Get data

We are going to combine data from two sources. First, we will get house price data from the publicly available Freddie Mac House Price Index and second we’ll use employment data from the Bureau of Labor Statistics (BLS).

For more details on these data see respectively:

• Visual Meditations on House Prices Part 1: data wrangling

• Wrangling employment data, plotting trends

Following the house price post, we’ll begin with a text file with metro area house prices, and following the second we’ll get employment data directly from BLS. To make things easier, I’ve post a link to the updated house price text file below, and copied the employment data code below that.

################################################
## Load libraries
################################################
library(tidyverse)

## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr

## Conflicts with tidy packages ----------------------------------------------

## filter(): dplyr, stats
## lag():    dplyr, stats

library(data.table)

## 
## Attaching package: 'data.table'

## The following objects are masked from 'package:dplyr':
## 
##     between, first, last

## The following object is masked from 'package:purrr':
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library(viridis)  #we want pretty colors later

## Loading required package: viridisLite

library(maps)

## 
## Attaching package: 'maps'

## The following object is masked from 'package:purrr':
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library(scales)

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#### HRBRTHEMES ################################

### Run to install #############################
#devtools::install_github("hrbrmstr/hrbrthemes")
library(hrbrthemes)
library(gcookbook)
library(extrafont)

## Registering fonts with R

################################################

source("code/multiplot.R")

data(us.cities) # from the package maps
cbsa.data <-fread("data/cbsa.city.txt") #our first utility file
cbsa.metro<-cbsa.data[metro.micro=="Metropolitan Statistical Area"]

#create lowercase names
cbsa.metro[,nameL:=tolower(name)]
us.cities<-data.table(us.cities)[,nameL:=tolower(name)]

d<-merge(cbsa.metro,us.cities,by="nameL")
#get rid of duplicates
# see: http://stackoverflow.com/questions/15776064/r-first-observation-by-group-using-data-table-self-join
d<-d[order(-pop)]
d<-d[d[,list(row1 = .I[1]), by = list(cbsa)][,row1]]

# load house price data
dm<-fread("data/fmhpi2016q4metro.txt")
dm$date<-as.Date(dm$date, format="%m/%d/%Y")
#compute year-over-year house price growth
dm[,hpa12:=hpi/shift(hpi,12,fill=NA)-1,by=metro]

# for merging:
setkey(d,cbsa.name)
setkey(dm,metro)
dm2<-merge(dm,d,by.y="cbsa.name",by.x="metro",all.x=T)

#load regions
regions<-fread("data/region.txt")
dm2<-merge(dm2,regions,by.x="state",by.y="statecode")

# Only keep necessary columns
dm2<-dm2[,c("state","statename","division","region","metro","cbsa","lat","long","date","year","month","hpi","hpa12","metro.pop"), with=F]

# Check data
library("htmlTable")
library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:data.table':
## 
##     hour, isoweek, mday, minute, month, quarter, second, wday,
##     week, yday, year

## The following object is masked from 'package:base':
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# make tables for viewing formatting dates with purr %>% operations
htmlTable(head(dm2 %>% map_if(is.Date, as.character,format="%b %d,%Y") %>% map_if(is.numeric, round,0) %>%as.data.frame() ,10), col.rgroup = c("none", "#F7F7F7"),caption="Merged house price data",
          tfoot="Source: Freddie Mac House Price Index, Census")

ggplot(data=dm2[metro=="Columbus, OH"],aes(x=date,y=hpi))+
  geom_line()+
  theme_ipsum()+  # try new theme
  labs(x="",y="House Price Index, (NSA, Dec 2000=100)",
       subtitle="Testing a line plot",
       title="House prices in Columbus, Ohio",
       caption="@lenkiefer Source: Freddie Mac House Price Index" )

