Non-financial accounts by sectors

Data - OECD

Author

François Geerolf


Info

source dataset Title .html .rData
oecd SNA_TABLE14A Non-financial accounts by sectors 2025-09-29 2024-06-30

Data on macro

source dataset Title .html .rData
eurostat nama_10_a10 Gross value added and income by A*10 industry breakdowns 2025-11-16 2025-11-16
eurostat nama_10_a10_e Employment by A*10 industry breakdowns 2025-11-16 2025-11-15
eurostat nama_10_gdp GDP and main components (output, expenditure and income) 2025-11-16 2025-11-15
eurostat nama_10_lp_ulc Labour productivity and unit labour costs 2025-11-16 2025-11-16
eurostat namq_10_a10 Gross value added and income A*10 industry breakdowns 2025-11-16 2025-11-15
eurostat namq_10_a10_e Employment A*10 industry breakdowns 2025-05-24 2025-11-16
eurostat namq_10_gdp GDP and main components (output, expenditure and income) 2025-10-27 2025-11-16
eurostat namq_10_lp_ulc Labour productivity and unit labour costs 2025-11-16 2025-11-13
eurostat namq_10_pc Main GDP aggregates per capita 2025-11-16 2025-11-15
eurostat nasa_10_nf_tr Non-financial transactions 2025-11-16 2025-11-16
eurostat nasq_10_nf_tr Non-financial transactions 2025-11-16 2025-11-13
fred gdp Gross Domestic Product 2025-11-15 2025-11-15
oecd QNA Quarterly National Accounts 2024-06-06 2025-05-24
oecd SNA_TABLE1 Gross domestic product (GDP) 2025-09-29 2025-05-24
oecd SNA_TABLE14A Non-financial accounts by sectors 2025-09-29 2024-06-30
oecd SNA_TABLE2 Disposable income and net lending - net borrowing 2024-07-01 2024-04-11
oecd SNA_TABLE6A Value added and its components by activity, ISIC rev4 2024-07-01 2024-06-30
wdi NE.RSB.GNFS.ZS External balance on goods and services (% of GDP) 2025-11-15 2025-11-15
wdi NY.GDP.MKTP.CD GDP (current USD) 2025-11-15 2025-11-15
wdi NY.GDP.MKTP.PP.CD GDP, PPP (current international D) 2025-11-15 2025-11-15
wdi NY.GDP.PCAP.CD GDP per capita (current USD) 2025-11-15 2025-11-15
wdi NY.GDP.PCAP.KD GDP per capita (constant 2015 USD) 2025-11-15 2025-11-15
wdi NY.GDP.PCAP.PP.CD GDP per capita, PPP (current international D) 2025-11-15 2025-11-15
wdi NY.GDP.PCAP.PP.KD GDP per capita, PPP (constant 2011 international D) 2025-11-15 2025-11-15

LAST_COMPILE

LAST_COMPILE
2025-11-17

Last

obsTime Nobs
2023 3

Sources

It presents the whole set of non financial accounts, from the production account to the acquisitions of non-financial assets accounts. For general government sector, property income, other current transfers and capital transfers are consolidated. It has been prepared from statistics reported to the OECD by Member countries in their answers to the new version of the annual national accounts questionnaire.

Layout - By Location

  • OECD Website. html

United States

Code 
ig_b("oecd", "SNA_TABLE14A")

France

Code 
ig_b("oecd", "SNA_TABLE14A_FRA")

Layout - By sector

SS1 - All sectors

Code 
ig_b("oecd", "SNA_TABLE14A_SS1")

SS11 - Non-financial corporations

Code 
ig_b("oecd", "SNA_TABLE14A_SS11")

SS12 - Financial corporations

Code 
ig_b("oecd", "SNA_TABLE14A_SS12")

SS13 - General government

Code 
ig_b("oecd", "SNA_TABLE14A_SS13")

SS14_S15 - Households and Non-profits

Code 
ig_b("oecd", "SNA_TABLE14A_SS14_S15")

