6.201 – Valeur ajoutée brute par branche à prix courants (En milliards d’euros)

Data - INSEE

year

Simplifié - 38 postes

t_6201 %>%
  group_by(year) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

Détaillé - 88 postes

t_6201d %>%
  group_by(year) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

year	Nobs
1999	112
2000	112
2001	112
2002	112
2003	112
2004	112
2005	112
2006	112
2007	112
2008	112
2009	112
2010	112
2011	112
2012	112
2013	112
2014	112
2015	112
2016	112
2017	112
2018	112
2019	112

2019

Simplifié - 38 postes

t_6201 %>%
  filter(year == "2020") %>%
  select(-year) %>%
  mutate(share = round(100*value/ value[sector == "TOTAL"], 1)) %>%
  mutate(value = round(value) %>% paste0(" Mds€")) %>%
  arrange(-share) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Détaillé - 88 postes

t_6201d %>%
  filter(year == "2019") %>%
  mutate(share = round(100*value/ value[sector == "TOTAL"], 1)) %>%
  mutate(value = round(value) %>% paste0(" Mds€")) %>%
  arrange(-share) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Branches Pourcentage

Industries alimentaires, Produits métalliques, automobile

t_6201d %>%
  filter(sector %in% c("A88.10", "A88.25", "A88.29", "TOTAL")) %>%
  year_to_date2() %>%
  group_by(date) %>%
  mutate(value = value/value[sector == "TOTAL"]) %>%
  filter(!(sector == "TOTAL")) %>%
  ggplot() + theme_minimal() + ylab("%") + xlab("") +
  geom_line(aes(x = date, y = value, color = Sector, linetype = Sector)) +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.54, 0.55),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 250, 0.1),
                labels = percent_format(accuracy = .1))