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mapy.R
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mapy.R
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rm(list = ls())
# načtení balíčků
library(dplyr)
library(sp)
library(sf)
library(ggplot2, quietly=T)
library(rgdal, quietly=T)
library(rgeos, quietly=T)
library(dplyr, quietly=T)
library(classInt, quietly=T)
library(eurostat, quietly=T)
#library(viridis)
library(RColorBrewer)
#download Eurostat NUTS 2016 shapefiles
url <- "https://gisco-services.ec.europa.eu/distribution/v2/nuts/download/ref-nuts-2016-01m.shp.zip" # location on the Eurostat website
download.file(url, basename(url), mode="wb") #download Eurostat country shapefiles
unzip("ref-nuts-2016-01m.shp.zip") # unzip the boundary data
unzip("NUTS_RG_01M_2016_4326_LEVL_2.shp.zip") # unzip the NUTS-2 folder
#download the country shp
url <- "https://gisco-services.ec.europa.eu/distribution/v2/countries/download/ref-countries-2013-01m.shp.zip" # location on the Eurostat website
download.file(url, basename(url), mode="wb") #download Eurostat country shapefiles
unzip("ref-countries-2013-01m.shp.zip") # unzip the boundary data
unzip("CNTR_RG_01M_2013_4326.shp.zip")
nuts2 <- readOGR(getwd(),
"NUTS_RG_01M_2016_4326_LEVL_2",
verbose = TRUE,
stringsAsFactors = FALSE)
#load country shapefile
cntry <- readOGR(getwd(),
"CNTR_RG_01M_2013_4326",
verbose = TRUE,
stringsAsFactors = FALSE)
#only European countries on the map
out <- c("MA", "TN", "DZ", "EG", "LY",
"JO", "IL", "PS", "SY", "SA",
"LB", "IQ", "IR", "GL")
cn <- subset(cntry, !FID%in%out)
c <- fortify(cn) #turn the SpatialPolygonsDataFrame into data.frame for plotting
toc <- eurostat::get_eurostat_toc()
k <- 'NUTS 2'
t <- subset(toc, grepl(k, title))
# tvorba grafů -------------------------
# employment rates
lfst_r_lfe2emprt <- eurostat::get_eurostat("lfst_r_lfe2emprt",
time_format = "num")
lfst_r_lfe2emprt_v2 <- lfst_r_lfe2emprt %>%
filter(time==2019, # only the year of 2019
age=="Y15-64", # ages 15-29
sex=="T") %>% # all genders
dplyr::select (geo, values)
names(lfst_r_lfe2emprt_v2)[1] <- "NUTS_ID"
lfst_r_lfe2emprt_v2
f1 <- merge(lfst_r_lfe2emprt_v2, nuts2, by="NUTS_ID")
e <- fortify(nuts2, region = "NUTS_ID") %>%
mutate(NUTS_ID = as.character(id))
d <- e %>% left_join(f1, by = "NUTS_ID")
filtr <- lfst_r_lfe2emprt_v2 %>%
filter(NUTS_ID %in% unique(e$NUTS_ID))
length(filtr$NUTS_ID)
ni = classIntervals(d$values,
n = 6,
style = 'equal')$brks
ni
hist(lfst_r_lfe2emprt_v2$values)
summary(lfst_r_lfe2emprt_v2$values)
labels <- c()
for(i in 1:length(ni)){
labels <- c(labels, paste0(round(ni[i], 0),
"–",
round(ni[i + 1], 0)))
}
(labels <- labels[1:length(labels)-1])
labels
d$cat <- cut(d$values,
breaks = ni,
labels = labels,
include.lowest = T)
levels(d$cat)
lvl <- levels(d$cat)
lvl[length(lvl) + 1] <- "No data"
d$cat <- factor(d$cat, levels = lvl)
d$cat[is.na(d$cat)] <- "No data"
levels(d$cat)
p <-
ggplot() +
geom_polygon(data = c, aes(x = long,
y = lat,
group = group),
fill = "grey80") +
geom_polygon(data = subset(d, !is.na(values)), aes(x = long,
y = lat,
group = group,
fill = cat)) +
geom_path(data = subset(d, !is.na(values)), aes(x = long,
y = lat,
group = group),
color = NA, size = 0) +
geom_path(data = c, aes(x = long,
y = lat,
group = group),
color = "white", size = 0.2)
#p
p1 <- p +