TRANSACT

Code 
SNA_TABLE14A %>%
  left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
  group_by(TRANSACT, Transact) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

LOCATION

Code 
SNA_TABLE14A %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, Location) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

SECTOR

Code 
SNA_TABLE14A_var$SECTOR %>%
  {if (is_html_output()) print_table(.) else .}
SECTOR Sector
NFAS 14A- NFAS : NON FINANCIAL ACCOUNTS BY SECTORS
S1_S2 Total economy and rest of the world
S1 Total economy
S11 Non-financial corporations
S11001 of which: Public non-financial corporations
S12 Financial corporations
S12001 of which: Public financial corporations
S13 General government
S14_S15 Households and non-profit institutions serving households
S14 Households
S15 Non-profit institutions serving households
SN Not sectorized
S2 Rest of the world

Saving Rate (%): NFB8GR (Gross Saving) / NFB6GR (Disposable Income, Gross)

All Sectors

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFB8GR", "NFB6GR"),
         SECTOR == "S1",
         LOCATION %in% c("FRA", "USA", "DEU")) %>%
  select(LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = NFB8GR/NFB6GR) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Gross saving rate (% of disposable income)") + xlab("")

Households

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFB8GR", "NFB6GR"),
         SECTOR == "S14_S15",
         LOCATION %in% c("FRA", "USA", "DEU")) %>%
  select(LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = NFB8GR/NFB6GR) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Gross saving rate (% of disposable income)") + xlab("")

Rents (% of GDP)

France, United States, Germany

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFD45R"),
         SECTOR == "S13",
         LOCATION %in% c("FRA", "USA", "DEU")) %>%
  select(LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFD45R) / B1_GE) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 0.1),
                     labels = scales::percent_format(accuracy = .1)) +
  ylab("Net Capital Formation (% of GDP) - Government") + xlab("") +
  geom_hline(yintercept = 0, linetype = "dashed")

Operating surplus, Non-Financial corporations

% of Value Added

France, United States, Germany, Italy, Spain

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFB2GP: Operating surplus, gross
  # NFB1GP: Gross domestic product / Gross value added
  filter(TRANSACT %in% c("NFB2GP", "NFB1GP"),
         SECTOR == "S11",
         LOCATION %in% c("FRA", "USA", "DEU", "ITA", "ESP")) %>%
  select(LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFB2GP) / NFB1GP) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_5flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Net operating surplus, gross (% of GDP)") + xlab("")

France, United States, Germany

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFB2GP: Operating surplus, gross
  # NFB1GP: Gross domestic product / Gross value added
  filter(TRANSACT %in% c("NFB2GP", "NFB1GP"),
         SECTOR == "S11",
         LOCATION %in% c("FRA", "USA", "DEU")) %>%
  select(LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFB2GP) / NFB1GP) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Net operating surplus, gross (% of GDP)") + xlab("")

Italy, Spain, Portugal

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFB2GP: Operating surplus, gross
  filter(TRANSACT %in% c("NFB2GP", "NFB1GP"),
         SECTOR == "S11",
         LOCATION %in% c("ITA", "ESP", "PRT")) %>%
  select(LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFB2GP) / NFB1GP) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "PRT", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Net operating surplus, gross (% of GDP)") + xlab("")

% of GDP

France, United States, Germany

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFB2GP: Operating surplus, gross
  # NFB1GP: Gross domestic product / Gross value added
  filter(TRANSACT %in% c("NFK1R", "NFB1GP", "NFB2GP"),
         SECTOR == "S11",
         LOCATION %in% c("FRA", "USA", "DEU")) %>%
  select(LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFB2GP) / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Net operating surplus, gross (% of GDP)") + xlab("")

Italy, Spain, Portugal

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFB2GP: Operating surplus, gross
  filter(TRANSACT %in% c("NFK1R", "NFB2GP"),
         SECTOR == "S1",
         LOCATION %in% c("ITA", "ESP", "PRT")) %>%
  select(LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFB2GP) / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "PRT", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Net operating surplus, gross (% of GDP)") + xlab("")