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ggplot(data=dm2[metro=="Miami-Fort Lauderdale-West Palm Beach, FL"],aes(x=date,y=hpa12))+
  geom_line()+
  theme_ipsum()+  # try new theme
  scale_y_percent()+  #use scale_y_percent function from hrbrtheme
  labs(x="",y="Annual Percentage Change in House Price Index, (NSA, Dec 2000=100)",
       subtitle="Testing a line plot",
       title="House price appreciation in Miami-Fort Lauderdale-West Palm Beach, FL",
       caption="@lenkiefer Source: Freddie Mac House Price Index" )+
  theme(panel.grid.major=element_line(color="lightgray"),
        panel.grid.minor=element_line(color="lightgray"))+
  geom_hline(yintercept=0) #darken the x axis at 0

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Employment data

Getting the employment data is exactly as in my wrangling employment data post, but we’ll recreate it here for completeness.

emp.data<-fread("https://download.bls.gov/pub/time.series/sm/sm.data.54.TotalNonFarm.All")

## Warning in fread("https://download.bls.gov/pub/time.series/sm/sm.data.
## 54.TotalNonFarm.All"): Bumped column 4 to type character on data row
## 521667, field contains '-'. Coercing previously read values in this
## column from logical, integer or numeric back to character which may not
## be lossless; e.g., if '00' and '000' occurred before they will now be just
## '0', and there may be inconsistencies with treatment of ',,' and ',NA,' too
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## column type detection uses a sample of 1,000 rows (100 rows at 10 points)
## so hopefully this message should be very rare. If reporting to datatable-
## help, please rerun and include the output from verbose=TRUE.

emp.series<-fread("https://download.bls.gov/pub/time.series/sm/sm.series")

emp.list<-emp.series[industry_code==0 # get all employment
                     & data_type_code==1 # get employment in thousands
                     & seasonal=="U",]  # get seasonally adjusted data]

emp.area<-fread("https://download.bls.gov/pub/time.series/sm/sm.area",
                col.names=c("area_code","area_name","drop"))[,c("area_code","area_name"),with=F]

## Warning in fread("https://download.bls.gov/pub/time.series/sm/sm.area", :
## Starting data input on line 2 and discarding line 1 because it has too few
## or too many items to be column names or data: area_code area_name

emp.st<-fread("https://download.bls.gov/pub/time.series/sm/sm.state",
              col.names=c("state_code","state_name","drop"))[,c("state_code","state_name"),with=F]

## Warning in fread("https://download.bls.gov/pub/time.series/sm/sm.state", :
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## or too many items to be column names or data: state_code state_name

# merge data
emp.dt<-merge(emp.data,emp.list,by="series_id",all.y=T)

#create month variable
emp.dt=emp.dt[,month:=as.numeric(substr(emp.dt$period,2,3))]
# (this assignment is to get around knitr/data table printing error)
# see e.g. http://stackoverflow.com/questions/15267018/knitr-gets-tricked-by-data-table-assignment

# M13 = Annual average, drop it:
emp.dt<-emp.dt[month<13,]

#create date variable
emp.dt$date<- as.Date(ISOdate(emp.dt$year,emp.dt$month,1) ) 

# merge on area and state codes
emp.dt<-merge(emp.dt,emp.area,by="area_code")
emp.dt<-merge(emp.dt,emp.st,by="state_code")
emp.dt=emp.dt[,c("state_name","area_name","date","year","month","value"),with=F]

emp.dt=emp.dt[,emp:=as.numeric(value)] #convert value to numeric

## Warning in eval(jsub, SDenv, parent.frame()): NAs introduced by coercion

# Compute year-over-year change in employment and year-over-year percent change
emp.dt=emp.dt[,emp.yoy:=emp-shift(emp,12,fill=NA),by=c("area_name","state_name")]
emp.dt=emp.dt[,emp.pc:=(emp-shift(emp,12,fill=NA))/shift(emp,12,fill=NA),by=c("area_name","state_name")]
emp.dt=emp.dt[,max.emp.st:=max(emp),by=c("state_name")]
emp.dt=emp.dt[,type:=ifelse(area_name=="Statewide","State","Metro")]

# drop states in c("Puerto Rico","Virgin Islands")
emp.dt=emp.dt[year>1989  &!(state_name %in% c("Puerto Rico","Virgin Islands")),]

Now that we have those data, let’s make a quick plot, replicating our employment plot for Ohio metros, but using the new theme:

