diff --git a/infrastructure-jobs/infrastructure-jobs.R b/infrastructure-jobs/infrastructure-jobs.R
new file mode 100644
index 0000000..ed1345b
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+++ b/infrastructure-jobs/infrastructure-jobs.R
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+# FiveThirtyEight.com
+# Article: "Using ‘Infrastructure Jobs’ as a Measuring Stick For State-Level Spending"
+# Published on: June 3, 2014
+# Article Author: Andrew Flowers (andrew.flowers@fivethirtyeight.com)
+# Article URL: http://fivethirtyeight.com/datalab/using-infrastructure-jobs-as-a-measuring-stick-for-state-level-spending/
+
+# Code Author: Andrew Flowers (andrew.flowers@fivethirtyeight.com)
+# Dependent files: payroll-states.csv
+
+# Purpose: Get state-level data on "Heavy Construction and Civil Engineering"  
+# Will produce statepayrolls.csv file after running
+
+# Get data
+temp<-tempfile()
+download.file("http://download.bls.gov/pub/time.series/sm/sm.data.62.Construction.Current",temp)
+statepay.raw<-read.table(temp,header=TRUE,sep="\t",stringsAsFactors=FALSE,strip.white=TRUE)
+unlink(temp)
+
+# Add series info
+series<-read.table("http://download.bls.gov/pub/time.series/sm/sm.series",sep="\t",header=TRUE,strip.white=TRUE)
+state<-read.csv("payroll-states.csv",header=TRUE,strip.white=TRUE)
+series<-merge(series,state,by="state_code")
+
+# Add industry info
+industry<-read.table("http://download.bls.gov/pub/time.series/sm/sm.industry", sep="\t", header=TRUE, strip.white=TRUE)
+industry$industry_name<-NULL
+industry$industry_name<-row.names(industry)
+row.names(industry)<-NULL
+names(industry)<-c("industry_name","industry_code")
+
+series<-merge(series,industry,by="industry_code")
+
+statepay<-merge(statepay.raw,series,by="series_id")
+
+# Take out heavy construction industry data (which is coded 20237000)
+heavyIndCodes<-c(20237000, 20237100, 20237200, 20237300, 20237900)
+statepay.heavy<-statepay[grep(heavyIndCodes[1], statepay$industry_code),]
+
+# Clean state data
+statepay.NSA<-subset(statepay.heavy,!period=="M13")
+statepay.NSA<-subset(statepay.NSA, area_code==0)
+statepay.NSA$date<-as.Date(paste(statepay.NSA$year,statepay.NSA$period,"01",sep="-"),"%Y-M%m-%d")
+statepay.NSA<-subset(statepay.NSA,select=c("series_id","date","state_name","value"))
+
+# Convert to time series
+require(reshape2)
+statepay.NSA.t<-dcast(statepay.NSA, date ~ state_name,value.var="value") #  ,fun.aggregate=mean)
+write.csv(statepay.NSA.t,file="statepayrolls.csv")
+
+
diff --git a/infrastructure-jobs/payroll-states.csv b/infrastructure-jobs/payroll-states.csv
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+++ b/infrastructure-jobs/payroll-states.csv
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+state_code,state_name
1,Alabama
2,Alaska
4,Arizona
5,Arkansas
6,California
8,Colorado
9,Connecticut
10,Delaware
11,District of Columbia
12,Florida
13,Georgia
15,Hawaii
16,Idaho
17,Illinois
18,Indiana
19,Iowa
20,Kansas
21,Kentucky
22,Louisiana
23,Maine
24,Maryland
25,Massachusetts
26,Michigan
27,Minnesota
28,Mississippi
29,Missouri
30,Montana
31,Nebraska
32,Nevada
33,New Hampshire
34,New Jersey
35,New Mexico
36,New York
37,North Carolina
38,North Dakota
39,Ohio
40,Oklahoma
41,Oregon
42,Pennsylvania
44,Rhode Island
45,South Carolina
46,South Dakota
47,Tennessee
48,Texas
49,Utah
50,Vermont
51,Virginia
53,Washington
54,West Virginia
55,Wisconsin
56,Wyoming
72,Puerto Rico
78,Virgin Islands
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