coord_map(xlim=c(-10.6600,44.07), ylim=c(32.5000,71.0500), projection="lambert", parameters=c(10.44,52.775)) +
expand_limits(x=c$long,y=c$lat)
#p1
p2 <- p1 +
labs(x = "",
title="Employment rates",
subtitle = "NUTS-2 level")
#p2
(barvy <- brewer.pal(6, "Blues"))
p3 <- p2 +
scale_fill_manual(name= "% of population aged 15-64",
values=c(barvy, "grey80"),
labels=levels(d$cat),
drop=F) +
guides(fill=guide_legend(
direction = "horizontal",
keyheight = unit(1.15, units = "mm"),
keywidth = unit(15, units = "mm"),
title.position = 'top',
title.hjust = 0.5,
label.hjust = .5,
nrow = 1,
byrow = T,
reverse = F,
label.position = "bottom"
)
)
#p3
p4 <- p3 + theme_minimal() +
theme(panel.background = element_blank(),
legend.background = element_blank(),
legend.position = c(.45, .07),
panel.border = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(color = "white", size = 0),
plot.title = element_text(size=15, color="black", hjust=0.5, vjust=-10, face = "bold"),
plot.subtitle = element_text(size=12, color="black", hjust=0.5, vjust=-15),
axis.title.x = element_text(size=6, color="grey60", hjust=0.5, vjust=5),
legend.text = element_text(size=9, color="grey20"),
legend.title = element_text(size=10, color="grey20"),
strip.text = element_text(size=12),
plot.margin = unit(c(t=-2, r=-2, b=-2, l=-2),"lines"), #added these narrower margins to enlarge map
axis.title.y = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank())
#p4
ggsave("Mapa_employ_rates_2019.png",
plot = last_plot(),
dpi = 300)
# Severe material deprivation
ilc_mddd21 <- eurostat::get_eurostat("ilc_mddd21",
time_format = "num")
ilc_mddd21_2 <- ilc_mddd21 %>%
filter(time==2019) %>% # only the year of 2019
dplyr::select (geo, values)
names(ilc_mddd21_2)[1] <- "NUTS_ID"
ilc_mddd21_2
filtr <- ilc_mddd21_2 %>%
filter(NUTS_ID %in% unique(e$NUTS_ID))
length(filtr$NUTS_ID)
f1 <- merge(ilc_mddd21_2, nuts2, by="NUTS_ID")
d <- e %>% left_join(f1, by = "NUTS_ID")
ni = classIntervals(d$values,
n = 6,
style = 'equal')$brks
ni
hist(ilc_mddd21_2$values)
summary(ilc_mddd21_2$values)
labels <- c()
for(i in 1:length(ni)){
labels <- c(labels, paste0(round(ni[i], 0),
"–",
round(ni[i + 1], 0)))
}
(labels <- labels[1:length(labels)-1])
labels
d$cat <- cut(d$values,
breaks = ni,
labels = labels,
include.lowest = T)
levels(d$cat)
lvl <- levels(d$cat)
lvl[length(lvl) + 1] <- "No data"
d$cat <- factor(d$cat, levels = lvl)
d$cat[is.na(d$cat)] <- "No data"
levels(d$cat)
p <-
ggplot() +
geom_polygon(data = c, aes(x = long,
y = lat,
group = group),
fill = "grey80") +
geom_polygon(data = subset(d, !is.na(values)), aes(x = long,
y = lat,
group = group,
fill = cat)) +
geom_path(data = subset(d, !is.na(values)), aes(x = long,
y = lat,
group = group),
color = NA, size = 0) +
geom_path(data = c, aes(x = long,
y = lat,
group = group),
color = "white", size = 0.2)
#p
p1 <- p +
coord_map(xlim=c(-10.6600,44.07), ylim=c(32.5000,71.0500), projection="lambert", parameters=c(10.44,52.775)) +
expand_limits(x=c$long,y=c$lat)
#p1
p2 <- p1 +
labs(x = "",
title="Severe material deprivation rates",
subtitle = "NUTS-2 level")
#p2
p3 <- p2 +
scale_fill_manual(name = "% of population",
values=c(barvy, "grey80"),
labels=levels(d$cat),
drop=F) +
guides(fill=guide_legend(
direction = "horizontal",
keyheight = unit(1.15, units = "mm"),
keywidth = unit(15, units = "mm"),
title.position = 'top',
title.hjust = 0.5,
label.hjust = .5,
nrow = 1,
byrow = T,