#set up recessions:
recessions.df = read.table(textConnection(
  "Peak, Trough
  1973-11-01, 1975-03-01
  1980-01-01, 1980-07-01
  1981-07-01, 1982-11-01
  1990-07-01, 1991-03-01
  2001-03-01, 2001-11-01
  2007-12-01, 2009-06-01"), sep=',',
colClasses=c('Date', 'Date'), header=TRUE)
# trim down
recessions.trim = subset(recessions.df, Peak >= "1990-01-01" )
# Plot employment series for Ohio:
g1<-
  ggplot(data=emp.dt[state_name=="Ohio" & type=="Metro"& year>1989])+
  geom_rect(data=recessions.trim, aes(xmin=Peak, xmax=Trough, ymin=-Inf, ymax=+Inf), fill='gray', alpha=0.5)+
  geom_line(aes(x=date,y=emp.pc,group=area_name))+
  theme_ipsum()+
  facet_wrap(~area_name,ncol=3)+scale_y_continuous(labels=percent)+
  geom_hline(color="black",yintercept=0)+
  labs(x="",y="",title="Annual percent change in total nonfarm employment",
       subtitle="Ohio Metro Areas (NSA)",
       caption="@lenkiefer Source: U.S. Bureau of Labor Statistics")+
  theme(panel.grid.major=element_line(color="lightgray"),
        panel.grid.minor=element_line(color="lightgray"))
g1

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Okay, we’ve got the data we want. Now let’s put it together. Things are about to get slightly interesting.

Combining data

We want to merge our house price data in dm2 with the employment data in emp.dt. Fortunately, we have a common key in metro names and for the most part names line up. However, it turns out that the metros in New England are recorded as NECTAs rather than MSAs.

There are some options, but we’ll choose the hacksaw rather than the scalpel. We’re really only going to be interested in the largest metro areas, and the only large metro area in New England is Boston, so we’ll take care of that and ignore the smaller metro areas for today. For our purposes the Boston NECTA and Boston MSA are close enough. For details see: https://www.census.gov/population/metro/data/def.html.

# replace Boston-Cambridge-Nashua, MA-NH NECTA with Boston-Cambridge-Newton, MA-NH
# FIX for Boston (approximate)
emp.dt<-emp.dt[,area_name:=ifelse(area_name=="Boston-Cambridge-Newton, MA NECTA Division",
                "Boston-Cambridge-Newton, MA-NH",
                area_name)]

# Create common key mergind date and metro
dm2<-dm2[,md:=paste(as.character(date),metro)]
emp.dt2<-emp.dt[,md:=paste(as.character(date),area_name)]
dt<-merge(dm2,emp.dt2[,c("md","emp","emp.yoy","emp.pc","type"),with=F],by="md")

Now that w ehave our merged data, let’s create a plot showing the relationship of year-over-year house price percentage changes to employment percentage changes by metro area in December of 2016.

ggplot(data=dt[date=="2016-12-01",],
       aes(x=emp.pc,y=hpa12,color=region,size=metro.pop))+
  geom_point(alpha=0.82)+
  theme_ipsum()+
  scale_color_ipsum() + # special theme colors
  scale_y_percent()+
  scale_x_percent()+
      geom_hline(yintercept=0,color="black")+
    guides(size=F)+
    geom_vline(xintercept=0,color="black")+
  labs(x="Annual percentage change\nin metro employment",
       y="Annual percentage change\nin metro house prices",
       title="Metros with stronger labor markets \nexperience stronger house price growth",
       subtitle="House prices and employment in December 2016",
       caption="@lenkiefer Source: Freddie Mac House Price Index, U.S. Bureau of Labor Statistics\nEach dot represents one metro area color coded by region and size based on metro area population (2015).")+
    theme(panel.grid.major=element_line(color="lightgray"),
          panel.grid.minor=element_line(color="lightgray"),
          legend.position="bottom")

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This chart tells a host of stories. In general house prices rise with stronger employment growth (we’ll soon see that’s true across most time periods). Also, there are regional differences. While I’ve color coded the regions, it might not jump out, so let’s use facet_wrap to create a small multiple by region and add a regression line.

