CODEWITHSUNDEEP
rggplot2scatter-plotcentroid
cyril
cyril

Reputation: 3006

R - add centroids to scatter plot

I have a dataset two continuous variables and one factor variable (two classes). I want to create a scatterplot with two centroids (one for each class) that includes error bars in R. The centroids should be positioned at the mean values for x and y for each class.

I can easily create the scatter plot using ggplot2, but I can't figure out how to add the centroids. Is it possible to do this using ggplot / qplot?

Here is some example code:

x <- c(1,2,3,4,5,2,3,5)
y <- c(10,11,14,5,7,9,8,5)
class <- c(1,1,1,0,0,1,0,0)
df <- data.frame(class, x, y)
qplot(x,y, data=df, color=as.factor(class))

Upvotes: 7

Views: 14635

Answers (2)

simpson
simpson

Reputation: 335

I could not get the exact code by @jlhoward to work for me (specifically with the error bars), so I made minor changes to remove errors and even remove warnings. So, you should be able to run the code from start to finish, and if @jlhoward wants to incorporate this into the existing answer, that's great.

centroids <- aggregate(cbind(mean.x = x, mean.y = y) ~ class, df, mean)
gg        <- merge(df, centroids, by = "class")
f         <- function(z) sd(z) / sqrt(length(z)) # function to calculate std.err
se        <- aggregate(cbind(se.x = x ,se.y = y) ~ class, df, f)
centroids <- merge(centroids, se, by = "class")    # add std.err column to centroids

ggplot(gg, aes(x = x, y = y, color = factor(class))) +
  geom_point(size = 3) +
  geom_point(data = centroids, aes(x = mean.x, y = mean.y), size = 5) +
  geom_errorbar(data = centroids, 
                aes(x = mean.x, y = mean.y, ymin = mean.y - se.y, ymax = mean.y + se.y),
                width = 0.1) +
  geom_errorbarh(data = centroids, inherit.aes=FALSE, # keeps ggplot from using first aes
                 aes(xmin = (mean.x - se.x), xmax = (mean.x + se.x), y = mean.y,
                 height = 0.1, color = factor(class))) +
  labs(x = "Label for x-axis", y = "Label for y-axis") +
  theme(legend.title = element_blank()) # remove legend title

Upvotes: 0

jlhoward
jlhoward

Reputation: 59355

Is this what you had in mind?

centroids <- aggregate(cbind(x,y)~class,df,mean)
ggplot(df,aes(x,y,color=factor(class))) +
  geom_point(size=3)+ geom_point(data=centroids,size=5)

This creates a separate data frame, centroids, with columns x, y, and class where x and y are the mean values by class. Then we add a second point geometry layer using centroid as the dataset.

This is a slightly more interesting version, useful in cluster analysis.

gg <- merge(df,aggregate(cbind(mean.x=x,mean.y=y)~class,df,mean),by="class")
ggplot(gg, aes(x,y,color=factor(class)))+geom_point(size=3)+
  geom_point(aes(x=mean.x,y=mean.y),size=5)+
  geom_segment(aes(x=mean.x, y=mean.y, xend=x, yend=y))

EDIT Response to OP's comment.

Vertical and horizontal error bars can be added using geom_errorbar(...) and geom_errorbarh(...).

centroids <- aggregate(cbind(x,y)~class,df,mean)
f         <- function(z)sd(z)/sqrt(length(z)) # function to calculate std.err
se        <- aggregate(cbind(se.x=x,se.y=y)~class,df,f)
centroids <- merge(centroids,se, by="class")    # add std.err column to centroids
ggplot(gg, aes(x,y,color=factor(class)))+
  geom_point(size=3)+
  geom_point(data=centroids, size=5)+
  geom_errorbar(data=centroids,aes(ymin=y-se.y,ymax=y+se.y),width=0.1)+
  geom_errorbarh(data=centroids,aes(xmin=x-se.x,xmax=x+se.x),height=0.1)

If you want to calculate, say, 95% confidence instead of std. error, replace 

f <- function(z)sd(z)/sqrt(length(z)) # function to calculate std.err

with

f <- function(z) qt(0.025,df=length(z)-1, lower.tail=F)* sd(z)/sqrt(length(z))

Upvotes: 20

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