R/shapefile_matching_functions.R
In gmellon/neaR: A package providing data processing functions for arts administrative data
#' Get Dataframe of Matching Metropolitan Area Codes
#'
#' This function returns a dataframe with MSA data for a vector of Latitudes
#' and Longitudes in a dataframe.
#'
#' The package operates by matching Lat/Long to shapefiles of Metropolitan
#' and Micropolitan Statistical Areas - from CBSA files. Then, the package matches Lat/Long
#' that have not yet been coded to Metropolitan and Micropolitan NECTAS
#'
#' It returns a dataframe with the following components:
#' GEOID: The GEOID of the MSA or NECTA
#' NAME_LSAD: The Name of the MSA or NECTA
#' LSAD: A Code indicating whether it is a Metro or Micro MSA or NECTA
#' -M1 are Metropolitan MSAs
#' -M2 are Micropolitan MSAs
#' -M5 are Metropolitan NECTAS
#' -M6 are Micropolitan NECTAS
#' Source: A constructed variable, indicating if the boundaries were based
#' on NECTA or MSAs
#'
#' @param Latitude vector of Latitudes
#' @param Longitude Vector of Longitudes
#' @return A datafame with associated MSA info. Will be in correct order
#' as original Lat/Long to make it easy to append the data
#' @examples msas<-get_msa_data(NEA$CoLatitude, NEA$CoLongitude)
#' NEA$MSA_GEOID<-msa$csba_GEOID
#' NEA$MSA_NameLSAD<-msa$cbsa_NameLSAD
#' @export
get_msa_data<-function(Latitude, Longitude, year=2016){
cbsa_sf<-tigris::core_based_statistical_areas(cb = F, year = year)
necta_sf<-tigris::new_england(type="necta", cb=F, year= year)
ID <- 1:length(Latitude)
spatial<-dtf(Latitude=Latitude, Longitude=Longitude, ID=ID)
complete_spatial<- spatial[!is.na(spatial$Latitude), ]
sp::coordinates(complete_spatial) <- c("Longitude", "Latitude")
sp::proj4string(complete_spatial)<-sp::proj4string(cbsa_sf)
complete_spatial$cbsa_GEOID <- NA
complete_spatial$cbsa_NAMELSAD <- NA
complete_spatial$cbsa_LSAD <- NA
complete_spatial$cbsa_GEOID <- sp::over(complete_spatial,cbsa_sf)$GEOID
complete_spatial$cbsa_NAMELSAD <- sp::over(complete_spatial,cbsa_sf)$NAMELSAD
complete_spatial$cbsa_LSAD <- sp::over(complete_spatial,cbsa_sf)$LSAD
matched_spatial <- dtf(ID = complete_spatial$ID,
cbsa_GEOID=complete_spatial$cbsa_GEOID,
cbsa_NAMELSAD=complete_spatial$cbsa_NAMELSAD,
cbsa_LSAD=complete_spatial$cbsa_LSAD, source = "MSA")
matched_spatial <- matched_spatial[!is.na(matched_spatial$cbsa_GEOID), ]
complete_spatial <- complete_spatial[which(is.na(complete_spatial$cbsa_GEOID)),]
sp::proj4string(complete_spatial)<-sp::proj4string(necta_sf)
complete_spatial$cbsa_GEOID <- sp::over(complete_spatial,necta_sf)$GEOID
complete_spatial$cbsa_NAMELSAD <- sp::over(complete_spatial,necta_sf)$NAMELSAD
complete_spatial$cbsa_LSAD <- sp::over(complete_spatial,necta_sf)$LSAD
matched_spatial <- rbind(matched_spatial, dtf(ID = complete_spatial$ID,
cbsa_GEOID=complete_spatial$cbsa_GEOID,
cbsa_NAMELSAD=complete_spatial$cbsa_NAMELSAD,
cbsa_LSAD=complete_spatial$cbsa_LSAD, source = "NECTA"))
output <- matched_spatial[match(spatial$ID, matched_spatial$ID), ]
all(output$ID==ID, na.rm = T)
all(is.na(Longitude)==is.na(output$ID))
output$ID <- NULL
return(output)
}
#' Get Dataframe of Matching Congressional District
#'
#' This function returns a dataframe with Congressional data for a vector of Latitudes
#' and Longitudes in a dataframe.
