Table 1.1.5. Gross Domestic Product (A) (Q)

Data - BEA

Author

François Geerolf

Info

source dataset .html .RData
bea T10105 2025-04-09 2025-02-12
bea T10106 2024-05-20 2025-02-12

Data on US macro

source dataset .html .RData
fred gdp 2025-04-01 2025-04-01
fred unr 2025-04-01 2025-04-01
oecd QNA 2024-06-06 2025-03-31
oecd SNA_TABLE1 2025-03-04 2025-03-04

LAST_COMPILE

LAST_COMPILE
2025-04-09

Last

TimePeriod Nobs
2024Q4 26

Layout

  • NIPA Website. html

LineNumber, LineDescription

Code 
T10105_A %>%
  group_by(LineNumber, LineDescription, SeriesCode) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()
LineNumber LineDescription SeriesCode Nobs
1 Gross domestic product A191RC 96
2 Personal consumption expenditures DPCERC 96
3 Goods DGDSRC 96
4 Durable goods DDURRC 96
5 Nondurable goods DNDGRC 96
6 Services DSERRC 96
7 Gross private domestic investment A006RC 96
8 Fixed investment A007RC 96
9 Nonresidential A008RC 96
10 Structures B009RC 96
11 Equipment Y033RC 96
12 Intellectual property products Y001RC 96
13 Residential A011RC 96
14 Change in private inventories A014RC 96
15 Net exports of goods and services A019RC 96
16 Exports B020RC 96
17 Goods A253RC 96
18 Services A646RC 96
19 Imports B021RC 96
20 Goods A255RC 96
21 Services B656RC 96
22 Government consumption expenditures and gross investment A822RC 96
23 Federal A823RC 96
24 National defense A824RC 96
25 Nondefense A825RC 96
26 State and local A829RC 96

Imports and Exports

Graph, Annual

Code 
T10105_A %>%
  year_to_date %>%
  filter(LineNumber %in% c(1, 16, 19)) %>%
  group_by(date) %>%
  transmute(date,
            `Exports` = DataValue[LineNumber == 16]/DataValue[LineNumber == 1],
            `Imports` = DataValue[LineNumber == 19]/DataValue[LineNumber == 1]) %>%
  gather(variable, value, -date) %>%
  ggplot(.) + theme_minimal() + xlab("") +  ylab("Exports, Imports (% of GDP)") +
  geom_line(aes(x = date, y = value, color = variable, linetype = variable)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = viridis(3)[1:2]) +
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.9)) +
  scale_y_continuous(breaks = seq(-0.06, 0.4, 0.02),
                     labels = scales::percent_format(accuracy = 1))

Goods, Services

Code 
T10105_A %>%
  year_to_date %>%
  filter(LineNumber %in% c(1, 17, 18, 20, 21)) %>%
  group_by(date) %>%
  transmute(date,
            `Exports - Goods` = DataValue[LineNumber == 17]/DataValue[LineNumber == 1],
            `Exports - Services` = DataValue[LineNumber == 18]/DataValue[LineNumber == 1],
            `Imports - Goods` = DataValue[LineNumber == 20]/DataValue[LineNumber == 1],
            `Imports - Services` = DataValue[LineNumber == 21]/DataValue[LineNumber == 1]) %>%
  gather(variable, value, -date) %>%
  ggplot(.) + theme_minimal() + xlab("") +  ylab("Exports, Imports (% of GDP)") +
  geom_line(aes(x = date, y = value, color = variable, linetype = variable)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = c(viridis(3)[1], viridis(3)[2], viridis(3)[1], viridis(3)[2])) +
  scale_linetype_manual(values = c("dashed", "dashed", "solid",  "solid")) +
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.9)) +
  scale_y_continuous(breaks = seq(-0.06, 0.4, 0.02),
                     labels = scales::percent_format(accuracy = 1))

