I am using R.
I found this website here that has a graph on unemployment data: https://www.bls.gov/charts/employment-situation/civilian-unemployment-rate.htm
I am trying to download the data for this graph (e.g. make a data frame in R).
I tried to first use Rvest to do this, but it seems like we are not allowed to scrape the data from this page.
I then tried to manually copy the data, and then tried to use the clipr r package to access the clipboard, but the formatting is coming out quite wrong.
Finally, I downloaded an SVG file corresponding to this graph. I am hoping that somewhere in the SVG file, the underlying data for this graph is contained. But while manually inspecting the source code, I can’t seem to find anything.
Does anyone know if it is possible to access the underlying data from a SVG file?
1
When I clicked on “Show table” I could select the data, paste into Google Sheets (which is usually pretty good at parsing html tables), and then paste that into R using the datapasta package to render as a data.frame.
<code>ggplot(df, aes(Month %>% lubridate::my(), Total)) +
geom_line()
</code>
<code>ggplot(df, aes(Month %>% lubridate::my(), Total)) +
geom_line()
</code>
ggplot(df, aes(Month %>% lubridate::my(), Total)) +
geom_line()
<code>df <- data.frame(
stringsAsFactors = FALSE,
check.names = FALSE,
Month = c("Aug 2004",
"Sept 2004","Oct 2004","Nov 2004",
"Dec 2004","Jan 2005","Feb 2005","Mar 2005",
"Apr 2005","May 2005","June 2005","July 2005",
"Aug 2005","Sept 2005","Oct 2005","Nov 2005",
"Dec 2005","Jan 2006","Feb 2006","Mar 2006",
"Apr 2006","May 2006","June 2006","July 2006",
"Aug 2006","Sept 2006","Oct 2006",
"Nov 2006","Dec 2006","Jan 2007","Feb 2007",
"Mar 2007","Apr 2007","May 2007","June 2007",
"July 2007","Aug 2007","Sept 2007","Oct 2007",
"Nov 2007","Dec 2007","Jan 2008","Feb 2008",
"Mar 2008","Apr 2008","May 2008","June 2008",
"July 2008","Aug 2008","Sept 2008",
"Oct 2008","Nov 2008","Dec 2008","Jan 2009",
"Feb 2009","Mar 2009","Apr 2009","May 2009",
"June 2009","July 2009","Aug 2009","Sept 2009",
"Oct 2009","Nov 2009","Dec 2009","Jan 2010",
"Feb 2010","Mar 2010","Apr 2010","May 2010",
"June 2010","July 2010","Aug 2010",
"Sept 2010","Oct 2010","Nov 2010","Dec 2010",
"Jan 2011","Feb 2011","Mar 2011","Apr 2011",
"May 2011","June 2011","July 2011","Aug 2011",
"Sept 2011","Oct 2011","Nov 2011","Dec 2011",
"Jan 2012","Feb 2012","Mar 2012","Apr 2012",
"May 2012","June 2012","July 2012","Aug 2012",
"Sept 2012","Oct 2012","Nov 2012",
"Dec 2012","Jan 2013","Feb 2013","Mar 2013",
"Apr 2013","May 2013","June 2013","July 2013",
"Aug 2013","Sept 2013","Oct 2013","Nov 2013",
"Dec 2013","Jan 2014","Feb 2014","Mar 2014",
"Apr 2014","May 2014","June 2014","July 2014",
"Aug 2014","Sept 2014","Oct 2014",
"Nov 2014","Dec 2014","Jan 2015","Feb 2015",
"Mar 2015","Apr 2015","May 2015","June 2015",
"July 2015","Aug 2015","Sept 2015","Oct 2015",
"Nov 2015","Dec 2015","Jan 2016","Feb 2016",
"Mar 2016","Apr 2016","May 2016","June 2016",
"July 2016","Aug 2016","Sept 2016",
"Oct 2016","Nov 2016","Dec 2016","Jan 2017",
"Feb 2017","Mar 2017","Apr 2017","May 2017",
"June 2017","July 2017","Aug 2017","Sept 2017",
"Oct 2017","Nov 2017","Dec 2017","Jan 2018",
"Feb 2018","Mar 2018","Apr 2018","May 2018",
"June 2018","July 2018","Aug 2018",
"Sept 2018","Oct 2018","Nov 2018","Dec 2018",
"Jan 2019","Feb 2019","Mar 2019","Apr 2019",
"May 2019","June 2019","July 2019","Aug 2019",
"Sept 2019","Oct 2019","Nov 2019","Dec 2019",
"Jan 2020","Feb 2020","Mar 2020","Apr 2020",
"May 2020","June 2020","July 2020",
"Aug 2020","Sept 2020","Oct 2020","Nov 2020",
"Dec 2020","Jan 2021","Feb 2021","Mar 2021",
"Apr 2021","May 2021","June 2021","July 2021",
"Aug 2021","Sept 2021","Oct 2021","Nov 2021",
"Dec 2021","Jan 2022","Feb 2022","Mar 2022",
"Apr 2022","May 2022","June 2022",
"July 2022","Aug 2022","Sept 2022","Oct 2022",
"Nov 2022","Dec 2022","Jan 2023","Feb 2023",
"Mar 2023","Apr 2023","May 2023","June 2023",
"July 2023","Aug 2023","Sept 2023","Oct 2023",
"Nov 2023","Dec 2023","Jan 2024","Feb 2024",
"Mar 2024","Apr 2024","May 2024","June 2024",
"July 2024","Aug 2024"),
Total = c(5.4,5.4,
5.5,5.4,5.4,5.3,5.4,5.2,5.2,5.1,5,5,
4.9,5,5,5,4.9,4.7,4.8,4.7,4.7,4.6,4.6,
4.7,4.7,4.5,4.4,4.5,4.4,4.6,4.5,4.4,
4.5,4.4,4.6,4.7,4.6,4.7,4.7,4.7,5,5,4.9,
5.1,5,5.4,5.6,5.8,6.1,6.1,6.5,6.8,
7.3,7.8,8.3,8.7,9,9.4,9.5,9.5,9.6,9.8,
10,9.9,9.9,9.8,9.8,9.9,9.9,9.6,9.4,9.4,
9.5,9.5,9.4,9.8,9.3,9.1,9,9,9.1,9,
9.1,9,9,9,8.8,8.6,8.5,8.3,8.3,8.2,8.2,
8.2,8.2,8.2,8.1,7.8,7.8,7.7,7.9,8,7.7,
7.5,7.6,7.5,7.5,7.3,7.2,7.2,7.2,6.9,
6.7,6.6,6.7,6.7,6.2,6.3,6.1,6.2,6.1,5.9,
5.7,5.8,5.6,5.7,5.5,5.4,5.4,5.6,5.3,
5.2,5.1,5,5,5.1,5,4.8,4.9,5,5.1,4.8,
4.9,4.8,4.9,5,4.9,4.7,4.7,4.7,4.6,4.4,
4.4,4.4,4.3,4.3,4.4,4.3,4.2,4.2,4.1,4,
4.1,4,4,3.8,4,3.8,3.8,3.7,3.8,3.8,
3.9,4,3.8,3.8,3.7,3.6,3.6,3.7,3.6,3.5,
3.6,3.6,3.6,3.6,3.5,4.4,14.8,13.2,11,
10.2,8.4,7.8,6.8,6.7,6.7,6.4,6.2,6.1,
6.1,5.8,5.9,5.4,5.1,4.7,4.5,4.1,3.9,4,