#'
#' The package operates by matching Lat/Long to shapefiles of 114th Congressional
#' Districts - valid from 2015-2017.
#'
#' It returns a dataframe with the following components:
#' GEOID: The GEOID of the CD
#' NAME_LSAD: The Name of the CD
#' @param Latitude vector of Latitudes
#' @param Longitude Vector of Longitudes
#' @return A datafame with associated CD info. Will be in correct order
#' as original Lat/Long to make it easy to append the data
#' @examples cds<-get_cd_data(NEA$CoLatitude, NEA$CoLongitude)
#' NEA$CD_GEOID<-cds$CD_GEOID
#' NEA$CD_NameLSAD<-cds$CD_NameLSAD
#' @export
get_cd_data<-function(Latitude, Longitude){
CD_sf <- tigris::congressional_districts(cb = F)
ID <- 1:length(Latitude)
spatial<-dtf(Latitude=Latitude, Longitude=Longitude, ID=ID)
complete_spatial<- spatial[!is.na(spatial$Latitude), ]
sp::coordinates(complete_spatial) <- c("Longitude", "Latitude")
sp::proj4string(complete_spatial)<-sp::proj4string(CD_sf)
complete_spatial$CD_GEOID <- NA
complete_spatial$CD_NAMELSAD <- NA
complete_spatial$CD_GEOID <- sp::over(complete_spatial,CD_sf)$GEOID
complete_spatial$CD_NAMELSAD <- sp::over(complete_spatial,CD_sf)$NAMELSAD
matched_spatial <- dtf(ID = complete_spatial$ID,
CD_GEOID=complete_spatial$CD_GEOID,
CD_NAMELSAD=complete_spatial$CD_NAMELSAD)
output <- matched_spatial[match(spatial$ID, matched_spatial$ID), ]
all(output$ID==ID, na.rm = T)
all(is.na(Longitude)==is.na(output$ID))
output$ID <- NULL
return(output)
}
#' Get Dataframe of Matching County Data
#'
#' This function returns a dataframe with County ID data for a vector of Latitudes
#' and Longitudes in a dataframe.
#'
#' It returns a dataframe with the following components:
#' GEOID: The GEOID of the County - i.e. FIPS Code
#' NAME_LSAD: The Name of the County
#' @param Latitude vector of Latitudes
#' @param Longitude Vector of Longitudes
#' @param year vintage of shapefiles, defaults to 2015 if none specified
#' @return A datafame with associated Ccountyinfo. Will be in correct order
#' as original Lat/Long to make it easy to append the data
#' @examples counties<-get_cunty_data(NEA$CoLatitude, NEA$CoLongitude)
#' NEA$county_GEOID<-counties$county_GEOID
#' NEA$county_NameLSAD<-counties$county_NameLSAD
#' @export
get_county_data<-function(Latitude, Longitude, year=2015){
county_sf<- tigris::counties(cb = F, year)
ID <- 1:length(Latitude)
spatial<-dtf(Latitude=Latitude, Longitude=Longitude, ID=ID)
complete_spatial<- spatial[!is.na(spatial$Latitude), ]
sp::coordinates(complete_spatial) <- c("Longitude", "Latitude")
sp::proj4string(complete_spatial)<-sp::proj4string(county_sf)
complete_spatial$county_GEOID <- NA
complete_spatial$county_NAMELSAD <- NA
complete_spatial$county_GEOID <- sp::over(complete_spatial,county_sf)$GEOID
complete_spatial$county_NAMELSAD <- sp::over(complete_spatial,county_sf)$NAMELSAD
matched_spatial <- dtf(ID = complete_spatial$ID,
county_GEOID=complete_spatial$county_GEOID,
county_NAMELSAD=complete_spatial$county_NAMELSAD)
output <- matched_spatial[match(spatial$ID, matched_spatial$ID), ]
all(output$ID==ID, na.rm = T)
all(is.na(Longitude)==is.na(output$ID))
output$ID <- NULL
return(output)
}
#' Get Dataframe of Matching Census Tract IDs
#'
#' This function returns a dataframe with Census Tract ID data for a vector of Latitudes
#' and Longitudes in a dataframe.