Goods, Services with NBER Recessions

Code 
T10105_A %>%
  year_to_date %>%
  filter(LineNumber %in% c(1, 17, 18, 20, 21)) %>%
  group_by(date) %>%
  transmute(date,
            `Exports - Goods` = DataValue[LineNumber == 17]/DataValue[LineNumber == 1],
            `Exports - Services` = DataValue[LineNumber == 18]/DataValue[LineNumber == 1],
            `Imports - Goods` = DataValue[LineNumber == 20]/DataValue[LineNumber == 1],
            `Imports - Services` = DataValue[LineNumber == 21]/DataValue[LineNumber == 1]) %>%
  gather(variable, value, -date) %>%
  ggplot(.) + theme_minimal() + xlab("") +  ylab("Exports, Imports (% of GDP)") +
  geom_line(aes(x = date, y = value, color = variable, linetype = variable)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = c(viridis(3)[1], viridis(3)[2], viridis(3)[1], viridis(3)[2])) +
  scale_linetype_manual(values = c("dashed", "dashed", "solid",  "solid")) +
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1928-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.9)) +
  scale_y_continuous(breaks = seq(-0.06, 0.4, 0.02),
                     labels = scales::percent_format(accuracy = 1))

Net Exports, Goods and Services

Graph with Presidents

Code 
US_presidents_extract <- US_presidents %>%
  filter(start >= as.Date("1945-01-01"))
T10105_Q %>%
  quarter_to_date %>%
  filter(LineNumber %in% c(1, 16:21)) %>%
  select(date, DataValue, LineNumber) %>%
  spread(LineNumber, DataValue) %>%
  transmute(date,
            `Net exports of goods and services` = (`16`-`19`)/`1`,
         `Net exports of goods` = (`17`-`20`)/`1`,
         `Net exports of services` = (`18`-`21`)/`1`) %>%
  gather(variable, value, -date) %>%
  mutate(variable = factor(variable, levels = c("Net exports of services",
                                                "Net exports of goods and services",
                                                "Net exports of goods"))) %>%
  ggplot(.) + theme_minimal() + xlab("") +  ylab("Net Exports (% of GDP)") +
  geom_line(aes(x = date, y = value, color = variable)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.25, 0.35),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_date(breaks = as.Date(US_presidents_extract$start),
               labels = date_format("%Y")) +
  geom_vline(aes(xintercept = as.numeric(start)), 
             data = US_presidents,
             colour = "grey50", alpha = 0.5) +
  geom_text(aes(x = start, y = new, label = name), 
            data = US_presidents_extract %>% mutate(new = -0.08 + 0.005 * (1:n() %% 2)),
            size = 2.5, vjust = 0, hjust = 0, nudge_x = 50) +
  geom_rect(aes(xmin = start, xmax = end, fill = party),
         ymin = -Inf, ymax = Inf, alpha = 0.1, data = US_presidents_extract) +
  scale_fill_manual(values = c("white", "grey")) +
  guides(color = guide_legend("party"), fill = FALSE) +
  scale_y_continuous(breaks = seq(-0.1, 0.05, 0.01),
                     labels = scales::percent_format(accuracy = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = viridis(3)[2])

Net Exports

Quarterly

Code 
T10105_Q %>%
  quarter_to_date %>%
  filter(LineNumber %in% c(1, 15)) %>%
  group_by(date) %>%
  mutate(net_exports_gdp = DataValue[LineNumber == 15]/DataValue[LineNumber == 1]) %>%
  ggplot(.) + theme_minimal() + xlab("") +  ylab("Net Exports (% of GDP)") +
  geom_line(aes(x = date, y = net_exports_gdp)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.8)) +
  scale_y_continuous(breaks = seq(-0.06, 0.05, 0.01),
                     labels = scales::percent_format(accuracy = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = viridis(3)[2])

