Industry (subsector data)

Data - ec

subsector

INDUSTRY_SUBSECTOR %>%
  group_by(subsector, Subsector) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional

subsector	Subsector	Nobs
10	Manufacture of food products	154524
11	Manufacture of beverages	152081
12	Manufacture of tobacco products	54567
13	Manufacture of textiles	155037
14	Manufacture of wearing apparel	149519
15	Manufacture of leather and related products	141190
16	Manuf. of wood and of prod. of wood and cork, except. furniture; manuf. of straw and plaiting materials	154520
17	Manufacture of paper and paper products	153918
18	Printing and reproduction of recorded media	152818
19	Manufacture of coke and refined petroleum products	93268
20	Manufacture of chemicals and chemical products	153214
21	Manufacture of basic pharmaceutical products and pharmaceutical preparations	129238
22	Manufacture of rubber and plastic products	154319
23	Manufacture of other non-metallic mineral products	156337
24	Manufacture of basic metals	150175
25	Manufacture of fabricated metal products, except machinery and equipment	156685
26	Manufacture of computer, electronic and optical products	145103
27	Manufacture of electrical equipment	152600
28	Manufacture of machinery and equipment n.e.c.	155927
29	Manufacture of motor vehicles, trailers and semi-trailers	147509
30	Manufacture of other transport equipment	126299
31	Manufacture of furniture	150301
32	Other manufacturing	145138
33	Repair and installation of machinery and equipment	132218

question

INDUSTRY_SUBSECTOR %>%
  group_by(question, Question) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional

question	Question	Nobs
1	Production trend observed in recent months	432987
2	Assessment of order-book levels	435594
3	Assessment of export order-book levels	425183
4	Assessment of stocks of finished products	433361
5	Production expectations for the months ahead	435441
6	Selling price expectations for the months ahead	432473
7	Employment expectations for the months ahead	388761
COF	Confidence Indicator (Q2 - Q4 + Q5) / 3	432705

answers

INDUSTRY_SUBSECTOR %>%
  group_by(answers, Answers) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional

answers	Answers	Nobs
B	Balance not seasonally adjusted (n.s.a)	1691076
BS	Balance seasonally adjusted (s.a)	1725429

country

INDUSTRY_SUBSECTOR %>%
  group_by(country, Country) %>%
  summarise(Nobs = n()) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Country))),
         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 .}

freq

INDUSTRY_SUBSECTOR %>%
  group_by(freq, Frequency) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional

freq	Frequency	Nobs
M	Monthly	3416505

Germany

Manufacture of motor vehicles

INDUSTRY_SUBSECTOR %>%
  filter(country == "DE",
         subsector %in% c("29"),
         question %in% c("2", "3", "4"),
         answers == "B") %>%
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = period, y = value, color = Question)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 2), "-01-01")),
               labels = date_format("%y")) +
  theme(legend.position = c(0.8, 0.1),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(-200, 200, 10))

Manufacture of chemicals and chemical products

INDUSTRY_SUBSECTOR %>%
  filter(country == "DE",
         subsector %in% c("20"),
         question %in% c("2", "3", "4"),
         answers == "B") %>%
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = period, y = value, color = Question)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 2), "-01-01")),
               labels = date_format("%y")) +
  theme(legend.position = c(0.8, 0.1),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(-200, 200, 10))