#'
#' Census Tract shapefiles can only be downloaded at the state level;
#' as such, this function runs in a loop, downloading and matching
#' the shapefile for each state. All shapefiles are the most precise boundary option
#' with the exception of CA, which offers only smaller boundary data
#'
#' It returns a dataframe with the following components:
#' CT_GEOID: The GEOID of the Census Tract
#' CT_NAME_LSAD: The Name of the Census Tract
#'
#' It is IMPORTANT to note that the CT GEOID is an 11 character digit
#' It is recommended that CT are matched at the same time that CT level
#' data will be matched. If the file is saved and reimported, the ID
#' must be padded to 11 digits, and must be set to not read out as scientific
#' notation.
#' @param Latitude vector of Latitudes
#' @param Longitude Vector of Longitudes
#' @return A datafame with associated CT info. Will be in correct order
#' as original Lat/Long to make it easy to append the data
#' @examples CT<-get_ct_data(NEA$CoLatitude, NEA$CoLongitude)
#' NEA$CT_GEOID<-CT$CT_GEOID
#' NEA$CT_NameLSAD<-CT$CT_NameLSAD
#' @export
get_ct_data<- function(Latitude, Longitude){
### Create Data Frame with State Fips and Names for Looping
State_FIPS<-c( "01" ,"02", "04", "05", "06", "08", "09", "10", "11" ,"12", "13", "15", "16",
"17", "18", "19", "20", "21", "22", "23", "24" ,"25",
"26", "27", "28", "29" ,"30" ,"31" ,"32" ,"33" ,"34",
"35" ,"36" ,"37" ,"38", "39" ,"40", "41" ,"42", "44", "45" ,"46" ,"47", "48",
"49" ,"50", "51" ,"53" ,"54", "55", "56")
State<-c( "Alabama" , "Alaska" , "Arizona" , "Arkansas" ,
"California" , "Colorado" , "Connecticut" , "Delaware" ,
"District of Columbia", "Florida" , "Geogia" , "Hawaii" ,
"Idaho" , "Illinois" , "Indiana" , "Iowa" ,
"Kansas" , "Kentucky" , "Louisiana" , "Maine" ,
"Maryland" , "Massachusetts", "Michigan" , "Minnesota" ,
"Mississippi" , "Missouri" , "Montana" , "Nebraska" ,
"Nevada" , "New Hampshire" , "New Jersey" , "New Mexico" ,
"New York" , "North Carolina" , "North Dakota" , "Ohio" ,
"Oklahoma" , "Oregon" , "Pennsylvania" , "Rhode Island" ,
"South Carolina" , "South Dakota" , "Tennessee" , "Texas" ,
"Utah" , "Vermont" , "Virginia" , "Washington" ,
"West Virginia" , "Wisconsin" , "Wyoming" )
State_Abbreviation<- c( "AL", "AK", "AZ", "AR" ,"CA" ,"CO" ,"CT", "DE" ,"DC" ,"FL", "GA", "HI" ,
"ID" ,"IL", "IN", "IA", "KS", "KY" ,"LA" ,"ME" ,"MD" ,"MA",
"MI", "MN", "MS" ,"MO", "MT", "NE" ,"NV" ,"NH" ,"NJ" ,"NM", "NY", "NC" ,"ND", "OH" ,"OK",
"OR", "PA", "RI" ,"SC" ,"SD" ,"TN" ,"TX",
"UT", "VT" ,"VA", "WA" ,"WV" ,"WI" ,"WY" )
state_fips<-dtf(State_FIPS, State, State_Abbreviation)
state_fips$State_FIPS<- stringr::str_pad(state_fips$State_FIPS,2,pad="0")
#set up master file
ID <- 1:length(Latitude)
spatial<-dtf(Latitude=Latitude, Longitude=Longitude, ID=ID)
complete_spatial<- spatial[!is.na(spatial$Latitude), ]
sp::coordinates(complete_spatial) <- c("Longitude", "Latitude")
complete_spatial$CT_GEOID <- NA
complete_spatial$CT_NAMELSAD <- NA
for( i in c(1:4, 6:nrow(state_fips))) {
census_tract_sf<-tigris::tracts(state_fips$State_FIPS[i] , cb=F)
sp::proj4string(complete_spatial)<-sp::proj4string(census_tract_sf)
complete_spatial$CT_GEOID[is.na(complete_spatial$CT_GEOID)] <- sp::over(complete_spatial[is.na(complete_spatial$CT_GEOID),], census_tract_sf)$GEOID
complete_spatial$CT_NAMELSAD[is.na(complete_spatial$CT_NAMELSAD)] <- sp::over(complete_spatial[is.na(complete_spatial$CT_NAMELSAD),], census_tract_sf)$NAMELSAD
}
#california - done last because there was originally an embedded problem with the shapefile on
