U.S. Transportation Employment
Joe Long
11/12/2020
Unemployment Rate - Transportation and Material Moving Occupations (LNU04032228)
indicator <-fredr_series_observations(series_id = "LNU04032228",
observation_start = as.Date("2015-01-01"))
colnames(indicator) <- c("date","series","miles")
indicator[,c(1,3)] %>% slice_sample(n = 10)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(2, T, color = "blue" ) | date | miles |
|---|---|
| 2015-11-01 | 6.8 |
| 2016-01-01 | 7.6 |
| 2016-02-01 | 6.6 |
| 2016-06-01 | 6.7 |
| 2017-03-01 | 6.2 |
| 2017-10-01 | 5.2 |
| 2018-11-01 | 4.0 |
| 2019-02-01 | 5.1 |
| 2019-10-01 | 3.9 |
| 2020-10-01 | 10.0 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-10-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=miles), color = "blue4",size=1) +
labs(title = "Unemployment Rate - Transportation and Material Moving Occupations (LNU04032228)",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Percent",
caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
All Employees, Transit and Ground Passenger Transportation (CES4348500001)
indicator <-fredr_series_observations(series_id = "CES4348500001",
observation_start = as.Date("2015-01-01"))
colnames(indicator) <- c("date","series","miles")
indicator[,c(1,3)] %>% slice_sample(n = 10)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(2, T, color = "blue" ) | date | miles |
|---|---|
| 2015-01-01 | 484.9 |
| 2016-03-01 | 490.4 |
| 2016-11-01 | 490.2 |
| 2017-05-01 | 498.8 |
| 2017-08-01 | 490.9 |
| 2017-12-01 | 494.5 |
| 2018-05-01 | 490.8 |
| 2018-09-01 | 496.3 |
| 2018-12-01 | 496.4 |
| 2019-08-01 | 493.1 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-11-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=miles), color = "blue4",size=1) +
labs(title = "All Employees, Transit and Ground Passenger Transportation (CES4348500001)",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Thousands of Persons",
caption = "Data source: FRED S. Louis Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
All Employees, Air Transportation (CEU4348100001)
indicator <-fredr_series_observations(series_id = "CES4348100001",
observation_start = as.Date("2015-01-01"))
colnames(indicator) <- c("date","series","miles")
indicator[,c(1,3)] %>% slice_sample(n = 10)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(2, T, color = "blue" ) | date | miles |
|---|---|
| 2015-01-01 | 450.0 |
| 2015-03-01 | 451.0 |
| 2015-05-01 | 454.1 |
| 2015-08-01 | 460.8 |
| 2015-10-01 | 465.2 |
| 2018-02-01 | 498.4 |
| 2019-06-01 | 503.3 |
| 2020-01-01 | 509.2 |
| 2020-05-01 | 383.8 |
| 2020-06-01 | 378.6 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-11-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=miles), color = "blue4",size=1) +
labs(title = "All Employees, Air Transportation (CEU4348100001)",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Thousands of Persons",
caption = "Data source: FRED S. Louis Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
All Employees, Pipeline Transportation (CES4348600001)
indicator <-fredr_series_observations(series_id = "CES4348600001",
observation_start = as.Date("2015-01-01"))
colnames(indicator) <- c("date","series","miles")
indicator[,c(1,3)] %>% slice_sample(n = 10)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(1:2, T, color = "blue" ) | date | miles |
|---|---|
| 2015-09-01 | 50.8 |
| 2016-03-01 | 49.9 |
| 2017-04-01 | 49.2 |
| 2017-10-01 | 48.5 |
| 2018-05-01 | 49.2 |
| 2018-06-01 | 49.8 |
| 2018-10-01 | 50.6 |
| 2019-02-01 | 50.5 |
| 2019-09-01 | 51.3 |
| 2020-09-01 | 49.4 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-11-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=miles), color = "blue4",size=1) +
labs(title = "All Employees, Pipeline Transportation (CES4348600001)",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Thousands of Persons",
caption = "Data source: FRED S. Louis Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
All Employees, Scenic and Sightseeing Transportation (CES4348700001)
indicator <-fredr_series_observations(series_id = "CES4348700001",
observation_start = as.Date("2015-01-01"))
