Electricity prices for household consumers - bi-annual data (from 2007 onwards)

Data - Eurostat

Author

François Geerolf

Info

source dataset .html .RData
eurostat nrg_pc_204 2024-11-01 2024-11-05

Data on energy

source dataset .html .RData
ec WOB 2024-09-15 2024-08-25
eurostat ei_isen_m 2024-11-05 2024-10-09
eurostat nrg_bal_c 2023-12-31 2024-10-08
eurostat nrg_pc_202 2024-11-05 2024-10-08
eurostat nrg_pc_203 2023-06-11 2024-10-08
eurostat nrg_pc_203_c 2024-11-05 2024-10-08
eurostat nrg_pc_203_h 2024-11-05 2024-11-05
eurostat nrg_pc_203_v 2024-11-05 2024-10-08
eurostat nrg_pc_204 2024-11-01 2024-11-05
eurostat nrg_pc_205 2023-06-11 2024-10-08
fred energy 2024-11-01 2024-11-01
iea world_energy_balances_highlights_2022 2024-06-20 2023-04-24
wb CMO 2024-06-20 2024-05-23
wdi EG.GDP.PUSE.KO.PP.KD 2024-09-18 2024-09-18
wdi EG.USE.PCAP.KG.OE 2024-09-18 2024-09-18
yahoo energy 2024-11-05 2024-11-05

LAST_COMPILE

LAST_COMPILE
2024-11-05

Last

Code 
nrg_pc_204 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()
time Nobs
2024S1 2052

Info

  • Electricity and gas prices in the first half of 2022. html / png
Code 
include_graphics("https://ec.europa.eu/eurostat/documents/4187653/14185664/Gas+and+Electricity+Prices+S1_2022.png")

  • html

  • Electricity prices for household consumers, second half 2022. html

Code 
include_graphics("https://ec.europa.eu/eurostat/statistics-explained/images/7/7a/Electricity_prices_for_household_consumers%2C_second_half_2022_v5.png")

nrg_cons

Code 
nrg_pc_204 %>%
  left_join(nrg_cons, by = "nrg_cons") %>%
  group_by(nrg_cons, Nrg_cons) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()
nrg_cons Nrg_cons Nobs
KWH1000-2499 Consumption from 1 000 kWh to 2 499 kWh - band DB 11418
KWH2500-4999 Consumption from 2 500 kWh to 4 999 kWh - band DC 11454
KWH5000-14999 Consumption from 5 000 kWh to 14 999 kWh - band DD 11418
KWH_GE15000 Consumption for 15 000 kWh or over - band DE 11391
KWH_LT1000 Consumption less than 1 000 kWh - band DA 11289
TOT_KWH Consumption of kWh - all bands 2046

currency

Code 
nrg_pc_204 %>%
  left_join(currency, by = "currency") %>%
  group_by(currency, Currency) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
currency Currency Nobs
EUR Euro 20494
NAC National currency 19702
PPS Purchasing Power Standard 18820

tax

Code 
nrg_pc_204 %>%
  left_join(tax, by = "tax") %>%
  group_by(tax, Tax) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
tax Tax Nobs
I_TAX All taxes and levies included 19677
X_VAT Excluding VAT and other recoverable taxes and levies 19677
X_TAX Excluding taxes and levies 19662

geo

Code 
nrg_pc_204 %>%
  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 
nrg_pc_204 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  print_table_conditional()

What do I_TAX, X_VAT, X_TAX mean ?