Annual

Code 
T10105_A %>%
  year_to_date %>%
  filter(LineNumber %in% c(1, 15)) %>%
  group_by(date) %>%
  mutate(net_exports_gdp = DataValue[LineNumber == 15]/DataValue[LineNumber == 1]) %>%
  ggplot(.) + theme_minimal() + xlab("") +  ylab("Net Exports (% of GDP)") +
  geom_line(aes(x = date, y = net_exports_gdp)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.8)) +
  scale_y_continuous(breaks = seq(-0.06, 0.05, 0.01),
                     labels = scales::percent_format(accuracy = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = viridis(3)[2])

Graph with Presidents

Code 
US_presidents_extract <- US_presidents %>%
  filter(start >= as.Date("1945-01-01"))
T10105_Q %>%
  quarter_to_date %>%
  filter(LineNumber %in% c(1, 15)) %>%
  group_by(date) %>%
  mutate(net_exports_gdp = DataValue[LineNumber == 15]/DataValue[LineNumber == 1]) %>%
  ggplot(.) + theme_minimal() + xlab("") +  ylab("Net Exports (% of GDP)") +
  geom_line(aes(x = date, y = net_exports_gdp)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.8)) +
  scale_x_date(breaks = as.Date(US_presidents_extract$start),
               labels = date_format("%Y")) +
  geom_vline(aes(xintercept = as.numeric(start)), 
             data = US_presidents,
             colour = "grey50", alpha = 0.5) +
  geom_text(aes(x = start, y = new, label = name), 
            data = US_presidents_extract %>% mutate(new = -0.08 + 0.005 * (1:n() %% 2)),
            size = 2.5, vjust = 0, hjust = 0, nudge_x = 50) +
  geom_rect(aes(xmin = start, xmax = end, fill = party),
         ymin = -Inf, ymax = Inf, alpha = 0.1, data = US_presidents_extract) +
  scale_fill_manual(values = c("white", "grey")) +
  guides(color = guide_legend("party"), fill = FALSE) +
  scale_y_continuous(breaks = seq(-0.06, 0.05, 0.01),
                     labels = scales::percent_format(accuracy = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = viridis(3)[2])

Data

Code 
T10105_Q %>%
  quarter_to_date %>%
  filter(SeriesCode %in% c("A191RC", "A019RC"),
         date >= as.Date("2016-12-01")) %>%
  select(SeriesCode, date, DataValue) %>%
  spread(SeriesCode, DataValue) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Data - Plots

Code 
T10105_Q %>%
  quarter_to_date %>%
  filter(SeriesCode %in% c("A191RC", "A019RC"),
         date >= as.Date("2017-01-01")) %>%
  select(SeriesCode, date, DataValue) %>%
  spread(SeriesCode, DataValue) %>%
  ggplot() + geom_line(aes(x = date, y = A019RC/1000)) +
  theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2024, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = seq(-2000, 2000, 100),
                labels = dollar_format(suffix = " Bn", accuracy = 1))