Operating surplus, gross (% of GDP) - All Economy

France, United States, Germany

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFB2GP: Operating surplus, gross
  filter(TRANSACT %in% c("NFK1R", "NFB2GP"),
         SECTOR == "S1",
         LOCATION %in% c("FRA", "USA", "DEU")) %>%
  select(LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFB2GP) / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Net operating surplus, gross (% of GDP)") + xlab("")

Italy, Spain, Portugal

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFB2GP: Operating surplus, gross
  filter(TRANSACT %in% c("NFK1R", "NFB2GP"),
         SECTOR == "S1",
         LOCATION %in% c("ITA", "ESP", "PRT")) %>%
  select(LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFB2GP) / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "PRT", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Net operating surplus, gross (% of GDP)") + xlab("")

Gross saving - investment (% of GDP) - NFB8GP-NFP5P

Germany

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFB8GP", "NFP5P"),
         SECTOR %in% c("S1", "S11", "S13", "S14_S15"),
         LOCATION %in% c("DEU")) %>%
  select(SECTOR, LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFB8GP - NFP5P) / B1_GE) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Sector)) +
  theme_minimal() + ylab("Gross Saving - Investment (% of GDP)") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1))

France

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFB8GP", "NFP5P"),
         SECTOR %in% c("S1", "S11", "S13", "S14_S15"),
         LOCATION %in% c("FRA")) %>%
  select(SECTOR, LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFB8GP - NFP5P) / B1_GE) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Sector)) +
  theme_minimal() + ylab("Gross Saving - Investment (% of GDP)") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1))

United States

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFB8GP", "NFP5P"),
         SECTOR %in% c("S1", "S11", "S13", "S14_S15"),
         LOCATION %in% c("USA")) %>%
  select(SECTOR, LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFB8GP - NFP5P) / B1_GE) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Sector)) +
  theme_minimal() + ylab("Gross Saving - Investment (% of GDP)") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1))

Italy

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFB8GP", "NFP5P"),
         SECTOR %in% c("S1", "S11", "S13", "S14_S15"),
         LOCATION %in% c("ITA")) %>%
  select(SECTOR, LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFB8GP - NFP5P) / B1_GE) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Sector)) +
  theme_minimal() + ylab("Gross Saving - Investment (% of GDP)") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1))

France, United States, Germany

All

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFB8GP", "NFP5P"),
         SECTOR == "S1",
         LOCATION %in% c("FRA", "USA", "DEU")) %>%
  select(LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFB8GP - NFP5P) / B1_GE) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Net Capital Formation (% of GDP) - Government") + xlab("") +
  geom_hline(yintercept = 0, linetype = "dashed")

S13 - Government

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFB8GP", "NFP5P"),
         SECTOR == "S13",
         LOCATION %in% c("FRA", "USA", "DEU")) %>%
  select(LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFB8GP - NFP5P) / B1_GE) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Net Capital Formation (% of GDP) - Government") + xlab("") +
  geom_hline(yintercept = 0, linetype = "dashed")

S11

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFB8GP", "NFP5P"),
         SECTOR == "S11",
         LOCATION %in% c("FRA", "USA", "DEU")) %>%
  select(LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFB8GP - NFP5P) / B1_GE) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Net Capital Formation (% of GDP) - Government") + xlab("") +
  geom_hline(yintercept = 0, linetype = "dashed")

S12

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFB8GP", "NFP5P"),
         SECTOR == "S12",
         LOCATION %in% c("FRA", "USA", "DEU")) %>%
  select(LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFB8GP - NFP5P) / B1_GE) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Net Capital Formation (% of GDP) - Government") + xlab("") +
  geom_hline(yintercept = 0, linetype = "dashed")

S14_S15

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFB8GP", "NFP5P"),
         SECTOR == "S14_S15",
         LOCATION %in% c("FRA", "USA", "DEU")) %>%
  select(LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFB8GP - NFP5P) / B1_GE) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Net Capital Formation (% of GDP) - Government") + xlab("") +
  geom_hline(yintercept = 0, linetype = "dashed")

