Retail trade - monthly data - index (2015 = 100) (NACE Rev. 2) - ei_isrt_m

Data - Eurostat

~/data/eurostat/

nace_r2

ei_isrt_m %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(nace_r2, Nace_r2) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional

nace_r2	Nace_r2	Nobs
G45	Wholesale and retail trade and repair of motor vehicles and motorcycles	53432
G47	Retail trade, except of motor vehicles and motorcycles	96208
G473	Retail sale of automotive fuel in specialised stores	89916
G47_FOOD	Retail sale of food, beverages and tobacco	93270
G47_NFOOD	Retail sale of non-food products (including fuel)	45542
G47_NFOOD_X_G473	Retail sale of non-food products (except fuel)	93294
G47_X_G473	Retail trade, except of motor vehicles, motorcyles and fuel	96868

s_adj

ei_isrt_m %>%
  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
SCA	Seasonally and calendar adjusted data	283918
CA	Calendar adjusted data, not seasonally adjusted data	269851
NSA	Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data)	14761

unit

ei_isrt_m %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional

unit	Unit	Nobs
I15	Index, 2015=100	305904
I21	Index, 2021=100	262626

indic

ei_isrt_m %>%
  left_join(indic, by = "indic") %>%
  group_by(indic, Indic) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional

indic	Indic	Nobs
TOVT	Index of turnover - Total	273116
TOVV	Index of deflated turnover	264978
IS-EPI	Number of persons employed index	30436

geo

ei_isrt_m %>%
  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

ei_isrt_m %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  print_table_conditional

France, Germany, Italy

Retail trade, except of motor vehicles and motorcycles

ei_isrt_m %>%
  filter(nace_r2 == "G47",
         geo %in% c("FR", "DE", "IT"),
         indic == "TOVT",
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2019M12"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  month_to_date %>%
  ggplot() + ylab("Retail trade, except of motor vehicles and motorcycles") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Geo)) +
  scale_color_manual(values = c("#0055a4", "#000000", "#008c45")) +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  geom_image(data = . %>%
               filter(date == as.Date("2004-01-01")) %>%
               mutate(date = as.Date("2004-01-01"),
                      image = paste0("../../icon/flag/", str_to_lower(Geo), ".png")),
             aes(x = date, y = values, image = image), asp = 1.5) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 10))

Wholesale and retail trade and repair of motor vehicles and motorcycles

ei_isrt_m %>%
  filter(nace_r2 == "G45",
         geo %in% c("FR", "DE", "IT"),
         indic == "TOVT",
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2019M12"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  month_to_date %>%
  ggplot() + ylab(" Wholesale and retail trade and repair of motor vehicles and motorcycles") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Geo)) +
  scale_color_manual(values = c("#0055a4", "#000000", "#008c45")) +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  geom_image(data = . %>%
               filter(date == as.Date("2004-01-01")) %>%
               mutate(date = as.Date("2004-01-01"),
                      image = paste0("../../icon/flag/", str_to_lower(Geo), ".png")),
             aes(x = date, y = values, image = image), asp = 1.5) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 10))

Retail sale of food, beverages and tobacco

ei_isrt_m %>%
  filter(nace_r2 == "G47_FOOD",
         geo %in% c("FR", "DE", "IT"),
         indic == "TOVT",
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2019M12"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  month_to_date %>%
  ggplot() + ylab("Retail sale of food, beverages and tobacco") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Geo)) +
  scale_color_manual(values = c("#0055a4", "#000000", "#008c45")) +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  geom_image(data = . %>%
               filter(date == as.Date("2004-01-01")) %>%
               mutate(date = as.Date("2004-01-01"),
                      image = paste0("../../icon/flag/", str_to_lower(Geo), ".png")),
             aes(x = date, y = values, image = image), asp = 1.5) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 10))

Retail sale of non-food products (including fuel)

ei_isrt_m %>%
  filter(nace_r2 == "G47_NFOOD",
         geo %in% c("FR", "DE", "IT"),
         indic == "TOVT",
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2019M12"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  month_to_date %>%
  ggplot() + ylab(" Retail sale of non-food products (including fuel)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Geo)) +
  scale_color_manual(values = c("#0055a4", "#000000", "#008c45")) +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  geom_image(data = . %>%
               filter(date == as.Date("2004-01-01")) %>%
               mutate(date = as.Date("2004-01-01"),
                      image = paste0("../../icon/flag/", str_to_lower(Geo), ".png")),
             aes(x = date, y = values, image = image), asp = 1.5) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 10))

Retail sale of non-food products (except fuel)

ei_isrt_m %>%
  filter(nace_r2 == "G47_NFOOD_X_G473",
         geo %in% c("FR", "DE", "IT"),
         indic == "TOVT",
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2019M12"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  month_to_date %>%
  ggplot() + ylab("Retail sale of non-food products (except fuel)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Geo)) +
  scale_color_manual(values = c("#0055a4", "#000000", "#008c45")) +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  geom_image(data = . %>%
               filter(date == as.Date("2004-01-01")) %>%
               mutate(date = as.Date("2004-01-01"),
                      image = paste0("../../icon/flag/", str_to_lower(Geo), ".png")),
             aes(x = date, y = values, image = image), asp = 1.5) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 10))

Retail trade, except of motor vehicles, motorcyles and fuel

ei_isrt_m %>%
  filter(nace_r2 == "G47_X_G473",
         geo %in% c("FR", "DE", "IT"),
         indic == "TOVT",
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2019M12"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  month_to_date %>%
  ggplot() + ylab("Retail trade, except of motor vehicles, motorcyles and fuel") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Geo)) +
  scale_color_manual(values = c("#0055a4", "#000000", "#008c45")) +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  geom_image(data = . %>%
               filter(date == as.Date("2004-01-01")) %>%
               mutate(date = as.Date("2004-01-01"),
                      image = paste0("../../icon/flag/", str_to_lower(Geo), ".png")),
             aes(x = date, y = values, image = image), asp = 1.5) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 10))

Retail sale of automotive fuel in specialised stores

ei_isrt_m %>%
  filter(nace_r2 == "G473",
         geo %in% c("FR", "DE", "IT"),
         indic == "TOVT",
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2019M12"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  month_to_date %>%
  ggplot() + ylab("Retail sale of automotive fuel in specialised stores") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Geo)) +
  scale_color_manual(values = c("#0055a4", "#000000", "#008c45")) +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  geom_image(data = . %>%
               filter(date == as.Date("2005-01-01")) %>%
               mutate(date = as.Date("2005-01-01"),
                      image = paste0("../../icon/flag/", str_to_lower(Geo), ".png")),
             aes(x = date, y = values, image = image), asp = 1.5) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 10))