eduardofox2/MHL_Hungria_script

## MHL_Hungria_script
##############Script escrito por Eduardo G P Fox em 14/02/2019 on RStudio Version 1.1.456 (OSX 10.13.6)###################

#Relativo a um artigo enfatizando o impacto da ciência húngara em diversas áreas, comparando ao Brasil e Singapura
#Publicado em: https://olivre.com.br/
#idealizado pelo Prof. Marcelo Hermes de Lima (UnB) e Eduardo G P Fox (aspirante ao Butantan)
#Por favor enviem comentarios sobre este script para meu site pessoal
####(https://ofoxofox.wixsite.com/research) e/ou Twitter @ofoxofox######

#Pacotes R utilizados
library(ggplot2)
library(reshape2)
library(cowplot)
library(magick)
library(ggsci)
library(scales)
library(plyr)
#Caso faltar no GUI local, utilizar install.packages() para rodar o script abaixo

#Caso caracteres em Português não estiverem mostrando corretamente, tem que mudar o locale
# no sistema operacional.
Sys.setlocale("LC_ALL", "pt_BR.UTF-8")

#Nova função para anotar o valor de R^2 e formula dos plots, obtida a partir do link
#https://stackoverflow.com/questions/48175294/why-the-lm-eqn-way-of-adding-regression-line-function-did-not-work-in-my-case
#Autoria de @Gari Guo & @CMichael
lm_eqn <- function(df, y, x){
    formula = as.formula(sprintf('%s ~ %s', y, x))
    m <- lm(formula, data=df);
    # formating the values into a summary string to print out
    # ~ give some space, but equal size and comma need to be quoted
    eq <- substitute(italic(target) == a + b %.%
italic(input)*","~~italic(r)^2~"="~r2*","~~p~"="~italic(pvalue),
         list(target = y,
              input = x,
              a = format(as.vector(coef(m)[1]), digits = 2),
              b = format(as.vector(coef(m)[2]), digits = 2),
             r2 = format(summary(m)$r.squared, digits = 3),
             # getting the pvalue is painful
             pvalue = format(summary(m)$coefficients[2,'Pr(>|t|)'], digits=1)
            )
          )
    as.character(as.expression(eq));
}

#Inserindo os dados, recolhidos pelo IC Matheus, a partir da scimago.com
#Organizados e analisados por Marcelo Hermes de Lima

dados.Fig.1<-data.frame(
Anos=seq(1996,2018,1),
BR=c(NA,2.37, 2.37, 2.63, 2.75, 2.25, 2.93, 2.83, 2.89, 3.96, 3.27,
3.33, 3.64, 3.64, 2.91, 3.45, 2.62, 2.95, 1.94, 1.59, 1.64, 1.71,
1.37),
HU=c(2.2, 2.1, 2.6, 2.4, 2.5, 3.5, 4.1, 4.3, 3.8, 4.2, 4.3, 4.5,
5.1, 4.5, 5.3, 5, 5.7, 5.7, 5.65, 5.7, 7.5, 7.9, 7),
SI=c(4.4, 3.4, 4.5, 3.4, 4.5, 4.5, 4.6, 5.4, 5.1, 6.25, 6.5, 6.5,
7.3, 8.2, 8.9, 9, 9.5, 9.5, 9.5, 9.25, 9.4, 9.7, 9.3)
)

dados.Fig.2<-data.frame(
Áreas=c("Bio-Agro","Bioquímica","Farmacologia","Micro e Imuno","Neurociência",
"a" , "Medicina","Psicologia","Veterinária",
"b" , "Física","Matemática","Meio Ambiente","Quimica","Geologia"),
Hungria=c(4.4,6.6,4.2,10,3.2, 0 , 9.6,4.3,2.4, 0 ,10,3.25,5.6,4.1,8.5),
Brasil=c(1.3,1.35,2.8,2.3,2.4, 0 , 1.25,0.65,0.8, 0 , 3.9,2.4,1.65,2.3,3.5)
)

dados.Fig.2_<-melt(dados.Fig.2)

dados.Fig.2_$Áreas<-factor(dados.Fig.2_$Áreas,levels=dados.Fig.2$Áreas)

dados.Fig.3A<-data.frame(
Anos=seq(2008,2018,1),
HU=c(3.8, 3.65, 4.65, 4.2, 4.9, 5.9, 5.3, 5.6, 7.5, 9.6, 9),
BR=c(1.8, 1.7, 1.85, 2.2, 1.7, 2.2, 2, 1.9, 1.9, 1.25, 1.6)
)

dados.Fig.3B<-data.frame(
Anos=seq(2007,2018,1),
HU=c(4.7, 4.35, 4.4, 4.9, 6.3, 9.5, 7.8, 5.7, 8, 9.4, 10, 9.3),
BR=c(2.9, 1.5, 2.4, 1.2, 3.2, 1.7, 2, 0.85, 1.95, 3.65, 3.9, 3.1)
)

dados.Fig.3C<-data.frame(
Anos=seq(2012,2018,1),
HU=c(2.5, 3.6, 3.2, 4.5, 6.4, 8.5, 9.4),
BR=c(2.25, 3.1, 1.8, 3.2, 3.55, 3.5, 4.2)
)