reverse = F,
label.position = "bottom"
)
)
#p3
p4 <- p3 + theme_minimal() +
theme(panel.background = element_blank(),
legend.background = element_blank(),
legend.position = c(.45, .07),
panel.border = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(color = "white", size = 0),
plot.title = element_text(size=15, color="black", hjust=0.5, vjust=-10, face = "bold"),
plot.subtitle = element_text(size=12, color="black", hjust=0.5, vjust=-15),
axis.title.x = element_text(size=6, color="grey60", hjust=0.5, vjust=5),
legend.text = element_text(size=9, color="grey20"),
legend.title = element_text(size=10, color="grey20"),
strip.text = element_text(size=12),
plot.margin = unit(c(t=-2, r=-2, b=-2, l=-2),"lines"), #added these narrower margins to enlarge map
axis.title.y = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank())
#p4
ggsave("Mapa_matdepr_rates_2019.png",
plot = last_plot(),
dpi = 300)
# Population density
tgs00024 <- eurostat::get_eurostat("tgs00024",
time_format = "num")
tgs00024 <- tgs00024 %>%
filter(time==2019) %>% # only the year of 2019
dplyr::select (geo, values)
names(tgs00024)[1] <- "NUTS_ID"
tgs00024
f1 <- merge(tgs00024, nuts2, by="NUTS_ID")
d <- e %>% left_join(f1, by = "NUTS_ID")
CZ <- d %>%
filter(CNTR_CODE == "CZ")
CZ %>% filter(FID == "CZ05")
ni = classIntervals(d$values,
n = 6,
style = 'quantile')$brks
ni
hist(tgs00024$values)
summary(tgs00024$values)
tgs00024[tgs00024 == 0.0] <- NA
sum(is.na(tgs00024$values))
#ni2 <- c(0, 50, 100, 150, 200, 250, 500, 1000)
#hist(d$values)
#summary(d$values)
labels <- c()
for(i in 1:length(ni)){
labels <- c(labels, paste0(round(ni[i], 0),
"–",
round(ni[i + 1], 0)))
}
(labels <- labels[1:length(labels)-1])
labels
d$cat <- cut(d$values,
breaks = ni,
labels = labels,
include.lowest = T)
levels(d$cat)
lvl <- levels(d$cat)
lvl[length(lvl) + 1] <- "No data"
d$cat <- factor(d$cat, levels = lvl)
d$cat[is.na(d$cat)] <- "No data"
levels(d$cat)
p <-
ggplot() +
geom_polygon(data = c, aes(x = long,
y = lat,
group = group),
fill = "grey80") +
geom_polygon(data = subset(d, !is.na(values)), aes(x = long,
y = lat,
group = group,
fill = cat)) +
geom_path(data = subset(d, !is.na(values)), aes(x = long,
y = lat,
group = group),
color = NA, size = 0) +
geom_path(data = c, aes(x = long,
y = lat,
group = group),
color = "white", size = 0.2)
#p
p1 <- p +
coord_map(xlim=c(-10.6600,44.07), ylim=c(32.5000,71.0500), projection="lambert", parameters=c(10.44,52.775)) +
expand_limits(x=c$long,y=c$lat)
#p1
p2 <- p1 +
labs(x = "",
title="Population density",
subtitle = "NUTS-2 level")
#p2
(barvy <- brewer.pal(6, "Blues"))
p3 <- p2 +
scale_fill_manual(name = "number of people per 1 km2",
values=c(barvy, "grey80"),
labels=levels(d$cat),
drop=F) +
guides(fill=guide_legend(
direction = "horizontal",
keyheight = unit(1.15, units = "mm"),
keywidth = unit(15, units = "mm"),
title.position = 'top',
title.hjust = 0.5,
label.hjust = .5,
nrow = 1,
byrow = T,
reverse = F,
label.position = "bottom"
)
)
#p3
p4 <- p3 + theme_minimal() +
theme(panel.background = element_blank(),
legend.background = element_blank(),
legend.position = c(.45, .07),
panel.border = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(color = "white", size = 0),
plot.title = element_text(size=15, color="black", hjust=0.5, vjust=-10, face = "bold"),
plot.subtitle = element_text(size=12, color="black", hjust=0.5, vjust=-15),