Gross saving (% of GDP)

Germany

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFB8GP"),
         SECTOR %in% c("S1", "S11", "S13", "S14_S15"),
         LOCATION %in% c("DEU")) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
  mutate(obsValue = obsValue / B1_GE) %>%
  year_to_date %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Sector)) +
  theme_minimal() + ylab("Gross Saving (% of GDP)") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
                     labels = scales::percent_format(accuracy = 1))

France

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFB8GP"),
         SECTOR %in% c("S1", "S11", "S13", "S14_S15"),
         LOCATION %in% c("FRA")) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
  mutate(obsValue = obsValue / B1_GE) %>%
  year_to_date %>%
  select(date, obsValue, Sector) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Sector)) +
  theme_minimal() + ylab("Gross Saving (% of GDP)") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
                     labels = scales::percent_format(accuracy = 1))

Gross capital formation (% of GDP)

Germany

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFP5P"),
         SECTOR %in% c("S1", "S11", "S13", "S14_S15"),
         LOCATION %in% c("DEU")) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
  mutate(obsValue = obsValue / B1_GE) %>%
  year_to_date %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Sector)) +
  theme_minimal() + ylab("Gross Capital Formation (% of GDP)") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
                     labels = scales::percent_format(accuracy = 1),
                     limits = c(0, 0.35))

France

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFP5P"),
         SECTOR %in% c("S1", "S11", "S13", "S14_S15"),
         LOCATION %in% c("FRA")) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
  mutate(obsValue = obsValue / B1_GE) %>%
  year_to_date %>%
  select(date, obsValue, Sector) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Sector)) +
  theme_minimal() + ylab("Gross Capital Formation (% of GDP)") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  
  theme(legend.position = c(0.65, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
                     labels = scales::percent_format(accuracy = 1),
                     limits = c(0, 0.35))

Italy

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFP5P"),
         SECTOR %in% c("S1", "S11", "S13", "S14_S15"),
         LOCATION %in% c("ITA")) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
  mutate(obsValue = obsValue / B1_GE) %>%
  year_to_date %>%
  select(date, obsValue, Sector) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Sector)) +
  theme_minimal() + ylab("Gross Capital Formation (% of GDP)") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  
  theme(legend.position = c(0.65, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
                     labels = scales::percent_format(accuracy = 1),
                     limits = c(0, 0.35))

Number of observations

Gross capital formation (% of GDP)

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  filter(TRANSACT %in% c("NFP5P", "B1_GE"), 
         SECTOR == "S1") %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  select_if(~ n_distinct(.) > 1) %>%
  select(LOCATION, Location, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  na.omit %>%
  mutate(NFP5P_B1_GE = (100*NFP5P / B1_GE)  %>% round(1) %>% paste("%")) %>%
  select(Location, obsTime, NFP5P_B1_GE) %>%
  group_by(Location) %>%
  summarise(year_first = first(obsTime),
            value_first = first(NFP5P_B1_GE),
            year_last = last(obsTime),
            value_last = last(NFP5P_B1_GE)) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Consumption of fixed capital (% of GDP)

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  filter(TRANSACT %in% c("NFK1R", "B1_GE"), 
         SECTOR == "S1") %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  select(Location, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  na.omit %>%
  mutate(NFK1R_B1_GE = (100*NFK1R / B1_GE)  %>% round(1) %>% paste("%")) %>%
  select(Location, obsTime, NFK1R_B1_GE) %>%
  group_by(Location) %>%
  summarise(year_first = first(obsTime),
            value_first = first(NFK1R_B1_GE),
            year_last = last(obsTime),
            value_last = last(NFK1R_B1_GE)) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Net capital formation (% of GDP)

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  filter(TRANSACT %in% c("NFK1R", "NFP5P", "B1_GE"), 
         SECTOR == "S1") %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  select(Location, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  na.omit %>%
  mutate(net_inv_B1_GE = (100*(NFP5P - NFK1R) / B1_GE)  %>% round(1) %>% paste("%")) %>%
  select(Location, obsTime, net_inv_B1_GE) %>%
  group_by(Location) %>%
  summarise(year_first = first(obsTime),
            value_first = first(net_inv_B1_GE),
            year_last = last(obsTime),
            value_last = last(net_inv_B1_GE)) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Adjustment for the change in net equity of households in pension funds (% of GDP)

Code 
SNA_TABLE14A %>%
  # NFD8P: Adjustment for the change in net equity of households in pension funds 
  filter(TRANSACT %in% c("NFD8P", "B1_GE"), 
         SECTOR == "S1") %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  select(Location, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  na.omit %>%
  mutate(NFD8P_B1_GE = (100*NFD8P / B1_GE)  %>% round(2) %>% paste("%")) %>%
  select(Location, obsTime, NFD8P_B1_GE) %>%
  group_by(Location) %>%
  summarise(year_first = first(obsTime),
            value_first = first(NFD8P_B1_GE),
            year_last = last(obsTime),
            value_last = last(NFD8P_B1_GE)) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Time Series

Gross capital formation (% of GDP)

All

S1

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFK1R", "NFP5P", "B1_GE"),
         SECTOR == "S1",
         LOCATION %in% c("FRA", "USA", "DEU", "JPN")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  select(Location, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFP5P) / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Gross Capital Formation (% of GDP)") + xlab("")

S11

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFP5P"),
         SECTOR == "S11",
         LOCATION %in% c("FRA", "USA", "DEU", "JPN")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  mutate(obsValue = obsValue / B1_GE) %>%
  year_to_date %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Gross Capital Formation (% of GDP)") + xlab("")

S13

5
Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFP5P"),
         SECTOR == "S13",
         LOCATION %in% c("FRA", "USA", "DEU", "JPN")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  mutate(obsValue = obsValue / B1_GE) %>%
  year_to_date %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Gross Capital Formation (% of GDP) - Government") + xlab("")

France, Unnited STates, Germany
Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFP5P"),
         SECTOR %in% c("S13"),
         LOCATION %in% c("FRA", "USA", "DEU")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  mutate(obsValue = obsValue / B1_GE) %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 0.5),
                     labels = scales::percent_format(accuracy = .1)) +
  ylab("Gross Capital Formation (% of GDP) - Government") + xlab("")

More countries
Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFP5P"),
         SECTOR %in% c("S13"),
         LOCATION %in% c("PRT", "USA", "DEU", "ITA", "ESP", "GRC")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  mutate(obsValue = obsValue / B1_GE) %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_6flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 0.5),
                     labels = scales::percent_format(accuracy = .1)) +
  ylab("Gross Capital Formation (% of GDP) - Government") + xlab("")

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFP5P"),
         SECTOR %in% c("S13"),
         LOCATION %in% c("PRT", "USA", "DEU", "ITA", "ESP", "GRC")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  mutate(obsValue = obsValue / B1_GE) %>%
  year_to_date %>%
  filter(date >= as.Date("2012-01-01")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_6flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 0.5),
                     labels = scales::percent_format(accuracy = .1)) +
  ylab("Gross Capital Formation (% of GDP) - Government") + xlab("")

1994-

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFK1R", "NFP5P", "B1_GE"),
         SECTOR == "S1",
         LOCATION %in% c("FRA", "USA", "DEU", "JPN")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  select(Location, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  na.omit %>%
  year_to_date %>%
  filter(date >= as.Date("1994-01-01")) %>%
  mutate(obsValue = (NFP5P) / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Gross Capital Formation (% of GDP)") + xlab("")

France

Disposable Income, GDP

All

Code 
SNA_TABLE14A %>%
  filter((SECTOR %in% c("S14_S15", "S1") & TRANSACT == "NFB6GP") |
           (SECTOR == "S1" & TRANSACT == "B1_GE"),
         LOCATION %in% c("FRA")) %>%
  year_to_date %>%
  left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
  left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
  group_by(TRANSACT, SECTOR) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = paste0(Transact, " - ", Sector))) + theme_minimal() +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank()) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(1000, 50000, 1000)) +
  ylab("Indice 1995 = 100") + xlab("")