3.8,3.6,3.7,3.6,3.6,3.5,3.6,3.5,3.6,3.6,
3.5,3.4,3.6,3.5,3.4,3.7,3.6,3.5,3.8,
3.8,3.8,3.7,3.7,3.7,3.9,3.8,3.9,4,4.1,
4.3,4.2),
`Men,.20.years.and.over` = c(5,5,4.9,
4.9,4.8,4.7,4.8,4.6,4.4,4.3,4.3,4.2,
4.3,4.5,4.3,4.3,4.3,4.1,4.2,4.1,4.2,4.2,
4,4.1,4.1,3.7,3.9,4,3.9,4.2,4.2,4,4,
4,4,4.2,4.1,4.2,4.3,4.2,4.4,4.5,4.4,
4.6,4.6,4.9,5.1,5.4,5.7,6.1,6.3,6.7,
7.4,7.9,8.5,9,9.5,9.8,9.9,9.8,10,10.1,
10.4,10.3,10.1,10.2,10.3,10.2,10.2,9.7,
9.7,9.6,9.6,9.6,9.4,9.9,9.4,9,8.9,8.8,
8.9,8.8,9,8.8,8.7,8.7,8.6,8.2,8,7.7,
7.7,7.7,7.6,7.7,7.7,7.7,7.6,7.3,7.2,7.2,
7.2,7.5,7,6.9,7.2,7.1,7,7,7.1,7.1,
6.9,6.6,6.3,6.2,6.3,6.1,5.9,5.9,5.7,5.7,
5.7,5.3,5.1,5.4,5.2,5.3,5.2,5.1,5,
5.2,4.8,4.8,4.7,4.7,4.7,4.8,4.6,4.3,4.4,
4.5,4.7,4.4,4.5,4.7,4.5,4.7,4.6,4.4,
4.4,4.3,4.2,4.2,3.9,3.9,3.9,4,4.1,3.9,
3.9,3.8,3.8,3.7,3.6,3.6,3.7,3.6,3.7,
3.4,3.5,3.5,3.6,3.4,3.6,3.6,3.4,3.5,3.4,
3.3,3.3,3.4,3.4,3.3,3.4,3.2,3.2,3.2,
3.2,4.1,13,11.5,10.1,9.4,7.9,7.3,6.7,
6.6,6.4,6.1,6,5.8,6,5.8,5.9,5.4,5.1,4.7,
4.2,3.9,3.6,3.8,3.5,3.4,3.5,3.4,3.4,
3.3,3.5,3.3,3.3,3.3,3.1,3.2,3.3,3.4,
3.3,3.5,3.4,3.4,3.7,3.8,3.7,3.7,3.5,3.6,
3.5,3.3,3.6,3.8,3.8,4,4),
`Women,.20.years.and.over` = c(4.7,4.6,
4.7,4.7,4.6,4.7,4.7,4.6,4.6,4.7,4.6,4.6,
4.4,4.6,4.5,4.6,4.4,4.3,4.3,4.2,4.3,
4.1,4.1,4.2,4.1,4.2,3.9,4,3.9,4,3.8,
3.8,3.9,3.8,3.9,4.2,4.1,4.1,4.1,4.2,4.4,
4.3,4.2,4.5,4.2,4.6,4.7,4.7,5.4,5,
5.4,5.7,5.9,6.5,6.8,7.1,7.1,7.5,7.6,7.7,
7.7,7.9,8,7.9,8,7.9,8,8.1,8.3,8.1,
7.7,7.9,8,8,8,8.4,8,7.9,7.9,7.8,8,8,8,
7.9,7.9,8.2,7.8,7.8,7.8,7.6,7.6,7.4,
7.4,7.4,7.4,7.4,7.3,7.1,7.1,7,7.4,7.2,
7,6.9,6.7,6.5,6.8,6.3,6.2,6.2,6.3,6.2,
6.1,5.8,5.9,6.2,5.6,5.8,5.3,5.6,5.6,
5.5,5.4,5.3,5.1,5,4.9,4.9,5,5.1,4.7,
4.8,4.6,4.6,4.6,4.6,4.5,4.5,4.5,4.7,4.6,
4.3,4.4,4.1,4.4,4.4,4.4,4.3,4.3,4.3,
4.2,4,4.1,4.1,3.9,3.9,4,3.9,3.7,3.7,
3.8,3.6,3.7,3.7,3.6,3.4,3.6,3.6,3.5,3.3,
3.4,3.5,3.5,3.6,3.4,3.4,3.2,3.3,3.3,
3.3,3.2,3.1,3.2,3.3,3.3,3.3,3.1,4,15.5,
13.9,11.2,10.5,8.3,7.8,6.4,6.2,6.3,6,
5.9,5.8,5.7,5.3,5.5,5,4.7,4.3,4.2,3.9,
3.6,3.6,3.7,3.4,3.3,3.4,3.3,3.1,3.2,
3.1,3.4,3.3,3.2,3.1,3.3,3.2,3.1,3.3,
3.1,3.1,3.2,3.1,3.2,3.1,3.3,3.2,3.5,3.6,
3.5,3.4,3.7,3.8,3.7),
`16.to.19.years.old` = c(16.7,16.6,
17.4,16.4,17.6,16.2,17.5,17.1,17.8,17.8,
16.3,16.1,16.1,15.5,16.1,17,14.9,15.1,
15.3,16.1,14.6,14,15.8,15.9,16,16.3,
15.2,14.8,14.6,14.8,14.9,14.9,15.9,15.9,
16.3,15.3,15.9,15.9,15.4,16.2,16.8,17.8,
16.6,16.1,15.9,19,19.2,20.7,18.6,19.1,
20,20.3,20.5,20.7,22.3,22.2,22.2,23.4,
24.7,24.3,25,25.9,27.2,26.9,26.7,26.1,
25.6,26.2,25.4,26.5,25.9,25.9,25.5,25.8,
27.2,24.8,25.3,25.7,24.1,24.4,24.7,23.9,
24.5,24.7,25,24.4,24.2,24.2,23.3,23.6,
23.8,24.8,25,24.4,23.3,23.6,24.2,23.8,
23.9,23.9,24.1,23.7,25.2,24,24.4,24.4,23.1,
23.3,22.5,21.3,22.2,20.7,20.4,20.4,
21.4,20.8,19.4,19.3,20.4,20.3,19.4,20.1,
18.7,17.2,16.9,18.5,16.9,17.5,17.2,17.8,
18,16.4,16.9,16.5,15.9,15.3,16.2,15.9,
15.4,16,16.1,15.9,16,15.2,15.7,16.1,15.8,
15.5,14.9,14.6,14.6,13.4,14.4,14.2,13.6,
13.2,13.9,12.9,14,16.2,13.6,13.8,14.4,
13.2,12.7,13.1,12.6,12.9,12.8,12.3,12.1,
12.4,12.6,13.2,13.8,12.7,13.1,12.9,
12.6,12.6,12.3,12.2,11.9,12,12.6,12.6,11.4,
14.3,32.8,30.4,22.4,19.2,16.6,16,13.9,
13.8,15.8,15,13.9,13,12,9.7,11.1,10.4,
11.1,11.3,11.3,10.9,11,11.2,10.2,10.3,
10.6,10.5,10.9,11.2,10.2,11.4,10.6,11.3,
10.5,10.5,11.1,9.9,9.3,10.3,11.2,11.3,
12.3,11.8,13.1,11.4,11.9,10.6,12.5,12.6,
11.7,12.3,12.1,12.4,14.1),
White = c(4.7,4.6,
4.6,4.6,4.5,4.5,4.6,4.5,4.4,4.4,4.3,4.2,
4.2,4.4,4.4,4.3,4.2,4.1,4.1,4,4.1,
4.1,4.1,4.1,4.1,3.9,3.9,4,3.9,4.2,4.1,
3.8,4,3.9,4.1,4.2,4.2,4.2,4.1,4.2,4.4,
4.4,4.4,4.5,4.4,4.8,5,5.2,5.4,5.4,5.9,
6.2,6.7,7.1,7.6,8,8.1,8.5,8.7,8.7,8.9,
8.9,9.2,9.2,9,8.8,8.9,8.9,9,8.7,8.6,
8.5,8.6,8.6,8.6,8.9,8.5,8.1,8.1,8,8.1,
7.9,8.1,8,7.9,7.9,7.9,7.7,7.5,7.4,
7.4,7.3,7.4,7.4,7.3,7.3,7.2,7,6.9,6.8,
6.9,7.1,6.8,6.7,6.7,6.7,6.6,6.5,6.4,6.3,
6.3,6.1,5.9,5.7,5.8,5.8,5.3,5.4,5.3,
5.3,5.3,5.1,4.9,5,4.7,4.9,4.7,4.8,4.7,
4.8,4.6,4.5,4.4,4.4,4.4,4.4,4.4,4.2,
4.2,4.3,4.4,4.2,4.3,4.2,4.4,4.4,4.4,4.2,
4.2,4.2,4,3.8,3.9,3.7,3.8,3.8,3.9,
3.7,3.6,3.7,3.7,3.5,3.6,3.5,3.6,3.5,3.5,
3.4,3.4,3.3,3.4,3.4,3.5,3.5,3.2,3.3,
3.2,3.2,3.3,3.4,3.4,3.2,3.2,3.2,3.2,3.1,
3,3.9,14.2,12.3,10,9.2,7.4,7,6,5.9,
6.1,5.7,5.6,5.4,5.3,5.1,5.2,4.8,4.5,
4.2,3.9,3.7,3.3,3.4,3.3,3.2,3.3,3.2,3.3,
3.1,3.2,3,3.2,3.2,3,3.1,3.2,3.2,3.1,
3.3,3.1,3.1,3.4,3.4,3.5,3.3,3.5,3.4,
3.4,3.4,3.5,3.5,3.5,3.8,3.8),
Black.or.African.American = c(10.5,10.3,
10.8,10.7,10.7,10.6,10.9,10.5,10.3,10.1,
10.2,9.2,9.7,9.4,9.1,10.6,9.2,8.9,9.5,
9.5,9.4,8.7,8.9,9.5,8.8,9,8.4,8.5,
8.3,7.9,8,8.4,8.3,8.3,8.5,8.1,7.6,8,8.5,
8.5,9,9.1,8.4,9.2,8.6,9.6,9.4,10,
10.6,11.3,11.4,11.5,12.1,12.7,13.7,13.7,15,
15,14.8,14.8,14.8,15.3,15.8,15.7,16.1,
16.5,16.1,16.8,16.6,15.5,15.2,15.6,15.9,
16,15.6,16.2,15.5,15.8,15.5,15.8,16.5,
16.3,16.2,15.9,16.4,15.9,14.6,15.6,15.4,
13.6,14,14,13.3,13.5,14.5,14.2,13.8,
13.6,14.1,13.3,14,13.7,13.8,13,13.3,13.4,
14.2,12.6,12.8,13,12.8,12.3,11.9,12.1,
11.8,12.1,11.6,11.4,11,11.6,11.4,11,
10.6,10.9,10.6,10.3,10.1,9.9,9.7,10.3,9.7,