axis.title.x = element_text(size=6, color="grey60", hjust=0.5, vjust=5),
legend.text = element_text(size=9, color="grey20"),
legend.title = element_text(size=10, color="grey20"),
strip.text = element_text(size=12),
plot.margin = unit(c(t=-2, r=-2, b=-2, l=-2),"lines"), #added these narrower margins to enlarge map
axis.title.y = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank())
#p4
ggsave("Mapa_popdensity_2019.png",
plot = last_plot(),
dpi = 300)
# Unemployment
tgs00010 <- eurostat::get_eurostat("tgs00010",
time_format = "num")
tgs00010 <- tgs00010 %>%
filter(time == 2019,
sex == "T",
isced11 == "TOTAL") %>%
dplyr::select (geo, values)
names(tgs00010)[1] <- "NUTS_ID"
tgs00010
f1 <- merge(tgs00010, nuts2, by="NUTS_ID")
d <- e %>% left_join(f1, by = "NUTS_ID")
ni = classIntervals(d$values,
n = 6,
style = 'quantile')$brks
ni
labels <- c()
for(i in 1:length(ni)){
labels <- c(labels, paste0(round(ni[i], 0),
"–",
round(ni[i + 1], 0)))
}
(labels <- labels[1:length(labels)-1])
labels
d$cat <- cut(d$values,
breaks = ni,
labels = labels,
include.lowest = T)
levels(d$cat)
lvl <- levels(d$cat)
lvl[length(lvl) + 1] <- "No data"
d$cat <- factor(d$cat, levels = lvl)
d$cat[is.na(d$cat)] <- "No data"
levels(d$cat)
p <-
ggplot() +
geom_polygon(data = c, aes(x = long,
y = lat,
group = group),
fill = "grey80") +
geom_polygon(data = subset(d, !is.na(values)), aes(x = long,
y = lat,
group = group,
fill = cat)) +
geom_path(data = subset(d, !is.na(values)), aes(x = long,
y = lat,
group = group),
color = NA, size = 0) +
geom_path(data = c, aes(x = long,
y = lat,
group = group),
color = "white", size = 0.2)
#p
p1 <- p +
coord_map(xlim=c(-10.6600,44.07), ylim=c(32.5000,71.0500), projection="lambert", parameters=c(10.44,52.775)) +
expand_limits(x=c$long,y=c$lat)
#p1
p2 <- p1 +
labs(x = "",
title="Unemployment rates",
subtitle = "NUTS-2 level")
#p2
(barvy <- brewer.pal(6, "Blues"))
p3 <- p2 +
scale_fill_manual(name = "% of economically active population",
values=c(barvy, "grey80"),
labels=levels(d$cat),
drop=F) +
guides(fill=guide_legend(
direction = "horizontal",
keyheight = unit(1.15, units = "mm"),
keywidth = unit(15, units = "mm"),
title.position = 'top',
title.hjust = 0.5,
label.hjust = .5,
nrow = 1,
byrow = T,
reverse = F,
label.position = "bottom"
)
)
#p3
p4 <- p3 + theme_minimal() +
theme(panel.background = element_blank(),
legend.background = element_blank(),
legend.position = c(.45, .07),
panel.border = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(color = "white", size = 0),
plot.title = element_text(size=15, color="black", hjust=0.5, vjust=-10, face = "bold"),
plot.subtitle = element_text(size=12, color="black", hjust=0.5, vjust=-15),
axis.title.x = element_text(size=6, color="grey60", hjust=0.5, vjust=5),
legend.text = element_text(size=9, color="grey20"),
legend.title = element_text(size=10, color="grey20"),
strip.text = element_text(size=12),
plot.margin = unit(c(t=-2, r=-2, b=-2, l=-2),"lines"), #added these narrower margins to enlarge map
axis.title.y = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank())
p4
ggsave("Mapa_unemploy_2019.png",
plot = last_plot(),
dpi = 300)
# Education ---------------------------------------------------------------
edat_lfs_9917 <- eurostat::get_eurostat("edat_lfs_9917",
time_format = "num")
edat_lfs_9917 <- edat_lfs_9917 %>%
filter(time == 2019,