dados.Fig.4<-data.frame(
Áreas=c("Engenharia","Computação","Materiais","Eng. Química","Energia","a",
 "Ciências Sociais","Artes e Human.","Adm. e Contab.","Economia", "b", "Multidisciplinar"),
Hungria=c(3.55,3.3,3.3,2.4,5.3, 0 , 1.95,3.2,5.8,2.3, 0, 4.3),
Brasil=c(3.05,2.8,2.1,2.6,2.7, 0 , 0.05,1.1,0.8,1.1, 0 , 3.4)
)

dados.Fig.4_<-melt(dados.Fig.4)

dados.Fig.4_$Áreas<-factor(dados.Fig.4_$Áreas,levels=dados.Fig.4$Áreas)

dados.Fig.5<-data.frame(
Anos=seq(1996,2018),
PIB.PPP=c(15354.64687, 15869.20022, 16526.5085, 17082.36124, 17893.93479,
18665.71659, 19606.46052, 20465.82873, 21500.42297, 22457.43917,
23399.10945, 23492.14161, 23782.36284, 22223.44986, 22421.69734,
22894.34764, 22674.18736, 23183.02905, 24220.73394, 25212.05311,
25842.87059, 27031.78002, 28464.5602),
rank=c(2.2, 2.1, 2.6, 2.4, 2.5, 3.5, 4.1, 4.3, 3.8, 4.2, 4.3, 4.5,
5.1, 4.5, 5.3, 5, 5.7, 5.7, 5.65, 5.7, 7.5, 7.9, 7)
)

dados.Fig.6<-data.frame(
Anos=c(1950,1960, 1973, 1976, 1989, 1998),
Czechoslovakia=c(76, 75, 57, 55, 50, 51.5),
East_Germany=c(67, 75, 60, 60, 50, 69),
Hungary=c(68, 66, 57, 56, 52, 55),
Poland=c(45, 43, 38, 36, 32, 38),
Romania=c(30, 36, 43, 47, 37, 30),
Bulgaria=c(25, 33, 35, 37, 32, 26)
)

#Fim dos dados brutos
#################################*********
#Plotando as Figuras segundo estética combinada com o primeiro autor MHL

#Imagens de icons obtidas a partir do website https://www.flaticon.com/packs/countrys-flags
#Icons made by <a href="https://www.flaticon.com/authors/freepik" title="Freepik">Freepik</a> from <a href="https://www.flaticon.com/" title="Flaticon">www.flaticon.com</a></div>
#Usando ícones de bandeiras baixadas no meu computador (Ajustar locale para o de quem usar este script)
brazil_icon<-image_read('/Users/egoncal2/Downloads/brazil.png')
hungria_icon<-image_read('/Users/egoncal2/Downloads/hungary.png')
singapura_icon<-image_read('/Users/egoncal2/Downloads/singapore.png')

Fig.1<-ggplot(dados.Fig.1)+
  geom_point(aes(y=BR, x=Anos), shape = 21, fill = "#009C3B", colour="#ffdf00", size = 3)+
  geom_smooth(aes(y=BR, x=Anos), method = "loess", se = FALSE, size = 1, span = 1, colour="#ffdf00", linetype = "dotted") +
  geom_point(aes(y=HU, x=Anos), shape = 21, fill = "#CD2A3E", colour="green", size = 3)+
  geom_smooth(aes(y=HU, x=Anos), method = "loess", se = FALSE, size = 1, span = 1, colour="#CD2A3E", linetype = "dotted") +
  geom_point(aes(y=SI, x=Anos), shape = 21, fill = "white", colour="#EF3340", size = 3)+
  geom_smooth(aes(y=SI, x=Anos), method = "loess", se = FALSE, size = 1, span = 1, colour="#EF3340", linetype = "dotted") +
  scale_x_continuous(limits=c(1996,2018), breaks=seq(1996,2018,3), expand = c(0.1, 0.1))+
       theme_cowplot(12)+
       labs(title = 'Rank-score\nHungria, Brasil e Singapura', x = NULL, y= NULL)+
       theme(
       legend.position = "none",
       plot.title = element_text(color="black", size=15, face="bold.italic"),
       axis.title.x = element_text(size=20, face="bold"),
       axis.title.y = element_text(size=20, face="bold"),
       axis.text.y=element_text(size=12),
       axis.text.x=element_text(size=12))

Final.Fig.1<-ggdraw(Fig.1) +
         draw_image(singapura_icon, scale=0.08, x=-0.25)+
         draw_image(hungria_icon, scale=0.08, x=0.21, y=0.12)+
         draw_image(brazil_icon, scale=0.08, x=0.21, y=-0.36)


Fig.2<-ggplot(dados.Fig.2_, aes(x=Áreas, y=value, fill = variable))+
       geom_bar(stat="identity", position = "dodge", width=0.6)+
       scale_fill_manual(values=c("#CD2A3E", "#009C3B"))+
       scale_y_continuous(name= NULL,limits=c(0,10), breaks=c(0,2.5,5.0,7.5,10.0),
        expand = c(0, 0.1))+
       theme_light()+
       labs(title = 'Rank-scores por área de estudo', x= NULL, font="bold", size=16, fill = "País")+
       scale_x_discrete(breaks=melt(dados.Fig.2)$Áreas[nchar(as.character(melt(dados.Fig.2)$Áreas))!=1])+
       theme(
       legend.position = "right",
       plot.title = element_text(color="red", size=15, face="bold.italic"),
       axis.title.x = element_text(size=20, face="bold"),
       axis.title.y = element_text(size=20, face="bold"),
       axis.text.x=element_text(size=14, angle = 45, hjust = 1),
       axis.text.y=element_text(size=12))


Fig.3A<-ggplot(dados.Fig.3A)+
         geom_smooth(aes(y=HU, x=Anos), size = 1, colour="#CD2A3E", linetype = "dashed", span = 0.3, se=FALSE)+
         geom_smooth(aes(y=BR, x=Anos), size = 1, colour="#ffdf00", linetype = "dashed",span = 0.3, se=FALSE)+
         geom_point(aes(y=BR, x=Anos), shape = 21, fill = "#009C3B", colour="#ffdf00", size = 3)+
         geom_point(aes(y=HU, x=Anos), shape = 21, fill = "#CD2A3E", colour="green", size = 3)+
         scale_y_continuous(limits=c(0,10), breaks=seq(0,10,2), expand = c(0.1, 0.1))+
         scale_x_continuous(limits=c(2008,2018), breaks=seq(2008,2018,3), expand = c(0.1, 0.1))+
         theme_cowplot(12)+
         labs(title = "Medicina", x=NULL, y=NULL)+
         theme(
         axis.title.x = element_text(size=14, face="bold"),
         axis.text.y=element_text(size=13),
         axis.text.x=element_text(size=13, angle = 45, hjust = 1))


Fig.3B<-ggplot(dados.Fig.3B)+
       geom_smooth(aes(x=Anos, y=HU), size = 1, colour="#CD2A3E", span = 2,linetype = "dashed", se=FALSE)+
       geom_smooth(aes(x=Anos, y=BR), size = 1, colour="#ffdf00", span = 2,linetype = "dashed", se=FALSE)+
       geom_point(aes(x=Anos, y=HU), shape = 21, fill = "#CD2A3E", colour="green", size = 3)+
       geom_point(aes(x=Anos, y=BR), shape = 21, fill = "#009C3B", colour="#ffdf00", size = 3)+
       scale_y_continuous(limits=c(0,10), breaks=seq(0,10,2), expand = c(0.1, 0.1))+
       scale_x_continuous(limits=c(2007,2019), breaks=seq(2007,2019,3), expand = c(0.1, 0.1))+
       theme_cowplot(12)+
         labs(title = "Física", x=NULL, y=NULL)+
         theme(
         axis.title.x = element_text(size=14, face="bold"),
         axis.text.y=element_text(size=13),
         axis.text.x=element_text(size=13, angle = 45, hjust = 1))

Fig.3C<-ggplot(dados.Fig.3C)+
         geom_smooth(aes(y=HU, x=Anos), size = 1, colour="#CD2A3E", span = 2, linetype = "dashed", se=FALSE)+
         geom_smooth(aes(y=BR, x=Anos), size = 1, colour="#ffdf00", span = 2, linetype = "dashed", se=FALSE)+
         geom_point(aes(y=HU, x=Anos), shape = 21, fill = "#CD2A3E", colour="green", size = 3)+
         geom_point(aes(y=BR, x=Anos), shape = 21, fill = "#009C3B", colour="#ffdf00", size = 3)+
         scale_y_continuous(limits=c(0,10), breaks=seq(0,10,2), expand = c(0.1, 0.1))+
         scale_x_continuous(limits=c(2012,2018), breaks=seq(2012,2018,2), expand = c(0.1, 0.1))+
         theme_cowplot(12)+
         labs(title = "Geologia", x=NULL, y=NULL)+
         theme(
         axis.title.x = element_text(size=14, face="bold"),
         axis.text.y=element_text(size=13),
         axis.text.x=element_text(size=13, angle = 45, hjust = 1))

Final.Fig.3<- plot_grid(Fig.3A, Fig.3B, Fig.3C, nrow=1)

#Adicionando texto acima :
title <- ggdraw() +
  draw_label(
    "Rank-scores\nBrasil vs. Hungria", size = 16,
    fontface = 'bold.italic',
    x = 0,
    hjust = 0
  ) +
  theme(
    # add margin on the left of the drawing canvas,
    # so title is aligned with left edge of first plot
    plot.margin = margin(0, 0, 0, 7)
  )
plot_grid(
  title, Final.Fig.3,
  ncol = 1,
  # rel_heights values control vertical title margins
  rel_heights = c(0.1, 1)
)


Fig.4<-ggplot(dados.Fig.4_, aes(x=Áreas, y=value, fill = variable))+
       geom_bar(stat="identity", position = "dodge", width=0.6)+
       scale_fill_manual(values=c("#CD2A3E", "#009C3B"))+
       scale_y_continuous(name= NULL,limits=c(0,10), breaks=c(0,2.5,5.0,7.5,10.0),
        expand = c(0, 0.1))+
       theme_light()+
       labs(title = 'Rank-scores por área de estudo', x= NULL, font="bold", size=16, fill = "País")+
       scale_x_discrete(breaks=melt(dados.Fig.4)$Áreas[nchar(as.character(melt(dados.Fig.4)$Áreas))!=1])+
       theme(
       legend.position = "right",
       plot.title = element_text(color="red", size=15, face="bold.italic"),
       axis.title.x = element_text(size=20, face="bold"),
       axis.title.y = element_text(size=20, face="bold"),
       axis.text.x=element_text(size=14, angle = 45, hjust = 1),
       axis.text.y=element_text(size=12))


Fig.5A<-ggplot(dados.Fig.5[,c(1,2)],aes(y=PIB.PPP, x=Anos))+
         geom_point(colour="black", size = 3)+
         scale_y_continuous(limits=c(0,35000), breaks=seq(0,35000,5000), expand = c(0.1, 0.1))+
         scale_x_continuous(limits=c(1996,2020), breaks=seq(1996,2020,4), expand = c(0.1, 0.1))+
         theme_light()+
         labs(title = "Hungria:\nPIB per capita (valor 2011)", x=NULL)+
         theme(plot.margin = margin(0, 0.1, 0, 0, "cm"),
         legend.position = "NULL",
         plot.title = element_text(color="black", size=16, face="bold"),
         axis.title.x = element_text(size=14, face="bold"),
         axis.title.y = element_text(size=14, face="bold",color="white"),
         axis.text.x=element_text(size=14),
         axis.text.y=element_text(size=12))

Fig.5B<-ggplot(dados.Fig.5[,c(1,3)],aes(y=rank, x=Anos))+
         geom_point(colour="#CD2A3E", size = 3)+
         scale_y_continuous(limits=c(0,10), breaks=seq(0,10,2), expand = c(0.1, 0.1))+
         scale_x_continuous(limits=c(1996,2020), breaks=seq(1996,2020,4), expand = c(0.1, 0.1))+
         theme_light()+
         labs(title = "Hungria:\nRank-score", x=NULL)+
         theme(plot.margin = margin(0, 0.1, 0, 0, "cm"),
         legend.position = "NULL",
         plot.title = element_text(color="black", size=16, face="bold"),
         axis.title.x = element_text(size=14, face="bold"),
         axis.title.y = element_text(size=14, face="bold",color="white"),
         axis.text.x=element_text(size=14),
         axis.text.y=element_text(size=12))

Fig.5C<-ggplot(dados.Fig.5[,c(2,3)],aes(y=rank, x=PIB.PPP))+
         geom_point(colour="blue", size = 3)+
         geom_smooth(method="lm", colour="red", alpha=0.1)+
         geom_text(x=22000,y=0, label=lm_eqn(dados.Fig.5[,c(2,3)], 'rank', 'PIB.PPP'), parse=T)+
         scale_y_continuous(limits=c(0,10), breaks=seq(0,10,2), expand = c(0.1, 0.1))+
         scale_x_continuous(limits=c(14000, 31000), expand = c(0.01, 0.01))+
         theme_light()+
         labs(title = "Hungria\nPIB vs. Rank-score", x=NULL)+
         theme(plot.margin = margin(0, 0.1, 0, 0, "cm"),
         legend.position = "NULL",
         plot.title = element_text(color="red", size=16, face="bold.italic"),
         axis.title.x = element_text(size=14, face="bold"),
         axis.title.y = element_text(size=14, face="bold",color="white"),
         axis.text.x=element_text(size=14),
         axis.text.y=element_text(size=12))

#Reparar como compor tamanhos diversos, gradualmente

Fig.5AB<-plot_grid(Fig.5A, Fig.5B, nrow=1)
Final.Fig.5<-plot_grid(Fig.5AB,Fig.5C, nrow=2)

Fig.6<-ggplot(melt(dados.Fig.6, id.vars = "Anos"), aes(fill=as.character(Anos), y=value, x=variable))+
geom_bar(position="dodge", stat="identity", width = 0.8)+
scale_fill_jco()+
scale_x_discrete(name= 'Países', labels = c("Czechoslovakia", "East Germany", "Hungary", "Poland",
"Romania", "Bulgaria"), expand = c(0.1, 0.1))+
scale_y_continuous(name= NULL, limits=c(0,100), breaks=c(0,25,50,75,100), expand = c(0, 0))+
labs(title = "Percentual do Trabalho da União Europeia", font="bold", size=20, fill = "Anos")+
theme_light()+
theme(
plot.title = element_text(color="red", size=20, face="bold.italic"),
axis.title.x = element_text(size=14),
axis.text.x=element_text(size=13),
axis.text.y=element_text(size=13),
legend.title=element_text(size=12))
legend.text=element_text(size=11),
axis.text.x=element_text(size=14, angle = 45, hjust = 1))

#**********************
	##############Script escrito por Eduardo G P Fox em 14/02/2019 on RStudio Version 1.1.456 (OSX 10.13.6)###################

	#Relativo a um artigo enfatizando o impacto da ciência húngara em diversas áreas, comparando ao Brasil e Singapura
	#Publicado em: https://olivre.com.br/
	#idealizado pelo Prof. Marcelo Hermes de Lima (UnB) e Eduardo G P Fox (aspirante ao Butantan)
	#Por favor enviem comentarios sobre este script para meu site pessoal
	####(https://ofoxofox.wixsite.com/research) e/ou Twitter @ofoxofox######

	#Pacotes R utilizados
	library(ggplot2)
	library(reshape2)
	library(cowplot)
	library(magick)
	library(ggsci)
	library(scales)
	library(plyr)
	#Caso faltar no GUI local, utilizar install.packages() para rodar o script abaixo

	#Caso caracteres em Português não estiverem mostrando corretamente, tem que mudar o locale
	# no sistema operacional.
	Sys.setlocale("LC_ALL", "pt_BR.UTF-8")

	#Nova função para anotar o valor de R^2 e formula dos plots, obtida a partir do link
	#https://stackoverflow.com/questions/48175294/why-the-lm-eqn-way-of-adding-regression-line-function-did-not-work-in-my-case
	#Autoria de @Gari Guo & @CMichael
	lm_eqn <- function(df, y, x){
	formula = as.formula(sprintf('%s ~ %s', y, x))
	m <- lm(formula, data=df);
	# formating the values into a summary string to print out
	# ~ give some space, but equal size and comma need to be quoted
	eq <- substitute(italic(target) == a + b %.%
	italic(input)","~~italic(r)^2~"="~r2","~~p~"="~italic(pvalue),
	list(target = y,
	input = x,
	a = format(as.vector(coef(m)[1]), digits = 2),
	b = format(as.vector(coef(m)[2]), digits = 2),
	r2 = format(summary(m)$r.squared, digits = 3),
	# getting the pvalue is painful
	pvalue = format(summary(m)$coefficients[2,'Pr(>\|t\|)'], digits=1)
	)
	)
	as.character(as.expression(eq));
	}

	#Inserindo os dados, recolhidos pelo IC Matheus, a partir da scimago.com
	#Organizados e analisados por Marcelo Hermes de Lima

	dados.Fig.1<-data.frame(
	Anos=seq(1996,2018,1),
	BR=c(NA,2.37, 2.37, 2.63, 2.75, 2.25, 2.93, 2.83, 2.89, 3.96, 3.27,
	3.33, 3.64, 3.64, 2.91, 3.45, 2.62, 2.95, 1.94, 1.59, 1.64, 1.71,
	1.37),
	HU=c(2.2, 2.1, 2.6, 2.4, 2.5, 3.5, 4.1, 4.3, 3.8, 4.2, 4.3, 4.5,
	5.1, 4.5, 5.3, 5, 5.7, 5.7, 5.65, 5.7, 7.5, 7.9, 7),
	SI=c(4.4, 3.4, 4.5, 3.4, 4.5, 4.5, 4.6, 5.4, 5.1, 6.25, 6.5, 6.5,
	7.3, 8.2, 8.9, 9, 9.5, 9.5, 9.5, 9.25, 9.4, 9.7, 9.3)
	)

	dados.Fig.2<-data.frame(
	Áreas=c("Bio-Agro","Bioquímica","Farmacologia","Micro e Imuno","Neurociência",
	"a" , "Medicina","Psicologia","Veterinária",
	"b" , "Física","Matemática","Meio Ambiente","Quimica","Geologia"),
	Hungria=c(4.4,6.6,4.2,10,3.2, 0 , 9.6,4.3,2.4, 0 ,10,3.25,5.6,4.1,8.5),
	Brasil=c(1.3,1.35,2.8,2.3,2.4, 0 , 1.25,0.65,0.8, 0 , 3.9,2.4,1.65,2.3,3.5)
	)

	dados.Fig.2_<-melt(dados.Fig.2)

	dados.Fig.2_$Áreas<-factor(dados.Fig.2_$Áreas,levels=dados.Fig.2$Áreas)

	dados.Fig.3A<-data.frame(
	Anos=seq(2008,2018,1),
	HU=c(3.8, 3.65, 4.65, 4.2, 4.9, 5.9, 5.3, 5.6, 7.5, 9.6, 9),
	BR=c(1.8, 1.7, 1.85, 2.2, 1.7, 2.2, 2, 1.9, 1.9, 1.25, 1.6)
	)

	dados.Fig.3B<-data.frame(
	Anos=seq(2007,2018,1),
	HU=c(4.7, 4.35, 4.4, 4.9, 6.3, 9.5, 7.8, 5.7, 8, 9.4, 10, 9.3),
	BR=c(2.9, 1.5, 2.4, 1.2, 3.2, 1.7, 2, 0.85, 1.95, 3.65, 3.9, 3.1)
	)

	dados.Fig.3C<-data.frame(
	Anos=seq(2012,2018,1),
	HU=c(2.5, 3.6, 3.2, 4.5, 6.4, 8.5, 9.4),
	BR=c(2.25, 3.1, 1.8, 3.2, 3.55, 3.5, 4.2)
	)

	dados.Fig.4<-data.frame(
	Áreas=c("Engenharia","Computação","Materiais","Eng. Química","Energia","a",
	"Ciências Sociais","Artes e Human.","Adm. e Contab.","Economia", "b", "Multidisciplinar"),
	Hungria=c(3.55,3.3,3.3,2.4,5.3, 0 , 1.95,3.2,5.8,2.3, 0, 4.3),
	Brasil=c(3.05,2.8,2.1,2.6,2.7, 0 , 0.05,1.1,0.8,1.1, 0 , 3.4)
	)

	dados.Fig.4_<-melt(dados.Fig.4)

	dados.Fig.4_$Áreas<-factor(dados.Fig.4_$Áreas,levels=dados.Fig.4$Áreas)

	dados.Fig.5<-data.frame(
	Anos=seq(1996,2018),
	PIB.PPP=c(15354.64687, 15869.20022, 16526.5085, 17082.36124, 17893.93479,
	18665.71659, 19606.46052, 20465.82873, 21500.42297, 22457.43917,
	23399.10945, 23492.14161, 23782.36284, 22223.44986, 22421.69734,
	22894.34764, 22674.18736, 23183.02905, 24220.73394, 25212.05311,
	25842.87059, 27031.78002, 28464.5602),
	rank=c(2.2, 2.1, 2.6, 2.4, 2.5, 3.5, 4.1, 4.3, 3.8, 4.2, 4.3, 4.5,
	5.1, 4.5, 5.3, 5, 5.7, 5.7, 5.65, 5.7, 7.5, 7.9, 7)
	)

	dados.Fig.6<-data.frame(
	Anos=c(1950,1960, 1973, 1976, 1989, 1998),
	Czechoslovakia=c(76, 75, 57, 55, 50, 51.5),
	East_Germany=c(67, 75, 60, 60, 50, 69),
	Hungary=c(68, 66, 57, 56, 52, 55),
	Poland=c(45, 43, 38, 36, 32, 38),
	Romania=c(30, 36, 43, 47, 37, 30),
	Bulgaria=c(25, 33, 35, 37, 32, 26)
	)

	#Fim dos dados brutos
	#################################*********
	#Plotando as Figuras segundo estética combinada com o primeiro autor MHL

	#Imagens de icons obtidas a partir do website https://www.flaticon.com/packs/countrys-flags
	#Icons made by <a href="https://www.flaticon.com/authors/freepik" title="Freepik">Freepik</a> from <a href="https://www.flaticon.com/" title="Flaticon">www.flaticon.com</a></div>
	#Usando ícones de bandeiras baixadas no meu computador (Ajustar locale para o de quem usar este script)
	brazil_icon<-image_read('/Users/egoncal2/Downloads/brazil.png')
	hungria_icon<-image_read('/Users/egoncal2/Downloads/hungary.png')
	singapura_icon<-image_read('/Users/egoncal2/Downloads/singapore.png')

	Fig.1<-ggplot(dados.Fig.1)+
	geom_point(aes(y=BR, x=Anos), shape = 21, fill = "#009C3B", colour="#ffdf00", size = 3)+
	geom_smooth(aes(y=BR, x=Anos), method = "loess", se = FALSE, size = 1, span = 1, colour="#ffdf00", linetype = "dotted") +
	geom_point(aes(y=HU, x=Anos), shape = 21, fill = "#CD2A3E", colour="green", size = 3)+
	geom_smooth(aes(y=HU, x=Anos), method = "loess", se = FALSE, size = 1, span = 1, colour="#CD2A3E", linetype = "dotted") +
	geom_point(aes(y=SI, x=Anos), shape = 21, fill = "white", colour="#EF3340", size = 3)+
	geom_smooth(aes(y=SI, x=Anos), method = "loess", se = FALSE, size = 1, span = 1, colour="#EF3340", linetype = "dotted") +
	scale_x_continuous(limits=c(1996,2018), breaks=seq(1996,2018,3), expand = c(0.1, 0.1))+
	theme_cowplot(12)+
	labs(title = 'Rank-score\nHungria, Brasil e Singapura', x = NULL, y= NULL)+
	theme(
	legend.position = "none",
	plot.title = element_text(color="black", size=15, face="bold.italic"),
	axis.title.x = element_text(size=20, face="bold"),
	axis.title.y = element_text(size=20, face="bold"),
	axis.text.y=element_text(size=12),
	axis.text.x=element_text(size=12))

	Final.Fig.1<-ggdraw(Fig.1) +
	draw_image(singapura_icon, scale=0.08, x=-0.25)+
	draw_image(hungria_icon, scale=0.08, x=0.21, y=0.12)+
	draw_image(brazil_icon, scale=0.08, x=0.21, y=-0.36)




	Fig.2<-ggplot(dados.Fig.2_, aes(x=Áreas, y=value, fill = variable))+
	geom_bar(stat="identity", position = "dodge", width=0.6)+
	scale_fill_manual(values=c("#CD2A3E", "#009C3B"))+
	scale_y_continuous(name= NULL,limits=c(0,10), breaks=c(0,2.5,5.0,7.5,10.0),
	expand = c(0, 0.1))+
	theme_light()+
	labs(title = 'Rank-scores por área de estudo', x= NULL, font="bold", size=16, fill = "País")+
	scale_x_discrete(breaks=melt(dados.Fig.2)$Áreas[nchar(as.character(melt(dados.Fig.2)$Áreas))!=1])+
	theme(
	legend.position = "right",
	plot.title = element_text(color="red", size=15, face="bold.italic"),
	axis.title.x = element_text(size=20, face="bold"),
	axis.title.y = element_text(size=20, face="bold"),
	axis.text.x=element_text(size=14, angle = 45, hjust = 1),
	axis.text.y=element_text(size=12))




	Fig.3A<-ggplot(dados.Fig.3A)+
	geom_smooth(aes(y=HU, x=Anos), size = 1, colour="#CD2A3E", linetype = "dashed", span = 0.3, se=FALSE)+
	geom_smooth(aes(y=BR, x=Anos), size = 1, colour="#ffdf00", linetype = "dashed",span = 0.3, se=FALSE)+
	geom_point(aes(y=BR, x=Anos), shape = 21, fill = "#009C3B", colour="#ffdf00", size = 3)+
	geom_point(aes(y=HU, x=Anos), shape = 21, fill = "#CD2A3E", colour="green", size = 3)+
	scale_y_continuous(limits=c(0,10), breaks=seq(0,10,2), expand = c(0.1, 0.1))+
	scale_x_continuous(limits=c(2008,2018), breaks=seq(2008,2018,3), expand = c(0.1, 0.1))+
	theme_cowplot(12)+
	labs(title = "Medicina", x=NULL, y=NULL)+
	theme(
	axis.title.x = element_text(size=14, face="bold"),
	axis.text.y=element_text(size=13),
	axis.text.x=element_text(size=13, angle = 45, hjust = 1))


	Fig.3B<-ggplot(dados.Fig.3B)+
	geom_smooth(aes(x=Anos, y=HU), size = 1, colour="#CD2A3E", span = 2,linetype = "dashed", se=FALSE)+
	geom_smooth(aes(x=Anos, y=BR), size = 1, colour="#ffdf00", span = 2,linetype = "dashed", se=FALSE)+
	geom_point(aes(x=Anos, y=HU), shape = 21, fill = "#CD2A3E", colour="green", size = 3)+
	geom_point(aes(x=Anos, y=BR), shape = 21, fill = "#009C3B", colour="#ffdf00", size = 3)+
	scale_y_continuous(limits=c(0,10), breaks=seq(0,10,2), expand = c(0.1, 0.1))+
	scale_x_continuous(limits=c(2007,2019), breaks=seq(2007,2019,3), expand = c(0.1, 0.1))+
	theme_cowplot(12)+
	labs(title = "Física", x=NULL, y=NULL)+
	theme(
	axis.title.x = element_text(size=14, face="bold"),
	axis.text.y=element_text(size=13),
	axis.text.x=element_text(size=13, angle = 45, hjust = 1))

	Fig.3C<-ggplot(dados.Fig.3C)+
	geom_smooth(aes(y=HU, x=Anos), size = 1, colour="#CD2A3E", span = 2, linetype = "dashed", se=FALSE)+
	geom_smooth(aes(y=BR, x=Anos), size = 1, colour="#ffdf00", span = 2, linetype = "dashed", se=FALSE)+
	geom_point(aes(y=HU, x=Anos), shape = 21, fill = "#CD2A3E", colour="green", size = 3)+
	geom_point(aes(y=BR, x=Anos), shape = 21, fill = "#009C3B", colour="#ffdf00", size = 3)+
	scale_y_continuous(limits=c(0,10), breaks=seq(0,10,2), expand = c(0.1, 0.1))+
	scale_x_continuous(limits=c(2012,2018), breaks=seq(2012,2018,2), expand = c(0.1, 0.1))+
	theme_cowplot(12)+
	labs(title = "Geologia", x=NULL, y=NULL)+
	theme(
	axis.title.x = element_text(size=14, face="bold"),
	axis.text.y=element_text(size=13),
	axis.text.x=element_text(size=13, angle = 45, hjust = 1))

	Final.Fig.3<- plot_grid(Fig.3A, Fig.3B, Fig.3C, nrow=1)

	#Adicionando texto acima :
	title <- ggdraw() +
	draw_label(
	"Rank-scores\nBrasil vs. Hungria", size = 16,
	fontface = 'bold.italic',
	x = 0,
	hjust = 0
	) +
	theme(
	# add margin on the left of the drawing canvas,
	# so title is aligned with left edge of first plot
	plot.margin = margin(0, 0, 0, 7)
	)
	plot_grid(
	title, Final.Fig.3,
	ncol = 1,
	# rel_heights values control vertical title margins
	rel_heights = c(0.1, 1)
	)