colnames(indicator) <- c("date","series","miles")
indicator[,c(1,3)] %>% slice_sample(n = 10)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(1:2, T, color = "blue" ) | date | miles |
|---|---|
| 2016-06-01 | 34.3 |
| 2016-11-01 | 33.9 |
| 2016-12-01 | 35.3 |
| 2017-09-01 | 35.2 |
| 2018-04-01 | 34.5 |
| 2018-09-01 | 33.9 |
| 2019-03-01 | 35.5 |
| 2019-05-01 | 35.8 |
| 2019-10-01 | 35.7 |
| 2019-12-01 | 35.9 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-11-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=miles), color = "blue4",size=1) +
labs(title = "All Employees, Scenic and Sightseeing Transportation (CES4348700001)",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Thousands of Persons",
caption = "Data source: FRED S. Louis Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
Employment Level - Transportation and Material Moving Occupations (LNU02032214)
indicator <-fredr_series_observations(series_id = "LNU02032214",
observation_start = as.Date("2015-01-01"))
colnames(indicator) <- c("date","series","miles")
indicator[,c(1,3)] %>% slice_sample(n = 10)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(1:2, T, color = "blue" ) | date | miles |
|---|---|
| 2015-01-01 | 9149 |
| 2015-04-01 | 8998 |
| 2016-02-01 | 9211 |
| 2017-09-01 | 9986 |
| 2017-11-01 | 9245 |
| 2018-01-01 | 8839 |
| 2018-02-01 | 9680 |
| 2018-10-01 | 10112 |
| 2019-03-01 | 10046 |
| 2020-09-01 | 10534 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-11-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=miles), color = "blue4",size=1) +
labs(title = "Employment Level - Transportation and Material Moving Occupations (LNU02032214)",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Thousands of Persons",
caption = "Data source: Bureau of Labor Statistics\nIllustration by @JoeLongSanDiego")+
theme_economist()
Unemployment Level - Transportation and Material Moving Occupations (LNU03032228)
indicator <-fredr_series_observations(series_id = "LNU03032228",
observation_start = as.Date("2015-01-01"))
colnames(indicator) <- c("date","series","miles")
indicator[,c(1,3)] %>% slice_sample(n = 10)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(1:2, T, color = "blue" ) | date | miles |
|---|---|
| 2016-11-01 | 568 |
| 2016-12-01 | 610 |
| 2017-01-01 | 693 |
| 2017-09-01 | 468 |
| 2018-01-01 | 649 |
| 2018-03-01 | 551 |
| 2018-04-01 | 457 |
| 2019-06-01 | 528 |
| 2020-04-01 | 2045 |
| 2020-05-01 | 2217 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-11-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=miles), color = "blue4",size=1) +
labs(title = "Unemployment Level - Transportation and Material Moving Occupations (LNU03032228)",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Thousands of Persons",
caption = "Data source: Bureau of Labor Statistics\nIllustration by @JoeLongSanDiego")+
theme_economist()
All Employees, Warehousing and Storage (CES4349300001)
indicator <-fredr_series_observations(series_id = "CES4349300001",
observation_start = as.Date("2015-01-01"))
colnames(indicator) <- c("date","series","miles")
indicator[,c(1,3)] %>% slice_sample(n = 10)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(1:2, T, color = "blue" ) | date | miles |
|---|---|
| 2015-05-01 | 817.4 |
| 2016-04-01 | 914.6 |
| 2016-09-01 | 950.4 |
| 2017-02-01 | 983.5 |
| 2017-05-01 | 1007.6 |
| 2019-04-01 | 1185.2 |
| 2019-05-01 | 1186.1 |
| 2019-08-01 | 1190.5 |
| 2020-01-01 | 1200.4 |
| 2020-02-01 | 1207.8 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-11-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=miles), color = "blue4",size=1) +
labs(title = "All Employees, Warehousing and Storage (CES4349300001)",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Thousands of Persons",
caption = "Data source: Bureau of Labor Statistics\nIllustration by @JoeLongSanDiego")+
theme_economist()
All Employees, Truck Transportation (CEU4348400001)
indicator <-fredr_series_observations(series_id = "CES4348400001",
observation_start = as.Date("2015-01-01"))
colnames(indicator) <- c("date","series","miles")
indicator[,c(1,3)] %>% slice_sample(n = 10)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(1:2, T, color = "blue" ) | date | miles |