sex == "T",
age == "Y15-64",
isced11 == "ED5-8",
c_birth == "TOTAL") %>%
dplyr::select (geo, values)
names(edat_lfs_9917)[1] <- "NUTS_ID"
edat_lfs_9917
f1 <- merge(edat_lfs_9917, nuts2, by="NUTS_ID")
d <- e %>% left_join(f1, by = "NUTS_ID")
ni = classIntervals(d$values,
n = 6,
style = 'quantile')$brks
ni
CZ_edu <- d %>%
filter(CNTR_CODE == "CZ")
hist(edat_lfs_9917$values)
summary(edat_lfs_9917$values)
#ni2 <- c(0, 50, 100, 150, 200, 250, 500, 1000)
#hist(d$values)
#summary(d$values)
labels <- c()
for(i in 1:length(ni)){
labels <- c(labels, paste0(round(ni[i], 0),
"–",
round(ni[i + 1], 0)))
}
(labels <- labels[1:length(labels)-1])
labels
d$cat <- cut(d$values,
breaks = ni,
labels = labels,
include.lowest = T)
levels(d$cat)
lvl <- levels(d$cat)
lvl[length(lvl) + 1] <- "No data"
d$cat <- factor(d$cat, levels = lvl)
d$cat[is.na(d$cat)] <- "No data"
levels(d$cat)
p <-
ggplot() +
geom_polygon(data = c, aes(x = long,
y = lat,
group = group),
fill = "grey80") +
geom_polygon(data = subset(d, !is.na(values)), aes(x = long,
y = lat,
group = group,
fill = cat)) +
geom_path(data = subset(d, !is.na(values)), aes(x = long,
y = lat,
group = group),
color = NA, size = 0) +
geom_path(data = c, aes(x = long,
y = lat,
group = group),
color = "white", size = 0.2)
#p
p1 <- p +
coord_map(xlim=c(-10.6600,44.07), ylim=c(32.5000,71.0500), projection="lambert", parameters=c(10.44,52.775)) +
expand_limits(x=c$long,y=c$lat)
#p1
p2 <- p1 +
labs(x = "",
title="Tertiary education attainment",
subtitle = "NUTS-2 level")
#p2
(barvy <- brewer.pal(6, "Blues"))
p3 <- p2 +
scale_fill_manual(name = "% of population",
values=c(barvy, "grey80"),
labels=levels(d$cat),
drop=F) +
guides(fill=guide_legend(
direction = "horizontal",
keyheight = unit(1.15, units = "mm"),
keywidth = unit(15, units = "mm"),
title.position = 'top',
title.hjust = 0.5,
label.hjust = .5,
nrow = 1,
byrow = T,
reverse = F,
label.position = "bottom"
)
)
#p3
p4 <- p3 + theme_minimal() +
theme(panel.background = element_blank(),
legend.background = element_blank(),
legend.position = c(.45, .07),
panel.border = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(color = "white", size = 0),
plot.title = element_text(size=15, color="black", hjust=0.5, vjust=-10, face = "bold"),
plot.subtitle = element_text(size=12, color="black", hjust=0.5, vjust=-15),
axis.title.x = element_text(size=6, color="grey60", hjust=0.5, vjust=5),
legend.text = element_text(size=9, color="grey20"),
legend.title = element_text(size=10, color="grey20"),
strip.text = element_text(size=12),
plot.margin = unit(c(t=-2, r=-2, b=-2, l=-2),"lines"), #added these narrower margins to enlarge map
axis.title.y = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank())
#p4
ggsave("Mapa_tertiaryedu_2019.png",
plot = last_plot(),
dpi = 300)
# Population - share 45-54 agegroup ---------------------------------------
demo_r_pjangroup <- eurostat::get_eurostat("demo_r_pjangroup",
time_format = "num")
demo_r_pjangroup_tot <- demo_r_pjangroup %>%
filter(time == 2019,
sex == "T",
age == "TOTAL") %>%
dplyr::select (geo, values)
names(demo_r_pjangroup_tot)[1] <- "NUTS_ID"
demo_r_pjangroup_tot
demo_r_pjangroup_45_49 <- demo_r_pjangroup %>%
filter(time == 2019,
sex == "T",
age == "Y45-49") %>%
dplyr::select (geo, values)
names(demo_r_pjangroup_45_49)[1] <- "NUTS_ID"
demo_r_pjangroup_45_49
demo_r_pjangroup_50_54 <- demo_r_pjangroup %>%
