Live fire: Census estimates release day

In [4]:

    

library(dplyr)
library(ggplot2)


    

    




Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

In [1]:

    

counties <- read.csv(url("https://www2.census.gov/programs-surveys/popest/datasets/2010-2017/counties/totals/co-est2017-alldata.csv"))


    

In [2]:

    

head(counties)


    

    






SUMLEV
REGION
DIVISION
STATE
COUNTY
STNAME
CTYNAME
CENSUS2010POP
ESTIMATESBASE2010
POPESTIMATE2010
⋯
RDOMESTICMIG2015
RDOMESTICMIG2016
RDOMESTICMIG2017
RNETMIG2011
RNETMIG2012
RNETMIG2013
RNETMIG2014
RNETMIG2015
RNETMIG2016
RNETMIG2017

	
40            
3             
6             
1             
0             
Alabama       
Alabama       
4779736       
4780135       
4785579       
⋯             
 -0.3172050   
 -0.404473    
  0.7888823   
 0.4507405    
 0.9393925    
 1.3642955    
 0.6942708    
  0.6785751   
  0.5589306   
  1.708218    
	
50            
3             
6             
1             
1             
Alabama       
Autauga County
  54571       
  54571       
  54750       
⋯             
 -1.9507393   
  4.831269    
  1.0471015   
 5.9118318    
-6.1021012    
-4.0502819    
 2.0993255    
 -1.6590399   
  5.1037088   
  1.317904    
	
50            
3             
6             
1             
3             
Alabama       
Baldwin County
 182265       
 182265       
 183110       
⋯             
 17.0478719   
 20.493601    
 22.3831750   
16.2859400    
17.1967858    
22.6152855    
20.3809040    
 17.9037487   
 21.3172439   
 23.163873    
	
50            
3             
6             
1             
5             
Alabama       
Barbour County
  27457       
  27457       
  27332       
⋯             
-16.2224360   
-18.755525    
-19.0423948   
 0.2560211    
-6.8224333    
-8.0189202    
-5.5497616    
-16.4110690   
-18.9476921   
-19.159940    
	
50            
3             
6             
1             
7             
Alabama       
Bibb County   
  22915       
  22919       
  22872       
⋯             
  0.9313878   
 -1.416117    
 -0.8829827   
-5.0419800    
-4.0966456    
-5.8900379    
 1.2434497    
  1.8184237   
 -0.5310439   
  0.000000    
	
50            
3             
6             
1             
9             
Alabama       
Blount County 
  57322       
  57324       
  57381       
⋯             
 -1.5633685   
 -1.736835    
  6.2124162   
 0.2435990    
-1.3546723    
-0.4860352    
-1.7713100    
 -0.5384936   
 -0.6599972   
  7.285313    

In [3]:

    

colnames(counties)


    

    






	
'SUMLEV'
	
'REGION'
	
'DIVISION'
	
'STATE'
	
'COUNTY'
	
'STNAME'
	
'CTYNAME'
	
'CENSUS2010POP'
	
'ESTIMATESBASE2010'
	
'POPESTIMATE2010'
	
'POPESTIMATE2011'
	
'POPESTIMATE2012'
	
'POPESTIMATE2013'
	
'POPESTIMATE2014'
	
'POPESTIMATE2015'
	
'POPESTIMATE2016'
	
'POPESTIMATE2017'
	
'NPOPCHG_2010'
	
'NPOPCHG_2011'
	
'NPOPCHG_2012'
	
'NPOPCHG_2013'
	
'NPOPCHG_2014'
	
'NPOPCHG_2015'
	
'NPOPCHG_2016'
	
'NPOPCHG_2017'
	
'BIRTHS2010'
	
'BIRTHS2011'
	
'BIRTHS2012'
	
'BIRTHS2013'
	
'BIRTHS2014'
	
'BIRTHS2015'
	
'BIRTHS2016'
	
'BIRTHS2017'
	
'DEATHS2010'
	
'DEATHS2011'
	
'DEATHS2012'
	
'DEATHS2013'
	
'DEATHS2014'
	
'DEATHS2015'
	