ggplot(data=dt[date=="2016-12-01",],
       aes(x=emp.pc,y=hpa12,color=region,size=metro.pop))+
  geom_point(alpha=0.82)+
  theme_ipsum()+
  scale_color_ipsum() + # special theme colors
  scale_y_percent()+
  scale_x_percent()+
      geom_hline(yintercept=0,color="black")+
    guides(size=F)+
    geom_vline(xintercept=0,color="black")+
  labs(x="Annual percentage change\nin metro employment",
       y="Annual percentage change\nin metro house prices",
       title="Metros with stronger labor markets \nexperience stronger house price growth",
       subtitle="House prices and employment in December 2016",
       caption="@lenkiefer Source: Freddie Mac House Price Index, U.S. Bureau of Labor Statistics\nEach dot represents one metro area color coded by region and size based on metro area population (2015).")+
    theme(panel.grid.major=element_line(color="lightgray"),
          panel.grid.minor=element_line(color="lightgray"),
          legend.position="bottom")+
  facet_wrap(~region)+stat_smooth(method="lm",size=0.75,formula=y~x,se=F)

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Here, we can see that the positive relationship between house price growth and employment appears to be strongest in the South and West Regions.

library(tweenr)
library(animation)

# Function for data prepartion
# Subset on December
myf<-function(in.y){
  dt2<-subset(dt, year==in.y & month==12)
  dt2 %>% map_if(is.character, as.factor) %>% as.data.frame -> dt.out
  dt.out$year<-factor(dt.out$year)
  return(dt.out)
}

# cycle through years
my.list<-lapply(c(2016,seq(1991,2016,1)),myf)

# Tweenr functions
tf <- tween_states(my.list, tweenlength= 2, statelength=3, ease=rep('cubic-in-out',3),
                   nframes=300)

tf<-data.table(tf)

# Loop to animate:
oopt = ani.options(interval = 0.15)
saveGIF({for (i in 1:max(tf$.frame)) {
  g<-
    ggplot(data=tf[.frame==i],
           aes(x=emp.pc,y=hpa12,group=metro,color=region,size=metro.pop))+
    geom_hline(yintercept=0,color="black")+
    guides(size=F)+
    geom_vline(xintercept=0,color="black")+
    geom_point(alpha=0.82)+theme_ipsum()+  scale_color_ipsum(name="Region") +
    scale_y_continuous(label=percent,limits=c(-.41,.4),breaks=seq(-.4,.4,.1))+
    scale_x_continuous(label=percent,limits=c(-.3,.2),breaks=seq(-.3,.2,.1))+
    theme(panel.grid.major=element_line(color="lightgray"),
          panel.grid.minor=element_line(color="lightgray"),
          legend.position="bottom")+
    labs(x="Annual percentage change in metro employment",
         y="Annual percentage change in metro house prices",
         title="Metros with stronger labor markets \nexperience stronger house price growth",
         subtitle=paste("House prices and employment in December",tf[.frame==i]$year),
         caption="@lenkiefer Source: Freddie Mac House Price Index, U.S. Bureau of Labor Statistics\nEach dot represents one metro area color coded by region and size based on metro area population (2015).")
  print(g)
  print(paste(i,"out of",max(tf$.frame)))
  ani.pause()}
},movie.name="emp hpi tween 02 20 2017.gif",ani.width = 650, ani.height = 550)

The composite static plot

Let’s first build our static plot for a particular metro area, Washington D.C.