filter(time == 2019,
sex == "T",
age == "Y50-54") %>%
dplyr::select (geo, values)
names(demo_r_pjangroup_50_54)[1] <- "NUTS_ID"
demo_r_pjangroup_50_54
(demo_r_pjangroup_45_54 <- tibble(demo_r_pjangroup_45_49[,1], values = NA))
for (i in 1:nrow(demo_r_pjangroup_45_54)){
demo_r_pjangroup_45_54[i,2] <- sum(demo_r_pjangroup_45_49[i,2], demo_r_pjangroup_50_54[i,2])
}
demo_r_pjangroup_45_54
(pop_shares <- left_join(demo_r_pjangroup_45_54, demo_r_pjangroup_tot, by = "NUTS_ID"))
(share_45_54 <- tibble(demo_r_pjangroup_45_54[,1], values = NA))
for (i in 1:nrow(share_45_54)){
share_45_54[i,2] <- (pop_shares[i,2] / pop_shares[i,3]) * 100
}
share_45_54
share_45_54 <- do.call(tibble,
lapply(share_45_54,
function(x) replace(x, is.infinite(x), NA)))
share_45_54[[2]] <- ifelse(share_45_54$values > 100, NA, share_45_54$values)
hist(share_45_54$values)
summary(share_45_54$values)
f1 <- merge(share_45_54, nuts2, by="NUTS_ID")
d <- e %>% left_join(f1, by = "NUTS_ID")
ni = classIntervals(d$values,
n = 6,
style = 'quantile')$brks
options(scipen = 100,
digits = 3)
ni
#ni2 <- c(0, 50, 100, 150, 200, 250, 500, 1000)
hist(d$values)
summary(d$values)
labels <- c()
for(i in 1:length(ni)){
labels <- c(labels, paste0(round(ni[i], 0),
"–",
round(ni[i + 1], 0)))
}
(labels <- labels[1:length(labels)-1])
labels
d$cat <- cut(d$values,
breaks = ni,
labels = labels,
include.lowest = T)
levels(d$cat)
lvl <- levels(d$cat)
lvl[length(lvl) + 1] <- "No data"
d$cat <- factor(d$cat, levels = lvl)
d$cat[is.na(d$cat)] <- "No data"
levels(d$cat)
table(d$cat)
p <-
ggplot() +
geom_polygon(data = c, aes(x = long,
y = lat,
group = group),
fill = "grey80") +
geom_polygon(data = subset(d, !is.na(values)), aes(x = long,
y = lat,
group = group,
fill = cat)) +
geom_path(data = subset(d, !is.na(values)), aes(x = long,
y = lat,
group = group),
color = NA, size = 0) +
geom_path(data = c, aes(x = long,
y = lat,
group = group),
color = "white", size = 0.2)
#p
p1 <- p +
coord_map(xlim=c(-10.6600,44.07), ylim=c(32.5000,71.0500), projection="lambert", parameters=c(10.44,52.775)) +
expand_limits(x=c$long,y=c$lat)
#p1
p2 <- p1 +
labs(x = "",
title="Share of people aged 45-54",
subtitle = "NUTS-2 level")
#p2
(barvy <- brewer.pal(6, "Blues"))
p3 <- p2 +
scale_fill_manual(name = "% of population",
values=c(barvy, "grey80"),
labels=levels(d$cat),
drop=F) +
guides(fill=guide_legend(
direction = "horizontal",
keyheight = unit(1.15, units = "mm"),
keywidth = unit(15, units = "mm"),
title.position = 'top',
title.hjust = 0.5,
label.hjust = .5,
nrow = 1,
byrow = T,
reverse = F,
label.position = "bottom"
)
)
#p3
p4 <- p3 + theme_minimal() +
theme(panel.background = element_blank(),
legend.background = element_blank(),
legend.position = c(.45, .07),
panel.border = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(color = "white", size = 0),
plot.title = element_text(size=15, color="black", hjust=0.5, vjust=-10, face = "bold"),
plot.subtitle = element_text(size=12, color="black", hjust=0.5, vjust=-15),
axis.title.x = element_text(size=6, color="grey60", hjust=0.5, vjust=5),
legend.text = element_text(size=9, color="grey20"),
legend.title = element_text(size=10, color="grey20"),
strip.text = element_text(size=12),
plot.margin = unit(c(t=-2, r=-2, b=-2, l=-2),"lines"), #added these narrower margins to enlarge map
axis.title.y = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank())