9.1,9.4,9.3,9,9.4,8.5,8.5,8.6,8.9,
8.8,8.2,8.7,8.2,8,8.5,8.5,8.2,8,7.5,8,
7.8,7.7,7.7,6.9,7.3,7.7,7.2,7.6,7.5,
6.7,7.5,6.8,6.7,6.4,5.9,6.4,6.5,6.3,6.1,
6.5,6.1,6.6,7,7.2,6.7,6.7,6.1,5.8,5.7,
5.3,5.3,5.5,5.5,6,6.4,6.1,7,16.9,
16.8,15.3,14.3,12.8,11.9,10.8,10.2,9.9,9.3,
9.9,9.9,10.2,9.1,9.1,8.1,8.6,7.7,7.6,
6.4,6.9,7,6.6,6.4,6.1,6.2,5.8,5.9,6.4,
5.8,5.8,5.6,5.7,5.4,5.7,5.1,4.8,5.7,
6,5.7,5.3,5.7,5.8,5.8,5.2,5.3,5.6,6.4,
5.6,6.1,6.3,6.3,6.1),
Asian = c(3.7,4.5,
4.9,4.1,4.2,4.1,4.3,3.9,4.2,4.2,3.6,4.8,
3.7,4.3,3.2,3.6,3.8,3,3,3.5,3.9,3.3,
3.1,2.4,3,3,2.8,3.1,2.5,3.1,2.6,3,
3.5,3.2,2.7,2.7,3.5,3.4,3.8,3.6,3.7,3.1,
2.9,3.6,3.5,4.1,4.2,3.7,4.5,4,3.9,4.8,
5.1,6.1,6.7,6.5,6.8,7,7.9,8.1,7.6,
7.6,7.6,7.2,8.4,8.3,8.2,7.6,7,7.8,7.4,
7.9,7.3,6.6,7.2,7.5,7.2,6.8,6.7,7.3,6.5,
7.2,6.5,7.4,7.2,8,7.4,6.4,6.8,6.7,
6.2,6.4,5.4,5.5,6.1,5.9,5.9,4.9,4.9,6.3,
6.6,6.4,6,5.1,5.3,4.5,4.7,5.3,5.1,5.4,
5.4,5.2,4.3,4.7,5.9,5.5,5.9,5.7,4.7,
4.2,4.5,4.4,5.1,4.8,4.4,4,4,3.2,4.4,
4.1,3.9,3.9,3.4,3.5,3.5,3.9,4.1,3.6,3.8,
3.9,3.8,4.1,3.6,3.8,4.1,3.8,3.4,3.1,
2.7,3.6,3.5,3.2,3.3,3.6,3.7,3.7,3.9,
3.6,3,3.1,2.6,2.9,3,3.1,2.9,2.2,3.2,3,
2.9,3.5,3.1,2.8,3.3,3,3.2,3,2.3,2.6,2,
2.8,2.8,2.5,2.9,2.6,2.6,3,2.5,4.1,
14.5,15,13.6,12,10.6,8.9,7.5,6.7,6,6.6,
5.1,5.9,5.7,5.7,5.6,5.3,4.5,4.2,4.2,
3.8,3.8,3.6,2.9,2.7,3,2.4,2.9,2.7,2.8,
2.5,3,2.6,2.4,2.9,3.4,2.8,2.8,3,3.1,
2.3,3.2,2.9,3.1,3.5,3.1,2.9,3.4,2.5,2.8,
3.1,4.1,3.7,4.1),
Hispanic.or.Latino = c(6.8,6.8,
6.8,6.7,6.6,6,6.3,5.8,6.4,6,5.7,5.5,
5.8,6.4,6,6.1,6.1,5.5,5.4,5.2,5.5,5,5.2,
5.2,5.3,5.5,4.8,5.1,5,5.5,5.1,5,5.6,
5.8,5.5,5.9,5.5,5.9,5.7,5.9,6.3,6.3,
6.2,6.9,7.1,6.9,7.6,7.5,8,8,8.8,8.7,
9.4,10.1,11.3,11.7,11.4,12.3,12.1,12.5,13,
12.6,12.8,12.4,12.8,12.9,12.7,12.9,
12.5,12,12.3,12.2,12,12.3,12.3,12.9,12.9,
12.3,11.8,11.6,11.9,11.6,11.5,11.2,11.2,
11.2,11.3,11.2,11.1,10.7,10.9,10.6,10.3,
10.9,10.9,10.2,10.1,9.7,10,9.9,9.6,
9.7,9.7,9.3,9,9,9.1,9.4,9.2,8.8,9.1,8.7,
8.3,8.3,8.2,7.9,7.2,7.7,7.8,7.7,7.4,
6.8,6.8,6.6,6.4,6.7,6.8,6.8,6.8,6.8,
6.7,6.9,6.6,6.2,6.3,6.4,6.2,5.9,5.5,5.6,
6.2,5.7,6,5.4,5.6,6.2,5.7,5.6,5.8,5.8,
5.6,5,5.2,5.2,5,5.1,5.1,5.1,5,4.9,5,
4.9,4.9,4.9,4.8,4.8,4.5,4.5,4.7,4.6,
4.4,4.7,4.4,4.8,4.2,4.7,4.2,4.2,4.3,
4.5,4.1,3.9,4.1,4.3,4.2,4.3,4.3,6,18.9,
17.6,14.5,12.9,10.5,10.2,8.8,8.6,9.3,
8.5,8.5,7.9,7.9,7.2,7.3,6.5,6.1,6.2,5.7,
5,4.6,4.7,4.3,4.2,4.2,4.4,4.3,4,4.5,
3.9,4.2,4,4.2,4.7,5.4,4.6,4.4,4.1,4.2,
4.4,4.9,4.6,4.8,4.6,5,5,5,4.5,4.8,5,
4.9,5.3,5.5)
)
</code>
<code>df <- data.frame(
stringsAsFactors = FALSE,
check.names = FALSE,
Month = c("Aug 2004",
"Sept 2004","Oct 2004","Nov 2004",
"Dec 2004","Jan 2005","Feb 2005","Mar 2005",
"Apr 2005","May 2005","June 2005","July 2005",
"Aug 2005","Sept 2005","Oct 2005","Nov 2005",
"Dec 2005","Jan 2006","Feb 2006","Mar 2006",
"Apr 2006","May 2006","June 2006","July 2006",
"Aug 2006","Sept 2006","Oct 2006",
"Nov 2006","Dec 2006","Jan 2007","Feb 2007",
"Mar 2007","Apr 2007","May 2007","June 2007",
"July 2007","Aug 2007","Sept 2007","Oct 2007",
"Nov 2007","Dec 2007","Jan 2008","Feb 2008",
"Mar 2008","Apr 2008","May 2008","June 2008",
"July 2008","Aug 2008","Sept 2008",
"Oct 2008","Nov 2008","Dec 2008","Jan 2009",
"Feb 2009","Mar 2009","Apr 2009","May 2009",
"June 2009","July 2009","Aug 2009","Sept 2009",
"Oct 2009","Nov 2009","Dec 2009","Jan 2010",
"Feb 2010","Mar 2010","Apr 2010","May 2010",
"June 2010","July 2010","Aug 2010",
"Sept 2010","Oct 2010","Nov 2010","Dec 2010",
"Jan 2011","Feb 2011","Mar 2011","Apr 2011",
"May 2011","June 2011","July 2011","Aug 2011",
"Sept 2011","Oct 2011","Nov 2011","Dec 2011",
"Jan 2012","Feb 2012","Mar 2012","Apr 2012",
"May 2012","June 2012","July 2012","Aug 2012",
"Sept 2012","Oct 2012","Nov 2012",
"Dec 2012","Jan 2013","Feb 2013","Mar 2013",
"Apr 2013","May 2013","June 2013","July 2013",
"Aug 2013","Sept 2013","Oct 2013","Nov 2013",
"Dec 2013","Jan 2014","Feb 2014","Mar 2014",
"Apr 2014","May 2014","June 2014","July 2014",
"Aug 2014","Sept 2014","Oct 2014",
"Nov 2014","Dec 2014","Jan 2015","Feb 2015",
"Mar 2015","Apr 2015","May 2015","June 2015",
"July 2015","Aug 2015","Sept 2015","Oct 2015",
"Nov 2015","Dec 2015","Jan 2016","Feb 2016",
"Mar 2016","Apr 2016","May 2016","June 2016",
"July 2016","Aug 2016","Sept 2016",
"Oct 2016","Nov 2016","Dec 2016","Jan 2017",
"Feb 2017","Mar 2017","Apr 2017","May 2017",
"June 2017","July 2017","Aug 2017","Sept 2017",
"Oct 2017","Nov 2017","Dec 2017","Jan 2018",
"Feb 2018","Mar 2018","Apr 2018","May 2018",
"June 2018","July 2018","Aug 2018",
"Sept 2018","Oct 2018","Nov 2018","Dec 2018",
"Jan 2019","Feb 2019","Mar 2019","Apr 2019",
"May 2019","June 2019","July 2019","Aug 2019",
"Sept 2019","Oct 2019","Nov 2019","Dec 2019",
"Jan 2020","Feb 2020","Mar 2020","Apr 2020",
"May 2020","June 2020","July 2020",
"Aug 2020","Sept 2020","Oct 2020","Nov 2020",
"Dec 2020","Jan 2021","Feb 2021","Mar 2021",
"Apr 2021","May 2021","June 2021","July 2021",
"Aug 2021","Sept 2021","Oct 2021","Nov 2021",