1938, 1958, 1978, 1998, 2018 Table

Percent

Code 
T10105_A %>%
  year_to_date %>%
  mutate(year = year(date)) %>%
  filter(year %in% c(1929, 1949, 1969, 1989, 2009, 2019)) %>%
  group_by(year) %>%
  mutate(value = round(100*DataValue/DataValue[1], 1)) %>%
  ungroup %>%
  select(2, 3, 6, 7) %>%
  spread(year, value) %>%
  mutate_at(vars(-1, -2), funs(ifelse(is.na(.), "", paste0(., " %")))) %>%
  select(-LineNumber) %>%
  setNames(c("", names(.)[2:7])) %>%
  knitr::kable(booktabs = TRUE,
               linesep = "") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                latex_options = c("striped", "hold_position")) %>%
  add_indent(c(2:26)) %>%
  add_indent(c(3:6, 8:14, 16:21, 23:26)) %>%
  add_indent(c(4:5, 9:13, 17:18, 20:21, 24:25)) %>%
  add_indent(c(10:12))
1929 1949 1969 1989 2009 2019
Gross domestic product 100 % 100 % 100 % 100 % 100 % 100 %
Personal consumption expenditures 74 % 65.4 % 59.3 % 63.4 % 68.3 % 67 %
Goods 41.9 % 39.7 % 29.9 % 25.2 % 22 % 21 %
Durable goods 9.4 % 9.8 % 8.9 % 8.8 % 7 % 7.1 %
Nondurable goods 32.5 % 29.9 % 21 % 16.5 % 15 % 14 %
Services 32.1 % 25.8 % 29.4 % 38.2 % 46.4 % 46 %
Gross private domestic investment 16.4 % 14.3 % 17.1 % 17.7 % 13.3 % 18.1 %
Fixed investment 14.9 % 15.3 % 16.2 % 17.2 % 14.4 % 17.7 %
Nonresidential 11.1 % 9.9 % 11.8 % 12.7 % 11.7 % 13.9 %
Structures 5.3 % 3.4 % 3.7 % 3.4 % 3.1 % 3.1 %
Equipment 5.3 % 5.8 % 6.4 % 6.6 % 4.6 % 5.8 %
Intellectual property products 0.6 % 0.7 % 1.7 % 2.7 % 3.9 % 5 %
Residential 3.9 % 5.4 % 4.4 % 4.5 % 2.7 % 3.8 %
Change in private inventories 1.5 % -1 % 0.9 % 0.5 % -1 % 0.3 %
Net exports of goods and services 0.4 % 1.9 % 0.1 % -1.5 % -2.9 % -2.7 %
Exports 5.7 % 5.3 % 5.1 % 8.9 % 10.9 % 11.8 %
Goods 5.1 % 4.5 % 3.8 % 6.6 % 7.3 % 7.6 %
Services 0.6 % 0.8 % 1.3 % 2.3 % 3.6 % 4.2 %
Imports 5.3 % 3.4 % 5 % 10.5 % 13.8 % 14.5 %
Goods 4.3 % 2.5 % 3.6 % 8.6 % 11 % 11.7 %
Services 1 % 0.9 % 1.3 % 1.9 % 2.9 % 2.8 %
Government consumption expenditures and gross investment 9.2 % 18.3 % 23.5 % 20.4 % 21.2 % 17.6 %
Federal 1.8 % 11.2 % 12.9 % 9.5 % 8.4 % 6.6 %
National defense 1 % 8.2 % 10 % 6.9 % 5.4 % 3.9 %
Nondefense 0.8 % 3 % 2.9 % 2.5 % 3 % 2.6 %
State and local 7.4 % 7.1 % 10.6 % 11 % 12.8 % 11 %

Percent - pdf

Code 
include_graphics2("https://fgeerolf.com/bib/bea/T10105_ex.png")

