Colouring Plot by Factor in R

Colouring plot by factor in R

data<-iris
plot(data$Sepal.Length, data$Sepal.Width, col=data$Species)
legend(7,4.3,unique(data$Species),col=1:length(data$Species),pch=1)

should do it for you. But I prefer ggplot2 and would suggest that for better graphics in R.

Colouring plot in R by multiple factor

As pointed out in the comments, the following works:

colors <- interaction(df$School, df$TimeOfTest)
plot(df$Score1, df$Score2, col=colors)

colouring plot by factor using custom colours in r

You can manually set the R palette used by your plot call like so:

palette(c("blue","pink","green"))

Which you can reset like so:

palette("default")

Try it out, creating two plots, one with default colours, one with the new colours specified:

# default plotting
palette("default")
plot(iris$Sepal.Length, iris$Sepal.Width, col=iris$Species, pch=19)

# after specifying custom palette
palette(c("blue","pink","green"))
plot(iris$Sepal.Length, iris$Sepal.Width, col=iris$Species, pch=19)

R plot color legend by factor

base R solution:

attach(v1)    
plot(x,y, pch=16, col=group)
legend("topleft", legend=levels(group), pch=16, col=unique(group))

ggplot2 solution

ggplot(v1)+
   geom_point(aes(x=x,y=y,colour=group))+
   theme_bw()

Again, I would strongly suggest the use of ggplot2 over base R unless you're only exploring the data. There are plenty of questions/answers on the matter on SO.

Manually setting colours for Voronio plot when colouring by factor using ggvoronoi

You can define a vector to attribute a color to each values of "cluster" variable and then pass them into the argument values = of the scale_fill_manual function as in the following :

library(ggplot2)
library(ggvoronoi)
library(dplyr)
for(i in df$years){
  #
  col  = c("1" = "green", "2" = "blue", "3" = "red")
  single_year <- df %>% 
    filter(years == i)
  #
  #
  plot <- ggplot(single_year,
                 aes(x=lat,
                     y=long)) +
    #
    geom_voronoi(aes(fill = cluster)) +
    #
    stat_voronoi(geom="path" )+
    #
    geom_point() +
    #
    labs(title = paste(i))+
    scale_fill_manual(values = col)
  #
  #
  ggsave(paste0(i,".jpeg"), plot = last_plot(), # Watch out for the SAVE!!!
         device = 'jpeg')
  #
}

and

Does it answer your question ?

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