Value added and its components by activity, ISIC rev4

Data - OECD

Author

Info

Related main macro datasets

Code

main_macro %>%
  arrange(-(dataset == "SNA_TABLE6A")) %>%
  mutate(Title = read_lines(paste0("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/", source, "/",dataset, ".qmd"), skip = 1, n_max = 1) %>% gsub("title: ", "", .) %>% gsub("\"", "", .)) %>%
  mutate(Download = as.Date(file.info(paste0("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/", source, "/", dataset, ".RData"))$mtime),
         Compile = as.Date(file.info(paste0("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/", source, "/", dataset, ".html"))$mtime)) %>%
  mutate(Compile = paste0("[", Compile, "](https://fgeerolf.com/data/", source, "/", dataset, '.html)')) %>%
  print_table_conditional_30()

source	dataset	Title	Download	Compile
oecd	SNA_TABLE6A	Value added and its components by activity, ISIC rev4	2024-06-30	[2024-07-01]
eurostat	nama_10_a10	Gross value added and income by A*10 industry breakdowns	2024-06-30	[2024-06-23]
eurostat	nama_10_a10_e	Employment by A*10 industry breakdowns	2024-06-30	[2024-07-01]
eurostat	nama_10_gdp	GDP and main components (output, expenditure and income)	2024-06-30	[2024-06-24]
eurostat	nama_10_lp_ulc	Labour productivity and unit labour costs	2024-06-30	[2024-06-24]
eurostat	namq_10_a10	Gross value added and income A*10 industry breakdowns	2024-06-30	[2024-06-24]
eurostat	namq_10_a10_e	Employment A*10 industry breakdowns	2024-06-30	[2024-06-24]
eurostat	namq_10_gdp	GDP and main components (output, expenditure and income)	2024-06-30	[2024-06-24]
eurostat	namq_10_lp_ulc	Labour productivity and unit labour costs	2024-06-30	[2024-06-24]
eurostat	namq_10_pc	Main GDP aggregates per capita	2024-06-30	[2024-06-24]
eurostat	nasa_10_nf_tr	Non-financial transactions	2024-06-30	[2024-06-24]
eurostat	nasq_10_nf_tr	Non-financial transactions	2024-06-30	[2024-06-24]
fred	gdp	Gross Domestic Product	2024-06-30	[2024-06-30]
oecd	QNA	Quarterly National Accounts, Per Capita	2024-06-30	[2024-06-06]
oecd	SNA_TABLE1	Gross domestic product (GDP)	2024-06-30	[2024-07-01]
oecd	SNA_TABLE14A	Non-financial accounts by sectors	2024-06-30	[2024-07-01]
oecd	SNA_TABLE2	Disposable income and net lending - net borrowing	2024-04-11	[2024-07-01]
wdi	NE.RSB.GNFS.ZS	External balance on goods and services (% of GDP)	2024-04-14	[2024-06-20]
wdi	NY.GDP.MKTP.CD	GDP (current USD)	2024-05-06	[2024-06-20]
wdi	NY.GDP.MKTP.PP.CD	GDP, PPP (current international D)	2024-04-14	[2024-06-20]
wdi	NY.GDP.PCAP.CD	GDP per capita (current USD)	2024-04-22	[2024-06-20]
wdi	NY.GDP.PCAP.KD	GDP per capita (constant 2015 USD)	2024-05-06	[2024-06-20]
wdi	NY.GDP.PCAP.PP.CD	GDP per capita, PPP (current international D)	2024-04-22	[2024-06-20]
wdi	NY.GDP.PCAP.PP.KD	GDP per capita, PPP (constant 2011 international D)	2024-05-06	[2024-06-20]

LAST_COMPILE

LAST_COMPILE
2024-07-01

Last

obsTime	Nobs
2023	3

ACTIVITY

Code

SNA_TABLE6A %>%
  left_join(SNA_TABLE6A_var$ACTIVITY, by = "ACTIVITY") %>%
  group_by(ACTIVITY, Activity) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

TRANSACT

Code

SNA_TABLE6A %>%
  left_join(SNA_TABLE6A_var$TRANSACT, by = "TRANSACT") %>%
  group_by(TRANSACT, Transact) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

TRANSACT	Transact	Nobs
B1GA	Gross value added	486869
K1A	Consumption of fixed capital	298130
P1A	Output	231627
P2A	Intermediate consumption	227487
D1A	Compensation of employees	103207
D29_D39A	Other taxes less other subsidies on production	102731
B2G_B3GA	Gross operating surplus and gross mixed income	95040
D11A	of which: gross wages and salaries	94809
B2N_B3NA	Net operating surplus and net mixed income	90737
B1_GE	Gross domestic product (expenditure approach)	2633

MEASURE

Code

SNA_TABLE6A %>%
  left_join(SNA_TABLE6A_var$MEASURE, by = "MEASURE") %>%
  group_by(MEASURE, Measure) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

MEASURE	Measure	Nobs
C	Current prices	896620
DOB	Deflator, OECD base year = 100	235895
V	Constant prices, national base year	247826
VOB	Constant prices, OECD base year	94644
VP	Constant prices, previous year prices	258285

LOCATION

Code

SNA_TABLE6A %>%
  left_join(SNA_TABLE6A_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 .}

obsTime

Code

SNA_TABLE6A %>%
  group_by(obsTime) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(obsTime)) %>%
  print_table_conditional()

Real Estate

Table

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VL", "VTOT"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(VARIABLE, LOCATION, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(value = B1GA_VL / B1_GE_VTOT) %>%
  filter(!is.na(value)) %>%
  group_by(LOCATION, Location) %>%
  summarise(year1 = first(year(date)),
            value1 = first(value),
            year2 = last(year(date)),
            value2 = last(value)) %>%
  mutate_at(vars(value1, value2), funs(round(100*., digits = 1))) %>%
  arrange(value2) %>%
  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 .}

France, Greece, Italy, Spain

All

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VL", "VTOT"),
         LOCATION %in% c("GRC", "FRA", "ITA", "ESP"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(VARIABLE, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(obsValue = B1GA_VL / B1_GE_VTOT) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_4flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 26, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Value Added in Real Estate (% of GDP)") + xlab("")

1995-

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VL", "VTOT"),
         LOCATION %in% c("GRC", "FRA", "ITA", "ESP"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(VARIABLE, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(obsValue = B1GA_VL / B1_GE_VTOT) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_4flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 26, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Value Added in Real Estate (% of GDP)") + xlab("")

France, Germany, US

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VL", "VTOT"),
         LOCATION %in% c("FRA", "DEU", "USA"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(VARIABLE, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(obsValue = B1GA_VL / B1_GE_VTOT) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 26, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Value Added in Real Estate (% of GDP)") + xlab("")

New Zealand, UK, US

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VL", "VTOT"),
         LOCATION %in% c("GBR", "NZL", "USA"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(VARIABLE, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(obsValue = B1GA_VL / B1_GE_VTOT) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 26, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Value Added in Real Estate (% of GDP)") + xlab("")

Low Manufacturing

Table

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VC", "VTOT"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(VARIABLE, LOCATION, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(value = B1GA_VC / B1_GE_VTOT) %>%
  filter(!is.na(value)) %>%
  group_by(LOCATION, Location) %>%
  summarise(year1 = first(year(date)),
            value1 = first(value),
            year2 = last(year(date)),
            value2 = last(value)) %>%
  mutate_at(vars(value1, value2), funs(round(100*., digits = 1))) %>%
  arrange(value2) %>%
  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 .}

Luxembourg, Cyprus, Australia

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VC", "VTOT"),
         LOCATION %in% c("LUX", "CYP", "AUS"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(VARIABLE, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(obsValue = B1GA_VC / B1_GE_VTOT) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 26, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Value Added in Manufacturing (% of GDP)") + xlab("")

Albania, Norway, Greece

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VC", "VTOT"),
         LOCATION %in% c("ALB", "NOR", "GRC"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(VARIABLE, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(obsValue = B1GA_VC / B1_GE_VTOT) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 26, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Value Added in Manufacturing (% of GDP)") + xlab("")

Malta, Iceland, United Kingdom

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VC", "VTOT"),
         LOCATION %in% c("MLT", "ISL", "GBR"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(LOCATION, VARIABLE, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(obsValue = B1GA_VC / B1_GE_VTOT) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "ISL", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 26, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Value Added in Manufacturing (% of GDP)") + xlab("")

Chile, Canada, France

All

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VC", "VTOT"),
         LOCATION %in% c("CHL", "CAN", "FRA"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(LOCATION, VARIABLE, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(obsValue = B1GA_VC / B1_GE_VTOT) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 26, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Value Added in Manufacturing (% of GDP)") + xlab("")

2007-

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VC", "VTOT"),
         LOCATION %in% c("CHL", "CAN", "FRA"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  filter(date >= as.Date("2007-01-01")) %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(LOCATION, VARIABLE, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(obsValue = B1GA_VC / B1_GE_VTOT) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 26, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Value Added in Manufacturing (% of GDP)") + xlab("")

New Zealand, Latvia, Brazil

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VC", "VTOT"),
         LOCATION %in% c("NZL", "LVA", "BRA"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(LOCATION, VARIABLE, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(obsValue = B1GA_VC / B1_GE_VTOT) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "ISL", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 26, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Value Added in Manufacturing (% of GDP)") + xlab("")