Netherlands

Code 
nrg_pc_204 %>%
  filter(geo %in% c("FR"),
         currency == "EUR",
         time %in% c("2021S2", "2022S1", "2022S2", "2023S1")) %>%
  select_if(~n_distinct(.) > 1) %>%
  #mutate(values = 1000* values) %>%
  spread(tax, values) %>%
  mutate(` Taxes except VAT` = X_VAT-X_TAX,
            `  VAT` = I_TAX-X_VAT,
            `Excluding taxes` = X_TAX) %>%
  print_table_conditional()
nrg_cons time I_TAX X_TAX X_VAT Taxes except VAT VAT Excluding taxes
KWH_GE15000 2021S2 0.1781 0.1169 0.1505 0.0336 0.0276 0.1169
KWH_GE15000 2022S1 0.1850 0.1394 0.1566 0.0172 0.0284 0.1394
KWH_GE15000 2022S2 0.1868 0.1457 0.1576 0.0119 0.0292 0.1457
KWH_GE15000 2023S1 0.2001 0.1657 0.1695 0.0038 0.0306 0.1657
KWH_LT1000 2021S2 0.4236 0.3161 0.3841 0.0680 0.0395 0.3161
KWH_LT1000 2022S1 0.4338 0.3421 0.3916 0.0495 0.0422 0.3421
KWH_LT1000 2022S2 0.4260 0.3417 0.3848 0.0431 0.0412 0.3417
KWH_LT1000 2023S1 0.4355 0.3585 0.3911 0.0326 0.0444 0.3585
KWH1000-2499 2021S2 0.2398 0.1665 0.2098 0.0433 0.0300 0.1665
KWH1000-2499 2022S1 0.2482 0.1893 0.2165 0.0272 0.0317 0.1893
KWH1000-2499 2022S2 0.2582 0.2049 0.2262 0.0213 0.0320 0.2049
KWH1000-2499 2023S1 0.2741 0.2258 0.2386 0.0128 0.0355 0.2258
KWH2500-4999 2021S2 0.2022 0.1356 0.1738 0.0382 0.0284 0.1356
KWH2500-4999 2022S1 0.2092 0.1566 0.1793 0.0227 0.0299 0.1566
KWH2500-4999 2022S2 0.2204 0.1723 0.1899 0.0176 0.0305 0.1723
KWH2500-4999 2023S1 0.2300 0.1893 0.1971 0.0078 0.0329 0.1893
KWH5000-14999 2021S2 0.1850 0.1219 0.1572 0.0353 0.0278 0.1219
KWH5000-14999 2022S1 0.1928 0.1445 0.1635 0.0190 0.0293 0.1445
KWH5000-14999 2022S2 0.1967 0.1533 0.1672 0.0139 0.0295 0.1533
KWH5000-14999 2023S1 0.2088 0.1720 0.1771 0.0051 0.0317 0.1720
TOT_KWH 2021S2 0.1970 0.1316 0.1687 0.0371 0.0283 0.1316
TOT_KWH 2022S1 0.2046 0.1539 0.1748 0.0209 0.0298 0.1539
TOT_KWH 2022S2 0.2106 0.1648 0.1805 0.0157 0.0301 0.1648
TOT_KWH 2023S1 0.2225 0.1834 0.1901 0.0067 0.0324 0.1834

Consumption < 1 000 kWh

bars

Code 
nrg_pc_204 %>%
  filter(geo %in% c("FR", "DE", "EA", "ES"),
         nrg_cons == "KWH_LT1000",
         currency == "EUR",
         time %in% c("2021S2", "2022S1", "2022S2", "2023S1")) %>%
  select_if(~n_distinct(.) > 1) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) %>%
  spread(tax, values) %>%
  transmute(time, Geo,
            ` Taxes except VAT` = X_VAT-X_TAX,
            `  VAT` = I_TAX-X_VAT,
            `Excluding taxes` = X_TAX) %>%
  gather(Tax, values, - time, -Geo) %>%
  ggplot(., aes(x = time, y = values, fill = Tax)) + 
  geom_bar(stat = "identity",
           position = "stack") +
  facet_grid(~ Geo) + theme_minimal() +
theme(legend.position = c(0.2, 0.9),
      legend.title = element_blank()) +
  scale_fill_manual(values = viridis(3)[1:3]) +
  scale_y_continuous(breaks = seq(-30, 30, .1),
                labels = dollar_format(a = .1, pre = "", su = "€/kWh"),
                limits = c(0, 0.6))  +
  xlab("Semester") + ylab("") +
  ggtitle("Electricity price, LOW Consumption")

France, Germany, EA19, Spain

All

Code 
nrg_pc_204 %>%
  filter(geo %in% c("FR", "DE", "EA", "ES"),
         tax %in% c("I_TAX", "X_TAX"),
         nrg_cons == "KWH_LT1000",
         currency == "EUR") %>%
  left_join(geo, by = "geo") %>%
  left_join(tax, by = "tax") %>%
  semester_to_date %>%
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color, linetype = Tax)) +
  scale_color_identity() + theme_minimal()  + add_8flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2025, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank()) +
  xlab("") + ylab("Electricity Prices - C < 1 000 kWh") +
  scale_y_log10(breaks = seq(-30, 30, .1),
                labels = dollar_format(a = .1, pre = "", su = "€"))

2020-

Code 
nrg_pc_204 %>%
  filter(geo %in% c("FR", "DE", "EA", "ES"),
         tax %in% c("I_TAX", "X_TAX"),
         nrg_cons == "KWH_LT1000",
         currency == "EUR") %>%
  left_join(geo, by = "geo") %>%
  left_join(tax, by = "tax") %>%
  semester_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color, linetype = Tax)) +
  scale_color_identity() + theme_minimal()  + add_8flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2025, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank()) +
  xlab("") + ylab("Band DA : Consumption < 1 000 kWh") +
  scale_y_log10(breaks = seq(-30, 30, .1),
                labels = dollar_format(a = .1, pre = "", su = "€"))

