Hourly Earnings (MEI)

Data - OECD

Author

François Geerolf

Info

source dataset Title .html .rData
oecd EAR_MEI Hourly Earnings (MEI) 2026-06-04 2024-04-16
oecd PRICES_CPI Consumer price indices (CPIs) 2024-04-16 2024-04-15

Data on wages

Code 
load_data("wages.RData")
wages %>%
  source_dataset_file_updates()
source dataset Title .html .rData
eurostat earn_mw_cur Monthly minimum wages - bi-annual data 2026-06-03 2026-04-26
eurostat ei_lmlc_q Labour cost index, nominal value - quarterly data 2026-06-03 2026-04-26
eurostat lc_lci_lev Labour cost levels by NACE Rev. 2 activity 2026-06-03 2026-04-26
eurostat lc_lci_r2_q Labour cost index by NACE Rev. 2 activity - nominal value, quarterly data 2026-06-03 2026-05-28
eurostat nama_10_lp_ulc Labour productivity and unit labour costs 2026-06-04 2026-04-26
eurostat namq_10_lp_ulc Labour productivity and unit labour costs 2026-06-04 2026-04-26
eurostat tps00155 Minimum wages 2026-06-04 2026-04-26
fred wage Wage 2026-06-04 2026-05-29
ilo EAR_4MTH_SEX_ECO_CUR_NB_A Mean nominal monthly earnings of employees by sex and economic activity -- Harmonized series 2024-06-20 2023-06-01
ilo EAR_XEES_SEX_ECO_NB_Q Mean nominal monthly earnings of employees by sex and economic activity -- Harmonized series 2024-06-20 2023-06-01
oecd AV_AN_WAGE Average annual wages 2026-06-04 2026-06-04
oecd AWCOMP Taxing Wages - Comparative tables 2026-06-04 2023-09-09
oecd EAR_MEI Hourly Earnings (MEI) 2026-06-04 2024-04-16
oecd HH_DASH Household Dashboard 2026-06-04 2023-09-09
oecd MIN2AVE Minimum relative to average wages of full-time workers - MIN2AVE 2026-02-22 2023-09-09
oecd RMW Real Minimum Wages - RMW 2026-06-04 2024-03-12
oecd ULC_EEQ Unit labour costs and labour productivity (employment based), Total economy 2026-06-04 2024-04-15

Last

obsTime FREQUENCY Nobs
2022 A 93
2021 A 95
2020 A 93
2023-11 M 5
2023-10 M 14
2023-09 M 32
2023-Q3 Q 71
2023-Q2 Q 86
2023-Q1 Q 91

Detail

The Hourly Earnings (MEI) dataset contains predominantly monthly statistics, and associated statistical methodological information, for the 38 OECD member countries and for selected non-member economies.

The MEI Earnings dataset provides monthly and quarterly data on employees’ earnings series. It includes earnings series in manufacturing and for the private economic sector. Mostly the sources of the data are business surveys covering different economic sectors, but in some cases administrative data are also used. The target series for hourly earnings correspond to seasonally adjusted average total earnings paid per employed person per hour, including overtime pay and regularly recurring cash supplements. Where hourly earnings series are not available, a series could refer to weekly or monthly earnings. In this case, a series for full-time or full-time equivalent employees is preferred to an all employees series.

SUBJECT

Code 
EAR_MEI %>%
  left_join(EAR_MEI_var$SUBJECT, by = "SUBJECT") %>%
  group_by(SUBJECT, Subject) %>%
  summarise(nobs = n()) %>%
  arrange(-nobs) %>%
  print_table_conditional()
SUBJECT Subject nobs
LCEAMN01_IXOBSA Manufacturing, Index, SA 19040
LCEAMN01_IXOB Manufacturing, Index 15978
LCEAPR_IXOBSA Private Sector, Index, SA 6568

FREQUENCY

Code 
EAR_MEI %>%
  left_join(EAR_MEI_var$FREQUENCY, by = "FREQUENCY") %>%
  group_by(FREQUENCY, Frequency) %>%
  summarise(nobs = n()) %>%
  arrange(-nobs) %>%
  print_table_conditional()
FREQUENCY Frequency nobs
M Monthly 22249
Q Quarterly 15555
A Annual 3782

LOCATION

All

Code 
EAR_MEI %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, Location) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         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 .}

Location x Frequency

Code 
EAR_MEI %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, Location, FREQUENCY) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         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 .}

Annual

Code 
EAR_MEI %>%
  filter(FREQUENCY == "A") %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, Location) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         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 .}

Quarterly

Code 
EAR_MEI %>%
  filter(FREQUENCY == "Q") %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, Location) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         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 .}

Monthly

Code 
EAR_MEI %>%
  filter(FREQUENCY == "M") %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, Location) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         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 
EAR_MEI %>%
  group_by(obsTime) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(obsTime)) %>%
  print_table_conditional()

Europe VS U.S.