Billions

Code 
T10105_A %>%
  year_to_date %>%
  mutate(year = year(date)) %>%
  filter(year %in% c(1929, 1949, 1969, 1989, 2009, 2019)) %>%
  group_by(year) %>%
  mutate(value = round(DataValue/1000)) %>%
  ungroup %>%
  select(2, 3, 6, 7) %>%
  spread(year, value) %>%
  mutate_at(vars(-1, -2), funs(ifelse(is.na(.), "", paste0("$ ",., " Bn")))) %>%
  select(-LineNumber) %>%
  setNames(c("", names(.)[2:7])) %>%
  knitr::kable(booktabs = TRUE,
               linesep = "") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                latex_options = c("striped", "hold_position")) %>%
  add_indent(c(2:26)) %>%
  add_indent(c(3:6, 8:14, 16:21, 23:26)) %>%
  add_indent(c(4:5, 9:13, 17:18, 20:21, 24:25)) %>%
  add_indent(c(10:12))
1929 1949 1969 1989 2009 2019
Gross domestic product $ 105 Bn $ 272 Bn $ 1018 Bn $ 5642 Bn $ 14478 Bn $ 21540 Bn
Personal consumption expenditures $ 77 Bn $ 178 Bn $ 604 Bn $ 3577 Bn $ 9891 Bn $ 14438 Bn
Goods $ 44 Bn $ 108 Bn $ 305 Bn $ 1424 Bn $ 3180 Bn $ 4533 Bn
Durable goods $ 10 Bn $ 27 Bn $ 90 Bn $ 494 Bn $ 1012 Bn $ 1524 Bn
Nondurable goods $ 34 Bn $ 81 Bn $ 214 Bn $ 929 Bn $ 2168 Bn $ 3009 Bn
Services $ 34 Bn $ 70 Bn $ 299 Bn $ 2153 Bn $ 6711 Bn $ 9905 Bn
Gross private domestic investment $ 17 Bn $ 39 Bn $ 174 Bn $ 1000 Bn $ 1930 Bn $ 3894 Bn
Fixed investment $ 16 Bn $ 42 Bn $ 164 Bn $ 972 Bn $ 2080 Bn $ 3821 Bn
Nonresidential $ 12 Bn $ 27 Bn $ 120 Bn $ 716 Bn $ 1690 Bn $ 2994 Bn
Structures $ 5 Bn $ 9 Bn $ 38 Bn $ 194 Bn $ 456 Bn $ 678 Bn
Equipment $ 5 Bn $ 16 Bn $ 65 Bn $ 372 Bn $ 670 Bn $ 1241 Bn
Intellectual property products $ 1 Bn $ 2 Bn $ 17 Bn $ 150 Bn $ 564 Bn $ 1075 Bn
Residential $ 4 Bn $ 15 Bn $ 44 Bn $ 256 Bn $ 390 Bn $ 827 Bn
Change in private inventories $ 2 Bn $ -3 Bn $ 9 Bn $ 28 Bn $ -151 Bn $ 73 Bn
Net exports of goods and services $ 0 Bn $ 5 Bn $ 1 Bn $ -87 Bn $ -419 Bn $ -577 Bn
Exports $ 6 Bn $ 14 Bn $ 52 Bn $ 504 Bn $ 1583 Bn $ 2539 Bn
Goods $ 5 Bn $ 12 Bn $ 39 Bn $ 375 Bn $ 1057 Bn $ 1645 Bn
Services $ 1 Bn $ 2 Bn $ 13 Bn $ 130 Bn $ 525 Bn $ 895 Bn
Imports $ 6 Bn $ 9 Bn $ 50 Bn $ 591 Bn $ 2002 Bn $ 3117 Bn
Goods $ 4 Bn $ 7 Bn $ 37 Bn $ 485 Bn $ 1588 Bn $ 2517 Bn
Services $ 1 Bn $ 2 Bn $ 14 Bn $ 106 Bn $ 414 Bn $ 600 Bn
Government consumption expenditures and gross investment $ 10 Bn $ 50 Bn $ 239 Bn $ 1152 Bn $ 3076 Bn $ 3786 Bn
Federal $ 2 Bn $ 30 Bn $ 131 Bn $ 534 Bn $ 1221 Bn $ 1420 Bn
National defense $ 1 Bn $ 22 Bn $ 102 Bn $ 391 Bn $ 788 Bn $ 850 Bn
Nondefense $ 1 Bn $ 8 Bn $ 29 Bn $ 143 Bn $ 433 Bn $ 570 Bn
State and local $ 8 Bn $ 19 Bn $ 108 Bn $ 618 Bn $ 1856 Bn $ 2366 Bn

Econ 102

U.S. GDP and Consumption (1929-2019)