We’ll call the multiplot function to combine ggplot graphs and wrap it in a function.

myplot<-function(df){

#make a map (sorry no AlbersUSA today)
  g.map<-
    ggplot(df, aes(x = long, y = lat)) +
    borders("state",  colour = "grey70",fill="lightgray",alpha=0.5)+
    theme_void()+
    theme(legend.position="none",
          plot.title=element_text(face="bold",size=18))+
    geom_point(alpha=0.82,color="black",size=3)+
    labs(title="House price & employment trends",
         subtitle=head(df,1)$metro,
         caption="@lenkiefer Source: Freddie Mac House Price Index, U.S. Bureau of Labor Statistics")+
    theme(plot.caption=element_text(hjust=0))

  #house price bar  
  g.bar<-
    ggplot(data=df,aes(x=date,y=hpa12,fill=hpa12))+geom_col()+
    theme_ipsum()+
    scale_y_continuous(label=percent,limits=c(-.45,.45),breaks=seq(-.45,.45,.15))+
    scale_fill_viridis(option="B",limits=c(-.45,.45))+
    labs(x="",y="",
         title="House Price Appreciation",
         subtitle="year-over-year percent change in index")+
    theme(plot.caption=element_text(hjust=0),
          panel.grid.major=element_line(color="lightgray"),
          panel.grid.minor=element_line(color="lightgray"),
          legend.position="none")
  
  #employment bar
  g.bar2<-
    ggplot(data=df,aes(x=date,y=emp.pc,fill=emp.pc))+geom_col()+
    theme_ipsum()+
    scale_y_continuous(label=percent,limits=c(-.11,.11),breaks=seq(-.1,.1,.05))+
    scale_fill_viridis(option="B",limits=c(-.11,.11))+
    labs(x="",y="",
         title="Employment growth",
         subtitle="year-over-year percent change metro employment")+
    theme(plot.caption=element_text(hjust=0),
          panel.grid.major=element_line(color="lightgray"),
          panel.grid.minor=element_line(color="lightgray"),
          legend.position="none")
  # scatter
  g.scatter<-
    ggplot(data=df,aes(x=emp.pc,y=hpa12))+
    geom_point()+theme_ipsum()+
    geom_hline(yintercept=0,color="black")+
    guides(size=F)+
    geom_vline(xintercept=0,color="black")+
    geom_point(alpha=0.82)+theme_ipsum()+  scale_color_ipsum(name="Region") +
    scale_y_continuous(label=percent,limits=c(-.41,.4),breaks=seq(-.4,.4,.1))+
    scale_x_continuous(label=percent,limits=c(-.11,.11),breaks=seq(-.1,.1,.05))+
    theme(panel.grid.major=element_line(color="lightgray"),
          panel.grid.minor=element_line(color="lightgray"),
          legend.position="bottom")+
    labs(x="Annual percentage change\n in metro employment",
         y="Annual percentage change\n in metro house prices",
         title="",
         subtitle="",
         caption="")
  
    #combine
  g<-multiplot(g.map,g.scatter,g.bar,g.bar2,
               layout=matrix(c(1,2,3,4), nrow=2, byrow=TRUE))
  
  return(g)
}

# test for Washington DC
df<-subset(dt,metro=="Washington-Arlington-Alexandria, DC-VA-MD-WV")
myplot(df)

## Warning: Removed 12 rows containing missing values (geom_point).

## Warning: Removed 12 rows containing missing values (geom_point).

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## NULL

This plot shows what you would expect, that periods of strong employment growth are generally periods of strong house price growth and vice versa.

Now, using our tweenr tricks, we can creat an animated tour:

# sort metros by metro.pop
cbsa.list3<-d[order(-metro.pop)]$cbsa.name


myf<-function(i){
  dt2<-subset(dt,metro==d[order(-metro.pop)]$cbsa.name[i] & year>1989)
  dt2 %>% map_if(is.character, as.factor) %>% as.data.frame -> dt.out
  return(dt.out)
}

# take top 20 metros
my.list<-lapply(c(seq(1,20),1),myf)

tf <- tween_states(my.list, tweenlength= 2, statelength=3, ease=rep('cubic-in-out',3),
                   nframes=200)

tf<-data.table(tf)

oopt = ani.options(interval = 0.15)
saveGIF({for (i in 1:max(tf$.frame)) {
  g<-myplot(tf[.frame==i])
  print(g)
  print(paste(i,"out of",max(tf$.frame)))
  ani.pause()}
},movie.name="geo tour emp hpi tween 02 20 2017.gif",ani.width = 650, ani.height = 600)

Running this generates: