Visualizations
my_colors <- c("#04536e", "#7c2817", "#f15c42", "#3d6a51", "#eca324","#5C92A1")
my_font <- "Ubuntu Condensed"
df %>%
select(year, countries, gov_exp) %>%
group_by(year, countries) %>%
summarise( mean = mean(gov_exp)) ->p
#==================== Geom_text_________
p %>%
filter(countries %in% c("Middle East")) %>%
filter (year == 2013)-> df_middle
p %>%
filter(countries %in% c("Northest Asia", "Southest Asia")) %>%
filter (year == 2013 )-> df_Asias
p %>%
filter(countries %in% c("Northen", "United & Europe")) %>%
filter (year == 2013 )-> df_europe
p %>%
filter(countries %in% c("Others")) %>%
filter (year == 2013 )-> Others
p %>%
ggplot(aes(year, mean, group=countries, color=countries))+
geom_line(size=1.3, show.legend = FALSE)+
scale_color_manual(values = my_colors)+
scale_fill_manual(values=my_colors)+
scale_x_continuous(limits = c(1995, 2014), breaks = seq(1995,2014,2))+
geom_text(data=df_middle, aes(year, mean, label=countries),
size=5, hjust= 0, vjust=2.5,family= my_font, show.legend = FALSE)+
geom_text(data=df_Asias, aes(year, mean, label=countries),
size=5, hjust= 0, vjust= 1, family= my_font, show.legend = FALSE)+
geom_text(data=df_europe, aes(year, mean, label=countries),
size=5, hjust= 0, vjust= 1,family= my_font, show.legend = FALSE)+
geom_text(data=Others, aes(year, mean, label=countries),
size=5, hjust= 0, vjust= - 1,family= my_font, show.legend = FALSE)+
theme_economist()+
theme(panel.grid.minor = element_blank())+
labs(x = NULL, y = NULL,
title = "World Governement's Composition of Expenditure on Health Care: 1995 - 2014",
subtitle = "Expenditure is calculated according to Purchasing Power Parity in 2010.",
caption = "Data Source: World Health Organization (WHO)") +
theme(plot.title = element_text(family = my_font, size = 15, colour = "grey10")) +
theme(plot.subtitle = element_text(family = my_font, size = 10, colour = "grey20")) +
theme(plot.caption = element_text(family = my_font, size = 10, colour = "grey30", face = "italic")) +
theme(legend.text = element_text(size = 10, color = "grey10", family = my_font)) +
theme(legend.position = c(0.15, 0.8)) +
theme(plot.margin = unit(c(1.2, 1.2, 1.2, 1.2), "cm"))
my_color2 <- c("#A8A9AD", "#5C92A1", "#6ED0F5", "#E7D3BA", "#822813","#5C92A1")
df %>%
select(year, countries, private) %>%
group_by(year, countries) %>%
summarise(mean = mean(private)) ->p1
ggplot(p1,aes(year, mean))+
geom_line(aes(color=countries),size= 1.2)+
scale_color_manual(values = my_color2)+
scale_fill_manual(values=my_color2)+
scale_x_continuous(limits = c(1995, 2014), breaks = seq(1995,2014,2))+
scale_y_continuous(limits = c(-1000,3000), breaks = seq(-1000,3000,500))+
theme_economist()
my_colors3 <- c("#04536e", "#7c2817", "#f15c42","#5C92A1")
df %>%
filter(countries == "Northest Asia") ->North_data
ggplot(North_data)+
geom_line(aes(year, gov_exp, color= country), size= 1.2 )+
scale_color_manual(name ="Countries", values = my_colors3)+
scale_x_continuous(limits = c(1995, 2014), breaks = seq(1995,2014,2))+
theme_economist()+
labs(x = NULL, y = NULL,
title = "Asia's Composition of Expenditure on Health Care: 1995 - 2014",
subtitle = "Expenditure is calculated according to Purchasing Power Parity in 2010.",
caption = "Data Source: World Health Organization (WHO)") +
theme(plot.title = element_text(family = my_font, size = 15, colour = "grey10")) +
theme(plot.subtitle = element_text(family = my_font, size = 10, colour = "grey20")) +
theme(plot.caption = element_text(family = my_font, size = 10, colour = "grey30", face = "italic")) +
theme(legend.text = element_text(size = 10, color = "grey10", family = my_font)) +
theme(legend.position = c(0.15, 0.8)) +
theme(plot.margin = unit(c(1.2, 1.2, 1.2, 1.2), "cm"))
df %>%
filter(countries == "United & Europe") -> Europe_data
my_color2 <- c("#A8A9AD", "#5C92A1", "#6ED0F5", "#E7D3BA", "#822813")
ggplot(Europe_data) +
geom_line(aes(year, gov_exp, color=country), size=2.1)+
scale_color_manual(name ="Countries", values = my_color2)+
scale_x_continuous(limits = c(1995, 2014), breaks = seq(1995,2014,2))+
theme_economist()+
labs(x = NULL, y = NULL,
title = "Powerful Government's Composition of Expenditure on Health Care: 1995 - 2014",
subtitle = "Expenditure is calculated according to Purchasing Power Parity in 2010.",
caption = "Data Source: World Health Organization (WHO)") +
theme(plot.title = element_text(family = my_font, size = 15, colour = "grey10")) +
theme(plot.subtitle = element_text(family = my_font, size = 10, colour = "grey20")) +
theme(plot.caption = element_text(family = my_font, size = 10, colour = "grey30", face = "italic")) +
