R/nass_count.R
In rdinter/usdarnass: USDA NASS Quick Stats API
Defines functions
nass_count
Documented in
nass_count
#' @title Get number of observations from Quick Stats query
#' @description Checks the number of observations that will be returned in a
#' data request. All queries to the Quick Stats are limited to 50,000
#' observations. This is a helpful function in determining how to structure a
#' data request to fit within the 50,000 limit.
#'
#' @param source_desc "Program" - Source of data ("CENSUS" or "SURVEY"). Census
#' program includes the Census of Ag as well as follow up projects. Survey
#' program includes national, state, and county surveys.
#' @param sector_desc "Sector" - Five high level, broad categories useful to
#' narrow down choices. ("ANIMALS & PRODUCTS", "CROPS", "DEMOGRAPHICS",
#' "ECONOMICS", or "ENVIRONMENTAL")
#' @param group_desc "Group" - Subsets within sector (e.g., under sector_desc =
#' "CROPS", the groups are "FIELD CROPS", "FRUIT & TREE NUTS", "HORTICULTURE",
#' and "VEGETABLES").
#' @param commodity_desc "Commodity" - The primary subject of interest (e.g.,
#' "CORN", "CATTLE", "LABOR", "TRACTORS", "OPERATORS").
#' @param class_desc Generally a physical attribute (e.g., variety, size, color,
#' gender) of the commodity.
#' @param prodn_practice_desc A method of production or action taken on the
#' commodity (e.g., IRRIGATED, ORGANIC, ON FEED).
#' @param util_practice_desc Utilizations (e.g., GRAIN, FROZEN, SLAUGHTER) or
#' marketing channels (e.g., FRESH MARKET, PROCESSING, RETAIL).
#' @param statisticcat_desc "Category" - The aspect of a commodity being
#' measured (e.g., "AREA HARVESTED", "PRICE RECEIVED", "INVENTORY", "SALES").
#' @param unit_desc The unit associated with the statistic category (e.g.,
#' ACRES, $ / LB, HEAD, $, OPERATIONS).
#' @param short_desc "Data Item" - A concatenation of six columns:
#' commodity_desc, class_desc, prodn_practice_desc, util_practice_desc,
#' statisticcat_desc, and unit_desc.
#' @param domain_desc "Domain" - Generally another characteristic of operations
#' that produce a particular commodity (e.g., "ECONOMIC CLASS", "AREA
#' OPERATED", "NAICS CLASSIFICATION", "SALES"). For chemical usage data, the
#' domain describes the type of chemical applied to the commodity. The
#' domain_desc = "TOTAL" will have no further breakouts; i.e., the data value
#' pertains completely to the short_desc.
#' @param domaincat_desc "Domain Category" - Categories or partitions within a
#' domain (e.g., under domain_desc = "SALES", domain categories include $1,000
#' TO $9,999, $10,000 TO $19,999, etc).
#' @param agg_level_desc "Geographic Level" - Aggregation level or geographic
#' granularity of the data. ("AGRICULTURAL DISTRICT", "COUNTY",
#' "INTERNATIONAL", "NATIONAL", "REGION : MULTI-STATE", "REGION : SUB-STATE",
#' "STATE", "WATERSHED", or "ZIP CODE")
#' @param state_ansi American National Standards Institute (ANSI) standard
#' 2-digit state codes.
#' @param state_fips_code NASS 2-digit state codes; include 99 and 98 for US
#' TOTAL and OTHER STATES, respectively; otherwise match ANSI codes.
#' @param state_alpha State abbreviation, 2-character alpha code.
#' @param state_name "State" - State full name.
#' @param asd_code NASS defined county groups, unique within a state, 2-digit ag
#' statistics district code.
#' @param asd_desc "Ag District" - Ag statistics district name.
#' @param county_ansi ANSI standard 3-digit county codes.
#' @param county_code NASS 3-digit county codes; includes 998 for OTHER
#' (COMBINED) COUNTIES and Alaska county codes; otherwise match ANSI codes.