"Dec 2021","Jan 2022","Feb 2022","Mar 2022",
"Apr 2022","May 2022","June 2022",
"July 2022","Aug 2022","Sept 2022","Oct 2022",
"Nov 2022","Dec 2022","Jan 2023","Feb 2023",
"Mar 2023","Apr 2023","May 2023","June 2023",
"July 2023","Aug 2023","Sept 2023","Oct 2023",
"Nov 2023","Dec 2023","Jan 2024","Feb 2024",
"Mar 2024","Apr 2024","May 2024","June 2024",
"July 2024","Aug 2024"),
Total = c(5.4,5.4,
5.5,5.4,5.4,5.3,5.4,5.2,5.2,5.1,5,5,
4.9,5,5,5,4.9,4.7,4.8,4.7,4.7,4.6,4.6,
4.7,4.7,4.5,4.4,4.5,4.4,4.6,4.5,4.4,
4.5,4.4,4.6,4.7,4.6,4.7,4.7,4.7,5,5,4.9,
5.1,5,5.4,5.6,5.8,6.1,6.1,6.5,6.8,
7.3,7.8,8.3,8.7,9,9.4,9.5,9.5,9.6,9.8,
10,9.9,9.9,9.8,9.8,9.9,9.9,9.6,9.4,9.4,
9.5,9.5,9.4,9.8,9.3,9.1,9,9,9.1,9,
9.1,9,9,9,8.8,8.6,8.5,8.3,8.3,8.2,8.2,
8.2,8.2,8.2,8.1,7.8,7.8,7.7,7.9,8,7.7,
7.5,7.6,7.5,7.5,7.3,7.2,7.2,7.2,6.9,
6.7,6.6,6.7,6.7,6.2,6.3,6.1,6.2,6.1,5.9,
5.7,5.8,5.6,5.7,5.5,5.4,5.4,5.6,5.3,
5.2,5.1,5,5,5.1,5,4.8,4.9,5,5.1,4.8,
4.9,4.8,4.9,5,4.9,4.7,4.7,4.7,4.6,4.4,
4.4,4.4,4.3,4.3,4.4,4.3,4.2,4.2,4.1,4,
4.1,4,4,3.8,4,3.8,3.8,3.7,3.8,3.8,
3.9,4,3.8,3.8,3.7,3.6,3.6,3.7,3.6,3.5,
3.6,3.6,3.6,3.6,3.5,4.4,14.8,13.2,11,
10.2,8.4,7.8,6.8,6.7,6.7,6.4,6.2,6.1,
6.1,5.8,5.9,5.4,5.1,4.7,4.5,4.1,3.9,4,
3.8,3.6,3.7,3.6,3.6,3.5,3.6,3.5,3.6,3.6,
3.5,3.4,3.6,3.5,3.4,3.7,3.6,3.5,3.8,
3.8,3.8,3.7,3.7,3.7,3.9,3.8,3.9,4,4.1,
4.3,4.2),
`Men,.20.years.and.over` = c(5,5,4.9,
4.9,4.8,4.7,4.8,4.6,4.4,4.3,4.3,4.2,
4.3,4.5,4.3,4.3,4.3,4.1,4.2,4.1,4.2,4.2,
4,4.1,4.1,3.7,3.9,4,3.9,4.2,4.2,4,4,
4,4,4.2,4.1,4.2,4.3,4.2,4.4,4.5,4.4,
4.6,4.6,4.9,5.1,5.4,5.7,6.1,6.3,6.7,
7.4,7.9,8.5,9,9.5,9.8,9.9,9.8,10,10.1,
10.4,10.3,10.1,10.2,10.3,10.2,10.2,9.7,
9.7,9.6,9.6,9.6,9.4,9.9,9.4,9,8.9,8.8,
8.9,8.8,9,8.8,8.7,8.7,8.6,8.2,8,7.7,
7.7,7.7,7.6,7.7,7.7,7.7,7.6,7.3,7.2,7.2,
7.2,7.5,7,6.9,7.2,7.1,7,7,7.1,7.1,
6.9,6.6,6.3,6.2,6.3,6.1,5.9,5.9,5.7,5.7,
5.7,5.3,5.1,5.4,5.2,5.3,5.2,5.1,5,
5.2,4.8,4.8,4.7,4.7,4.7,4.8,4.6,4.3,4.4,
4.5,4.7,4.4,4.5,4.7,4.5,4.7,4.6,4.4,
4.4,4.3,4.2,4.2,3.9,3.9,3.9,4,4.1,3.9,
3.9,3.8,3.8,3.7,3.6,3.6,3.7,3.6,3.7,
3.4,3.5,3.5,3.6,3.4,3.6,3.6,3.4,3.5,3.4,
3.3,3.3,3.4,3.4,3.3,3.4,3.2,3.2,3.2,
3.2,4.1,13,11.5,10.1,9.4,7.9,7.3,6.7,
6.6,6.4,6.1,6,5.8,6,5.8,5.9,5.4,5.1,4.7,
4.2,3.9,3.6,3.8,3.5,3.4,3.5,3.4,3.4,
3.3,3.5,3.3,3.3,3.3,3.1,3.2,3.3,3.4,
3.3,3.5,3.4,3.4,3.7,3.8,3.7,3.7,3.5,3.6,
3.5,3.3,3.6,3.8,3.8,4,4),
`Women,.20.years.and.over` = c(4.7,4.6,
4.7,4.7,4.6,4.7,4.7,4.6,4.6,4.7,4.6,4.6,
4.4,4.6,4.5,4.6,4.4,4.3,4.3,4.2,4.3,
4.1,4.1,4.2,4.1,4.2,3.9,4,3.9,4,3.8,
3.8,3.9,3.8,3.9,4.2,4.1,4.1,4.1,4.2,4.4,
4.3,4.2,4.5,4.2,4.6,4.7,4.7,5.4,5,
5.4,5.7,5.9,6.5,6.8,7.1,7.1,7.5,7.6,7.7,
7.7,7.9,8,7.9,8,7.9,8,8.1,8.3,8.1,
7.7,7.9,8,8,8,8.4,8,7.9,7.9,7.8,8,8,8,
7.9,7.9,8.2,7.8,7.8,7.8,7.6,7.6,7.4,
7.4,7.4,7.4,7.4,7.3,7.1,7.1,7,7.4,7.2,
7,6.9,6.7,6.5,6.8,6.3,6.2,6.2,6.3,6.2,
6.1,5.8,5.9,6.2,5.6,5.8,5.3,5.6,5.6,
5.5,5.4,5.3,5.1,5,4.9,4.9,5,5.1,4.7,
4.8,4.6,4.6,4.6,4.6,4.5,4.5,4.5,4.7,4.6,
4.3,4.4,4.1,4.4,4.4,4.4,4.3,4.3,4.3,
4.2,4,4.1,4.1,3.9,3.9,4,3.9,3.7,3.7,
3.8,3.6,3.7,3.7,3.6,3.4,3.6,3.6,3.5,3.3,
3.4,3.5,3.5,3.6,3.4,3.4,3.2,3.3,3.3,
3.3,3.2,3.1,3.2,3.3,3.3,3.3,3.1,4,15.5,
13.9,11.2,10.5,8.3,7.8,6.4,6.2,6.3,6,
5.9,5.8,5.7,5.3,5.5,5,4.7,4.3,4.2,3.9,
3.6,3.6,3.7,3.4,3.3,3.4,3.3,3.1,3.2,
3.1,3.4,3.3,3.2,3.1,3.3,3.2,3.1,3.3,
3.1,3.1,3.2,3.1,3.2,3.1,3.3,3.2,3.5,3.6,
3.5,3.4,3.7,3.8,3.7),
`16.to.19.years.old` = c(16.7,16.6,
17.4,16.4,17.6,16.2,17.5,17.1,17.8,17.8,
16.3,16.1,16.1,15.5,16.1,17,14.9,15.1,
15.3,16.1,14.6,14,15.8,15.9,16,16.3,
15.2,14.8,14.6,14.8,14.9,14.9,15.9,15.9,
16.3,15.3,15.9,15.9,15.4,16.2,16.8,17.8,
16.6,16.1,15.9,19,19.2,20.7,18.6,19.1,
20,20.3,20.5,20.7,22.3,22.2,22.2,23.4,
24.7,24.3,25,25.9,27.2,26.9,26.7,26.1,
25.6,26.2,25.4,26.5,25.9,25.9,25.5,25.8,
27.2,24.8,25.3,25.7,24.1,24.4,24.7,23.9,
24.5,24.7,25,24.4,24.2,24.2,23.3,23.6,
23.8,24.8,25,24.4,23.3,23.6,24.2,23.8,
23.9,23.9,24.1,23.7,25.2,24,24.4,24.4,23.1,
23.3,22.5,21.3,22.2,20.7,20.4,20.4,
21.4,20.8,19.4,19.3,20.4,20.3,19.4,20.1,
18.7,17.2,16.9,18.5,16.9,17.5,17.2,17.8,
18,16.4,16.9,16.5,15.9,15.3,16.2,15.9,
15.4,16,16.1,15.9,16,15.2,15.7,16.1,15.8,
15.5,14.9,14.6,14.6,13.4,14.4,14.2,13.6,
13.2,13.9,12.9,14,16.2,13.6,13.8,14.4,
13.2,12.7,13.1,12.6,12.9,12.8,12.3,12.1,
12.4,12.6,13.2,13.8,12.7,13.1,12.9,
12.6,12.6,12.3,12.2,11.9,12,12.6,12.6,11.4,
14.3,32.8,30.4,22.4,19.2,16.6,16,13.9,
13.8,15.8,15,13.9,13,12,9.7,11.1,10.4,
11.1,11.3,11.3,10.9,11,11.2,10.2,10.3,
10.6,10.5,10.9,11.2,10.2,11.4,10.6,11.3,
10.5,10.5,11.1,9.9,9.3,10.3,11.2,11.3,
12.3,11.8,13.1,11.4,11.9,10.6,12.5,12.6,
11.7,12.3,12.1,12.4,14.1),
White = c(4.7,4.6,
4.6,4.6,4.5,4.5,4.6,4.5,4.4,4.4,4.3,4.2,
4.2,4.4,4.4,4.3,4.2,4.1,4.1,4,4.1,
4.1,4.1,4.1,4.1,3.9,3.9,4,3.9,4.2,4.1,
3.8,4,3.9,4.1,4.2,4.2,4.2,4.1,4.2,4.4,
4.4,4.4,4.5,4.4,4.8,5,5.2,5.4,5.4,5.9,
6.2,6.7,7.1,7.6,8,8.1,8.5,8.7,8.7,8.9,
8.9,9.2,9.2,9,8.8,8.9,8.9,9,8.7,8.6,
8.5,8.6,8.6,8.6,8.9,8.5,8.1,8.1,8,8.1,
7.9,8.1,8,7.9,7.9,7.9,7.7,7.5,7.4,
7.4,7.3,7.4,7.4,7.3,7.3,7.2,7,6.9,6.8,