1990-

Code 
SNA_TABLE14A %>%
  filter((SECTOR %in% c("S14_S15", "S1") & TRANSACT == "NFB6GP") |
           (SECTOR == "S1" & TRANSACT == "B1_GE"),
         LOCATION %in% c("FRA")) %>%
  year_to_date %>%
  filter(date >= as.Date("1990-01-01")) %>%
  left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
  left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
  group_by(TRANSACT, SECTOR) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  mutate(Variable = paste0(Transact, " - ", Sector)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Variable)) + 
  theme_minimal() + ylab("Indice 1990 = 100") + xlab("") +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 300, 10))

1995-

Code 
SNA_TABLE14A %>%
  filter((SECTOR %in% c("S14_S15", "S1") & TRANSACT == "NFB6GP") |
           (SECTOR == "S1" & TRANSACT == "B1_GE"),
         LOCATION %in% c("FRA")) %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
  left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
  group_by(TRANSACT, SECTOR) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  mutate(Variable = paste0(Transact, " - ", Sector)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Variable)) + 
  theme_minimal() + ylab("Indice 1995 = 100") + xlab("") +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 300, 5))

1996-

Code 
SNA_TABLE14A %>%
  filter((SECTOR %in% c("S14_S15", "S1") & TRANSACT == "NFB6GP") |
           (SECTOR == "S1" & TRANSACT == "B1_GE"),
         LOCATION %in% c("FRA")) %>%
  year_to_date %>%
  filter(date >= as.Date("1996-01-01")) %>%
  left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
  left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
  group_by(TRANSACT, SECTOR) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  mutate(Variable = paste0(Transact, " - ", Sector)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Variable)) + 
  theme_minimal() + ylab("Indice 1996 = 100") + xlab("") +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 300, 5))

2008-

Code 
SNA_TABLE14A %>%
  filter((SECTOR %in% c("S14_S15", "S1") & TRANSACT == "NFB6GP") |
           (SECTOR == "S1" & TRANSACT == "B1_GE"),
         LOCATION %in% c("FRA")) %>%
  year_to_date %>%
  filter(date >= as.Date("2008-01-01")) %>%
  left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
  left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
  group_by(TRANSACT, SECTOR) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  mutate(Variable = paste0(Transact, " - ", Sector)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Variable)) + 
  theme_minimal() + ylab("Indice 2008 = 100") + xlab("") +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 300, 5))

B6NS1 - Net National Disposable Income / GDP

B6GS1 - Gross National Disposable Income / GDP

Households (% of GDP)

France, Germany, Belgium, Netherlands, Italy

Code 
SNA_TABLE14A %>%
  filter((SECTOR == "S14_S15" & TRANSACT == "NFB6GP") |
           (SECTOR == "S1" & TRANSACT == "B1_GE"),
         LOCATION %in% c("FRA", "DEU", "BEL", "ESP", "NLD", "ITA")) %>%
  year_to_date %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(obsValue = NFB6GP / B1_GE) %>%
  select(Location, date, obsValue) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(Location == "Netherlands", color2, color)) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_6flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 120, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Gross National Disposable Income (% of GDP)") + xlab("")

France, Germany, U.S., Denmark

Code 
SNA_TABLE14A %>%
  filter((SECTOR == "S14_S15" & TRANSACT == "NFB6GP") |
           (SECTOR == "S1" & TRANSACT == "B1_GE"),
         LOCATION %in% c("FRA", "DEU", "USA", "DNK")) %>%
  year_to_date %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(obsValue = NFB6GP / B1_GE) %>%
  select(Location, date, obsValue) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(Location == "Netherlands", color2, color)) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 120, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Gross National Disposable Income (% of GDP)") + xlab("")

Corporations (% of GDP)