#' @param county_name "County" - County name.
#' @param region_desc "Region" - NASS defined geographic entities not readily
#' defined by other standard geographic levels. A region can be a less than a
#' state (SUB-STATE) or a group of states (MULTI-STATE), and may be specific
#' to a commodity.
#' @param zip_5 "Zip Code" - US Postal Service 5-digit zip code.
#' @param watershed_code US Geological Survey (USGS) 8-digit Hydrologic Unit
#' Code (HUC) for watersheds.
#' @param watershed_desc "Watershed" - Name assigned to the HUC.
#' @param congr_district_code US Congressional District 2-digit code.
#' @param country_code US Census Bureau, Foreign Trade Division 4-digit country
#' code, as of April, 2007.
#' @param country_name Country name.
#' @param location_desc Full description for the location dimension.
#' @param year "Year" - The numeric year of the data and can be either a
#' character or numeric vector. Conditional values are also possible, for
#' example a character vector of ">=1999" of "1999<=" will give years greater
#' than or equal to 1999. Right now the logical values can either be
#' greater/less than or equal to with the logical at either the beginning or
#' end of a string with the year.
#' @param freq_desc "Period Type" - Length of time covered ("ANNUAL", "SEASON",
#' "MONTHLY", "WEEKLY", "POINT IN TIME"). "MONTHLY" often covers more than one
#' month. "POINT IN TIME" is as of a particular day.
#' @param begin_code If applicable, a 2-digit code corresponding to the
#' beginning of the reference period (e.g., for freq_desc = MONTHLY,
#' begin_code ranges from 01 (January) to 12 (December)).
#' @param end_code If applicable, a 2-digit code corresponding to the end of the
#' reference period (e.g., the reference period of JAN THRU MAR will have
#' begin_code = 01 and end_code = 03).
#' @param reference_period_desc "Period" - The specific time frame, within a
#' freq_desc.
#' @param week_ending Week ending date, used when freq_desc = WEEKLY.
#' @param key API key, default is to use the value stored in \code{.Renviron}
#' which is stored from the \code{\link{nass_set_key}} function. If there is
#' no API key stored in the environment, a character string can be provided.
#' @param \\dots Not used.
#' @return Number of observations.
#' @export nass_count
#' @examples
#'
#' \dontrun{
#' # Determine all the observations in NASS
#' nass_count()
#' }
#'
#' \dontrun{
#' # Find the number of observations for Wake County in North Carolina
#' nass_count(state_name = "NORTH CAROLINA", county_name = "WAKE")
#' }
nass_count <- function(source_desc = NULL,
sector_desc = NULL,
group_desc = NULL,
commodity_desc = NULL,
class_desc = NULL,
prodn_practice_desc = NULL,
util_practice_desc = NULL,
statisticcat_desc = NULL,
unit_desc = NULL,
short_desc = NULL,
domain_desc = NULL,
domaincat_desc = NULL,
agg_level_desc = NULL,
state_ansi = NULL,
state_fips_code = NULL,
state_alpha = NULL,
state_name = NULL,
asd_code = NULL,
asd_desc = NULL,
county_ansi = NULL,
county_code = NULL,
county_name = NULL,
region_desc = NULL,
zip_5 = NULL,
watershed_code = NULL,
watershed_desc = NULL,
congr_district_code = NULL,
country_code = NULL,
country_name = NULL,
location_desc = NULL,
year = NULL,
freq_desc = NULL,
begin_code = NULL,
end_code = NULL,
reference_period_desc = NULL,
week_ending = NULL,
key = check_key(), ...){
# Pass the arguments through formatting
calls <- match.call(expand.dots = TRUE)
calls[[1]] <- as.name("args_list")
arguments <- eval.parent(calls)
temp <- httr::GET("http://quickstats.nass.usda.gov/api/get_counts/",
query = arguments)
tt <- check_response(temp)
if (names(tt) == "count") {
count_data <- as.numeric(tt[["count"]])
} else {
stop("Parameter entered is not valid")
}
return(count_data)
}
rdinter/usdarnass documentation built on Feb. 10, 2020, 7:28 p.m.
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Defines functions nass_count
Documented in nass_count
#' @title Get number of observations from Quick Stats query
#' @description Checks the number of observations that will be returned in a
#' data request. All queries to the Quick Stats are limited to 50,000
#' observations. This is a helpful function in determining how to structure a
#' data request to fit within the 50,000 limit.
#'
#' @param source_desc "Program" - Source of data ("CENSUS" or "SURVEY"). Census
#' program includes the Census of Ag as well as follow up projects. Survey
#' program includes national, state, and county surveys.
#' @param sector_desc "Sector" - Five high level, broad categories useful to
#' narrow down choices. ("ANIMALS & PRODUCTS", "CROPS", "DEMOGRAPHICS",
#' "ECONOMICS", or "ENVIRONMENTAL")
#' @param group_desc "Group" - Subsets within sector (e.g., under sector_desc =
#' "CROPS", the groups are "FIELD CROPS", "FRUIT & TREE NUTS", "HORTICULTURE",
#' and "VEGETABLES").
#' @param commodity_desc "Commodity" - The primary subject of interest (e.g.,
#' "CORN", "CATTLE", "LABOR", "TRACTORS", "OPERATORS").
#' @param class_desc Generally a physical attribute (e.g., variety, size, color,
#' gender) of the commodity.
#' @param prodn_practice_desc A method of production or action taken on the
#' commodity (e.g., IRRIGATED, ORGANIC, ON FEED).
#' @param util_practice_desc Utilizations (e.g., GRAIN, FROZEN, SLAUGHTER) or
#' marketing channels (e.g., FRESH MARKET, PROCESSING, RETAIL).
#' @param statisticcat_desc "Category" - The aspect of a commodity being
#' measured (e.g., "AREA HARVESTED", "PRICE RECEIVED", "INVENTORY", "SALES").
#' @param unit_desc The unit associated with the statistic category (e.g.,
#' ACRES, $ / LB, HEAD, $, OPERATIONS).
#' @param short_desc "Data Item" - A concatenation of six columns:
#' commodity_desc, class_desc, prodn_practice_desc, util_practice_desc,
#' statisticcat_desc, and unit_desc.
#' @param domain_desc "Domain" - Generally another characteristic of operations
#' that produce a particular commodity (e.g., "ECONOMIC CLASS", "AREA
#' OPERATED", "NAICS CLASSIFICATION", "SALES"). For chemical usage data, the
#' domain describes the type of chemical applied to the commodity. The
#' domain_desc = "TOTAL" will have no further breakouts; i.e., the data value
#' pertains completely to the short_desc.
#' @param domaincat_desc "Domain Category" - Categories or partitions within a
#' domain (e.g., under domain_desc = "SALES", domain categories include $1,000
#' TO $9,999, $10,000 TO $19,999, etc).
#' @param agg_level_desc "Geographic Level" - Aggregation level or geographic
#' granularity of the data. ("AGRICULTURAL DISTRICT", "COUNTY",
#' "INTERNATIONAL", "NATIONAL", "REGION : MULTI-STATE", "REGION : SUB-STATE",
#' "STATE", "WATERSHED", or "ZIP CODE")
#' @param state_ansi American National Standards Institute (ANSI) standard
#' 2-digit state codes.
#' @param state_fips_code NASS 2-digit state codes; include 99 and 98 for US
#' TOTAL and OTHER STATES, respectively; otherwise match ANSI codes.
#' @param state_alpha State abbreviation, 2-character alpha code.
#' @param state_name "State" - State full name.
#' @param asd_code NASS defined county groups, unique within a state, 2-digit ag
#' statistics district code.
#' @param asd_desc "Ag District" - Ag statistics district name.
#' @param county_ansi ANSI standard 3-digit county codes.
#' @param county_code NASS 3-digit county codes; includes 998 for OTHER
#' (COMBINED) COUNTIES and Alaska county codes; otherwise match ANSI codes.
#' @param county_name "County" - County name.
#' @param region_desc "Region" - NASS defined geographic entities not readily
#' defined by other standard geographic levels. A region can be a less than a
#' state (SUB-STATE) or a group of states (MULTI-STATE), and may be specific
#' to a commodity.
#' @param zip_5 "Zip Code" - US Postal Service 5-digit zip code.
#' @param watershed_code US Geological Survey (USGS) 8-digit Hydrologic Unit
#' Code (HUC) for watersheds.
#' @param watershed_desc "Watershed" - Name assigned to the HUC.
#' @param congr_district_code US Congressional District 2-digit code.
#' @param country_code US Census Bureau, Foreign Trade Division 4-digit country
#' code, as of April, 2007.
#' @param country_name Country name.
#' @param location_desc Full description for the location dimension.
#' @param year "Year" - The numeric year of the data and can be either a
#' character or numeric vector. Conditional values are also possible, for
#' example a character vector of ">=1999" of "1999<=" will give years greater
#' than or equal to 1999. Right now the logical values can either be
#' greater/less than or equal to with the logical at either the beginning or
#' end of a string with the year.
#' @param freq_desc "Period Type" - Length of time covered ("ANNUAL", "SEASON",
#' "MONTHLY", "WEEKLY", "POINT IN TIME"). "MONTHLY" often covers more than one
#' month. "POINT IN TIME" is as of a particular day.
#' @param begin_code If applicable, a 2-digit code corresponding to the
#' beginning of the reference period (e.g., for freq_desc = MONTHLY,
#' begin_code ranges from 01 (January) to 12 (December)).
#' @param end_code If applicable, a 2-digit code corresponding to the end of the
#' reference period (e.g., the reference period of JAN THRU MAR will have
#' begin_code = 01 and end_code = 03).
#' @param reference_period_desc "Period" - The specific time frame, within a
#' freq_desc.
#' @param week_ending Week ending date, used when freq_desc = WEEKLY.
#' @param key API key, default is to use the value stored in \code{.Renviron}
#' which is stored from the \code{\link{nass_set_key}} function. If there is
#' no API key stored in the environment, a character string can be provided.
#' @param \\dots Not used.
#' @return Number of observations.
#' @export nass_count
#' @examples
#'
#' \dontrun{
#' # Determine all the observations in NASS
#' nass_count()
#' }
#'
#' \dontrun{
#' # Find the number of observations for Wake County in North Carolina
#' nass_count(state_name = "NORTH CAROLINA", county_name = "WAKE")
#' }
nass_count <- function(source_desc = NULL,
sector_desc = NULL,
group_desc = NULL,
commodity_desc = NULL,
class_desc = NULL,
prodn_practice_desc = NULL,
util_practice_desc = NULL,
statisticcat_desc = NULL,
unit_desc = NULL,
short_desc = NULL,
domain_desc = NULL,
domaincat_desc = NULL,
agg_level_desc = NULL,
state_ansi = NULL,
state_fips_code = NULL,
state_alpha = NULL,
state_name = NULL,
asd_code = NULL,
asd_desc = NULL,
county_ansi = NULL,
county_code = NULL,
county_name = NULL,
region_desc = NULL,
zip_5 = NULL,
watershed_code = NULL,
watershed_desc = NULL,
congr_district_code = NULL,
country_code = NULL,
country_name = NULL,
location_desc = NULL,
year = NULL,
freq_desc = NULL,
begin_code = NULL,
end_code = NULL,
reference_period_desc = NULL,
week_ending = NULL,
key = check_key(), ...){
# Pass the arguments through formatting
calls <- match.call(expand.dots = TRUE)
calls[[1]] <- as.name("args_list")
arguments <- eval.parent(calls)
temp <- httr::GET("http://quickstats.nass.usda.gov/api/get_counts/",
query = arguments)
tt <- check_response(temp)
if (names(tt) == "count") {
count_data <- as.numeric(tt[["count"]])
} else {
stop("Parameter entered is not valid")
}
return(count_data)
}
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