Code
hlth_cd_ycdr2 %>%
left_join(unit, by = "unit") %>%
group_by(unit, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) print_table(.) else .}| unit | Unit | Nobs |
|---|---|---|
| RT | Rate | 39496312 |
Data - Eurostat
hlth_cd_ycdr2 %>%
left_join(unit, by = "unit") %>%
group_by(unit, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) print_table(.) else .}| unit | Unit | Nobs |
|---|---|---|
| RT | Rate | 39496312 |
hlth_cd_ycdr2 %>%
left_join(sex, by = "sex") %>%
group_by(sex, Sex) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) print_table(.) else .}| sex | Sex | Nobs |
|---|---|---|
| T | Total | 13618053 |
| F | Females | 13016289 |
| M | Males | 12861970 |
hlth_cd_ycdr2 %>%
left_join(age, by = "age") %>%
group_by(age, Age) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}hlth_cd_ycdr2 %>%
left_join(icd10, by = "icd10") %>%
group_by(icd10, Icd10) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}hlth_cd_ycdr2 %>%
left_join(geo, by = "geo") %>%
group_by(geo, Geo) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}hlth_cd_ycdr2 %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}hlth_cd_ycdr2 %>%
filter(time == "2015",
age == "TOTAL",
sex == "T",
geo == "FR") %>%
left_join(icd10, by = "icd10") %>%
select(icd10, Icd10, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}hlth_cd_ycdr2 %>%
filter(time == "2015",
age == "TOTAL",
sex == "T",
icd10 %in% c("X60-X84_Y870", "A-R_V-Y")) %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, icd10, values) %>%
spread(icd10, values) %>%
mutate(`%` = round(100*`X60-X84_Y870`/`A-R_V-Y`, 2)) %>%
arrange(-`%`) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}hlth_cd_ycdr2 %>%
filter(time == "2015",
age == "TOTAL",
sex == "T",
icd10 %in% c("X60-X84_Y870", "A-R_V-Y")) %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, icd10, values) %>%
spread(icd10, values) %>%
right_join(europe_NUTS2, by = "geo") %>%
filter(long >= -13.5, lat >= 33) %>%
ggplot(., aes(x = long, y = lat, group = group, fill = `X60-X84_Y870`/`A-R_V-Y`)) +
geom_polygon() + coord_map() +
scale_fill_viridis_c(na.value = "white",
labels = scales::percent_format(accuracy = .1),
breaks = 0.01*seq(0, 100, 0.5),
values = c(0, 0.1, 0.3, 0.4, 0.5, 0.6, 1)) +
theme_void() + theme(legend.position = c(0.15, 0.85)) +
labs(fill = "Employment \nPrimary Education (%)")
hlth_cd_ycdr2 %>%
filter(time == "2013",
age == "TOTAL",
sex == "T",
icd10 %in% c("X60-X84_Y870", "A-R_V-Y")) %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, icd10, values) %>%
spread(icd10, values) %>%
right_join(europe_NUTS2, by = "geo") %>%
filter(long >= -13.5, lat >= 33) %>%
ggplot(., aes(x = long, y = lat, group = group, fill = `X60-X84_Y870`/`A-R_V-Y`)) +
geom_polygon() + coord_map() +
scale_fill_viridis_c(na.value = "white",
labels = scales::percent_format(accuracy = .1),
breaks = 0.01*seq(0, 100, 0.5),
values = c(0, 0.1, 0.3, 0.4, 0.5, 0.6, 1)) +
theme_void() + theme(legend.position = c(0.15, 0.85)) +
labs(fill = "Employment \nPrimary Education (%)")