6.202 – Valeur ajoutée brute par branche en volume aux prix de l année précédente chaînés (En milliards d’euros 2014) - t_6202

Data - INSEE

year

Simplifié - 38 postes

t_6202 %>%
  group_by(year) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

Détaillé - 88 postes

t_6202d %>%
  group_by(year) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

year	Nobs
1999	111
2000	111
2001	111
2002	111
2003	111
2004	111
2005	111
2006	111
2007	111
2008	111
2009	111
2010	111
2011	111
2012	111
2013	111
2014	111
2015	111
2016	111
2017	111
2018	111
2019	111

2019

Simplifié - 38 postes

t_6202 %>%
  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_6202d %>%
  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_6202d %>%
  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_color_manual(values = viridis(4)[1:3]) +
  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))