Turnover and volume of sales in wholesale and retail trade - quarterly data - sts_trtu_q

Data - Eurostat

Author

François Geerolf

Info

Last observation: Quarterly: 2026Q1 (N = 8,119)

First observation: Quarterly: 1991Q1 (N = 345)

Last data update: 23 jul 2026, 23:06. Last compile: 24 jul 2026, 03:58

Structure

France: Retail Turnover vs. Volume of Sales

Value vs. Volume, Retail Trade (G47)

Code

sts_trtu_q %>%
  filter(geo == "FR",
         nace_r2 == "G47",
         s_adj == "SCA",
         unit == "I21") %>%
  quarter_to_date %>%

  ggplot + geom_line(aes(x = date, y = values, color = Indic_bt)) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("Retail trade index (2021 = 100)")

Germany: Wholesale vs. Retail Turnover

G46 (Wholesale) vs. G47 (Retail)

Code

sts_trtu_q %>%
  filter(geo == "DE",
         nace_r2 %in% c("G46", "G47"),
         s_adj == "SCA",
         unit == "I21",
         indic_bt == "NETTUR") %>%
  quarter_to_date %>%

  ggplot + geom_line(aes(x = date, y = values, color = Nace_r2)) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("Net turnover (2021 = 100)")

Latest Quarter: Retail Turnover Growth by Country

Code

latest_q <- sts_trtu_q %>%
  filter(geo == "FR",
         nace_r2 == "G47",
         s_adj == "CA",
         unit == "PCH_SM",
         indic_bt == "NETTUR",
         !is.na(values)) %>%
  summarise(m = max(time)) %>%
  pull(m)

sts_trtu_q %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "NL", "EU27_2020"),
         nace_r2 == "G47",
         s_adj == "CA",
         unit == "PCH_SM",
         indic_bt == "NETTUR",
         time == latest_q) %>%
  mutate(values = values/100) %>%
  select(Geo, values) %>%
  arrange(-values) %>%
  print_table_conditional()

Geo	values
Spain	0.056
European Union - 27 countries (from 2020)	0.034
France	0.033
Italy	0.025
Germany	0.022
Netherlands	0.017

Turnover and volume of sales in wholesale and retail trade

France, Germany, Italy

All

Code

sts_trtu_q %>%
  filter(nace_r2 == "G47",
         unit == "I15",
         indic_bt == "NETTUR",
         geo %in% c("FR", "DE", "IT"),
         s_adj == "SCA") %>%
  select(geo, Geo, time, values) %>%
  group_by(geo) %>%

  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  quarter_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, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_3flags + 
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 10))

2010-

Code

sts_trtu_q %>%
  filter(nace_r2 == "G47",
         unit == "I15",
         indic_bt == "NETTUR",
         geo %in% c("FR", "DE", "IT"),
         s_adj == "SCA") %>%
  select(geo, Geo, time, values) %>%
  group_by(geo) %>%

  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2010-01-01")) %>%
  ggplot() + ylab("Retail trade, except of motor vehicles and motorcycles - Since 2010") + 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, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_3flags + 
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 10))