Hourly Earnings (MEI)
Data - OECD
François Geerolf
Info
| source | dataset | .html | .RData |
|---|---|---|---|
| oecd | EAR_MEI | 2024-04-16 | 2024-04-16 |
| oecd | PRICES_CPI | 2024-04-16 | 2024-04-15 |
Data on wages
| source | dataset | .html | .RData |
|---|---|---|---|
| eurostat | earn_mw_cur | 2024-04-15 | 2024-04-09 |
| eurostat | ei_lmlc_q | 2024-04-15 | 2024-04-15 |
| eurostat | lc_lci_lev | 2024-04-15 | 2024-04-09 |
| eurostat | lc_lci_r2_q | 2024-04-15 | 2024-04-09 |
| eurostat | nama_10_lp_ulc | 2024-04-15 | 2024-04-15 |
| eurostat | namq_10_lp_ulc | 2024-04-15 | 2024-04-09 |
| eurostat | tps00155 | 2024-04-15 | 2024-04-09 |
| fred | wage | 2024-04-15 | 2024-04-15 |
| ilo | EAR_4MTH_SEX_ECO_CUR_NB_A | 2023-06-01 | 2023-06-01 |
| ilo | EAR_XEES_SEX_ECO_NB_Q | 2023-06-01 | 2023-06-01 |
| oecd | AV_AN_WAGE | 2024-04-16 | 2023-09-09 |
| oecd | AWCOMP | 2024-04-16 | 2023-09-09 |
| oecd | EAR_MEI | 2024-04-16 | 2024-04-16 |
| oecd | HH_DASH | 2024-04-16 | 2023-09-09 |
| oecd | MIN2AVE | 2024-04-16 | 2023-09-09 |
| oecd | RMW | 2024-04-16 | 2024-03-12 |
| oecd | ULC_EEQ | 2024-04-16 | 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
EAR_MEI %>%
left_join(EAR_MEI_var$SUBJECT, by = "SUBJECT") %>%
group_by(SUBJECT, Subject) %>%
summarise(nobs = n()) %>%
arrange(-nobs) %>%
print_table_conditional()| SUBJECT | Subject | nobs |
|---|---|---|
| LCEAMN01_IXOBSA | Manufacturing, Index, SA | 19040 |
| LCEAMN01_IXOB | Manufacturing, Index | 15978 |
| LCEAPR_IXOBSA | Private Sector, Index, SA | 6568 |
FREQUENCY
EAR_MEI %>%
left_join(EAR_MEI_var$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
EAR_MEI %>%
left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION, Location) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Location x Frequency
EAR_MEI %>%
left_join(EAR_MEI_var$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
EAR_MEI %>%
filter(FREQUENCY == "A") %>%
left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION, Location) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Quarterly
EAR_MEI %>%
filter(FREQUENCY == "Q") %>%
left_join(EAR_MEI_var$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
EAR_MEI %>%
filter(FREQUENCY == "M") %>%
left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION, Location) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}obsTime
Europe VS U.S.
LCEAMN01_IXOBSA - Manufacturing, Index, SA
EAR_MEI %>%
filter(SUBJECT == "LCEAMN01_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("USA", "EA19")) %>%
quarter_to_date %>%
filter(date >= as.Date("2019-01-01")) %>%
left_join(EAR_MEI_var$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
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$LOCATION, by = "LOCATION") %>%
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
group_by(Location) %>%
arrange(date) %>%
mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-01-01")]) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_2flags +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(10, 500, 5),
labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
ylab("Private Sector, Index, SA") + xlab("")
Eurozone, Japan, United States, United Kingdom
All
EAR_MEI %>%
filter(SUBJECT == "LCEAPR_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("USA", "JPN", "EA19", "GBR", "HUN", "POL")) %>%
quarter_to_date %>%
left_join(EAR_MEI_var$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-
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$LOCATION, by = "LOCATION") %>%
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
group_by(Location) %>%
mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_6flags +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(10, 500, 10),
labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
ylab("Private Sector, Index, SA") + xlab("")
Eurozone, United States, United Kingdom
All
EAR_MEI %>%
filter(SUBJECT == "LCEAPR_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("USA", "EA19", "GBR")) %>%
quarter_to_date %>%
left_join(EAR_MEI_var$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
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$LOCATION, by = "LOCATION") %>%
left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
group_by(Location) %>%
mutate(obsValue = 100*obsValue / obsValue[date == as.Date("1996-01-01")]) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(10, 500, 5),
labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
ylab("Private Sector, Index, SA") + xlab("")
Real
EAR_MEI %>%
filter(SUBJECT == "LCEAPR_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("USA", "EA19")) %>%
quarter_to_date %>%
filter(date >= as.Date("1996-01-01")) %>%
left_join(EAR_MEI_var$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
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$LOCATION, by = "LOCATION") %>%
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
group_by(Location) %>%
mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) + add_6flags +
scale_y_log10(breaks = seq(10, 500, 5),
labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
ylab("Private Sector, Index, SA") + xlab("")
Real
All
EAR_MEI %>%
filter(SUBJECT == "LCEAPR_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("USA", "EA19", "GBR")) %>%
quarter_to_date %>%
