High-tech trade by high-tech group of products in million euro (from 2007, SITC Rev. 4)

Data - Eurostat

Author

Info

LAST_DOWNLOAD

Code

tibble(source = c("eurostat", "oecd", "oecd"),
       dataset = c("htec_trd_group4", "GBARD_NABS2007", "BERD_MA_SOF")) %>%
  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()

source	dataset	Title	Download	Compile
eurostat	htec_trd_group4	High-tech trade by high-tech group of products in million euro (from 2007, SITC Rev. 4)	2024-10-08	[2024-11-05]
oecd	GBARD_NABS2007	Government budget allocations for R and D	2023-11-22	[2024-04-16]
oecd	BERD_MA_SOF	Business enterprise R&D expenditure by main activity (focussed) and source of funds	2023-09-09	[2024-04-16]

LAST_COMPILE

LAST_COMPILE
2024-11-08

Last

Code

htec_trd_group4 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()

time	Nobs
2022	1846

stk_flow

Code

htec_trd_group4 %>%
  left_join(stk_flow, by = "stk_flow") %>%
  group_by(stk_flow, Stk_flow) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional

stk_flow	Stk_flow	Nobs
IMP	Imports	14796
EXP	Exports	14749

sitc06

Code

htec_trd_group4 %>%
  left_join(sitc06, by = "sitc06") %>%
  group_by(sitc06, Sitc06) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Sector = gsub(" ", "-", str_to_lower(Sitc06)),
         Sector = paste0('<img src="../../icon/sector/vsmall/', Sector, '.png" alt="Sector">')) %>%
  select(Sector, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

partner

Code

htec_trd_group4 %>%
  left_join(partner, by = "partner") %>%
  group_by(partner, Partner) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional

partner	Partner	Nobs
WORLD	All countries of the world	9858
EXT_EU27_2020	Extra-EU27 (from 2020)	9853
INT_EU27_2020	Intra-EU27 (from 2020)	9834

geo

Code

htec_trd_group4 %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../icon/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

time

Code

htec_trd_group4 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  print_table_conditional()

time	Nobs
2022	1846
2021	1853
2020	1858
2019	1859
2018	1859
2017	1856
2016	1858
2015	1854
2014	1839
2013	1839
2012	1841
2011	1836
2010	1839
2009	1839
2008	1833
2007	1836

France, Germany, Italy

Exports - EXP

Code

htec_trd_group4 %>%
  filter(partner == "WORLD",
         time == "2018",
         stk_flow == "EXP",
         geo %in% c("DE", "FR", "IT", "UK")) %>%
  left_join(geo, by = "geo") %>%
  left_join(sitc06, by = "sitc06") %>%
  select(sitc06, Sitc06, Geo, values) %>%
  spread(Geo, values) %>%
  mutate(Sector = gsub(" ", "-", str_to_lower(Sitc06)),
         Sector = paste0('<img src="../../icon/sector/vsmall/', Sector, '.png" alt="Sector">')) %>%
  select(Sector, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Imports - IMP

Code

htec_trd_group4 %>%
  filter(partner == "WORLD",
         time == "2018",
         stk_flow == "IMP",
         geo %in% c("DE", "FR", "IT", "UK")) %>%
  left_join(geo, by = "geo") %>%
  left_join(sitc06, by = "sitc06") %>%
  select(sitc06, Sitc06, Geo, values) %>%
  spread(Geo, values) %>%
  mutate(Sector = gsub(" ", "-", str_to_lower(Sitc06)),
         Sector = paste0('<img src="../../icon/sector/vsmall/', Sector, '.png" alt="Sector">')) %>%
  select(Sector, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Balance - EXP-IMP

Code

htec_trd_group4 %>%
  filter(partner == "WORLD",
         time == "2018",
         geo %in% c("DE", "FR", "IT", "UK", "EU27_2020")) %>%
  left_join(geo, by = "geo") %>%
  left_join(sitc06, by = "sitc06") %>%
  select(sitc06, Sitc06, stk_flow, Geo, values) %>%
  spread(stk_flow, values) %>%
  mutate(values = EXP-IMP) %>%
  select(-EXP, -IMP) %>%
  spread(Geo, values) %>%
  mutate(Sector = gsub(" ", "-", str_to_lower(Sitc06)),
         Sector = paste0('<img src="../../icon/sector/vsmall/', Sector, '.png" alt="Sector">')) %>%
  select(Sector, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Exports high tech products

Values

Code

htec_trd_group4 %>%
  filter(partner == "WORLD",
         geo %in% c("DE", "IT", "FR", "EU27_2020"),
         sitc06 == "TOT_HT") %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  select_if(~ n_distinct(.) > 1) %>%
  mutate(Geo = ifelse(geo == "EU27_2020", "Europe", Geo)) %>%
  select(date, stk_flow, Geo, values) %>%
  spread(stk_flow, values) %>%
  mutate(values = EXP-IMP) %>%
  ggplot + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal()

% of GDP

Code

htec_trd_group4 %>%
  filter(partner == "WORLD",
         geo %in% c("DE", "IT", "FR", "EU27_2020"),
         sitc06 == "TOT_HT") %>%
  left_join(gdp, by = c("geo", "time")) %>%
  mutate(values = values/gdp) %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  select_if(~ n_distinct(.) > 1) %>%
  mutate(Geo = ifelse(geo == "EU27_2020", "Europe", Geo)) %>%
  select(date, stk_flow, Geo, values) %>%
  spread(stk_flow, values) %>%
  mutate(values = EXP-IMP) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Net Exports, High Tech Products (% of GDP)") +
  scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + add_4flags +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.5),
                     labels = percent_format(accuracy = .1))