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

Data - INSEE

year

Simplifié - 38 postes

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

Détaillé - 88 postes

Code
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

Code
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

Code
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

Code
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))