#' Colorado State Demography Office ggplot2 Theme
#'
#' Custom \code{ggplot2} theme that borrows heavily from the
#'\code{theme_fivethirtyeight()} in ggthemes.
#'
#' @param base_size Base font size.
#' @param base_family Plot text font family.
theme_codemog <- function(base_size = 12, base_family = "sans"){
codemog_pal=c(
dkblu=rgb(31,73,125, max=255),
dkred=rgb(192,80,77, max=255),
dkgray = rgb(78, 87, 88, max = 255),
medgray = rgb(210, 210, 210, max = 255),
ltgray = rgb(208, 210, 211, max = 255),
green = rgb(119, 171, 67, max = 255)
)
theme(
line = element_line(),
rect = element_blank(),
text = element_text(colour = codemog_pal['dkgray'], size=base_size),
axis.title = element_text(family=base_family, colour=codemog_pal['dkgray']),
axis.text = element_text(colour=codemog_pal['dkgray'], family=base_family),
axis.ticks = element_blank(),
axis.line = element_blank(),
legend.background = element_rect(),
legend.position = "bottom",
legend.direction = "horizontal",
legend.box = "vertical",
panel.grid = element_line(colour = NULL),
panel.grid.major = element_line(colour = codemog_pal['medgray'], size=base_size*.05),
panel.grid.minor = element_line(colour = codemog_pal['medgray'], size=base_size*.05),
plot.title = element_text(hjust = 0, size = rel(1.5), face = "bold"),
plot.margin = unit(c(.2, .2, .2, .2), "lines"),
strip.background=element_rect())
}
#' Colorado State Demography Office Color Palette for ggplot2
#'
#'Custom color palette using a mix of SDO colors and DOLA
#' Brand Colors from Brand Colorado.
#'
#'
codemog_pal=c(
dkblu=rgb(31,73,125, max=255),
dkred=rgb(192,80,77, max=255),
dkgray = rgb(78, 87, 88, max = 255),
medgray = rgb(210, 210, 210, max = 255),
ltgray = rgb(208, 210, 211, max = 255),
green = rgb(119, 171, 67, max = 255)
)
#' Creates a \code{ggplot2} chart of the population for a CO county
#'
#' Takes some basic input on the timeperiod and county then creates a
#' plot of the data in \code{ggplot2}. Similar to the county_ts_data()
#' function. Can create timeseries from 1990 to 2040 (beyond 2013 are
#' forecasts).
#' Note: Requires dplyr, ggplot2, ggthemes, scales, and grid R packages.
#'
#' @param fips The County FIPS number (without leading Zeros)
#' @param beginyear The first year in the timeseries Defaults to 1990.
#' @param endyear The first year in the timeseries Defaults to 2013.
#' @param base Base font size.
county_ts_chart=function(fips, beginyear=1990, base=12){
require(dplyr, quietly=TRUE)
require(ggplot2, quietly=TRUE)
require(scales, quietly=TRUE)
require(grid, quietly=TRUE)
fips=as.numeric(fips)
d=county_est%>%
select(countyfips, county, year, totalPopulation)%>%
# bind_rows(select(county_hist, countyfips, county, year, totalPopulation))
bind_rows(county_hist)%>%
filter(countyfips==fips, year>=beginyear)%>%
group_by(county,countyfips, year)%>%
summarise(totalPopulation=sum(totalPopulation))
p=d%>%
ggplot(aes(x=as.factor(year), y=as.integer(totalPopulation), group=countyfips))+
geom_line(color=codemog_pal['dkblu'], size=1.75)+
labs(x="Year", y="Population", title=paste(d$county,"County Population,", beginyear, "to", max(d$year), sep=" "))+
scale_y_continuous(label=comma)+
theme_codemog(base_size=base)+
theme(axis.text.x=element_text(angle=90))
return(p)
}
#' Creates a \code{ggplot2} chart of the population for a CO municipality
#'
#' Takes some basic input on the timeperiod and county then creates a
#' plot of the data in \code{ggplot2}. Similar to the muni_ts_data()
#' function. Can create timeseries from 1990 to 2040 (beyond 2013 are
#' forecasts).
#' Note: Requires dplyr, ggplot2, ggthemes, scales, and grid R packages.
#'
#' @param fips The County FIPS number (without leading Zeros)
#' @param beginyear The first year in the timeseries Defaults to 1990.
#' @param endyear The first year in the timeseries Defaults to 2013.
#' @param base Base font size.
muni_ts_chart=function(fips, beginyear=1990, base=12){
require(dplyr, quietly=TRUE)
require(tidyr, quietly=TRUE)
require(ggplot2, quietly=TRUE)
require(scales, quietly=TRUE)
require(grid, quietly=TRUE)
fips=as.numeric(fips)
d=muni_est%>%
mutate(
placefips=as.numeric(as.character(placefips)),
geonum=as.numeric(as.character(geonum)))%>%
select(geonum, placefips, municipality, year, totalPopulation)%>%
bind_rows(muni_hist%>%select(-countyfips))%>%
filter(year>=beginyear)%>%
group_by(placefips,municipality,year)%>%
summarise(totalPopulation=sum(totalPopulation))%>%
filter(placefips==fips)
p=d%>%
ggplot(aes(x=as.factor(year), y=as.integer(totalPopulation), group=placefips))+
geom_line(color=codemog_pal['dkblu'], size=1.75)+
labs(x="Year", y="Population", title=paste(d$municipality,"Population,", beginyear, "to", max(d$year), sep=" "))+
scale_y_continuous(label=comma)+
theme_codemog(base_size=base)+
theme(axis.text.x=element_text(angle=90))
return(p)
}
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