6.9,7.1,6.8,6.7,6.7,6.7,6.6,6.5,6.4,6.3,
6.3,6.1,5.9,5.7,5.8,5.8,5.3,5.4,5.3,
5.3,5.3,5.1,4.9,5,4.7,4.9,4.7,4.8,4.7,
4.8,4.6,4.5,4.4,4.4,4.4,4.4,4.4,4.2,
4.2,4.3,4.4,4.2,4.3,4.2,4.4,4.4,4.4,4.2,
4.2,4.2,4,3.8,3.9,3.7,3.8,3.8,3.9,
3.7,3.6,3.7,3.7,3.5,3.6,3.5,3.6,3.5,3.5,
3.4,3.4,3.3,3.4,3.4,3.5,3.5,3.2,3.3,
3.2,3.2,3.3,3.4,3.4,3.2,3.2,3.2,3.2,3.1,
3,3.9,14.2,12.3,10,9.2,7.4,7,6,5.9,
6.1,5.7,5.6,5.4,5.3,5.1,5.2,4.8,4.5,
4.2,3.9,3.7,3.3,3.4,3.3,3.2,3.3,3.2,3.3,
3.1,3.2,3,3.2,3.2,3,3.1,3.2,3.2,3.1,
3.3,3.1,3.1,3.4,3.4,3.5,3.3,3.5,3.4,
3.4,3.4,3.5,3.5,3.5,3.8,3.8),
Black.or.African.American = c(10.5,10.3,
10.8,10.7,10.7,10.6,10.9,10.5,10.3,10.1,
10.2,9.2,9.7,9.4,9.1,10.6,9.2,8.9,9.5,
9.5,9.4,8.7,8.9,9.5,8.8,9,8.4,8.5,
8.3,7.9,8,8.4,8.3,8.3,8.5,8.1,7.6,8,8.5,
8.5,9,9.1,8.4,9.2,8.6,9.6,9.4,10,
10.6,11.3,11.4,11.5,12.1,12.7,13.7,13.7,15,
15,14.8,14.8,14.8,15.3,15.8,15.7,16.1,
16.5,16.1,16.8,16.6,15.5,15.2,15.6,15.9,
16,15.6,16.2,15.5,15.8,15.5,15.8,16.5,
16.3,16.2,15.9,16.4,15.9,14.6,15.6,15.4,
13.6,14,14,13.3,13.5,14.5,14.2,13.8,
13.6,14.1,13.3,14,13.7,13.8,13,13.3,13.4,
14.2,12.6,12.8,13,12.8,12.3,11.9,12.1,
11.8,12.1,11.6,11.4,11,11.6,11.4,11,
10.6,10.9,10.6,10.3,10.1,9.9,9.7,10.3,9.7,
9.1,9.4,9.3,9,9.4,8.5,8.5,8.6,8.9,
8.8,8.2,8.7,8.2,8,8.5,8.5,8.2,8,7.5,8,
7.8,7.7,7.7,6.9,7.3,7.7,7.2,7.6,7.5,
6.7,7.5,6.8,6.7,6.4,5.9,6.4,6.5,6.3,6.1,
6.5,6.1,6.6,7,7.2,6.7,6.7,6.1,5.8,5.7,
5.3,5.3,5.5,5.5,6,6.4,6.1,7,16.9,
16.8,15.3,14.3,12.8,11.9,10.8,10.2,9.9,9.3,
9.9,9.9,10.2,9.1,9.1,8.1,8.6,7.7,7.6,
6.4,6.9,7,6.6,6.4,6.1,6.2,5.8,5.9,6.4,
5.8,5.8,5.6,5.7,5.4,5.7,5.1,4.8,5.7,
6,5.7,5.3,5.7,5.8,5.8,5.2,5.3,5.6,6.4,
5.6,6.1,6.3,6.3,6.1),
Asian = c(3.7,4.5,
4.9,4.1,4.2,4.1,4.3,3.9,4.2,4.2,3.6,4.8,
3.7,4.3,3.2,3.6,3.8,3,3,3.5,3.9,3.3,
3.1,2.4,3,3,2.8,3.1,2.5,3.1,2.6,3,
3.5,3.2,2.7,2.7,3.5,3.4,3.8,3.6,3.7,3.1,
2.9,3.6,3.5,4.1,4.2,3.7,4.5,4,3.9,4.8,
5.1,6.1,6.7,6.5,6.8,7,7.9,8.1,7.6,
7.6,7.6,7.2,8.4,8.3,8.2,7.6,7,7.8,7.4,
7.9,7.3,6.6,7.2,7.5,7.2,6.8,6.7,7.3,6.5,
7.2,6.5,7.4,7.2,8,7.4,6.4,6.8,6.7,
6.2,6.4,5.4,5.5,6.1,5.9,5.9,4.9,4.9,6.3,
6.6,6.4,6,5.1,5.3,4.5,4.7,5.3,5.1,5.4,
5.4,5.2,4.3,4.7,5.9,5.5,5.9,5.7,4.7,
4.2,4.5,4.4,5.1,4.8,4.4,4,4,3.2,4.4,
4.1,3.9,3.9,3.4,3.5,3.5,3.9,4.1,3.6,3.8,
3.9,3.8,4.1,3.6,3.8,4.1,3.8,3.4,3.1,
2.7,3.6,3.5,3.2,3.3,3.6,3.7,3.7,3.9,
3.6,3,3.1,2.6,2.9,3,3.1,2.9,2.2,3.2,3,
2.9,3.5,3.1,2.8,3.3,3,3.2,3,2.3,2.6,2,
2.8,2.8,2.5,2.9,2.6,2.6,3,2.5,4.1,
14.5,15,13.6,12,10.6,8.9,7.5,6.7,6,6.6,
5.1,5.9,5.7,5.7,5.6,5.3,4.5,4.2,4.2,
3.8,3.8,3.6,2.9,2.7,3,2.4,2.9,2.7,2.8,
2.5,3,2.6,2.4,2.9,3.4,2.8,2.8,3,3.1,
2.3,3.2,2.9,3.1,3.5,3.1,2.9,3.4,2.5,2.8,
3.1,4.1,3.7,4.1),
Hispanic.or.Latino = c(6.8,6.8,
6.8,6.7,6.6,6,6.3,5.8,6.4,6,5.7,5.5,
5.8,6.4,6,6.1,6.1,5.5,5.4,5.2,5.5,5,5.2,
5.2,5.3,5.5,4.8,5.1,5,5.5,5.1,5,5.6,
5.8,5.5,5.9,5.5,5.9,5.7,5.9,6.3,6.3,
6.2,6.9,7.1,6.9,7.6,7.5,8,8,8.8,8.7,
9.4,10.1,11.3,11.7,11.4,12.3,12.1,12.5,13,
12.6,12.8,12.4,12.8,12.9,12.7,12.9,
12.5,12,12.3,12.2,12,12.3,12.3,12.9,12.9,
12.3,11.8,11.6,11.9,11.6,11.5,11.2,11.2,
11.2,11.3,11.2,11.1,10.7,10.9,10.6,10.3,
10.9,10.9,10.2,10.1,9.7,10,9.9,9.6,
9.7,9.7,9.3,9,9,9.1,9.4,9.2,8.8,9.1,8.7,
8.3,8.3,8.2,7.9,7.2,7.7,7.8,7.7,7.4,
6.8,6.8,6.6,6.4,6.7,6.8,6.8,6.8,6.8,
6.7,6.9,6.6,6.2,6.3,6.4,6.2,5.9,5.5,5.6,
6.2,5.7,6,5.4,5.6,6.2,5.7,5.6,5.8,5.8,
5.6,5,5.2,5.2,5,5.1,5.1,5.1,5,4.9,5,
4.9,4.9,4.9,4.8,4.8,4.5,4.5,4.7,4.6,
4.4,4.7,4.4,4.8,4.2,4.7,4.2,4.2,4.3,
4.5,4.1,3.9,4.1,4.3,4.2,4.3,4.3,6,18.9,
17.6,14.5,12.9,10.5,10.2,8.8,8.6,9.3,
8.5,8.5,7.9,7.9,7.2,7.3,6.5,6.1,6.2,5.7,
5,4.6,4.7,4.3,4.2,4.2,4.4,4.3,4,4.5,
3.9,4.2,4,4.2,4.7,5.4,4.6,4.4,4.1,4.2,
4.4,4.9,4.6,4.8,4.6,5,5,5,4.5,4.8,5,
4.9,5.3,5.5)
)
</code>
df <- data.frame(
stringsAsFactors = FALSE,
check.names = FALSE,
Month = c("Aug 2004",
"Sept 2004","Oct 2004","Nov 2004",
"Dec 2004","Jan 2005","Feb 2005","Mar 2005",
"Apr 2005","May 2005","June 2005","July 2005",
"Aug 2005","Sept 2005","Oct 2005","Nov 2005",
"Dec 2005","Jan 2006","Feb 2006","Mar 2006",
"Apr 2006","May 2006","June 2006","July 2006",
"Aug 2006","Sept 2006","Oct 2006",
"Nov 2006","Dec 2006","Jan 2007","Feb 2007",
"Mar 2007","Apr 2007","May 2007","June 2007",
"July 2007","Aug 2007","Sept 2007","Oct 2007",
"Nov 2007","Dec 2007","Jan 2008","Feb 2008",
"Mar 2008","Apr 2008","May 2008","June 2008",
"July 2008","Aug 2008","Sept 2008",
"Oct 2008","Nov 2008","Dec 2008","Jan 2009",
"Feb 2009","Mar 2009","Apr 2009","May 2009",
"June 2009","July 2009","Aug 2009","Sept 2009",
"Oct 2009","Nov 2009","Dec 2009","Jan 2010",
"Feb 2010","Mar 2010","Apr 2010","May 2010",
"June 2010","July 2010","Aug 2010",
"Sept 2010","Oct 2010","Nov 2010","Dec 2010",
"Jan 2011","Feb 2011","Mar 2011","Apr 2011",
"May 2011","June 2011","July 2011","Aug 2011",
"Sept 2011","Oct 2011","Nov 2011","Dec 2011",
"Jan 2012","Feb 2012","Mar 2012","Apr 2012",
"May 2012","June 2012","July 2012","Aug 2012",
"Sept 2012","Oct 2012","Nov 2012",
"Dec 2012","Jan 2013","Feb 2013","Mar 2013",
"Apr 2013","May 2013","June 2013","July 2013",