	Fig.4<-ggplot(dados.Fig.4_, aes(x=Áreas, y=value, fill = variable))+
	geom_bar(stat="identity", position = "dodge", width=0.6)+
	scale_fill_manual(values=c("#CD2A3E", "#009C3B"))+
	scale_y_continuous(name= NULL,limits=c(0,10), breaks=c(0,2.5,5.0,7.5,10.0),
	expand = c(0, 0.1))+
	theme_light()+
	labs(title = 'Rank-scores por área de estudo', x= NULL, font="bold", size=16, fill = "País")+
	scale_x_discrete(breaks=melt(dados.Fig.4)$Áreas[nchar(as.character(melt(dados.Fig.4)$Áreas))!=1])+
	theme(
	legend.position = "right",
	plot.title = element_text(color="red", size=15, face="bold.italic"),
	axis.title.x = element_text(size=20, face="bold"),
	axis.title.y = element_text(size=20, face="bold"),
	axis.text.x=element_text(size=14, angle = 45, hjust = 1),
	axis.text.y=element_text(size=12))



	Fig.5A<-ggplot(dados.Fig.5[,c(1,2)],aes(y=PIB.PPP, x=Anos))+
	geom_point(colour="black", size = 3)+
	scale_y_continuous(limits=c(0,35000), breaks=seq(0,35000,5000), expand = c(0.1, 0.1))+
	scale_x_continuous(limits=c(1996,2020), breaks=seq(1996,2020,4), expand = c(0.1, 0.1))+
	theme_light()+
	labs(title = "Hungria:\nPIB per capita (valor 2011)", x=NULL)+
	theme(plot.margin = margin(0, 0.1, 0, 0, "cm"),
	legend.position = "NULL",
	plot.title = element_text(color="black", size=16, face="bold"),
	axis.title.x = element_text(size=14, face="bold"),
	axis.title.y = element_text(size=14, face="bold",color="white"),
	axis.text.x=element_text(size=14),
	axis.text.y=element_text(size=12))

	Fig.5B<-ggplot(dados.Fig.5[,c(1,3)],aes(y=rank, x=Anos))+
	geom_point(colour="#CD2A3E", size = 3)+
	scale_y_continuous(limits=c(0,10), breaks=seq(0,10,2), expand = c(0.1, 0.1))+
	scale_x_continuous(limits=c(1996,2020), breaks=seq(1996,2020,4), expand = c(0.1, 0.1))+
	theme_light()+
	labs(title = "Hungria:\nRank-score", x=NULL)+
	theme(plot.margin = margin(0, 0.1, 0, 0, "cm"),
	legend.position = "NULL",
	plot.title = element_text(color="black", size=16, face="bold"),
	axis.title.x = element_text(size=14, face="bold"),
	axis.title.y = element_text(size=14, face="bold",color="white"),
	axis.text.x=element_text(size=14),
	axis.text.y=element_text(size=12))

	Fig.5C<-ggplot(dados.Fig.5[,c(2,3)],aes(y=rank, x=PIB.PPP))+
	geom_point(colour="blue", size = 3)+
	geom_smooth(method="lm", colour="red", alpha=0.1)+
	geom_text(x=22000,y=0, label=lm_eqn(dados.Fig.5[,c(2,3)], 'rank', 'PIB.PPP'), parse=T)+
	scale_y_continuous(limits=c(0,10), breaks=seq(0,10,2), expand = c(0.1, 0.1))+
	scale_x_continuous(limits=c(14000, 31000), expand = c(0.01, 0.01))+
	theme_light()+
	labs(title = "Hungria\nPIB vs. Rank-score", x=NULL)+
	theme(plot.margin = margin(0, 0.1, 0, 0, "cm"),
	legend.position = "NULL",
	plot.title = element_text(color="red", size=16, face="bold.italic"),
	axis.title.x = element_text(size=14, face="bold"),
	axis.title.y = element_text(size=14, face="bold",color="white"),
	axis.text.x=element_text(size=14),
	axis.text.y=element_text(size=12))

	#Reparar como compor tamanhos diversos, gradualmente

	Fig.5AB<-plot_grid(Fig.5A, Fig.5B, nrow=1)
	Final.Fig.5<-plot_grid(Fig.5AB,Fig.5C, nrow=2)

	Fig.6<-ggplot(melt(dados.Fig.6, id.vars = "Anos"), aes(fill=as.character(Anos), y=value, x=variable))+
	geom_bar(position="dodge", stat="identity", width = 0.8)+
	scale_fill_jco()+
	scale_x_discrete(name= 'Países', labels = c("Czechoslovakia", "East Germany", "Hungary", "Poland",
	"Romania", "Bulgaria"), expand = c(0.1, 0.1))+
	scale_y_continuous(name= NULL, limits=c(0,100), breaks=c(0,25,50,75,100), expand = c(0, 0))+
	labs(title = "Percentual do Trabalho da União Europeia", font="bold", size=20, fill = "Anos")+
	theme_light()+
	theme(
	plot.title = element_text(color="red", size=20, face="bold.italic"),
	axis.title.x = element_text(size=14),
	axis.text.x=element_text(size=13),
	axis.text.y=element_text(size=13),
	legend.title=element_text(size=12))
	legend.text=element_text(size=11),
	axis.text.x=element_text(size=14, angle = 45, hjust = 1))

	#**********************