LCEAMN01_IXOBSA - Manufacturing, Index, SA

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2019-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  group_by(Location) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_2flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + 
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Manufacturing, Index, SA") + xlab("")

LCEAPR_IXOBSA

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2019-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  group_by(Location) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_2flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + 
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Eurozone, Japan, United States, United Kingdom

All

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "JPN", "EA19", "GBR", "HUN", "POL")) %>%
  quarter_to_date %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_6flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 500, 10),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k", prefix = "")) +
  ylab("Manufacturing, Index, SA") + xlab("")

2015-

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "JPN", "EA19", "GBR", "HUN", "POL")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_6flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + 
  scale_y_log10(breaks = seq(10, 500, 10),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Eurozone, United States, United Kingdom

All

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR")) %>%
  quarter_to_date %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 500, 10),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k", prefix = "")) +
  ylab("Manufacturing, Index, SA") + xlab("")

1996-

Nominal

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("1996-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("1996-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Real

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("1996-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("1996-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_2flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

2015-

Nominal

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Real

All
Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

2019-

Nominal

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2019-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + 
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Real

All
Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2019-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + 
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

Eurozone, United States, United Kingdom, France, Germany, Italy, Belgium

2015-

Nominal

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "DEU", "BEL")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Real

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "DEU", "BEL")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

Poland, Hungary

2015-

Nominal

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("POL", "HUN")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "FRA", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + 
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + add_2flags +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Real

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("POL", "HUN")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "FRA", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_2flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

Sweden, Netherlands, Luxembourg, Hungary, Poland, Finland

2015-

Nominal

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("SWE", "NLD", "LUX", "FIN", "DNK", "IRL")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "FRA", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + 
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + add_4flags +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Real

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("SWE", "NLD", "LUX", "FIN", "DNK", "IRL")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "FRA", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

Eurozone, United States, United Kingdom, France, Germany, Italy, Belgium

2015-

Nominal

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("FRA", "DEU", "ITA", "BEL", "ESP", "PRT", "AUT")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "FRA", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + 
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Real

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("FRA", "DEU", "ITA", "BEL", "ESP", "PRT", "AUT")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "FRA", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_6flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

2020-

Nominal

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("FRA", "DEU", "ITA", "BEL", "ESP", "PRT", "AUT")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2019-10-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "FRA", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-10-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_6flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_date(breaks = seq(as.Date("2015-01-01"), Sys.Date(), "3 months"),
               labels = date_format("%b %Y")) +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Real

Code 
df <- EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("FRA", "DEU", "ITA", "BEL", "ESP", "PRT", "AUT")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2019-10-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "FRA", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-10-01")]) %>%
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

  ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_6flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

2022-

Real

Code 
df <- EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("FRA", "DEU", "ITA", "BEL", "ESP", "PRT", "AUT")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2021-10-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "FRA", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2021-10-01")]) %>%
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

  ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_6flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

Eurozone, United States, United Kingdom, France

2015-

Nominal

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR", "FRA")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Real

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR", "FRA")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

Eurozone, United States, United Kingdom, Germany

2015-

Nominal

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR", "DEU")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Real

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR", "DEU")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  arrange(date) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

2021-

Code 
df <- EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR", "DEU")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2021-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  arrange(date) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2021-01-01")]) %>%
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

  ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

US, Eurozone, Germany, France

2019Q4-

Code 
df <- EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "DEU", "FRA")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2019-10-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  arrange(date) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-10-01")]) %>%
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

  ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

2017-

Code 
df <- EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "DEU", "FRA")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  arrange(date) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2017-01-01")]) %>%
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

  ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

2019-

Code 
df <- EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "DEU", "FRA")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2019-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  arrange(date) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-01-01")]) %>%
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

  ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

2021-

Code 
df <- EAR_MEI %>%
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "DEU", "FRA")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2021-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  arrange(date) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2021-01-01")]) %>%
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

  ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

Manufacturing, Index, SA

Table

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAMN01_IXOBSA") %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, Location) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         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 .}

U.S., Europe, U.K., Germany

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR", "DEU")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Manufacturing Sector, Index, SA, Real") + xlab("")

U.S., Europe, U.K., Germany, France

2015-

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR", "DEU", "FRA")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_5flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Manufacturing Sector, Index, SA, Real") + xlab("")

2019-Q4-

Code 
df <- EAR_MEI %>%
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR", "DEU", "FRA")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2019-10-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-10-01")]) %>%
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

  ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_5flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Manufacturing Sector, Index, SA, Real") + xlab("")

Plus Italy, Spain, etc.

2019-Q4-

Code 
df <- EAR_MEI %>%
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR", "DEU", "FRA", "ITA", "ESP")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2019-10-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-10-01")]) %>%
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_7flags + add_6flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Manufacturing Sector, Index, SA, Real") + xlab("")

Germany, Italy, Netherlands, Spain, France

2019-Q4-

Code 
df <- EAR_MEI %>%
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("DEU", "ITA", "NLD", "ESP", "FRA")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2019-10-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-10-01")]) %>%
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_5flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Manufacturing Sector, Index, SA, Real") + xlab("")

Germany, Italy, Netherlands, Spain, France

2019-Q4-

Code 
df <- EAR_MEI %>%
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("DEU", "ITA", "NLD", "ESP", "FRA")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2019-10-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-10-01")]) %>%
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + 
  add_5flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Manufacturing Sector, Index, SA, Real") + xlab("")

United States, Euro, UK

Code 
df <- EAR_MEI %>%
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))


ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_3flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Manufacturing Sector, Index, SA, Real") + xlab("")

United States, Euro, UK

2015-

Code 
df <- EAR_MEI %>%
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))


ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_2flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Manufacturing Sector, Index, SA, Real") + xlab("")

2017-

Code 
df <- EAR_MEI %>%
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2017-01-01")]) %>%
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))


ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_2flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Manufacturing Sector, Index, SA, Real") + xlab("")

Switzerland, Australia, Mexico

Code 
EAR_MEI %>%
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "A",
         LOCATION %in% c("AUS", "MEX", "CHE")) %>%
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 500, 10),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k", prefix = "")) +
  ylab("Manufacturing, Index, SA") + xlab("")