Linear

Code 
plot_linear <- T10105_A %>%
  year_to_date %>%
  filter(LineNumber %in% c(1, 2)) %>%
  ggplot(.) + xlab("") + ylab("U.S. GDP") +
  geom_line(aes(x = date, y = DataValue/1000000, color = LineDescription)) + 
  theme_minimal() +
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1928-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = nber_recessions$Peak,
               labels = date_format("%y")) +
  scale_y_continuous(breaks = 1*seq(0, 40, 2.5),
                     labels = scales::dollar_format(accuracy = 0.1, suffix = "Tn"))

plot_linear

Log

Code 
plot_log <- plot_linear +
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1928-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  scale_y_log10(breaks = c(0.1, 0.2, 0.5, 1, 2, 5, 8, 10, 20),
                     labels = scales::dollar_format(accuracy = 0.1, suffix = "Tn"))

plot_log

Both

Code 
ggpubr::ggarrange(plot_linear + ggtitle("Linear Scale"),
                  plot_log + ggtitle("Log Scale") + ylab(""),
                  common.legend = T)

U.S. GDP (1929-2019)

(ref:gdp-only) U.S. GDP (1929-2019).

Code 
T10105_A %>%
  year_to_date %>%
  filter(LineNumber == 1) %>%
  ggplot(.) + xlab("") + ylab("U.S. GDP") +
  geom_line(aes(x = date, y = DataValue/1000000)) + 
  theme_minimal() +
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1928-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = nber_recessions$Peak,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 1*seq(0, 40, 2.5),
                     labels = scales::dollar_format(accuracy = 0.1, suffix = "Tn"))

(ref:gdp-only)

U.S. GDP (1929-2019) - Log Scale

(ref:log-gdp-only) U.S. GDP (1929-2019) - Log Scale.

Code 
T10105_A %>%
  year_to_date %>%
  filter(LineNumber == 1) %>%
  ggplot(.) + xlab("") + ylab("U.S. GDP (Log Scale)") +
  geom_line(aes(x = date, y = DataValue/1000000)) + theme_minimal() +
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1928-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = nber_recessions$Peak,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = 20*2^seq(-9, 1, 1),
                labels = scales::dollar_format(accuracy = 0.1, suffix = "Tn"))

(ref:log-gdp-only)

Government Purchases

HP Filter

(ref:g-purchases) Government Purchases.

Code 
T10105_A %>%
  year_to_date %>%
  filter(LineNumber %in% c(1, 22)) %>%
  group_by(date) %>%
  summarise(government_gdp = DataValue[LineNumber == 22]/DataValue[LineNumber == 1]) %>%
  select(date, government_gdp) %>%
  inner_join(gdp_real_A, by = "date") %>%
  mutate(year = year(date),
         gdp_real_A_hp10000 = exp(hpfilter(log(gdp_real_A), freq = 10000)$trend),
         gdp_real_A_LLtrend = lm(log(gdp_real_A) ~ year) %>% fitted %>% exp) %>%
  transmute(date,
            `Government Purchases (% of GDP)` = government_gdp,
            `Government Purchases (% of Log-Linear GDP Trend)` = government_gdp * gdp_real_A / gdp_real_A_LLtrend,
            `Government Purchases (% of HP GDP Trend)` = government_gdp * gdp_real_A / gdp_real_A_hp10000) %>%
  gather(variable, value, -date) %>%
  ggplot(.) + geom_line(aes(x = date, y = value, color = variable, linetype = variable)) + theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.15)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1928-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  scale_x_date(breaks = nber_recessions$Peak,
               labels = date_format("%Y"),
               limits = as.Date(paste0(c(1947, 2019), "-01-01"))) +
  scale_y_continuous(breaks = seq(0.12, 0.29, 0.01),
                     labels = scales::percent_format(accuracy = 1),
                     limits = c(0.12, 0.29)) + 
  xlab("") + ylab("Government Purchases (% of GDP)")

(ref:g-purchases)