filter(date >= as.Date("2015-01-01")) %>%
left_join(EAR_MEI_var$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
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$LOCATION, by = "LOCATION") %>%
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
group_by(Location) %>%
arrange(date) %>%
mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-01-01")]) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(10, 500, 5),
labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
ylab("Private Sector, Index, SA") + xlab("")
Real
All
EAR_MEI %>%
filter(SUBJECT == "LCEAPR_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("USA", "EA19", "GBR")) %>%
quarter_to_date %>%
filter(date >= as.Date("2019-01-01")) %>%
left_join(EAR_MEI_var$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
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$LOCATION, by = "LOCATION") %>%
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
group_by(Location) %>%
mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) + add_6flags +
scale_y_log10(breaks = seq(10, 500, 5),
labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
ylab("Private Sector, Index, SA") + xlab("")
Real
EAR_MEI %>%
filter(SUBJECT == "LCEAPR_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("USA", "DEU", "BEL")) %>%
quarter_to_date %>%
filter(date >= as.Date("2015-01-01")) %>%
left_join(EAR_MEI_var$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
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$LOCATION, by = "LOCATION") %>%
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "FRA", color2, color)) %>%
group_by(Location) %>%
mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) + add_2flags +
scale_y_log10(breaks = seq(10, 500, 5),
labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
ylab("Private Sector, Index, SA") + xlab("")
Real
EAR_MEI %>%
filter(SUBJECT == "LCEAPR_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("POL", "HUN")) %>%
quarter_to_date %>%
filter(date >= as.Date("2015-01-01")) %>%
left_join(EAR_MEI_var$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
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$LOCATION, by = "LOCATION") %>%
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "FRA", color2, color)) %>%
group_by(Location) %>%
mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) + add_4flags +
scale_y_log10(breaks = seq(10, 500, 5),
labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
ylab("Private Sector, Index, SA") + xlab("")
Real
EAR_MEI %>%
filter(SUBJECT == "LCEAPR_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("SWE", "NLD", "LUX", "FIN", "DNK", "IRL")) %>%
quarter_to_date %>%
filter(date >= as.Date("2015-01-01")) %>%
left_join(EAR_MEI_var$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
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$LOCATION, by = "LOCATION") %>%
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "FRA", color2, color)) %>%
group_by(Location) %>%
mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) + add_6flags +
scale_y_log10(breaks = seq(10, 500, 5),
labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
ylab("Private Sector, Index, SA") + xlab("")
Real
EAR_MEI %>%
filter(SUBJECT == "LCEAPR_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("FRA", "DEU", "ITA", "BEL", "ESP", "PRT", "AUT")) %>%
quarter_to_date %>%
filter(date >= as.Date("2015-01-01")) %>%
left_join(EAR_MEI_var$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
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$LOCATION, by = "LOCATION") %>%
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "FRA", color2, color)) %>%
group_by(Location) %>%
mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-10-01")]) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_6flags +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_x_date(breaks = seq(as.Date("2015-01-01"), Sys.Date(), "3 months"),
labels = date_format("%b %Y")) +
scale_y_log10(breaks = seq(10, 500, 5),
labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
ylab("Private Sector, Index, SA") + xlab("")
Real
df <- EAR_MEI %>%
filter(SUBJECT == "LCEAPR_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("FRA", "DEU", "ITA", "BEL", "ESP", "PRT", "AUT")) %>%
quarter_to_date %>%
filter(date >= as.Date("2019-10-01")) %>%
left_join(EAR_MEI_var$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
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$date), by = 0.25)) +
scale_y_log10(breaks = seq(10, 500, 1),
labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
ylab("Private Sector, Index, SA, Real") + xlab("")
Eurozone, United States, United Kingdom, France
2015-
Nominal
EAR_MEI %>%
filter(SUBJECT == "LCEAPR_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("USA", "EA19", "GBR", "FRA")) %>%
quarter_to_date %>%
filter(date >= as.Date("2015-01-01")) %>%
left_join(EAR_MEI_var$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
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$LOCATION, by = "LOCATION") %>%
left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
group_by(Location) %>%
mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_4flags +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(10, 500, 1),
labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