|---|---|
| 2015-10-01 | 1453 |
| 2016-11-01 | 1452 |
| 2017-05-01 | 1454 |
| 2018-04-01 | 1483 |
| 2018-05-01 | 1486 |
| 2019-04-01 | 1530 |
| 2019-09-01 | 1528 |
| 2019-12-01 | 1526 |
| 2020-03-01 | 1523 |
| 2020-07-01 | 1440 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-11-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=miles), color = "blue4",size=1) +
labs(title = "All Employees, Truck Transportation (CEU4348400001)",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Thousands of Persons",
caption = "Data source: Bureau of Labor Statistics\nIllustration by @JoeLongSanDiego")+
theme_economist()
Moving 12-Month Total Vehicle Miles Traveled (M12MTVUSM227NFWA)
indicator <-fredr_series_observations(series_id = "M12MTVUSM227NFWA",
observation_start = as.Date("2015-01-01"))
colnames(indicator) <- c("date","series","miles")
indicator[,c(1,3)] %>% slice_sample(n = 10)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(2, T, color = "blue" ) | date | miles |
|---|---|
| 2016-07-01 | 3141648 |
| 2017-01-01 | 3177328 |
| 2017-10-01 | 3206161 |
| 2017-12-01 | 3212346 |
| 2018-01-01 | 3214482 |
| 2018-02-01 | 3216597 |
| 2018-11-01 | 3237913 |
| 2019-03-01 | 3243523 |
| 2019-10-01 | 3263387 |
| 2020-03-01 | 3226317 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-10-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=miles), color = "blue4",size=1) +
labs(title = "Moving 12-Month Total Vehicle Miles Traveled_ Total",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Millions of Miles",
caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
Vehicle Miles Traveled (TRFVOLUSM227NFWA)
indicator <-fredr_series_observations(series_id = "TRFVOLUSM227NFWA",
observation_start = as.Date("2019-01-01"))
colnames(indicator) <- c("date","series","miles")
indicator[,c(1,3)] %>% slice_sample(n = 10)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(2, T, color = "blue" ) | date | miles |
|---|---|
| 2019-01-01 | 248208 |
| 2019-02-01 | 226734 |
| 2019-06-01 | 280936 |
| 2019-08-01 | 286531 |
| 2019-12-01 | 274113 |
| 2020-03-01 | 220020 |
| 2020-04-01 | 168463 |
| 2020-06-01 | 243890 |
| 2020-07-01 | 262380 |
| 2020-08-01 | 251265 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-10-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=miles), color = "blue4",size=1) +
labs(title = "Vehicle Miles Traveled",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Millions of Miles",
caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
Load Factor for U.S. Air Carrier Domestic and International, Scheduled Passenger Flights (LOADFACTOR)
indicator <-fredr_series_observations(series_id = "LOADFACTOR",
observation_start = as.Date("2000-01-01"))
colnames(indicator) <- c("date","series","percent")
indicator[,c(1,3)] %>% slice_sample(n = 10)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(2, T, color = "blue" ) | date | percent |
|---|---|
| 2003-03-01 | 72.1 |
| 2004-07-01 | 82.6 |
| 2005-04-01 | 76.8 |
| 2006-02-01 | 75.2 |
| 2009-02-01 | 73.3 |
| 2010-06-01 | 86.2 |
| 2010-12-01 | 80.7 |
| 2011-07-01 | 86.9 |
| 2019-11-01 | 81.7 |
| 2020-01-01 | 80.1 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-10-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=percent), color = "blue4",size=1) +
labs(title = "U.S. Air Passenger _ Load Factor",
subtitle = str_glue("Domestic & International\nFrom {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Percent of load",
caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
tail(indicator,n=12) %>%
ggplot() +
geom_line(mapping = aes(x=date,y=percent), color = "blue4",size=1) +
labs(title = "U.S. Air Passenger _ Load Factor",
subtitle = "May 2019 to May 2020",
x="Time", y="Percent of load",
caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
Public Transit Ridership (TRANSIT)
indicator <-fredr_series_observations(series_id = "TRANSIT",
observation_start = as.Date("2000-01-01"))
colnames(indicator) <- c("date","series","value")
indicator[,c(1,3)] %>% slice_sample(n = 12)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(2, T, color = "blue" ) | date | value |