'DEATHS2016'
	
'DEATHS2017'
	
'NATURALINC2010'
	
'NATURALINC2011'
	
'NATURALINC2012'
	
'NATURALINC2013'
	
'NATURALINC2014'
	
'NATURALINC2015'
	
'NATURALINC2016'
	
'NATURALINC2017'
	
'INTERNATIONALMIG2010'
	
'INTERNATIONALMIG2011'
	
'INTERNATIONALMIG2012'
	
'INTERNATIONALMIG2013'
	
'INTERNATIONALMIG2014'
	
'INTERNATIONALMIG2015'
	
'INTERNATIONALMIG2016'
	
'INTERNATIONALMIG2017'
	
'DOMESTICMIG2010'
	
'DOMESTICMIG2011'
	
'DOMESTICMIG2012'
	
'DOMESTICMIG2013'
	
'DOMESTICMIG2014'
	
'DOMESTICMIG2015'
	
'DOMESTICMIG2016'
	
'DOMESTICMIG2017'
	
'NETMIG2010'
	
'NETMIG2011'
	
'NETMIG2012'
	
'NETMIG2013'
	
'NETMIG2014'
	
'NETMIG2015'
	
'NETMIG2016'
	
'NETMIG2017'
	
'RESIDUAL2010'
	
'RESIDUAL2011'
	
'RESIDUAL2012'
	
'RESIDUAL2013'
	
'RESIDUAL2014'
	
'RESIDUAL2015'
	
'RESIDUAL2016'
	
'RESIDUAL2017'
	
'GQESTIMATESBASE2010'
	
'GQESTIMATES2010'
	
'GQESTIMATES2011'
	
'GQESTIMATES2012'
	
'GQESTIMATES2013'
	
'GQESTIMATES2014'
	
'GQESTIMATES2015'
	
'GQESTIMATES2016'
	
'GQESTIMATES2017'
	
'RBIRTH2011'
	
'RBIRTH2012'
	
'RBIRTH2013'
	
'RBIRTH2014'
	
'RBIRTH2015'
	
'RBIRTH2016'
	
'RBIRTH2017'
	
'RDEATH2011'
	
'RDEATH2012'
	
'RDEATH2013'
	
'RDEATH2014'
	
'RDEATH2015'
	
'RDEATH2016'
	
'RDEATH2017'
	
'RNATURALINC2011'
	
'RNATURALINC2012'
	
'RNATURALINC2013'
	
'RNATURALINC2014'
	
'RNATURALINC2015'
	
'RNATURALINC2016'
	
'RNATURALINC2017'
	
'RINTERNATIONALMIG2011'
	
'RINTERNATIONALMIG2012'
	
'RINTERNATIONALMIG2013'
	
'RINTERNATIONALMIG2014'
	
'RINTERNATIONALMIG2015'
	
'RINTERNATIONALMIG2016'
	
'RINTERNATIONALMIG2017'
	
'RDOMESTICMIG2011'
	
'RDOMESTICMIG2012'
	
'RDOMESTICMIG2013'
	
'RDOMESTICMIG2014'
	
'RDOMESTICMIG2015'
	
'RDOMESTICMIG2016'
	
'RDOMESTICMIG2017'
	
'RNETMIG2011'
	
'RNETMIG2012'
	
'RNETMIG2013'
	
'RNETMIG2014'
	
'RNETMIG2015'
	
'RNETMIG2016'
	
'RNETMIG2017'

In [11]:

    

nebraska <- counties %>% filter(STNAME == "Nebraska") %>% filter(SUMLEV == 50) %>% mutate(change = ((POPESTIMATE2017-POPESTIMATE2016)/POPESTIMATE2016)*100, pos = change >0)


    

In [15]:

    

nebraskasorted <- arrange(nebraska, desc(change)) %>% mutate(CTYNAME = factor(CTYNAME, CTYNAME))


    

In [16]:

    

ggplot(nebraskasorted, aes(x=CTYNAME, y=change, fill=pos)) + geom_bar(stat='identity', position='identity') + coord_flip()


    

In [ ]: