~/data/bea/FixedAssets.html
Ex 1: Private fixed assets
By sector
FAAt307ESI %>%
filter(Line %in% c(1, 55, 11)) %>%
rename(variable = LineDescription) %>%
left_join(gdp_adjustment, by = "date") %>%
mutate(value = 1000*value/ GDP) %>%
ggplot + geom_line(aes(x = date, y = value, color = variable, linetype = variable)) +
ylab("% of GDP") + xlab("") +
theme_minimal()+
geom_rect(data = nber_recessions %>%
filter(Peak > as.Date("1948-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_x_date(breaks = nber_recessions$Peak,
minor_breaks = "5 years",
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(0, 500, 20),
labels = scales::percent_format(accuracy = 1)) +
scale_color_manual(values = viridis(5)[1:4]) +
theme(legend.position = c(0.2, 0.9),
legend.title = element_blank(),
legend.text = element_text(size = 8),
legend.key.size = unit(0.9, 'lines'))

Agriculture, Mining, Utilities
FAAt307ESI %>%
filter(Line %in% c(2, 5, 9)) %>%
rename(variable = LineDescription) %>%
left_join(gdp_adjustment, by = "date") %>%
mutate(value = 1000*value/ GDP) %>%
ggplot + geom_line(aes(x = date, y = value, color = variable, linetype = variable)) +
ylab("% of GDP") + xlab("") +
theme_minimal()+
geom_rect(data = nber_recessions %>%
filter(Peak > as.Date("1948-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_x_date(breaks = nber_recessions$Peak,
minor_breaks = "5 years",
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(0, 500, 2),
labels = scales::percent_format(accuracy = 1)) +
scale_color_manual(values = viridis(5)[1:4]) +
theme(legend.position = c(0.2, 0.9),
legend.title = element_blank(),
legend.text = element_text(size = 8),
legend.key.size = unit(0.9, 'lines'))

Agriculture, Mining, Wholesale
FAAt307ESI %>%
filter(Line %in% c(10, 11, 33)) %>%
rename(variable = LineDescription) %>%
left_join(gdp_adjustment, by = "date") %>%
mutate(value = 1000*value/ GDP) %>%
ggplot + geom_line(aes(x = date, y = value, color = variable, linetype = variable)) +
ylab("% of GDP") + xlab("") +
theme_minimal()+
geom_rect(data = nber_recessions %>%
filter(Peak > as.Date("1948-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_x_date(breaks = nber_recessions$Peak,
minor_breaks = "5 years",
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(0, 500, 2),
labels = scales::percent_format(accuracy = 1)) +
scale_color_manual(values = viridis(5)[1:4]) +
theme(legend.position = c(0.2, 0.9),
legend.title = element_blank(),
legend.text = element_text(size = 8),
legend.key.size = unit(0.9, 'lines'))

Ex 2: 1938, 1958, 1978, 1998, 2018 Table
Percent
FAAt307ESI %>%
mutate(year = year(date)) %>%
filter(year %in% c(1938, 1958, 1978, 1998, 2018)) %>%
group_by(year) %>%
mutate(value = round(100*value/value[1], 1)) %>%
ungroup %>%
select(Line, LineDescription, year, value) %>%
spread(year, value) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}
Billions
FAAt307ESI %>%
mutate(year = year(date)) %>%
filter(year %in% c(1938, 1958, 1978, 1998, 2018)) %>%
group_by(year) %>%
mutate(value = round(value)) %>%
ungroup %>%
select(Line, LineDescription, year, value) %>%
spread(year, value) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}