# tigris. Has been fixed in more recent updates.
census_tract_sf<-tigris::tracts(state="06" , cb=F)
sp::proj4string(complete_spatial) <- sp::proj4string(census_tract_sf)
complete_spatial$CT_GEOID[is.na(complete_spatial$CT_GEOID)] <- sp::over(complete_spatial[is.na(complete_spatial$CT_GEOID),], census_tract_sf)$GEOID
matched_spatial <- data.frame(ID = complete_spatial$ID,
CT_GEOID = complete_spatial$CT_GEOID,
CT_NAMELSAD = complete_spatial$CT_NAMELSAD, stringsAsFactors = FALSE)
output <- matched_spatial[match(spatial$ID, matched_spatial$ID), ]
all(output$ID==ID, na.rm = T)
all(is.na(Longitude)==is.na(output$ID))
output$ID <- NULL
return(output)
}
gmellon/neaR documentation built on May 14, 2019, 2:42 p.m.
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#' Get Dataframe of Matching Metropolitan Area Codes
#'
#' This function returns a dataframe with MSA data for a vector of Latitudes
#' and Longitudes in a dataframe.
#'
#' The package operates by matching Lat/Long to shapefiles of Metropolitan
#' and Micropolitan Statistical Areas - from CBSA files. Then, the package matches Lat/Long
#' that have not yet been coded to Metropolitan and Micropolitan NECTAS
#'
#' It returns a dataframe with the following components:
#' GEOID: The GEOID of the MSA or NECTA
#' NAME_LSAD: The Name of the MSA or NECTA
#' LSAD: A Code indicating whether it is a Metro or Micro MSA or NECTA
#' -M1 are Metropolitan MSAs
#' -M2 are Micropolitan MSAs
#' -M5 are Metropolitan NECTAS
#' -M6 are Micropolitan NECTAS
#' Source: A constructed variable, indicating if the boundaries were based
#' on NECTA or MSAs
#'
#' @param Latitude vector of Latitudes
#' @param Longitude Vector of Longitudes
#' @return A datafame with associated MSA info. Will be in correct order
#' as original Lat/Long to make it easy to append the data
#' @examples msas<-get_msa_data(NEA$CoLatitude, NEA$CoLongitude)
#' NEA$MSA_GEOID<-msa$csba_GEOID
#' NEA$MSA_NameLSAD<-msa$cbsa_NameLSAD
#' @export
get_msa_data<-function(Latitude, Longitude, year=2016){
cbsa_sf<-tigris::core_based_statistical_areas(cb = F, year = year)
necta_sf<-tigris::new_england(type="necta", cb=F, year= year)
ID <- 1:length(Latitude)
spatial<-dtf(Latitude=Latitude, Longitude=Longitude, ID=ID)
complete_spatial<- spatial[!is.na(spatial$Latitude), ]
sp::coordinates(complete_spatial) <- c("Longitude", "Latitude")
sp::proj4string(complete_spatial)<-sp::proj4string(cbsa_sf)
complete_spatial$cbsa_GEOID <- NA
complete_spatial$cbsa_NAMELSAD <- NA
complete_spatial$cbsa_LSAD <- NA
complete_spatial$cbsa_GEOID <- sp::over(complete_spatial,cbsa_sf)$GEOID
complete_spatial$cbsa_NAMELSAD <- sp::over(complete_spatial,cbsa_sf)$NAMELSAD
complete_spatial$cbsa_LSAD <- sp::over(complete_spatial,cbsa_sf)$LSAD
matched_spatial <- dtf(ID = complete_spatial$ID,
