#'Get the FIPS code for the selected data.
#'@name get_FIPS
#'@description The function will fetch and return the FIPS for the counties of interest.
#' As some counties will change due to various causes, it is easier to track the counties
#' by FIPS code for a long-term analysis. Description of FIPS codes is available via
#' \code{[here](https://en.wikipedia.org/wiki/FIPS_county_code)}
#'
#'@param data the dataset to work with, generally the full usfertilizer or its subsets.
#'@param counties counties of interest, defalut: all avaible counties.
#'@param states states of interest, defalt: all avaialble states.
#'@param overlap_state_county Logic. If true, the function will overlaping
#' the input of states and counties. If false, the function will return
#' results either in the states or in the counties.
#'@param combine_state_county Logic. If true, the county will be changed into
#' county, state, e.g. Wake, NC; If false, no changes.
#'@return A tibble with tidy data.
#'@export
#'@import dplyr
#'@seealso \code{link(get_data)}
#'@examples
#' data = fertilizer
#' get_FIPS(data, counties = "Wake", states = "NC")
#' get_FIPS(data, states = "NC")
#'
get_FIPS <- function(data,
counties = NULL,
states = NULL,
overlap_state_county = TRUE,
combine_state_county = TRUE
){
output = get_data(
data = data,
counties = counties,
states = states,
overlap_state_county = overlap_state_county,
combine_state_county = combine_state_county
)
# only selece three rows.
output = select(output, c(FIPS, County, Year))
# fetch duplicated counties.
output %>%
group_by(FIPS)%>%
filter(n()>=1) %>%
summarise(County = County[1])
# return format
# FIPS County
# 01049 DeKalb, AL
}
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