theme(legend.text = element_text(size = 10, color = "grey10", family = my_font)) +
theme(legend.position = c(0.15, 0.8)) +
theme(plot.margin = unit(c(1.2, 1.2, 1.2, 1.2), "cm"))
## # A tibble: 40 x 8
## year country gov_exp total_exp private gov_rate countries priv_rate
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <fct> <dbl>
## 1 2014 South Korea 1368. 2531. 1163. 0.541 Northest Asia 0.459
## 2 2014 United Stat~ 4541. 9403. 4861. 0.483 United & Eur~ 0.517
## 3 2013 South Korea 1293. 2380. 1087. 0.543 Northest Asia 0.457
## 4 2013 United Stat~ 4279. 8988. 4709. 0.476 United & Eur~ 0.524
## 5 2012 South Korea 1233. 2246. 1012. 0.549 Northest Asia 0.451
## 6 2012 United Stat~ 4154. 8790. 4636. 0.473 United & Eur~ 0.527
## 7 2011 South Korea 1197. 2140. 943. 0.559 Northest Asia 0.441
## 8 2011 United Stat~ 4035. 8524. 4489. 0.473 United & Eur~ 0.527
## 9 2010 South Korea 1174. 2070. 895. 0.567 Northest Asia 0.433
## 10 2010 United Stat~ 3926. 8269. 4343. 0.475 United & Eur~ 0.525
## # ... with 30 more rows
#======== labeling
Ko_Ame %>%
filter(year==2014) -> label_table
Ko_Ame %>%
ggplot(aes(year, gov_rate, color=country))+
geom_line(size=1.4)+
theme_economist()+
scale_x_continuous(breaks = seq(1995,2014,1))+
scale_y_continuous(labels = scales::percent)+
geom_text(data= label_table,aes(year, gov_rate, label= country),
vjust= 2.5, face="bold", size=4.5)+
labs(y = "Governement Expenditure", x = "Year",
title = "Share of Government Spending on Health Care: South Korea and United States",
subtitle = "Expenditure is calculated according to Purchasing Power Parity in 2010.",
caption = "Data Source: World Health Organization (WHO)") +
theme(plot.title = element_text(family = my_font, size = 15, colour = "grey10")) +
theme(plot.subtitle = element_text(family = my_font, size = 13, colour = "grey20")) +
theme(plot.caption = element_text(family = my_font, size = 10, colour = "grey30",
face = "italic")) +
theme(legend.text = element_text(size = 12, color = "grey10", family = my_font)) +
theme(plot.margin = unit(c(1.2, 1.2, 1.2, 1.2), "cm"))
## # A tibble: 20 x 7
## year gov_exp total_exp private gov_rate countries priv_rate
## <fct> <dbl> <dbl> <dbl> <fct> <fct> <fct>
## 1 2014 1368. 2531. 1163. 0.5405106359436~ Northest As~ 0.459489364056~
## 2 2013 1293. 2380. 1087. 0.5432691095781~ Northest As~ 0.456730890421~
## 3 2012 1233. 2246. 1012. 0.54925519360513 Northest As~ 0.450744806394~
## 4 2011 1197. 2140. 943. 0.55928948856071 Northest As~ 0.440710511439~
## 5 2010 1174. 2070. 895. 0.5674090535774~ Northest As~ 0.432590946422~
## 6 2009 1066. 1890. 823. 0.5643116421322~ Northest As~ 0.435688357867~
## 7 2008 966. 1768. 802. 0.5463335745951~ Northest As~ 0.453666425404~
## 8 2007 914. 1669. 755. 0.5477607378508~ Northest As~ 0.452239262149~
## 9 2006 814. 1489. 675. 0.5463911294384~ Northest As~ 0.453608870561~
## 10 2005 683. 1291. 608. 0.5289450234396~ Northest As~ 0.471054976560~
## 11 2004 598. 1139. 541. 0.5252137727599~ Northest As~ 0.474786227240~
## 12 2003 549. 1054. 504. 0.52136192347486 Northest As~ 0.478638076525~
## 13 2002 519. 959. 440. 0.5411638313185~ Northest As~ 0.458836168681~
## 14 2001 505. 914. 409. 0.5523101840705~ Northest As~ 0.447689815929~
## 15 2000 374. 765. 390. 0.4896867438362~ Northest As~ 0.510313256163~
## 16 1999 334. 689. 355. 0.4845392253643~ Northest As~ 0.515460774635~
## 17 1998 278. 580. 303. 0.4782518784035~ Northest As~ 0.521748121596~
## 18 1997 257. 598. 341. 0.4295502782559~ Northest As~ 0.570449721744~
## 19 1996 227. 561. 334. 0.4042837779997~ Northest As~ 0.595716222000~
## 20 1995 185. 493. 308. 0.37528905837965 Northest As~ 0.624710941620~
color <- c("#04536e" ,"#12a4dc")
Korea %>%
ggplot()+
geom_col(aes(x=year, y=total_exp, fill="Government"))+
geom_col(aes(x=year, y=gov_exp, fill="Private"))+
theme_economist()+
scale_fill_manual(values = color, name= NULL)+
labs(x = NULL, y = NULL,
title = "South Korea's Composition of Expenditure on Health Care: 1995 - 2014",
subtitle = "Expenditure is calculated according to Purchasing Power Parity in 2010.",
caption = "Data Source: World Health Organization (WHO)") +
theme(plot.title = element_text(family = my_font, size = 15, colour = "grey10")) +
theme(plot.subtitle = element_text(family = my_font, size = 10, colour = "grey20")) +
theme(plot.caption = element_text(family = my_font, size = 10, colour = "grey30", face = "italic")) +
theme(legend.text = element_text(size = 10, color = "grey10", family = my_font)) +
theme(legend.position = c(0.15, 0.8)) +
theme(plot.margin = unit(c(1.2, 1.2, 1.2, 1.2), "cm"))
