Consumer price indices (CPIs)

Data - OECD

Author

François Geerolf

Info

source dataset Title .html .rData
oecd PRICES_ALL Consumer price indices (CPIs) 2025-09-29 2025-11-16

Last

Monthly

obsTime Nobs
2025-10 2167

Quarterly

obsTime Nobs
2025-Q3 2558

Annual

obsTime Nobs
2025 395

FREQ

Code 
PRICES_ALL %>%
  left_join(FREQ, by = "FREQ") %>%
  group_by(FREQ, Freq) %>%
  summarise(Nobs = sum(!is.na(obsValue))) %>%
  arrange(-Nobs) %>%
  print_table_conditional
FREQ Freq Nobs
M Monthly 2104805
Q Quarterly 439181
A Annual 203849

MEASURE

Code 
PRICES_ALL %>%
  left_join(MEASURE, by = "MEASURE") %>%
  group_by(MEASURE, Measure) %>%
  summarise(Nobs = sum(!is.na(obsValue))) %>%
  arrange(-Nobs) %>%
  print_table_conditional
MEASURE Measure Nobs
CPI Consumer price index 2706779
IT_W Item weights 41056

ADJUSTMENT

Code 
PRICES_ALL %>%
  left_join(ADJUSTMENT, by = "ADJUSTMENT") %>%
  group_by(ADJUSTMENT, Adjustment) %>%
  summarise(Nobs = sum(!is.na(obsValue))) %>%
  arrange(-Nobs) %>%
  print_table_conditional
ADJUSTMENT Adjustment Nobs
N Neither seasonally adjusted nor calendar adjusted 2741101
S Seasonally adjusted, not calendar adjusted 6734

METHODOLOGY

Code 
PRICES_ALL %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  group_by(METHODOLOGY, Methodology) %>%
  summarise(Nobs = sum(!is.na(obsValue))) %>%
  arrange(-Nobs) %>%
  print_table_conditional
METHODOLOGY Methodology Nobs
N National 1960593
HICP Eurostat harmonised index of consumer prices (HICP) 787242

UNIT_MEASURE

Code 
PRICES_ALL %>%
  left_join(UNIT_MEASURE, by = "UNIT_MEASURE") %>%
  group_by(UNIT_MEASURE, Unit_measure) %>%
  summarise(Nobs = sum(!is.na(obsValue))) %>%
  arrange(-Nobs) %>%
  print_table_conditional
UNIT_MEASURE Unit_measure Nobs
IX Index 865322
PC Percentage change 852804
PA Percent per annum 837142
PD Percentage points 151511
10P3EXP_CNSMR Per 1 000 of consumer expenditure 41056

EXPENDITURE

Code 
PRICES_ALL %>%
  left_join(EXPENDITURE, by = "EXPENDITURE") %>%
  group_by(EXPENDITURE, Expenditure) %>%
  summarise(Nobs = sum(!is.na(obsValue))) %>%
  arrange(-Nobs) %>%
  print_table_conditional
EXPENDITURE Expenditure Nobs
_T Total 198106
CP01 Food and non-alcoholic beverages 167126
CP045_0722 Energy 148894
CP045 Electricity, gas and other fuels 116813
CP041 Actual rentals for housing 113915
_TXCP01_NRG All items non-food non-energy 112285
CP02 Alcoholic beverages, tobacco and narcotics 108928
CP04 Housing, water, electricity, gas and other fuels 108928
CP08 Communication 108928
CP06 Health 108673
CP03 Clothing and footwear 108340
CP05 Furnishings, household equipment and routine household maintenance 108340
CP09 Recreation and culture 108340
CP11 Restaurants and hotels 107891
CP0722 Fuels and lubricants for personal transport equipment 107579
CP07 Transport 107575
CP12 Miscellaneous goods and services 107551
CP10 Education 106141
SERV Services 101787
CP043 Maintenance and repair of the dwelling 98355
CP044 Water supply and miscellaneous services relating to the dwelling 97525
CP041T043X042 Housing excluding imputed rentals for housing 55330
GD Goods 52953
CP041T043 Housing 49696
_TXNRG_01_02 Overall index excluding energy, food, alcohol and tobacco 36922
SERVXCP041_042_0432 Services less housing 31952
CP042 Imputed rentals for housing 31856
SERVXCP041_0432 Services less housing (Housing excluding imputed rentals for housing) 30514
CPRES Residuals 6592

TRANSFORMATION

Code 
PRICES_ALL %>%
  left_join(TRANSFORMATION, by = "TRANSFORMATION") %>%
  group_by(TRANSFORMATION, Transformation) %>%
  summarise(Nobs = sum(!is.na(obsValue))) %>%
  arrange(-Nobs) %>%
  print_table_conditional
TRANSFORMATION Transformation Nobs
_Z Not applicable 906378
G1 Growth rate, period on period 852804
GY Growth rate, over 1 year 837142
GOY Contribution to growth rate, over 1 year 151511

REF_AREA

Code 
PRICES_ALL %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  group_by(REF_AREA, Ref_area) %>%
  summarise(Nobs = sum(!is.na(obsValue))) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Ref_area)),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

obsTime

Code 
PRICES_ALL %>%
  filter(!is.na(obsValue)) %>%
  group_by(obsTime) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(obsTime)) %>%
  print_table_conditional

Europe vs. US

1996-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "_T",
         REF_AREA %in% c("EA20", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

1996-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "_T",
         REF_AREA %in% c("EA20", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("2002-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

2017-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "_T",
         REF_AREA %in% c("EA20", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("2017-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

France vs. Germany

1996-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "_T",
         REF_AREA %in% c("FRA", "DEU"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

1999-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "_T",
         REF_AREA %in% c("FRA", "DEU"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("1999-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

2017-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "_T",
         REF_AREA %in% c("FRA", "DEU"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("2017-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

France vs. USA

CP041 - rents

1996-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "CP041",
         REF_AREA %in% c("FRA", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index (CP041)") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

2002-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "CP041",
         REF_AREA %in% c("FRA", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("2002-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index (CP041)") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

2010-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "CP041",
         REF_AREA %in% c("FRA", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("2010-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index (CP041)") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

GD

1996-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "GD",
         REF_AREA %in% c("FRA", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index (CP09)") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

2002-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "GD",
         REF_AREA %in% c("FRA", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("2002-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index (CP09)") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

CP07

1996-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "CP07",
         REF_AREA %in% c("FRA", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index (CP09)") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

2002-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "CP07",
         REF_AREA %in% c("FRA", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("2002-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index (CP09)") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

2017-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "CP07",
         REF_AREA %in% c("FRA", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("2017-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index (CP07)") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

2020-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "CP07",
         REF_AREA %in% c("FRA", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("2020-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index (CP07)") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

CP09

1996-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "CP09",
         REF_AREA %in% c("FRA", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index (CP09)") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

2002-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "CP09",
         REF_AREA %in% c("FRA", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("2002-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index (CP09)") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

CP08

1996-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "CP08",
         REF_AREA %in% c("FRA", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index (CP08)") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

2002-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "CP08",
         REF_AREA %in% c("FRA", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("2002-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index (CP08)") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

All

1996-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "_T",
         REF_AREA %in% c("FRA", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

2002-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "_T",
         REF_AREA %in% c("FRA", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("2002-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

2017-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "_T",
         REF_AREA %in% c("FRA", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("2017-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

2019-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "_T",
         REF_AREA %in% c("FRA", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("2019-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_flags +
  theme_minimal() + xlab("") + ylab("Price Index") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))