wos<- read.csv("wos_otter.csv")
str(wos)

library(ggplot2)

#Plot divided by region
mytheme<-theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
min_y <- min(0)
max_y <- max(60)
wos_plot<-ggplot(wos, aes(x=Coastal_ecosystem_type, ..count..)) + 
  geom_bar(aes(fill = Otter_population), position = "dodge") +
  scale_fill_manual(values = c("black", "grey"))+
  scale_y_continuous(limits=c(min_y,max_y))+
  theme_bw() +xlab("Coastal ecosystem type") +
  ylab("Number of studies")+
  guides(fill=FALSE)+
  ggtitle('A')+theme(plot.title=element_text(hjust=0)) + 
  theme(text=element_text(size=16))
wos_plot

wos_plot2<-ggplot(wos, aes(within_ecosystem_type, ..count..)) + 
  geom_bar(aes(fill = Otter_population), position = "dodge")+
  scale_fill_manual(values = c("black", "grey"))+
  theme_bw() +xlab("Within ecosystem type") +
  ylab("Number of studies")+
  ggtitle('B')+theme(plot.title=element_text(hjust=0)) + 
  theme(legend.position = c(0.8, 0.8))+
  guides(fill=guide_legend(title="Otter population"))+
  theme(text=element_text(size=16))+
  theme(axis.text.x = element_text(size=12))
wos_plot2

# ggsave(p, filename = "otter_wos.pdf", width=12, height=6)#### WOS Figure
pdf("otter_wos.pdf", width=14, height=6)
gridExtra::grid.arrange(wos_plot, wos_plot2,nrow = 1, ncol=2)
dev.off()