"Aug 2013","Sept 2013","Oct 2013","Nov 2013",
"Dec 2013","Jan 2014","Feb 2014","Mar 2014",
"Apr 2014","May 2014","June 2014","July 2014",
"Aug 2014","Sept 2014","Oct 2014",
"Nov 2014","Dec 2014","Jan 2015","Feb 2015",
"Mar 2015","Apr 2015","May 2015","June 2015",
"July 2015","Aug 2015","Sept 2015","Oct 2015",
"Nov 2015","Dec 2015","Jan 2016","Feb 2016",
"Mar 2016","Apr 2016","May 2016","June 2016",
"July 2016","Aug 2016","Sept 2016",
"Oct 2016","Nov 2016","Dec 2016","Jan 2017",
"Feb 2017","Mar 2017","Apr 2017","May 2017",
"June 2017","July 2017","Aug 2017","Sept 2017",
"Oct 2017","Nov 2017","Dec 2017","Jan 2018",
"Feb 2018","Mar 2018","Apr 2018","May 2018",
"June 2018","July 2018","Aug 2018",
"Sept 2018","Oct 2018","Nov 2018","Dec 2018",
"Jan 2019","Feb 2019","Mar 2019","Apr 2019",
"May 2019","June 2019","July 2019","Aug 2019",
"Sept 2019","Oct 2019","Nov 2019","Dec 2019",
"Jan 2020","Feb 2020","Mar 2020","Apr 2020",
"May 2020","June 2020","July 2020",
"Aug 2020","Sept 2020","Oct 2020","Nov 2020",
"Dec 2020","Jan 2021","Feb 2021","Mar 2021",
"Apr 2021","May 2021","June 2021","July 2021",
"Aug 2021","Sept 2021","Oct 2021","Nov 2021",
"Dec 2021","Jan 2022","Feb 2022","Mar 2022",
"Apr 2022","May 2022","June 2022",
"July 2022","Aug 2022","Sept 2022","Oct 2022",
"Nov 2022","Dec 2022","Jan 2023","Feb 2023",
"Mar 2023","Apr 2023","May 2023","June 2023",
"July 2023","Aug 2023","Sept 2023","Oct 2023",
"Nov 2023","Dec 2023","Jan 2024","Feb 2024",
"Mar 2024","Apr 2024","May 2024","June 2024",
"July 2024","Aug 2024"),
Total = c(5.4,5.4,
5.5,5.4,5.4,5.3,5.4,5.2,5.2,5.1,5,5,
4.9,5,5,5,4.9,4.7,4.8,4.7,4.7,4.6,4.6,
4.7,4.7,4.5,4.4,4.5,4.4,4.6,4.5,4.4,
4.5,4.4,4.6,4.7,4.6,4.7,4.7,4.7,5,5,4.9,
5.1,5,5.4,5.6,5.8,6.1,6.1,6.5,6.8,
7.3,7.8,8.3,8.7,9,9.4,9.5,9.5,9.6,9.8,
10,9.9,9.9,9.8,9.8,9.9,9.9,9.6,9.4,9.4,
9.5,9.5,9.4,9.8,9.3,9.1,9,9,9.1,9,
9.1,9,9,9,8.8,8.6,8.5,8.3,8.3,8.2,8.2,
8.2,8.2,8.2,8.1,7.8,7.8,7.7,7.9,8,7.7,
7.5,7.6,7.5,7.5,7.3,7.2,7.2,7.2,6.9,
6.7,6.6,6.7,6.7,6.2,6.3,6.1,6.2,6.1,5.9,
5.7,5.8,5.6,5.7,5.5,5.4,5.4,5.6,5.3,
5.2,5.1,5,5,5.1,5,4.8,4.9,5,5.1,4.8,
4.9,4.8,4.9,5,4.9,4.7,4.7,4.7,4.6,4.4,
4.4,4.4,4.3,4.3,4.4,4.3,4.2,4.2,4.1,4,
4.1,4,4,3.8,4,3.8,3.8,3.7,3.8,3.8,
3.9,4,3.8,3.8,3.7,3.6,3.6,3.7,3.6,3.5,
3.6,3.6,3.6,3.6,3.5,4.4,14.8,13.2,11,
10.2,8.4,7.8,6.8,6.7,6.7,6.4,6.2,6.1,
6.1,5.8,5.9,5.4,5.1,4.7,4.5,4.1,3.9,4,
3.8,3.6,3.7,3.6,3.6,3.5,3.6,3.5,3.6,3.6,
3.5,3.4,3.6,3.5,3.4,3.7,3.6,3.5,3.8,
3.8,3.8,3.7,3.7,3.7,3.9,3.8,3.9,4,4.1,
4.3,4.2),
`Men,.20.years.and.over` = c(5,5,4.9,
4.9,4.8,4.7,4.8,4.6,4.4,4.3,4.3,4.2,
4.3,4.5,4.3,4.3,4.3,4.1,4.2,4.1,4.2,4.2,
4,4.1,4.1,3.7,3.9,4,3.9,4.2,4.2,4,4,
4,4,4.2,4.1,4.2,4.3,4.2,4.4,4.5,4.4,
4.6,4.6,4.9,5.1,5.4,5.7,6.1,6.3,6.7,
7.4,7.9,8.5,9,9.5,9.8,9.9,9.8,10,10.1,
10.4,10.3,10.1,10.2,10.3,10.2,10.2,9.7,
9.7,9.6,9.6,9.6,9.4,9.9,9.4,9,8.9,8.8,
8.9,8.8,9,8.8,8.7,8.7,8.6,8.2,8,7.7,
7.7,7.7,7.6,7.7,7.7,7.7,7.6,7.3,7.2,7.2,
7.2,7.5,7,6.9,7.2,7.1,7,7,7.1,7.1,
6.9,6.6,6.3,6.2,6.3,6.1,5.9,5.9,5.7,5.7,
5.7,5.3,5.1,5.4,5.2,5.3,5.2,5.1,5,
5.2,4.8,4.8,4.7,4.7,4.7,4.8,4.6,4.3,4.4,
4.5,4.7,4.4,4.5,4.7,4.5,4.7,4.6,4.4,
4.4,4.3,4.2,4.2,3.9,3.9,3.9,4,4.1,3.9,
3.9,3.8,3.8,3.7,3.6,3.6,3.7,3.6,3.7,
3.4,3.5,3.5,3.6,3.4,3.6,3.6,3.4,3.5,3.4,
3.3,3.3,3.4,3.4,3.3,3.4,3.2,3.2,3.2,
3.2,4.1,13,11.5,10.1,9.4,7.9,7.3,6.7,
6.6,6.4,6.1,6,5.8,6,5.8,5.9,5.4,5.1,4.7,
4.2,3.9,3.6,3.8,3.5,3.4,3.5,3.4,3.4,
3.3,3.5,3.3,3.3,3.3,3.1,3.2,3.3,3.4,
3.3,3.5,3.4,3.4,3.7,3.8,3.7,3.7,3.5,3.6,
3.5,3.3,3.6,3.8,3.8,4,4),
`Women,.20.years.and.over` = c(4.7,4.6,
4.7,4.7,4.6,4.7,4.7,4.6,4.6,4.7,4.6,4.6,
4.4,4.6,4.5,4.6,4.4,4.3,4.3,4.2,4.3,
4.1,4.1,4.2,4.1,4.2,3.9,4,3.9,4,3.8,
3.8,3.9,3.8,3.9,4.2,4.1,4.1,4.1,4.2,4.4,
4.3,4.2,4.5,4.2,4.6,4.7,4.7,5.4,5,
5.4,5.7,5.9,6.5,6.8,7.1,7.1,7.5,7.6,7.7,
7.7,7.9,8,7.9,8,7.9,8,8.1,8.3,8.1,
7.7,7.9,8,8,8,8.4,8,7.9,7.9,7.8,8,8,8,
7.9,7.9,8.2,7.8,7.8,7.8,7.6,7.6,7.4,
7.4,7.4,7.4,7.4,7.3,7.1,7.1,7,7.4,7.2,
7,6.9,6.7,6.5,6.8,6.3,6.2,6.2,6.3,6.2,
6.1,5.8,5.9,6.2,5.6,5.8,5.3,5.6,5.6,
5.5,5.4,5.3,5.1,5,4.9,4.9,5,5.1,4.7,
4.8,4.6,4.6,4.6,4.6,4.5,4.5,4.5,4.7,4.6,
4.3,4.4,4.1,4.4,4.4,4.4,4.3,4.3,4.3,
4.2,4,4.1,4.1,3.9,3.9,4,3.9,3.7,3.7,
3.8,3.6,3.7,3.7,3.6,3.4,3.6,3.6,3.5,3.3,
3.4,3.5,3.5,3.6,3.4,3.4,3.2,3.3,3.3,
3.3,3.2,3.1,3.2,3.3,3.3,3.3,3.1,4,15.5,
13.9,11.2,10.5,8.3,7.8,6.4,6.2,6.3,6,
5.9,5.8,5.7,5.3,5.5,5,4.7,4.3,4.2,3.9,
3.6,3.6,3.7,3.4,3.3,3.4,3.3,3.1,3.2,
3.1,3.4,3.3,3.2,3.1,3.3,3.2,3.1,3.3,
3.1,3.1,3.2,3.1,3.2,3.1,3.3,3.2,3.5,3.6,
3.5,3.4,3.7,3.8,3.7),
`16.to.19.years.old` = c(16.7,16.6,
17.4,16.4,17.6,16.2,17.5,17.1,17.8,17.8,
16.3,16.1,16.1,15.5,16.1,17,14.9,15.1,
15.3,16.1,14.6,14,15.8,15.9,16,16.3,
15.2,14.8,14.6,14.8,14.9,14.9,15.9,15.9,
16.3,15.3,15.9,15.9,15.4,16.2,16.8,17.8,
16.6,16.1,15.9,19,19.2,20.7,18.6,19.1,
20,20.3,20.5,20.7,22.3,22.2,22.2,23.4,
24.7,24.3,25,25.9,27.2,26.9,26.7,26.1,
25.6,26.2,25.4,26.5,25.9,25.9,25.5,25.8,
27.2,24.8,25.3,25.7,24.1,24.4,24.7,23.9,
24.5,24.7,25,24.4,24.2,24.2,23.3,23.6,
