source | dataset | Title | Download | Compile |
---|---|---|---|---|
oecd | EO | Economic Outlook | 2024-04-15 | 2024-04-16 |
oecd | EO112_INTERNET | Economic Outlook No 112 - November 2022 | 2023-03-18 | 2024-04-15 |
oecd | EO111_INTERNET | Economic Outlook No 111 - June 2022 | 2023-03-18 | 2024-04-15 |
oecd | EO110_INTERNET | Economic Outlook No 110 - December 2021 | 2023-03-18 | 2024-04-15 |
oecd | EO109_INTERNET | Economic Outlook No 109 - May 2021 | 2023-03-18 | 2024-04-15 |
oecd | EO108_INTERNET | Economic Outlook No 108 - December 2020 | 2020-12-05 | 2024-04-15 |
LAST_COMPILE |
---|
2024-04-16 |
obsTime | Nobs |
---|---|
2020-Q4 | 3879 |
EO108_INTERNET %>%
left_join(EO108_INTERNET_var$FREQUENCY, by = "FREQUENCY") %>%
group_by(FREQUENCY, Frequency) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) print_table(.) else .}
FREQUENCY | Frequency | Nobs |
---|---|---|
Q | Quarterly | 715318 |
A | Annual | 357547 |
EO108_INTERNET %>%
filter(FREQUENCY == "A",
VARIABLE == "GAP",
LOCATION %in% c("DEU", "ESP", "GRC", "ITA")) %>%
left_join(EO108_INTERNET_var$LOCATION, by = "LOCATION") %>%
year_to_date %>%
select(Location, date, obsValue) %>%
arrange(Location, date) %>%
mutate(obsValue = obsValue/100) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot() +
geom_line(aes(x = date, y = obsValue, color = color)) +
theme_minimal() + scale_color_identity() + add_4flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
labels = scales::percent_format(accuracy = 1)) +
ylab("Output Gap (% of GDP)") + xlab("")
EO108_INTERNET %>%
filter(FREQUENCY == "A",
VARIABLE == "GAP",
LOCATION %in% c("CHE", "FRA", "DEU", "USA")) %>%
left_join(EO108_INTERNET_var$LOCATION, by = "LOCATION") %>%
year_to_date %>%
select(Location, date, obsValue) %>%
arrange(Location, date) %>%
mutate(obsValue = obsValue/100) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot() +
geom_line(aes(x = date, y = obsValue, color = color)) +
theme_minimal() + scale_color_identity() + add_4flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
labels = scales::percent_format(accuracy = 1)) +
ylab("Output Gap (% of GDP)") + xlab("")
EO108_INTERNET %>%
filter(FREQUENCY == "Q",
VARIABLE == "UNR",
LOCATION %in% c("DEU", "ITA", "FRA", "USA")) %>%
left_join(EO108_INTERNET_var$LOCATION, by = "LOCATION") %>%
quarter_to_date %>%
select(Location, date, obsValue) %>%
arrange(Location, date) %>%
mutate(obsValue = obsValue/100) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot() +
geom_line(aes(x = date, y = obsValue, color = color)) +
theme_minimal() + scale_color_identity() + add_4flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Unemployment Rate (%)") + xlab("")
EO108_INTERNET %>%
filter(FREQUENCY == "Q",
VARIABLE == "UNR",
LOCATION %in% c("DEU", "ITA", "FRA", "USA")) %>%
left_join(EO108_INTERNET_var$LOCATION, by = "LOCATION") %>%
quarter_to_date %>%
filter(date >= as.Date("1990-01-01")) %>%
select(Location, date, obsValue) %>%
arrange(Location, date) %>%
mutate(obsValue = obsValue/100) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot() +
geom_line(aes(x = date, y = obsValue, color = color)) +
theme_minimal() + scale_color_identity() + add_4flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Unemployment Rate (%)") + xlab("")