|---|---|
| 2001-06-01 | 807301 |
| 2002-07-01 | 782849 |
| 2003-03-01 | 820157 |
| 2004-05-01 | 781133 |
| 2004-07-01 | 754430 |
| 2006-11-01 | 851564 |
| 2009-01-01 | 827360 |
| 2012-07-01 | 848428 |
| 2015-06-01 | 878666 |
| 2016-10-01 | 889002 |
| 2018-11-01 | 812423 |
| 2020-02-01 | 790589 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-10-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=value), color = "blue4",size=1) +
labs(title = "Public Transit Ridership",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Thousands of trip tickets",
caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
tail(indicator,n=12) %>%
ggplot() +
geom_line(mapping = aes(x=date,y=value), color = "blue4",size=1) +
labs(title = "Public Transit Ridership",
subtitle = "Most recent 12 months",
x="Time", y="Thousands of trip tickets",
caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
Rail Passenger Miles (RAILPM)
indicator <-fredr_series_observations(series_id = "RAILPM",
observation_start = as.Date("2000-01-01"))
colnames(indicator) <- c("date","series","value")
indicator[,c(1,3)] %>% slice_sample(n = 12)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(2, T, color = "blue" ) | date | value |
|---|---|
| 2001-02-01 | 366288212 |
| 2001-03-01 | 448912584 |
| 2002-06-01 | 499734575 |
| 2008-05-01 | 550928592 |
| 2009-03-01 | 456227991 |
| 2011-03-01 | 566357389 |
| 2012-07-01 | 663965533 |
| 2013-10-01 | 543695520 |
| 2017-06-01 | 636313142 |
| 2019-04-01 | 532041046 |
| 2019-06-01 | 618224369 |
| 2020-07-01 | 160734686 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-10-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=value/1000000), color = "blue4",size=1) +
labs(title = "Rail Passenger Miles",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Millions of Miles",
caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
tail(indicator,n=12) %>%
ggplot() +
geom_line(mapping = aes(x=date,y=value/1000000), color = "blue4",size=1) +
labs(title = "Rail Passenger Miles",
subtitle = "Most recent 12 months",
x="Time", y="Millions of Miles",
caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
Air Revenue Passenger Miles (AIRRPMTSID11)
A revenue passenger mile (RPM) is a transportation industry metric that shows the number of miles traveled by paying passengers and is typically an airline traffic statistic.
Revenue passenger miles are calculated by multiplying the number of paying passengers by the distance traveled. For example, an airplane with 100 passengers that flies 250 miles has generated 25,000 RPM.
Revenue passenger mile (RPM) is a transportation industry metric primarily used by the airline industry to show the number of miles traveled by paying passengers.
indicator <-fredr_series_observations(series_id = "AIRRPMTSID11",
observation_start = as.Date("2000-01-01"))
colnames(indicator) <- c("date","series","value")
indicator[,c(1,3)] %>% slice_sample(n = 12)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(2, T, color = "blue" ) | date | value |
|---|---|
| 2000-02-01 | 58168008 |
| 2003-11-01 | 59194919 |
| 2004-04-01 | 62296043 |
| 2004-09-01 | 63175083 |
| 2005-07-01 | 66942007 |
| 2007-10-01 | 70826612 |
| 2009-03-01 | 63843795 |
| 2010-05-01 | 66608999 |
| 2012-10-01 | 68899197 |
| 2014-11-01 | 72807914 |
| 2019-08-01 | 88577542 |
| 2019-10-01 | 89115893 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-10-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=value/1000), color = "blue4",size=1) +
labs(title = "Air Revenue Passenger Miles (RPM) _ AIRRPMTSID11",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Millions",
caption = "Data source: FRED St. Louis Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
tail(indicator,n=12) %>%
ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-10-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=value/1000), color = "blue4",size=1) +
labs(title = "Air Revenue Passenger Miles (RMP) _ AIRRPMTSID11",
subtitle = "Most recent 12 months period",
x="Time", y="Millions",
caption = "Data source: FRED St. Louis Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()