23.8,24.8,25,24.4,23.3,23.6,24.2,23.8,
23.9,23.9,24.1,23.7,25.2,24,24.4,24.4,23.1,
23.3,22.5,21.3,22.2,20.7,20.4,20.4,
21.4,20.8,19.4,19.3,20.4,20.3,19.4,20.1,
18.7,17.2,16.9,18.5,16.9,17.5,17.2,17.8,
18,16.4,16.9,16.5,15.9,15.3,16.2,15.9,
15.4,16,16.1,15.9,16,15.2,15.7,16.1,15.8,
15.5,14.9,14.6,14.6,13.4,14.4,14.2,13.6,
13.2,13.9,12.9,14,16.2,13.6,13.8,14.4,
13.2,12.7,13.1,12.6,12.9,12.8,12.3,12.1,
12.4,12.6,13.2,13.8,12.7,13.1,12.9,
12.6,12.6,12.3,12.2,11.9,12,12.6,12.6,11.4,
14.3,32.8,30.4,22.4,19.2,16.6,16,13.9,
13.8,15.8,15,13.9,13,12,9.7,11.1,10.4,
11.1,11.3,11.3,10.9,11,11.2,10.2,10.3,
10.6,10.5,10.9,11.2,10.2,11.4,10.6,11.3,
10.5,10.5,11.1,9.9,9.3,10.3,11.2,11.3,
12.3,11.8,13.1,11.4,11.9,10.6,12.5,12.6,
11.7,12.3,12.1,12.4,14.1),
White = c(4.7,4.6,
4.6,4.6,4.5,4.5,4.6,4.5,4.4,4.4,4.3,4.2,
4.2,4.4,4.4,4.3,4.2,4.1,4.1,4,4.1,
4.1,4.1,4.1,4.1,3.9,3.9,4,3.9,4.2,4.1,
3.8,4,3.9,4.1,4.2,4.2,4.2,4.1,4.2,4.4,
4.4,4.4,4.5,4.4,4.8,5,5.2,5.4,5.4,5.9,
6.2,6.7,7.1,7.6,8,8.1,8.5,8.7,8.7,8.9,
8.9,9.2,9.2,9,8.8,8.9,8.9,9,8.7,8.6,
8.5,8.6,8.6,8.6,8.9,8.5,8.1,8.1,8,8.1,
7.9,8.1,8,7.9,7.9,7.9,7.7,7.5,7.4,
7.4,7.3,7.4,7.4,7.3,7.3,7.2,7,6.9,6.8,
6.9,7.1,6.8,6.7,6.7,6.7,6.6,6.5,6.4,6.3,
6.3,6.1,5.9,5.7,5.8,5.8,5.3,5.4,5.3,
5.3,5.3,5.1,4.9,5,4.7,4.9,4.7,4.8,4.7,
4.8,4.6,4.5,4.4,4.4,4.4,4.4,4.4,4.2,
4.2,4.3,4.4,4.2,4.3,4.2,4.4,4.4,4.4,4.2,
4.2,4.2,4,3.8,3.9,3.7,3.8,3.8,3.9,
3.7,3.6,3.7,3.7,3.5,3.6,3.5,3.6,3.5,3.5,
3.4,3.4,3.3,3.4,3.4,3.5,3.5,3.2,3.3,
3.2,3.2,3.3,3.4,3.4,3.2,3.2,3.2,3.2,3.1,
3,3.9,14.2,12.3,10,9.2,7.4,7,6,5.9,
6.1,5.7,5.6,5.4,5.3,5.1,5.2,4.8,4.5,
4.2,3.9,3.7,3.3,3.4,3.3,3.2,3.3,3.2,3.3,
3.1,3.2,3,3.2,3.2,3,3.1,3.2,3.2,3.1,
3.3,3.1,3.1,3.4,3.4,3.5,3.3,3.5,3.4,
3.4,3.4,3.5,3.5,3.5,3.8,3.8),
Black.or.African.American = c(10.5,10.3,
10.8,10.7,10.7,10.6,10.9,10.5,10.3,10.1,
10.2,9.2,9.7,9.4,9.1,10.6,9.2,8.9,9.5,
9.5,9.4,8.7,8.9,9.5,8.8,9,8.4,8.5,
8.3,7.9,8,8.4,8.3,8.3,8.5,8.1,7.6,8,8.5,
8.5,9,9.1,8.4,9.2,8.6,9.6,9.4,10,
10.6,11.3,11.4,11.5,12.1,12.7,13.7,13.7,15,
15,14.8,14.8,14.8,15.3,15.8,15.7,16.1,
16.5,16.1,16.8,16.6,15.5,15.2,15.6,15.9,
16,15.6,16.2,15.5,15.8,15.5,15.8,16.5,
16.3,16.2,15.9,16.4,15.9,14.6,15.6,15.4,
13.6,14,14,13.3,13.5,14.5,14.2,13.8,
13.6,14.1,13.3,14,13.7,13.8,13,13.3,13.4,
14.2,12.6,12.8,13,12.8,12.3,11.9,12.1,
11.8,12.1,11.6,11.4,11,11.6,11.4,11,
10.6,10.9,10.6,10.3,10.1,9.9,9.7,10.3,9.7,
9.1,9.4,9.3,9,9.4,8.5,8.5,8.6,8.9,
8.8,8.2,8.7,8.2,8,8.5,8.5,8.2,8,7.5,8,
7.8,7.7,7.7,6.9,7.3,7.7,7.2,7.6,7.5,
6.7,7.5,6.8,6.7,6.4,5.9,6.4,6.5,6.3,6.1,
6.5,6.1,6.6,7,7.2,6.7,6.7,6.1,5.8,5.7,
5.3,5.3,5.5,5.5,6,6.4,6.1,7,16.9,
16.8,15.3,14.3,12.8,11.9,10.8,10.2,9.9,9.3,
9.9,9.9,10.2,9.1,9.1,8.1,8.6,7.7,7.6,
6.4,6.9,7,6.6,6.4,6.1,6.2,5.8,5.9,6.4,
5.8,5.8,5.6,5.7,5.4,5.7,5.1,4.8,5.7,
6,5.7,5.3,5.7,5.8,5.8,5.2,5.3,5.6,6.4,
5.6,6.1,6.3,6.3,6.1),
Asian = c(3.7,4.5,
4.9,4.1,4.2,4.1,4.3,3.9,4.2,4.2,3.6,4.8,
3.7,4.3,3.2,3.6,3.8,3,3,3.5,3.9,3.3,
3.1,2.4,3,3,2.8,3.1,2.5,3.1,2.6,3,
3.5,3.2,2.7,2.7,3.5,3.4,3.8,3.6,3.7,3.1,
2.9,3.6,3.5,4.1,4.2,3.7,4.5,4,3.9,4.8,
5.1,6.1,6.7,6.5,6.8,7,7.9,8.1,7.6,
7.6,7.6,7.2,8.4,8.3,8.2,7.6,7,7.8,7.4,
7.9,7.3,6.6,7.2,7.5,7.2,6.8,6.7,7.3,6.5,
7.2,6.5,7.4,7.2,8,7.4,6.4,6.8,6.7,
6.2,6.4,5.4,5.5,6.1,5.9,5.9,4.9,4.9,6.3,
6.6,6.4,6,5.1,5.3,4.5,4.7,5.3,5.1,5.4,
5.4,5.2,4.3,4.7,5.9,5.5,5.9,5.7,4.7,
4.2,4.5,4.4,5.1,4.8,4.4,4,4,3.2,4.4,
4.1,3.9,3.9,3.4,3.5,3.5,3.9,4.1,3.6,3.8,
3.9,3.8,4.1,3.6,3.8,4.1,3.8,3.4,3.1,
2.7,3.6,3.5,3.2,3.3,3.6,3.7,3.7,3.9,
3.6,3,3.1,2.6,2.9,3,3.1,2.9,2.2,3.2,3,
2.9,3.5,3.1,2.8,3.3,3,3.2,3,2.3,2.6,2,
2.8,2.8,2.5,2.9,2.6,2.6,3,2.5,4.1,
14.5,15,13.6,12,10.6,8.9,7.5,6.7,6,6.6,
5.1,5.9,5.7,5.7,5.6,5.3,4.5,4.2,4.2,
3.8,3.8,3.6,2.9,2.7,3,2.4,2.9,2.7,2.8,
2.5,3,2.6,2.4,2.9,3.4,2.8,2.8,3,3.1,
2.3,3.2,2.9,3.1,3.5,3.1,2.9,3.4,2.5,2.8,
3.1,4.1,3.7,4.1),
Hispanic.or.Latino = c(6.8,6.8,
6.8,6.7,6.6,6,6.3,5.8,6.4,6,5.7,5.5,
5.8,6.4,6,6.1,6.1,5.5,5.4,5.2,5.5,5,5.2,
5.2,5.3,5.5,4.8,5.1,5,5.5,5.1,5,5.6,
5.8,5.5,5.9,5.5,5.9,5.7,5.9,6.3,6.3,
6.2,6.9,7.1,6.9,7.6,7.5,8,8,8.8,8.7,
9.4,10.1,11.3,11.7,11.4,12.3,12.1,12.5,13,
12.6,12.8,12.4,12.8,12.9,12.7,12.9,
12.5,12,12.3,12.2,12,12.3,12.3,12.9,12.9,
12.3,11.8,11.6,11.9,11.6,11.5,11.2,11.2,
11.2,11.3,11.2,11.1,10.7,10.9,10.6,10.3,
10.9,10.9,10.2,10.1,9.7,10,9.9,9.6,
9.7,9.7,9.3,9,9,9.1,9.4,9.2,8.8,9.1,8.7,
8.3,8.3,8.2,7.9,7.2,7.7,7.8,7.7,7.4,
6.8,6.8,6.6,6.4,6.7,6.8,6.8,6.8,6.8,
6.7,6.9,6.6,6.2,6.3,6.4,6.2,5.9,5.5,5.6,
6.2,5.7,6,5.4,5.6,6.2,5.7,5.6,5.8,5.8,
5.6,5,5.2,5.2,5,5.1,5.1,5.1,5,4.9,5,
4.9,4.9,4.9,4.8,4.8,4.5,4.5,4.7,4.6,
4.4,4.7,4.4,4.8,4.2,4.7,4.2,4.2,4.3,
4.5,4.1,3.9,4.1,4.3,4.2,4.3,4.3,6,18.9,
17.6,14.5,12.9,10.5,10.2,8.8,8.6,9.3,
8.5,8.5,7.9,7.9,7.2,7.3,6.5,6.1,6.2,5.7,
5,4.6,4.7,4.3,4.2,4.2,4.4,4.3,4,4.5,
3.9,4.2,4,4.2,4.7,5.4,4.6,4.4,4.1,4.2,
4.4,4.9,4.6,4.8,4.6,5,5,5,4.5,4.8,5,
4.9,5.3,5.5)
)
HTML table is there and accessible, though according to Automated retrieval program (robot) activity they seem to expect contact details in User Agent header of obvious non-browser requests. With httr2 this is somewhat more convenient to execute.
