Retail trade - monthly data - index (2015 = 100) (NACE Rev. 2) - ei_isrt_m
Data - Eurostat
Info
Last observation: Monthly: 2026M05 (N = 828)
First observation: Monthly: 1991M01 (N = 44)
Last data update: 23 jul 2026, 22:15. Last compile: 24 jul 2026, 01:26
Structure
France, Germany, Italy
Retail trade, except of motor vehicles and motorcycles
Code
ei_isrt_m %>%
filter(nace_r2 == "G47",
geo %in% c("FR", "DE", "IT"),
indic_bt == "NETTUR",
s_adj == "SCA") %>%
select(geo, Geo, time, values) %>%
group_by(geo) %>%
mutate(values = 100*values/values[time == "2019M12"]) %>%
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, 2100, 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
Code
ei_isrt_m %>%
filter(nace_r2 == "G47_FOOD",
geo %in% c("FR", "DE", "IT"),
indic_bt == "NETTUR",
s_adj == "SCA") %>%
select(geo, Geo, time, values) %>%
group_by(geo) %>%
mutate(values = 100*values/values[time == "2019M12"]) %>%
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, 2100, 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)
Code
ei_isrt_m %>%
filter(nace_r2 == "G47_NFOOD",
geo %in% c("FR", "DE", "IT"),
indic_bt == "NETTUR",
s_adj == "SCA") %>%
select(geo, Geo, time, values) %>%
group_by(geo) %>%
mutate(values = 100*values/values[time == "2019M12"]) %>%
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, 2100, 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)
Code
ei_isrt_m %>%
filter(nace_r2 == "G47_NFOOD_X_G473",
geo %in% c("FR", "DE", "IT"),
indic_bt == "NETTUR",
s_adj == "SCA") %>%
select(geo, Geo, time, values) %>%
group_by(geo) %>%
mutate(values = 100*values/values[time == "2019M12"]) %>%
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, 2100, 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
Code
ei_isrt_m %>%
filter(nace_r2 == "G47_X_G473",
geo %in% c("FR", "DE", "IT"),
indic_bt == "NETTUR",
s_adj == "SCA") %>%
select(geo, Geo, time, values) %>%
group_by(geo) %>%
mutate(values = 100*values/values[time == "2019M12"]) %>%
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, 2100, 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
Code
ei_isrt_m %>%
filter(nace_r2 == "G473",
geo %in% c("FR", "DE", "IT"),
indic_bt == "NETTUR",
s_adj == "SCA") %>%
select(geo, Geo, time, values) %>%
group_by(geo) %>%
mutate(values = 100*values/values[time == "2019M12"]) %>%
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, 2100, 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))