#' Creates a chart with two plots, one for population trend, one for components of change
#'
#' This function creates two seperate time-series plots, a line chart for popultion trends
#' and a dodged bar chart for components of change over time. The two plots are stacked together and
#' placed into one chart.
#'
#' @param fips is the fips code for the main area to be compared
#' @param state is the state that the original fips
#' @param base is the base text size for the ggplot2 object and codemog_theme()
#'
#'
cp_migjobs=function(fips, countyname, base=12){
require(codemog, quietly = TRUE)
require(ggplot2, quietly = TRUE)
require(grid, quietly = TRUE)
require(dplyr, quietly = TRUE)
require(tidyr, quietly = TRUE)
j=county_jobs%>%
filter(sector_id==0, year<=2014)%>%
select(countyfips, year, jobs)%>%
group_by(countyfips)%>%
arrange(year)%>%
mutate(jobs=as.numeric(jobs),
jobChange=jobs-lag(jobs))
nm=county_profile%>%
filter(year>2000)%>%
select(year, countyfips, netMigration)
p=inner_join(j,nm, by=c("countyfips", "year"))%>%
filter(countyfips==as.numeric(fips), year>2001)%>%
ggplot()+
geom_bar(stat="identity",aes(x=year, y=jobChange, fill="jobChange"))+
geom_line(aes(x=year, y=netMigration,color="netMigration"), size=1.15)+
scale_x_continuous(breaks=2002:2014)+
scale_color_manual(" ",values=c("jobChange"=rgb(31,74,126, max=255), "netMigration"=rgb(191,32,38, max=255)),
labels=c("Net Migration"))+
scale_fill_manual("", values=rgb(31,74,126, max=255), labels="Job Change")+
theme_codemog(base_size=base)+
theme(legend.box="horizontal",
plot.title=element_text(size=rel(1.25)))+
labs(x="Year", y="Count", title=paste(countyname, "County Job Change and Net Migration, 2002 to 2014\nSource: State Demography Office"))
return(p)
}
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