Netherlands, United States, Costa Rica

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VC", "VTOT"),
         LOCATION %in% c("NLD", "USA", "CRI"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(LOCATION, VARIABLE, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(obsValue = B1GA_VC / B1_GE_VTOT) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "ISL", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 26, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Value Added in Manufacturing (% of GDP)") + xlab("")

Spain, Colombia, Portugal

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VC", "VTOT"),
         LOCATION %in% c("ESP", "COL", "PRT"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(LOCATION, VARIABLE, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(obsValue = B1GA_VC / B1_GE_VTOT) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "ISL", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 26, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Value Added in Manufacturing (% of GDP)") + xlab("")

High Manufacturing

Table

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VC", "VTOT"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(VARIABLE, LOCATION, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(value = B1GA_VC / B1_GE_VTOT) %>%
  filter(!is.na(value)) %>%
  group_by(LOCATION, Location) %>%
  summarise(year1 = first(year(date)),
            value1 = first(value),
            year2 = last(year(date)),
            value2 = last(value)) %>%
  mutate_at(vars(value1, value2), funs(round(100*., digits = 1))) %>%
  arrange(-value2) %>%
  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 .}

Ireland, South Korea, Czech Republic

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VC", "VTOT"),
         LOCATION %in% c("IRL", "KOR", "CZE"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(VARIABLE, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(obsValue = B1GA_VC / B1_GE_VTOT) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 40, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Value Added in Manufacturing (% of GDP)") + xlab("")

Slovenia, Germany, Romania

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VC", "VTOT"),
         LOCATION %in% c("SVN", "DEU", "ROU"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(VARIABLE, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(obsValue = B1GA_VC / B1_GE_VTOT) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 40, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Value Added in Manufacturing (% of GDP)") + xlab("")

Turkey, Switzerland, Slovak Republic

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VC", "VTOT"),
         LOCATION %in% c("TUR", "CHE", "SVK"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(LOCATION, VARIABLE, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(obsValue = B1GA_VC / B1_GE_VTOT) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "SVK", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 40, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Value Added in Manufacturing (% of GDP)") + xlab("")

Manufacturing

Table

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VC", "VTOT"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(VARIABLE, LOCATION, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(value = B1GA_VC / B1_GE_VTOT) %>%
  filter(!is.na(value)) %>%
  group_by(LOCATION, Location) %>%
  summarise(year1 = first(year(date)),
            value1 = first(value),
            year2 = last(year(date)),
            value2 = last(value)) %>%
  mutate_at(vars(value1, value2), funs(round(100*., digits = 1))) %>%
  arrange(value2) %>%
  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 .}

France, Germany, US

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VC", "VTOT"),
         LOCATION %in% c("FRA", "DEU", "USA"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(VARIABLE, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(obsValue = B1GA_VC / B1_GE_VTOT) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 26, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Value Added in Manufacturing (% of GDP)") + xlab("")

Denmark, Switzerland, UK

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VC", "VTOT"),
         LOCATION %in% c("GBR", "DNK", "CHE"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(VARIABLE, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(obsValue = B1GA_VC / B1_GE_VTOT) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 26, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Value Added in Manufacturing (% of GDP)") + xlab("")

Spain, Italy, Portugal

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VC", "VTOT"),
         LOCATION %in% c("PRT", "ESP", "ITA"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(VARIABLE, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(obsValue = B1GA_VC / B1_GE_VTOT) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 26, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Value Added in Manufacturing (% of GDP)") + xlab("")

Norway, Sweden, Finland

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VC", "VTOT"),
         LOCATION %in% c("NOR", "SWE", "FIN"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(VARIABLE, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(obsValue = B1GA_VC / B1_GE_VTOT) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 40, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Value Added in Manufacturing (% of GDP)") + xlab("")

South Korea, Australia, New Zealand

Code

SNA_TABLE6A %>%
  filter(TRANSACT %in% c("B1GA", "B1_GE"),
         ACTIVITY %in% c("VC", "VTOT"),
         LOCATION %in% c("KOR", "NZL", "AUS"),
         MEASURE == "C") %>%
  left_join(SNA_TABLE6A_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  mutate(VARIABLE = paste0(TRANSACT, "_", ACTIVITY)) %>%
  select(VARIABLE, Location, date, obsValue) %>%
  spread(VARIABLE, obsValue) %>%
  mutate(obsValue = B1GA_VC / B1_GE_VTOT) %>%
  na.omit %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 40, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Value Added in Manufacturing (% of GDP)") + xlab("")