <- RcensusPkg::tiger_zctas_sf(
    vintage = 2020,
    general = TRUE,
    do_progress = TRUE,
    express = express,
    output_dir = output_dir,
    delete_files = FALSE
  )

## End(Not run)

vote2020 data

Description

Dataframe shows national presidential voting results for 2020 by state. Letters "D" and "R" under the Party column indicate respectively Democrate or Republican win for a state.

Usage

vote2020

Format

An object of class data.table (inherits from data.frame) with 61 rows and 22 columns.

Details

wide_to_long

Description

Reshape a data frame from a "wide" format to a "long" format.

Function is a helper in calling data.table's melt() function to reshape a wide data frame to a long form.

Usage

wide_to_long(
  dt = NULL,
  id_v = c("NAME", "GEOID"),
  measure_v = NULL,
  variable_name = "variable",
  value_name = "estimate",
  na_rm = FALSE
)

Arguments

Value

A reshaped data frame in the "long" format.

Examples

## Not run: 
  # Requires Census Bureau API key
  library(data.table)
  library(downloader)
  library(jsonlite)
  library(RcensusPkg)
  # Request for data from Census Bureau which comes in the "wide" form
  B19001_1yr_wide_dt <- RcensusPkg::get_vintage_data(
    dataset = "acs/acs1",
    vintage = 2016,
    group = "B19001",
    region = "state"
  )
  # Convert the returned data.table into "long" form
  B19001_1yr_long_dt <- RcensusPkg::wide_to_long(
    dt = B19001_1yr_wide_dt,
    id_v = c("NAME","GEOID")
  )

## End(Not run)