#p4
ggsave("Mapa_share_45-54_2019.png",
plot = last_plot(),
dpi = 300)
# Self-reported unmet needs -----------------------------------------------
hlth_silc_08_r <- eurostat::get_eurostat("hlth_silc_08_r",
time_format = "num")
hlth_silc_08_r <- hlth_silc_08_r %>%
filter(time == 2019,
reason == "NO_UNMET") %>%
dplyr::select (geo, values)
names(hlth_silc_08_r)[1] <- "NUTS_ID"
hlth_silc_08_r
f1 <- merge(hlth_silc_08_r, nuts2, by="NUTS_ID")
d <- e %>% left_join(f1, by = "NUTS_ID")
ni = classIntervals(d$values,
n = 6,
style = 'quantile')$brks
ni
hist(hlth_silc_08_r$values)
summary(hlth_silc_08_r$values)
#ni2 <- c(0, 50, 100, 150, 200, 250, 500, 1000)
#hist(d$values)
#summary(d$values)
labels <- c()
for(i in 1:length(ni)){
labels <- c(labels, paste0(round(ni[i], 0),
"–",
round(ni[i + 1], 0)))
}
(labels <- labels[1:length(labels)-1])
labels
d$cat <- cut(d$values,
breaks = ni,
labels = labels,
include.lowest = T)
levels(d$cat)
lvl <- levels(d$cat)
lvl[length(lvl) + 1] <- "No data"
d$cat <- factor(d$cat, levels = lvl)
d$cat[is.na(d$cat)] <- "No data"
levels(d$cat)
p <-
ggplot() +
geom_polygon(data = c, aes(x = long,
y = lat,
group = group),
fill = "grey80") +
geom_polygon(data = subset(d, !is.na(values)), aes(x = long,
y = lat,
group = group,
fill = cat)) +
geom_path(data = subset(d, !is.na(values)), aes(x = long,
y = lat,
group = group),
color = NA, size = 0) +
geom_path(data = c, aes(x = long,
y = lat,
group = group),
color = "white", size = 0.2)
#p
p1 <- p +
coord_map(xlim=c(-10.6600,44.07), ylim=c(32.5000,71.0500), projection="lambert", parameters=c(10.44,52.775)) +
expand_limits(x=c$long,y=c$lat)
#p1
tit <- "No unmet needs for medical examination"
p2 <- p1 +
labs(x = "",
title= tit,
subtitle = "NUTS-2 level")
#p2
(barvy <- brewer.pal(6, "Blues"))
p3 <- p2 +
scale_fill_manual(name = "% of population",
values=c(barvy, "grey80"),
labels=levels(d$cat),
drop=F) +
guides(fill=guide_legend(
direction = "horizontal",
keyheight = unit(1.15, units = "mm"),
keywidth = unit(15, units = "mm"),
title.position = 'top',
title.hjust = 0.5,
label.hjust = .5,
nrow = 1,
byrow = T,
reverse = F,
label.position = "bottom"
)
)
#p3
p4 <- p3 + theme_minimal() +
theme(panel.background = element_blank(),
legend.background = element_blank(),
legend.position = c(.45, .07),
panel.border = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(color = "white", size = 0),
plot.title = element_text(size=15, color="black", hjust=0.5, vjust=-10, face = "bold"),
plot.subtitle = element_text(size=12, color="black", hjust=0.5, vjust=-15),
axis.title.x = element_text(size=6, color="grey60", hjust=0.5, vjust=5),
legend.text = element_text(size=9, color="grey20"),
legend.title = element_text(size=10, color="grey20"),
strip.text = element_text(size=12),
plot.margin = unit(c(t=-2, r=-2, b=-2, l=-2),"lines"), #added these narrower margins to enlarge map
axis.title.y = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank())
#p4
ggsave("Mapa_unmetNeeds_2019.png",
plot = last_plot(),
dpi = 300)
# GDP -----------------------------------------------
nama_10r_2gdp <- eurostat::get_eurostat("nama_10r_2gdp",
time_format = "num")
nama_10r_2gdp <- nama_10r_2gdp %>%
filter(time == 2019,
unit == "MIO_EUR") %>%
dplyr::select (geo, values)
names(nama_10r_2gdp)[1] <- "NUTS_ID"
nama_10r_2gdp
nama_10r_2gdp[[2]] <- nama_10r_2gdp[[2]]/1000