Code 
SNA_TABLE14A %>%
  filter((SECTOR == "S13" & TRANSACT == "NFB6GP") |
           (SECTOR == "S1" & TRANSACT == "B1_GE"),
         LOCATION %in% c("FRA", "DEU", "BEL", "ESP", "NLD", "ITA")) %>%
  year_to_date %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(obsValue = NFB6GP / B1_GE) %>%
  select(Location, date, obsValue) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(Location == "Netherlands", color2, color)) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_6flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 120, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Gross National Disposable Income (% of GDP)") + xlab("")

Governments (% of GDP)

Code 
SNA_TABLE14A %>%
  filter((SECTOR == "S11" & TRANSACT == "NFB6GP") |
           (SECTOR == "S1" & TRANSACT == "B1_GE"),
         LOCATION %in% c("FRA", "DEU", "BEL", "ESP", "NLD", "ITA")) %>%
  year_to_date %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(obsValue = NFB6GP / B1_GE) %>%
  select(Location, date, obsValue) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(Location == "Netherlands", color2, color)) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_6flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 120, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Gross National Disposable Income (% of GDP)") + xlab("")

All (% of GDP)

Code 
SNA_TABLE14A %>%
  filter((SECTOR == "S1" & TRANSACT == "NFB6GP") |
           (SECTOR == "S1" & TRANSACT == "B1_GE"),
         LOCATION %in% c("FRA", "DEU", "BEL", "ESP", "NLD", "ITA")) %>%
  year_to_date %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(obsValue = NFB6GP / B1_GE) %>%
  select(Location, date, obsValue) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(Location == "Netherlands", color2, color)) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_6flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 120, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Gross National Disposable Income (% of GDP)") + xlab("")

Net capital formation (% of GDP)

All

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFK1R", "NFP5P", "B1_GE"),
         SECTOR == "S1",
         LOCATION %in% c("FRA", "USA", "DEU", "JPN")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  select(Location, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFP5P - NFK1R) / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Net Capital Formation (% of GDP)") + xlab("")

S13

France, United States, Germany

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFK1R", "NFP5P"),
         SECTOR == "S13",
         LOCATION %in% c("FRA", "USA", "DEU")) %>%
  select(LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFP5P - NFK1R) / B1_GE) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 0.1),
                     labels = scales::percent_format(accuracy = .1)) +
  ylab("Net Capital Formation (% of GDP) - Government") + xlab("") +
  geom_hline(yintercept = 0, linetype = "dashed")

Germany, United States, Germany

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFK1R", "NFP5P"),
         SECTOR == "S13",
         LOCATION %in% c("DEU", "LVA", "SVK", "CZE")) %>%
  select(LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFP5P - NFK1R) / B1_GE) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "SVK", "#EE1C25", color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Net Capital Formation (% of GDP) - Government") + xlab("") +
  geom_hline(yintercept = 0, linetype = "dashed")

Table

Code 
SNA_TABLE14A %>%
  # NFK1R: Consumption of fixed capital
  # NFP5P: Gross capital formation
  filter(TRANSACT %in% c("NFK1R", "NFP5P"),
         SECTOR == "S13") %>%
  select(LOCATION, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = (NFP5P - NFK1R) / B1_GE) %>%
  filter(date >= as.Date("1995-01-01"),
         date <= as.Date("2019-01-01")) %>%
  group_by(LOCATION, Location) %>%
  filter(n() == 25) %>%
  summarise(`Avg Net Inv.` = round(100*mean(obsValue), 2)) %>%
  arrange(`Avg Net Inv.`) %>%
  mutate(`Avg Net Inv.` = paste0(`Avg Net Inv.`, " %")) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Operating surplus and mixed income; gross (% of GDP)

Code 
SNA_TABLE14A %>%
  # NFB2G_B3GP: Operating surplus and mixed income
  filter(TRANSACT %in% c("NFB2G_B3GP", "B1_GE"),
         # S1: Total economy
         SECTOR == "S1",
         LOCATION %in% c("FRA", "USA", "DEU", "JPN")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  select(Location, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = NFB2G_B3GP / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Operating surplus and mixed income (% of GDP)") + xlab("")