Residential investment

Code 
T10105_A %>%
  year_to_date %>%
  filter(LineNumber %in% c(1, 13)) %>%
  group_by(date) %>%
  summarise(government_gdp = DataValue[LineNumber == 13]/DataValue[LineNumber == 1]) %>%
  select(date, government_gdp) %>%
  inner_join(gdp_real_A, by = "date") %>%
  mutate(year = year(date),
         gdp_real_A_hp10000 = log(gdp_real_A) %>% hpfilter(freq = 10000) %>% pluck("trend") %>% exp,
         gdp_real_A_LLtrend = lm(log(gdp_real_A) ~ year) %>% fitted %>% exp) %>%
  transmute(date,
            `Residential Investment (% of GDP)` = government_gdp,
            `Residential Investment (% of Log-Linear GDP Trend)` = government_gdp * gdp_real_A / gdp_real_A_LLtrend,
            `Residential Investment (% of HP GDP Trend)` = government_gdp * gdp_real_A / gdp_real_A_hp10000) %>%
  gather(variable, value, -date) %>%
  ggplot(.) + geom_line(aes(x = date, y = value, color = variable, linetype = variable)) + theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.15)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1928-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  scale_x_date(breaks = nber_recessions$Peak,
               labels = date_format("%Y"),
               limits = as.Date(paste0(c(1947, 2019), "-01-01"))) +
  scale_y_continuous(breaks = seq(0, 0.29, 0.01),
                     labels = scales::percent_format(accuracy = 1)) + 
  xlab("") + ylab("Government Purchases (% of GDP)")

Net Exports

HP Filter

Code 
T10105_A %>%
  year_to_date %>%
  filter(LineNumber %in% c(1, 15)) %>%
  group_by(date) %>%
  summarise(net_exports_gdp = DataValue[LineNumber == 15]/DataValue[LineNumber == 1]) %>%
  select(date, net_exports_gdp) %>%
  inner_join(gdp_real_A, by = "date") %>%
  mutate(year = year(date),
         gdp_real_A_hp10000 = log(gdp_real_A) %>% hpfilter(freq = 10000) %>% pluck("trend") %>% exp,
         gdp_real_A_LLtrend = lm(log(gdp_real_A) ~ year) %>% fitted %>% exp) %>%
  transmute(date,
            `Net Exports (% of GDP)` = net_exports_gdp,
            `Net Exports (% of Log-Linear GDP Trend)` = net_exports_gdp * gdp_real_A / gdp_real_A_LLtrend,
            `Net Exports (% of HP GDP Trend)` = net_exports_gdp * gdp_real_A / gdp_real_A_hp10000) %>%
  gather(variable, value, -date) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Net Exports (% of GDP)") +
  geom_line(aes(x = date, y = value, color = variable, linetype = variable)) +
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black") +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.title = element_blank(),
        legend.position = c(0.75, 0.85)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + 
  scale_y_continuous(breaks = seq(-0.06, 0.05, 0.01),
                     labels = scales::percent_format(accuracy = 1))

No HP Filter

Code 
T10105_A %>%
  year_to_date %>%
  filter(LineNumber %in% c(1, 15)) %>%
  group_by(date) %>%
  summarise(net_exports_gdp = DataValue[LineNumber == 15]/DataValue[LineNumber == 1]) %>%
  select(date, net_exports_gdp) %>%
  inner_join(gdp_real_A, by = "date") %>%
  mutate(year = year(date),
         gdp_real_A_LLtrend = exp(fitted(lm(log(gdp_real_A) ~ year)))) %>%
  transmute(date,
            `Net Exports (% of GDP)` = net_exports_gdp,
            `Net Exports (% of Log-Linear GDP Trend)` = net_exports_gdp * gdp_real_A / gdp_real_A_LLtrend) %>%
  gather(variable, value, -date) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Net Exports (% of GDP)") +
  geom_line(aes(x = date, y = value, color = variable, linetype = variable)) +
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black") +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.title = element_blank(),
        legend.position = c(0.75, 0.85)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + 
  scale_y_continuous(breaks = seq(-0.06, 0.05, 0.01),
                     labels = scales::percent_format(accuracy = 1))