France, Germany, Italy, Spain

Code 
nrg_pc_204 %>%
  filter(geo %in% c("FR", "DE", "IT", "ES"),
         tax %in% c("I_TAX", "X_TAX"),
         nrg_cons == "KWH_LT1000",
         currency == "EUR") %>%
  left_join(geo, by = "geo") %>%
  left_join(tax, by = "tax") %>%
  semester_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color, linetype = Tax)) +
  scale_color_identity() + theme_minimal()  + add_8flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2025, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank()) +
  xlab("") + ylab("Electricity Prices - C < 1 000 kWh") +
  scale_y_log10(breaks = seq(-30, 30, .1),
                labels = dollar_format(a = .1, pre = "", su = "€"))

Netherlands

Code 
nrg_pc_204 %>%
  filter(geo %in% c("NL"),
         tax %in% c("I_TAX", "X_TAX"),
         nrg_cons == "KWH_LT1000",
         currency == "EUR") %>%
  left_join(geo, by = "geo") %>%
  left_join(tax, by = "tax") %>%
  semester_to_date %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color, linetype = Tax)) +
  scale_color_identity() + theme_minimal()  + add_2flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2025, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.3),
        legend.title = element_blank()) +
  xlab("") + ylab("Electricity Prices - C < 1 000 kWh") +
  scale_y_continuous(breaks = seq(-30, 30, .1),
                labels = dollar_format(a = .1, pre = "", su = "€"))

Greece, Belgium, Austria, Portugal

Code 
nrg_pc_204 %>%
  filter(geo %in% c("EL", "BE", "AT", "PT"),
         tax %in% c("I_TAX", "X_TAX"),
         nrg_cons == "KWH_LT1000",
         currency == "EUR") %>%
  left_join(geo, by = "geo") %>%
  left_join(tax, by = "tax") %>%
  semester_to_date %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color, linetype = Tax)) +
  scale_color_identity() + theme_minimal()  + add_8flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2025, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank()) +
  xlab("") + ylab("Electricity Prices - C < 1 000 kWh") +
  scale_y_log10(breaks = seq(-30, 30, .1),
                labels = dollar_format(a = .1, pre = "", su = "€"))

1 000 kWh < Consumption < 2 500 kWh

France, Germany, Italy, Spain

Code 
nrg_pc_204 %>%
  filter(geo %in% c("FR", "DE", "IT", "ES"),
         tax %in% c("I_TAX", "X_TAX"),
         nrg_cons == "KWH1000-2499",
         currency == "EUR") %>%
  left_join(geo, by = "geo") %>%
  left_join(tax, by = "tax") %>%
  semester_to_date %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color, linetype = Tax)) +
  scale_color_identity() + theme_minimal()  + add_8flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2025, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank()) +
  xlab("") + ylab("Electricity Prices - 1 000 kWh < C < 2 500 kWh") +
  scale_y_log10(breaks = c(0.12, 0.15, 0.18, 0.2, 0.25, 0.3, 0.35),
                labels = dollar_format(a = .01, pre = "", su = "€"))

Band DE : Consumption > 15 000 kWh

France, Germany, EA19, Spain

bars

Code 
nrg_pc_204 %>%
  filter(geo %in% c("FR", "DE", "EA", "ES"),
         nrg_cons == "KWH_GE15000",
         currency == "EUR",
         time %in% c("2021S2", "2022S1", "2022S2")) %>%
  select_if(~n_distinct(.) > 1) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) %>%
  spread(tax, values) %>%
  transmute(time, Geo,
            ` Taxes except VAT` = X_VAT-X_TAX,
            `  VAT` = I_TAX-X_VAT,
            `Excluding taxes` = X_TAX) %>%
  gather(Tax, values, - time, -Geo) %>%
  ggplot(., aes(x = time, y = values, fill = Tax)) + 
  geom_bar(stat = "identity",
           position = "stack") +
  facet_grid(~ Geo) + theme_minimal() +
theme(legend.position = "none",
      legend.title = element_blank()) +
  scale_fill_manual(values = viridis(3)[1:3]) +
  scale_y_continuous(breaks = seq(-30, 30, .1),
                labels = dollar_format(a = .1, pre = "", su = "€/kWh"),
                limits = c(0, 0.6))  +
  xlab("Semester") + ylab("") +
  ggtitle("Electricity price, HIGH Consumption")

