Business surveys - NACE Rev. 2 activity - Services - monthly data - ei_bsse_m_r2

Data - Eurostat

Author

François Geerolf

indic

English

Code 
ei_bsse_m_r2 %>%
  left_join(indic, by = "indic") %>%
  group_by(indic, Indic) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional
indic Indic Nobs
BS-SARM Evolution of demand over the past 3 months 18599
BS-SABC Business situation development over the past 3 months 18549
BS-SERM Evolution of employment over the past 3 months 18549
BS-SAEM Expectation of the demand over the next 3 months 18534
BS-SCI Services confidence indicator 18482
BS-SEEM Expectation of the employment over the next 3 months 18343
BS-PE3M Expectations of the prices over the next 3 months 16184

French

Code 
load_data("eurostat/indic_fr.RData")
ei_bsse_m_r2 %>%
  left_join(indic, by = "indic") %>%
  group_by(indic, Indic) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional
indic Indic Nobs
BS-SARM Evolution de la demande au cours des 3 derniers mois 18599
BS-SABC Evolution de l'activité au cours des 3 derniers mois 18549
BS-SERM Evolution de l'emploi au cours des 3 derniers mois 18549
BS-SAEM Evolution probable de la demande au cours des 3 prochains mois 18534
BS-SCI Services, indicateur de confiance 18482
BS-SEEM Evolution probable de l'emploi au cours des 3 prochains mois 18343
BS-PE3M Evolution probable des prix au cours des 3 prochains mois 16184

s_adj

Code 
ei_bsse_m_r2 %>%
  left_join(s_adj, by = "s_adj") %>%
  group_by(s_adj, S_adj) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional
s_adj S_adj Nobs
SA Seasonally adjusted data, not calendar adjusted data 64088
NSA Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) 63152

unit

Code 
ei_bsse_m_r2 %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional
unit Unit Nobs
BAL Balance 127240

geo

Code 
ei_bsse_m_r2 %>%
  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="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

time

Code 
ei_bsse_m_r2 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional

Covid Crisis

Table

Code 
ei_bsse_m_r2 %>%
  filter(indic == "BS-SARM",
         time %in% c("2019M09", "2020M03", "2020M04", "2020M09"),
         s_adj == "NSA") %>%
  select(geo, time, values) %>%
  left_join(geo, by = "geo") %>%
  spread(time, values) %>%
  arrange(`2020M04`) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

France, Germany, Italy, Europe

Code 
ei_bsse_m_r2 %>%
  filter(indic == "BS-SARM",
         geo %in% c("FR", "DE", "IT", "EU27_2020"),
         s_adj == "NSA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "1997M01"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo),
         Geo = ifelse(geo == "EU27_2020", "Europe", Geo)) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01"),
         date <= as.Date("2020-09-01")) %>%
  ggplot() + ylab("Construction Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Geo)) +
  scale_color_manual(values = c("#003399", "#ED2939", "#000000", "#008c45")) +
  scale_x_date(breaks = "1 month",
               labels = date_format("%b %y")) +
  theme(legend.position = c(0.1, 0.2)) +
  scale_y_continuous(breaks = seq(-3000, 2000, 100))

France, Germany, Italy

BS-SARM - Evolution of demand over the past 3 months

Code 
ei_bsse_m_r2 %>%
  filter(indic == "BS-SARM",
         geo %in% c("FR", "DE", "IT", "EU27_2020"),
         s_adj == "NSA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "1997M01"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo),
         Geo = ifelse(geo == "EU27_2020", "Europe", Geo)) %>%
  month_to_date %>%
  ggplot() + ylab("Evolution of demand") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Geo)) +
  scale_color_manual(values = c("#003399", "#ED2939", "#000000", "#008c45")) +
  scale_x_date(breaks = "5 years",
               labels = date_format("%y")) +
  theme(legend.position = c(0.1, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(-3000, 2000, 100))

BS-SABC - Business situation development over the past 3 months

Code 
ei_bsse_m_r2 %>%
  filter(indic == "BS-SABC",
         geo %in% c("FR", "DE", "IT", "EU27_2020"),
         s_adj == "SA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo),
         Geo = ifelse(geo == "EU27_2020", "Europe", Geo)) %>%
  month_to_date %>%
  ggplot() + ylab("Business situation development") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Geo)) +
  scale_color_manual(values = c("#003399", "#ED2939", "#000000", "#008c45")) +
  scale_x_date(breaks = "5 years",
               labels = date_format("%y")) +
  theme(legend.position = c(0.1, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(-3000, 2000, 10))

BS-SERM - Evolution of employment over the past 3 months

Code 
ei_bsse_m_r2 %>%
  filter(indic == "BS-SERM",
         geo %in% c("FR", "DE", "IT", "EU27_2020"),
         s_adj == "SA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo),
         Geo = ifelse(geo == "EU27_2020", "Europe", Geo)) %>%
  month_to_date %>%
  ggplot() + ylab("Evolution of employment") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Geo)) +
  scale_color_manual(values = c("#003399", "#ED2939", "#000000", "#008c45")) +
  scale_x_date(breaks = "5 years",
               labels = date_format("%y")) +
  theme(legend.position = c(0.1, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(-3000, 2000, 10))

BS-SAEM - Expectation of demand

Code 
ei_bsse_m_r2 %>%
  filter(indic == "BS-SAEM",
         geo %in% c("FR", "DE", "IT", "EU27_2020"),
         s_adj == "SA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo),
         Geo = ifelse(geo == "EU27_2020", "Europe", Geo)) %>%
  month_to_date %>%
  ggplot() + ylab("Expectation of demand") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Geo)) +
  scale_color_manual(values = c("#003399", "#ED2939", "#000000", "#008c45")) +
  scale_x_date(breaks = "5 years",
               labels = date_format("%y")) +
  theme(legend.position = c(0.1, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(-3000, 2000, 10))

BS-SEEM - Expectation of the employment

Code 
ei_bsse_m_r2 %>%
  filter(indic == "BS-SEEM",
         geo %in% c("FR", "DE", "IT", "EU27_2020"),
         s_adj == "SA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo),
         Geo = ifelse(geo == "EU27_2020", "Europe", Geo)) %>%
  month_to_date %>%
  ggplot() + ylab("Expectation of employment") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Geo)) +
  scale_color_manual(values = c("#003399", "#ED2939", "#000000", "#008c45")) +
  scale_x_date(breaks = "5 years",
               labels = date_format("%y")) +
  theme(legend.position = c(0.1, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(-3000, 2000, 10))

BS-PE3M - Expectations of the prices

Code 
ei_bsse_m_r2 %>%
  filter(indic == "BS-PE3M",
         geo %in% c("FR", "DE", "IT", "EU27_2020"),
         s_adj == "SA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo),
         Geo = ifelse(geo == "EU27_2020", "Europe", Geo)) %>%
  month_to_date %>%
  ggplot() + ylab("Expectations of the prices ") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Geo)) +
  scale_color_manual(values = c("#003399", "#ED2939", "#000000", "#008c45")) +
  scale_x_date(breaks = "5 years",
               labels = date_format("%y")) +
  theme(legend.position = c(0.1, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(-3000, 2000, 10))