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

Data - Eurostat

Info

Last observation: 2026Q2 (N = 4641)

First observation: 1991Q1 (N = 315)

Last data update: 15 aoû 2026, 23:10. Last compile: 18 aoû 2026, 04:25

Structure

freq

indic_bt

nace_r2

s_adj

unit

geo

France: Retail Turnover vs. Volume of Sales

Value vs. Volume, Retail Trade (G47)

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)

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

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	0.059
Italy	0.043
European Union - 27 countries (from 2020)	0.040
France	0.038
Netherlands	0.034
Germany	0.020

Turnover and volume of sales in wholesale and retail trade

France, Germany, Italy

All

sts_trtu_q |>
  filter(nace_r2 == "G47",
         unit == "I21",
         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() |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + ylab("Retail trade, except of motor vehicles and motorcycles") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_flags +
  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_flags + 
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 10))

2010-

sts_trtu_q |>
  filter(nace_r2 == "G47",
         unit == "I21",
         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")) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + ylab("Retail trade, except of motor vehicles and motorcycles - Since 2010") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_flags +
  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_flags + 
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 10))