cbsa_GEOID=complete_spatial$cbsa_GEOID,
cbsa_NAMELSAD=complete_spatial$cbsa_NAMELSAD,
cbsa_LSAD=complete_spatial$cbsa_LSAD, source = "MSA")
matched_spatial <- matched_spatial[!is.na(matched_spatial$cbsa_GEOID), ]
complete_spatial <- complete_spatial[which(is.na(complete_spatial$cbsa_GEOID)),]
sp::proj4string(complete_spatial)<-sp::proj4string(necta_sf)
complete_spatial$cbsa_GEOID <- sp::over(complete_spatial,necta_sf)$GEOID
complete_spatial$cbsa_NAMELSAD <- sp::over(complete_spatial,necta_sf)$NAMELSAD
complete_spatial$cbsa_LSAD <- sp::over(complete_spatial,necta_sf)$LSAD
matched_spatial <- rbind(matched_spatial, dtf(ID = complete_spatial$ID,
cbsa_GEOID=complete_spatial$cbsa_GEOID,
cbsa_NAMELSAD=complete_spatial$cbsa_NAMELSAD,
cbsa_LSAD=complete_spatial$cbsa_LSAD, source = "NECTA"))
output <- matched_spatial[match(spatial$ID, matched_spatial$ID), ]
all(output$ID==ID, na.rm = T)
all(is.na(Longitude)==is.na(output$ID))
output$ID <- NULL
return(output)
}
#' Get Dataframe of Matching Congressional District
#'
#' This function returns a dataframe with Congressional data for a vector of Latitudes
#' and Longitudes in a dataframe.
#'
#' The package operates by matching Lat/Long to shapefiles of 114th Congressional
#' Districts - valid from 2015-2017.
#'
#' It returns a dataframe with the following components:
#' GEOID: The GEOID of the CD
#' NAME_LSAD: The Name of the CD
#' @param Latitude vector of Latitudes
#' @param Longitude Vector of Longitudes
#' @return A datafame with associated CD info. Will be in correct order
#' as original Lat/Long to make it easy to append the data
#' @examples cds<-get_cd_data(NEA$CoLatitude, NEA$CoLongitude)
#' NEA$CD_GEOID<-cds$CD_GEOID
#' NEA$CD_NameLSAD<-cds$CD_NameLSAD
#' @export
get_cd_data<-function(Latitude, Longitude){
CD_sf <- tigris::congressional_districts(cb = F)
ID <- 1:length(Latitude)
spatial<-dtf(Latitude=Latitude, Longitude=Longitude, ID=ID)
complete_spatial<- spatial[!is.na(spatial$Latitude), ]
sp::coordinates(complete_spatial) <- c("Longitude", "Latitude")
sp::proj4string(complete_spatial)<-sp::proj4string(CD_sf)
complete_spatial$CD_GEOID <- NA
complete_spatial$CD_NAMELSAD <- NA
complete_spatial$CD_GEOID <- sp::over(complete_spatial,CD_sf)$GEOID
complete_spatial$CD_NAMELSAD <- sp::over(complete_spatial,CD_sf)$NAMELSAD
matched_spatial <- dtf(ID = complete_spatial$ID,
CD_GEOID=complete_spatial$CD_GEOID,
CD_NAMELSAD=complete_spatial$CD_NAMELSAD)
output <- matched_spatial[match(spatial$ID, matched_spatial$ID), ]
all(output$ID==ID, na.rm = T)
all(is.na(Longitude)==is.na(output$ID))
output$ID <- NULL
return(output)
}
#' Get Dataframe of Matching County Data
#'
#' This function returns a dataframe with County ID data for a vector of Latitudes
#' and Longitudes in a dataframe.