2020-

Code 
nrg_pc_204 %>%
  filter(geo %in% c("FR", "DE", "EA", "ES"),
         tax %in% c("I_TAX", "X_TAX"),
         nrg_cons == "KWH_GE15000",
         currency == "EUR") %>%
  left_join(geo, by = "geo") %>%
  left_join(tax, by = "tax") %>%
  semester_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color, linetype = Tax)) +
  scale_color_identity() + theme_minimal()  + add_8flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2025, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank()) +
  xlab("") + ylab("Band DE : Consumption > 15 000 kWh") +
  scale_y_log10(breaks = seq(-30, 30, .05),
                labels = dollar_format(a = .01, pre = "", su = "€"))

Euros

Euro Area EA

All

Code 
nrg_pc_204 %>%
  filter(geo == "EA",
         currency == "EUR") %>%
  left_join(tax, by = "tax") %>%
  left_join(nrg_cons, by = "nrg_cons") %>%
  select_if(~ n_distinct(.) > 1) %>%
  semester_to_date %>%
  ggplot + geom_line(aes(x = date, y = values, color = paste0(Tax, " - ", Nrg_cons))) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 1, 0.01)) +
  theme(legend.position = c(0.45, 0.5),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))

2020-

Code 
nrg_pc_204 %>%
  filter(geo == "EA",
         currency == "EUR") %>%
  left_join(tax, by = "tax") %>%
  left_join(nrg_cons, by = "nrg_cons") %>%
  select_if(~ n_distinct(.) > 1) %>%
  semester_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = paste0(Tax, " - ", Nrg_cons))) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 1, 0.01)) +
  theme(legend.position = c(0.45, 0.5),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))

France

All

Code 
nrg_pc_204 %>%
  filter(geo == "FR",
         currency == "EUR") %>%
  left_join(tax, by = "tax") %>%
  left_join(nrg_cons, by = "nrg_cons") %>%
  select_if(~ n_distinct(.) > 1) %>%
  semester_to_date %>%
  ggplot + geom_line(aes(x = date, y = values, color = Nrg_cons, linetype = Tax)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 1, 0.01)) +
  theme(legend.position = c(0.45, 0.5),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))

2020-

Code 
nrg_pc_204 %>%
  filter(geo == "FR",
         currency == "EUR") %>%
  left_join(tax, by = "tax") %>%
  left_join(nrg_cons, by = "nrg_cons") %>%
  select_if(~ n_distinct(.) > 1) %>%
  semester_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = paste0(Tax, " - ", Nrg_cons))) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 1, 0.01)) +
  theme(legend.position = c(0.45, 0.5),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))

Germany

Code 
nrg_pc_204 %>%
  filter(geo == "DE",
         unit == "KWH",
         currency == "EUR") %>%
  left_join(tax, by = "tax") %>%
  left_join(nrg_cons, by = "nrg_cons") %>%
  select_if(~ n_distinct(.) > 1) %>%
  semester_to_date %>%
  ggplot + geom_line(aes(x = date, y = values, color = paste0(Tax, " - ", Nrg_cons))) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 1, 0.01)) +
  theme(legend.position = c(0.45, 0.5),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))

Italy

Code 
nrg_pc_204 %>%
  filter(geo == "IT",
         unit == "KWH",
         currency == "EUR") %>%
  left_join(tax, by = "tax") %>%
  left_join(nrg_cons, by = "nrg_cons") %>%
  select_if(~ n_distinct(.) > 1) %>%
  semester_to_date %>%
  ggplot + geom_line(aes(x = date, y = values, color = paste0(Tax, " - ", Nrg_cons))) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 1, 0.01)) +
  theme(legend.position = c(0.45, 0.5),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))

Spain

Code 
nrg_pc_204 %>%
  filter(geo == "ES",
         currency == "EUR") %>%
  left_join(tax, by = "tax") %>%
  left_join(nrg_cons, by = "nrg_cons") %>%
  select_if(~ n_distinct(.) > 1) %>%
  semester_to_date %>%
  ggplot + geom_line(aes(x = date, y = values, color = paste0(Tax, " - ", Nrg_cons))) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 1, 0.01)) +
  theme(legend.position = c(0.45, 0.5),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))

Netherlands

Code 
nrg_pc_204 %>%
  filter(geo == "NL",
         currency == "EUR") %>%
  left_join(tax, by = "tax") %>%
  left_join(nrg_cons, by = "nrg_cons") %>%
  select_if(~ n_distinct(.) > 1) %>%
  semester_to_date %>%
  ggplot + geom_line(aes(x = date, y = values, color = paste0(Tax, " - ", Nrg_cons))) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 1, 0.01)) +
  theme(legend.position = c(0.45, 0.5),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))