ordinal_data

da <- read.csv("https://raw.githubusercontent.com/juanchiem/agro_data/master/spores.csv", 
                sep = ";")
da %>% 
  group_by(meter, spore_nuclei, .drop=FALSE) %>%
  summarise(count = mean(count)) %>% 
  mutate(percent = count/sum(count)) %>%   
  # group_by(meter, spore_nuclei) %>% 
  # mutate(tot = sum(count)) 
  ggplot(aes(x=meter, y=percent, fill=factor(spore_nuclei) ))+ 
  geom_bar(position="fill", stat="identity")+
  labs(y="Type of spore frequency")+
  scale_fill_viridis_d(direction = -1)+
  geom_text(aes(label = round(percent,2)), position = position_stack(vjust = .5), 
            col = "white", fontface = "bold", size = 3)+
  theme_bw()

# si fuera una escala de severidad agregamos la escala traffic:

mycols <- colorRampPalette(c("red2", "orange", "gold1", "forestgreen"))
myvals <- c(0, .25, .5, .75, 1)

da %>% 
  group_by(meter, spore_nuclei, .drop=FALSE) %>%
  summarise(count = mean(count)) %>% 
  mutate(percent = count/sum(count)) %>%   
  ggplot(aes(x=meter, y=percent, fill=fct_rev(factor(spore_nuclei))))+ 
  geom_bar(position="fill", stat="identity")+
  labs(y="Type of spore frequency")+
  # scale_fill_viridis_d(direction = -1)+
  scale_fill_manual(values=mycols(5), name="Escala sev")  +
  geom_text(aes(label = round(percent,2)), position = position_stack(vjust = .5), 
            col = "white", fontface = "bold", size = 3)