#'
#' It returns a dataframe with the following components:
#' GEOID: The GEOID of the County - i.e. FIPS Code
#' NAME_LSAD: The Name of the County
#' @param Latitude vector of Latitudes
#' @param Longitude Vector of Longitudes
#' @param year vintage of shapefiles, defaults to 2015 if none specified
#' @return A datafame with associated Ccountyinfo. Will be in correct order
#' as original Lat/Long to make it easy to append the data
#' @examples counties<-get_cunty_data(NEA$CoLatitude, NEA$CoLongitude)
#' NEA$county_GEOID<-counties$county_GEOID
#' NEA$county_NameLSAD<-counties$county_NameLSAD
#' @export
get_county_data<-function(Latitude, Longitude, year=2015){
county_sf<- tigris::counties(cb = F, year)
ID <- 1:length(Latitude)
spatial<-dtf(Latitude=Latitude, Longitude=Longitude, ID=ID)
complete_spatial<- spatial[!is.na(spatial$Latitude), ]
sp::coordinates(complete_spatial) <- c("Longitude", "Latitude")
sp::proj4string(complete_spatial)<-sp::proj4string(county_sf)
complete_spatial$county_GEOID <- NA
complete_spatial$county_NAMELSAD <- NA
complete_spatial$county_GEOID <- sp::over(complete_spatial,county_sf)$GEOID
complete_spatial$county_NAMELSAD <- sp::over(complete_spatial,county_sf)$NAMELSAD
matched_spatial <- dtf(ID = complete_spatial$ID,
county_GEOID=complete_spatial$county_GEOID,
county_NAMELSAD=complete_spatial$county_NAMELSAD)
output <- matched_spatial[match(spatial$ID, matched_spatial$ID), ]
all(output$ID==ID, na.rm = T)
all(is.na(Longitude)==is.na(output$ID))
output$ID <- NULL
return(output)
}
#' Get Dataframe of Matching Census Tract IDs
#'
#' This function returns a dataframe with Census Tract ID data for a vector of Latitudes
#' and Longitudes in a dataframe.
#'
#' Census Tract shapefiles can only be downloaded at the state level;
#' as such, this function runs in a loop, downloading and matching
#' the shapefile for each state. All shapefiles are the most precise boundary option
#' with the exception of CA, which offers only smaller boundary data
#'
#' It returns a dataframe with the following components:
#' CT_GEOID: The GEOID of the Census Tract
#' CT_NAME_LSAD: The Name of the Census Tract
#'
#' It is IMPORTANT to note that the CT GEOID is an 11 character digit
#' It is recommended that CT are matched at the same time that CT level
#' data will be matched. If the file is saved and reimported, the ID
#' must be padded to 11 digits, and must be set to not read out as scientific
#' notation.