Note that you should probably consider their public API – https://www.bls.gov/developers/ – and there’s also blsR package.
<code>library(httr2)
library(rvest)
library(dplyr, warn.conflicts = FALSE)
library(ggplot2)
url_ <- "https://www.bls.gov/charts/employment-situation/civilian-unemployment-rate.htm"
html <-
request(url_) |>
req_user_agent("Contact: [email protected]") |>
req_perform() |>
resp_body_html()
html_element(html, "table") |>
html_table() |>
print() |>
# pivot & plot
tidyr::pivot_longer(-Month, names_to = "Series", values_to = "Percent") |>
mutate(
Date = lubridate::my(Month), .keep = "unused",
Series = factor(Series, levels = unique(Series))
) |>
ggplot(aes(x = Date, y = Percent, color = Series)) +
geom_line() +
scale_color_brewer(palette = "Dark2") +
theme_minimal() +
theme(legend.position = "top", legend.title = element_blank(), legend.byrow = TRUE)
#> # A tibble: 241 × 9
#> Month Total `Men, 20 years and over` `Women, 20 years and over`
#> <chr> <dbl> <dbl> <dbl>
#> 1 Aug 2004 5.4 5 4.7
#> 2 Sept 2004 5.4 5 4.6
#> 3 Oct 2004 5.5 4.9 4.7
#> 4 Nov 2004 5.4 4.9 4.7
#> 5 Dec 2004 5.4 4.8 4.6
#> 6 Jan 2005 5.3 4.7 4.7
#> 7 Feb 2005 5.4 4.8 4.7
#> 8 Mar 2005 5.2 4.6 4.6
#> 9 Apr 2005 5.2 4.4 4.6
#> 10 May 2005 5.1 4.3 4.7
#> # ℹ 231 more rows
#> # ℹ 5 more variables: `16 to 19 years old` <dbl>, White <dbl>,
#> # `Black or African American` <dbl>, Asian <dbl>, `Hispanic or Latino` <dbl>
</code>
<code>library(httr2)
library(rvest)
library(dplyr, warn.conflicts = FALSE)
library(ggplot2)
url_ <- "https://www.bls.gov/charts/employment-situation/civilian-unemployment-rate.htm"
html <-
request(url_) |>
req_user_agent("Contact: [email protected]") |>
req_perform() |>
resp_body_html()
html_element(html, "table") |>
html_table() |>
print() |>
# pivot & plot
tidyr::pivot_longer(-Month, names_to = "Series", values_to = "Percent") |>
mutate(
Date = lubridate::my(Month), .keep = "unused",
Series = factor(Series, levels = unique(Series))
) |>
ggplot(aes(x = Date, y = Percent, color = Series)) +
geom_line() +
scale_color_brewer(palette = "Dark2") +
theme_minimal() +
theme(legend.position = "top", legend.title = element_blank(), legend.byrow = TRUE)
#> # A tibble: 241 × 9
#> Month Total `Men, 20 years and over` `Women, 20 years and over`
#> <chr> <dbl> <dbl> <dbl>
#> 1 Aug 2004 5.4 5 4.7
#> 2 Sept 2004 5.4 5 4.6
#> 3 Oct 2004 5.5 4.9 4.7
#> 4 Nov 2004 5.4 4.9 4.7
#> 5 Dec 2004 5.4 4.8 4.6
#> 6 Jan 2005 5.3 4.7 4.7
#> 7 Feb 2005 5.4 4.8 4.7
#> 8 Mar 2005 5.2 4.6 4.6
#> 9 Apr 2005 5.2 4.4 4.6
#> 10 May 2005 5.1 4.3 4.7
#> # ℹ 231 more rows
#> # ℹ 5 more variables: `16 to 19 years old` <dbl>, White <dbl>,
#> # `Black or African American` <dbl>, Asian <dbl>, `Hispanic or Latino` <dbl>
</code>
library(httr2)
library(rvest)
library(dplyr, warn.conflicts = FALSE)
library(ggplot2)
url_ <- "https://www.bls.gov/charts/employment-situation/civilian-unemployment-rate.htm"
html <-
request(url_) |>
req_user_agent("Contact: [email protected]") |>
req_perform() |>
resp_body_html()
html_element(html, "table") |>
html_table() |>
print() |>
# pivot & plot
tidyr::pivot_longer(-Month, names_to = "Series", values_to = "Percent") |>
mutate(
Date = lubridate::my(Month), .keep = "unused",
Series = factor(Series, levels = unique(Series))
) |>
ggplot(aes(x = Date, y = Percent, color = Series)) +
geom_line() +
scale_color_brewer(palette = "Dark2") +
theme_minimal() +
theme(legend.position = "top", legend.title = element_blank(), legend.byrow = TRUE)
#> # A tibble: 241 × 9
#> Month Total `Men, 20 years and over` `Women, 20 years and over`
#> <chr> <dbl> <dbl> <dbl>
#> 1 Aug 2004 5.4 5 4.7
#> 2 Sept 2004 5.4 5 4.6
#> 3 Oct 2004 5.5 4.9 4.7
#> 4 Nov 2004 5.4 4.9 4.7
#> 5 Dec 2004 5.4 4.8 4.6
#> 6 Jan 2005 5.3 4.7 4.7
#> 7 Feb 2005 5.4 4.8 4.7
#> 8 Mar 2005 5.2 4.6 4.6
#> 9 Apr 2005 5.2 4.4 4.6
#> 10 May 2005 5.1 4.3 4.7
#> # ℹ 231 more rows
#> # ℹ 5 more variables: `16 to 19 years old` <dbl>, White <dbl>,
#> # `Black or African American` <dbl>, Asian <dbl>, `Hispanic or Latino` <dbl>
2
This is not a matter of finding the best tools for scraping the website for data, this is a question of finding the right data on the website. The U.S. Bureau of Labor Statistics work under the Freedom of Information Act Requests, so it is their job to provide data for anyone interested.
Under the section for unemployment you will find a number of tools and resources for digging up data about unemployment. There is a link to the raw data in text/space separated files. That is a bit difficult to work with, but there is also a link for “Data Finder”. As I see it, Data Finder can be used for picking out exactly what you need from different surveys. That looks useful. It will give you a chart of the data, but also the option of downloading the data as either an Excel Spreadsheet or a CSV file.