Adjustment for the change in net equity of households in pension funds -

Code 
SNA_TABLE14A %>%
  # NFD8P: Adjustment for the change in net equity of households in pension funds 
  filter(TRANSACT %in% c("NFD8P", "B1_GE"),
         # S1: Total economy
         SECTOR == "S1",
         LOCATION %in% c("DEU", "USA", "CAN", "JPN")) %>%
  left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
  select(Location, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  na.omit %>%
  year_to_date %>%
  mutate(obsValue = NFD8P / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Operating surplus and mixed income (% of GDP)") + xlab("")

2018

2017 - United States

Code 
SNA_TABLE14A %>%
  filter(LOCATION == "USA",
         obsTime == "2017",
         !(SECTOR %in% c("S2", "S14", "S15"))) %>%
  left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  mutate(obsValue = round(100*obsValue / B1_GE, 1) %>% paste0(., "%")) %>%
  select(SECTOR, TRANSACT, Transact, obsValue) %>%
  mutate(SECTOR = paste0('<img src="../../icon/sector/vsmall/', SECTOR, '.png" alt="All">')) %>%
  spread(SECTOR, obsValue) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

2018 - Germany

Code 
SNA_TABLE14A %>%
  filter(LOCATION == "DEU",
         obsTime == "2018",
         !(SECTOR %in% c("S2", "S14", "S15"))) %>%
  left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  mutate(obsValue = round(100*obsValue / B1_GE, 1) %>% paste0(., "%")) %>%
  select(SECTOR, TRANSACT, Transact, obsValue) %>%
  mutate(SECTOR = paste0('<img src="../../icon/sector/vsmall/', SECTOR, '.png" alt="All">')) %>%
  spread(SECTOR, obsValue) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

2018 - France

Code 
SNA_TABLE14A %>%
  filter(LOCATION == "FRA",
         obsTime == "2018",
         !(SECTOR %in% c("S2", "S14", "S15"))) %>%
  left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  mutate(obsValue = round(100*obsValue / B1_GE, 1) %>% paste0(., "%")) %>%
  select(SECTOR, TRANSACT, Transact, obsValue) %>%
  mutate(SECTOR = paste0('<img src="../../icon/sector/vsmall/', SECTOR, '.png" alt="All">')) %>%
  spread(SECTOR, obsValue) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

2017 - Japan

Code 
SNA_TABLE14A %>%
  filter(LOCATION == "JPN",
         obsTime == "2017",
         !(SECTOR %in% c("S2", "S14", "S15"))) %>%
  left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  mutate(obsValue = round(100*obsValue / B1_GE, 1) %>% paste0(., "%")) %>%
  select(SECTOR, TRANSACT, Transact, obsValue) %>%
  mutate(SECTOR = paste0('<img src="../../icon/sector/vsmall/', SECTOR, '.png" alt="All">')) %>%
  spread(SECTOR, obsValue) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

2018 - United Kingdom

Code 
SNA_TABLE14A %>%
  filter(LOCATION == "GBR",
         obsTime == "2018",
         !(SECTOR %in% c("S2", "S14", "S15"))) %>%
  left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  mutate(obsValue = round(100*obsValue / B1_GE, 1) %>% paste0(., "%")) %>%
  select(SECTOR, TRANSACT, Transact, obsValue) %>%
  mutate(SECTOR = paste0('<img src="../../icon/sector/vsmall/', SECTOR, '.png" alt="All">')) %>%
  spread(SECTOR, obsValue) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

2016 - China

Code 
SNA_TABLE14A %>%
  filter(LOCATION == "CHN",
         obsTime == "2016",
         !(SECTOR %in% c("S2", "S14", "S15"))) %>%
  left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C") %>%
              select(obsTime, LOCATION, B1_GE = obsValue), 
            by = c("LOCATION", "obsTime")) %>%
  mutate(obsValue = round(100*obsValue / B1_GE, 1) %>% paste0(., "%")) %>%
  select(SECTOR, TRANSACT, Transact, obsValue) %>%
  mutate(SECTOR = paste0('<img src="../../icon/sector/vsmall/', SECTOR, '.png" alt="All">')) %>%
  spread(SECTOR, obsValue) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}