#' @param Latitude vector of Latitudes
#' @param Longitude Vector of Longitudes
#' @return A datafame with associated CT info. Will be in correct order
#' as original Lat/Long to make it easy to append the data
#' @examples CT<-get_ct_data(NEA$CoLatitude, NEA$CoLongitude)
#' NEA$CT_GEOID<-CT$CT_GEOID
#' NEA$CT_NameLSAD<-CT$CT_NameLSAD
#' @export
get_ct_data<- function(Latitude, Longitude){
### Create Data Frame with State Fips and Names for Looping
State_FIPS<-c( "01" ,"02", "04", "05", "06", "08", "09", "10", "11" ,"12", "13", "15", "16",
"17", "18", "19", "20", "21", "22", "23", "24" ,"25",
"26", "27", "28", "29" ,"30" ,"31" ,"32" ,"33" ,"34",
"35" ,"36" ,"37" ,"38", "39" ,"40", "41" ,"42", "44", "45" ,"46" ,"47", "48",
"49" ,"50", "51" ,"53" ,"54", "55", "56")
State<-c( "Alabama" , "Alaska" , "Arizona" , "Arkansas" ,
"California" , "Colorado" , "Connecticut" , "Delaware" ,
"District of Columbia", "Florida" , "Geogia" , "Hawaii" ,
"Idaho" , "Illinois" , "Indiana" , "Iowa" ,
"Kansas" , "Kentucky" , "Louisiana" , "Maine" ,
"Maryland" , "Massachusetts", "Michigan" , "Minnesota" ,
"Mississippi" , "Missouri" , "Montana" , "Nebraska" ,
"Nevada" , "New Hampshire" , "New Jersey" , "New Mexico" ,
"New York" , "North Carolina" , "North Dakota" , "Ohio" ,
"Oklahoma" , "Oregon" , "Pennsylvania" , "Rhode Island" ,
"South Carolina" , "South Dakota" , "Tennessee" , "Texas" ,
"Utah" , "Vermont" , "Virginia" , "Washington" ,
"West Virginia" , "Wisconsin" , "Wyoming" )
State_Abbreviation<- c( "AL", "AK", "AZ", "AR" ,"CA" ,"CO" ,"CT", "DE" ,"DC" ,"FL", "GA", "HI" ,
"ID" ,"IL", "IN", "IA", "KS", "KY" ,"LA" ,"ME" ,"MD" ,"MA",
"MI", "MN", "MS" ,"MO", "MT", "NE" ,"NV" ,"NH" ,"NJ" ,"NM", "NY", "NC" ,"ND", "OH" ,"OK",
"OR", "PA", "RI" ,"SC" ,"SD" ,"TN" ,"TX",
"UT", "VT" ,"VA", "WA" ,"WV" ,"WI" ,"WY" )
state_fips<-dtf(State_FIPS, State, State_Abbreviation)
state_fips$State_FIPS<- stringr::str_pad(state_fips$State_FIPS,2,pad="0")
#set up master file
ID <- 1:length(Latitude)
spatial<-dtf(Latitude=Latitude, Longitude=Longitude, ID=ID)
complete_spatial<- spatial[!is.na(spatial$Latitude), ]
sp::coordinates(complete_spatial) <- c("Longitude", "Latitude")
complete_spatial$CT_GEOID <- NA
complete_spatial$CT_NAMELSAD <- NA
for( i in c(1:4, 6:nrow(state_fips))) {
census_tract_sf<-tigris::tracts(state_fips$State_FIPS[i] , cb=F)
sp::proj4string(complete_spatial)<-sp::proj4string(census_tract_sf)
complete_spatial$CT_GEOID[is.na(complete_spatial$CT_GEOID)] <- sp::over(complete_spatial[is.na(complete_spatial$CT_GEOID),], census_tract_sf)$GEOID
complete_spatial$CT_NAMELSAD[is.na(complete_spatial$CT_NAMELSAD)] <- sp::over(complete_spatial[is.na(complete_spatial$CT_NAMELSAD),], census_tract_sf)$NAMELSAD
}
#california - done last because there was originally an embedded problem with the shapefile on
# tigris. Has been fixed in more recent updates.
census_tract_sf<-tigris::tracts(state="06" , cb=F)
sp::proj4string(complete_spatial) <- sp::proj4string(census_tract_sf)
complete_spatial$CT_GEOID[is.na(complete_spatial$CT_GEOID)] <- sp::over(complete_spatial[is.na(complete_spatial$CT_GEOID),], census_tract_sf)$GEOID
matched_spatial <- data.frame(ID = complete_spatial$ID,
CT_GEOID = complete_spatial$CT_GEOID,
CT_NAMELSAD = complete_spatial$CT_NAMELSAD, stringsAsFactors = FALSE)
output <- matched_spatial[match(spatial$ID, matched_spatial$ID), ]
all(output$ID==ID, na.rm = T)
all(is.na(Longitude)==is.na(output$ID))
output$ID <- NULL
return(output)
}
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