2015 County FIPS Code Changes

[keberwein]

There were several FIPS code changes in 2015. Looks like the "regions" used in choroplethr reflect the codes for 2012-2014. Therefore, any county data that is 2015 or newer won't map.

Is there a plan to update the FIPS codes or provide a crosswalk function? I've included a reproducible example below that attempts to map data from the BLS for May 2015 as well as a hyperlink that outlines some of the recent FIPS changes.

library(dplyr)
library(choroplethr)
temp<-tempfile()
download.file("http://www.bls.gov/lau/laucntycur14.txt", temp)
df <- read.csv(temp,
               fill=TRUE,header=FALSE,sep="|",skip=6,stringsAsFactors=FALSE,strip.white=TRUE)
colnames(df) <- c("area_code", "fips_state", "fips_county", "area_title", "period",
                         "labor_force", "employed", "unemployed", "unemployed_rate")
unlink(temp)
df$fips_county <- formatC(df$fips_county, width = 3, format = "d", flag = "0")
df$fips_state <- formatC(df$fips_state, width = 2, format = "d", flag = "0")
df$fips <- paste(df$fips_state,df$fips_county,sep="")
df <- df %>%
    subset(period == "May-15" & fips_state != 72, 
           select = c("fips", "labor_force")) %>%
    rename("region"=fips, "value"=labor_force) %>%
    mutate(region = as.numeric(region)) %>%
    mutate(value = as.numeric(gsub(",", "", as.character(value))))

county_choropleth(df)

[arilamstein]

Please close this issue and repost it on the arilamstein/choroplethr repo. That's where active development is.

On Thu, Jul 28, 2016 at 6:45 PM, Keberwein notifications@github.com wrote:

There were several FIPS code changes in 2015. Looks like the "regions" used in choroplethr reflect the codes for 2014. Therefore, any county data that is 2015 or newer won't map. Is there a plan to update the FIPS codes or provide a crosswalk function? I've included a reproducible example below that attempts to map data from the BLS for May 2015 as well as a hyperlink that outlines some of the recent FIPS changes https://www.economy.com/support/blog/buffet.aspx?did=50094FC4-C32C-4CCA-862A-264BC890E13B .

`library(dplyr) library(choroplethr) temp<-tempfile() download.file("http://www.bls.gov/lau/laucntycur14.txt", temp) df <- read.csv(temp,

fill=TRUE,header=FALSE,sep="|",skip=6,stringsAsFactors=FALSE,strip.white=TRUE) colnames(df) <- c("area_code", "fips_state", "fips_county", "area_title", "period", "labor_force", "employed", "unemployed", "unemployed_rate") unlink(temp) df$fips_county <- formatC(df$fips_county, width = 3, format = "d", flag = "0") df$fips_state <- formatC(df$fips_state, width = 2, format = "d", flag = "0") df$fips <- paste(df$fips_state,df$fips_county,sep="") df <- df %>% subset(period == "May-15" & fips_state != 72, select = c("fips", "labor_force")) %>% rename("region"=fips, "value"=labor_force) %>% mutate(region = as.numeric(region)) %>% mutate(value = as.numeric(gsub(",", "", as.character(value))))

county_choropleth(df)`

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[keberwein]

Thanks.