ylab("Private Sector, Index, SA, Real") + xlab("")
Eurozone, United States, United Kingdom, Germany
2015-
Nominal
EAR_MEI %>%
filter(SUBJECT == "LCEAPR_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("USA", "EA19", "GBR", "DEU")) %>%
quarter_to_date %>%
filter(date >= as.Date("2015-01-01")) %>%
left_join(EAR_MEI_var$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
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$LOCATION, by = "LOCATION") %>%
left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
arrange(date) %>%
mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
group_by(Location) %>%
mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_4flags +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) + add_6flags +
scale_y_log10(breaks = seq(10, 500, 1),
labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
ylab("Private Sector, Index, SA, Real") + xlab("")
2021-
df <- EAR_MEI %>%
filter(SUBJECT == "LCEAPR_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("USA", "EA19", "GBR", "DEU")) %>%
quarter_to_date %>%
filter(date >= as.Date("2021-01-01")) %>%
left_join(EAR_MEI_var$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-
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$date), by = 0.25)) +
scale_y_log10(breaks = seq(10, 500, 1),
labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
ylab("Private Sector, Index, SA, Real") + xlab("")
2017-
df <- EAR_MEI %>%
filter(SUBJECT == "LCEAPR_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("USA", "EA19", "DEU", "FRA")) %>%
quarter_to_date %>%
filter(date >= as.Date("2017-01-01")) %>%
left_join(EAR_MEI_var$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-
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$date), by = 0.25)) +
scale_y_log10(breaks = seq(10, 500, 1),
labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
ylab("Private Sector, Index, SA, Real") + xlab("")
2021-
df <- EAR_MEI %>%
filter(SUBJECT == "LCEAPR_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("USA", "EA19", "DEU", "FRA")) %>%
quarter_to_date %>%
filter(date >= as.Date("2021-01-01")) %>%
left_join(EAR_MEI_var$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
EAR_MEI %>%
filter(SUBJECT == "LCEAMN01_IXOBSA") %>%
left_join(EAR_MEI_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION, Location) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}U.S., Europe, U.K., Germany
EAR_MEI %>%
filter(SUBJECT == "LCEAMN01_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("USA", "EA19", "GBR", "DEU")) %>%
quarter_to_date %>%
filter(date >= as.Date("2015-01-01")) %>%
left_join(EAR_MEI_var$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-
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$LOCATION, by = "LOCATION") %>%
left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) %>%
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
mutate(obsValue = obsValue/CPALTT01_IXOB) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "EA19", color2, color)) %>%
group_by(Location) %>%
mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_5flags +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) + add_6flags +
scale_y_log10(breaks = seq(10, 500, 1),
labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
ylab("Manufacturing Sector, Index, SA, Real") + xlab("")
2019-Q4-
df <- EAR_MEI %>%
filter(SUBJECT == "LCEAMN01_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("USA", "EA19", "GBR", "DEU", "FRA")) %>%
quarter_to_date %>%
filter(date >= as.Date("2019-10-01")) %>%
left_join(EAR_MEI_var$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-
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$date), by = 0.25)) +
scale_y_log10(breaks = seq(10, 500, 1),
labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
ylab("Manufacturing Sector, Index, SA, Real") + xlab("")
Germany, Italy, Netherlands, Spain, France
2019-Q4-
df <- EAR_MEI %>%
filter(SUBJECT == "LCEAMN01_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("DEU", "ITA", "NLD", "ESP", "FRA")) %>%
quarter_to_date %>%
filter(date >= as.Date("2019-10-01")) %>%
left_join(EAR_MEI_var$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-
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$date), by = 0.25)) +
add_6flags +
scale_y_log10(breaks = seq(10, 500, 1),
labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
ylab("Manufacturing Sector, Index, SA, Real") + xlab("")
United States, Euro, UK
df <- EAR_MEI %>%
filter(SUBJECT == "LCEAMN01_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("USA", "EA19", "GBR")) %>%
quarter_to_date %>%
filter(date >= as.Date("2015-01-01")) %>%
left_join(EAR_MEI_var$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-
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$date), by = 0.25)) +
add_6flags +
scale_y_log10(breaks = seq(10, 500, 1),
labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
ylab("Manufacturing Sector, Index, SA, Real") + xlab("")
2017-
df <- EAR_MEI %>%
filter(SUBJECT == "LCEAMN01_IXOBSA",
FREQUENCY == "Q",
LOCATION %in% c("USA", "EA19")) %>%
quarter_to_date %>%
filter(date >= as.Date("2017-01-01")) %>%
left_join(EAR_MEI_var$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
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("")