I have written a code that generates x and y data and am able to plot it.

# Number of observations
n <- 250
# x randomly drawn from a continuous uniform distribution with bounds [0,10]
x <- runif(min = 0, max = 1, n = sample(n))
# Error term from Normal distribution
error <- rnorm(n = n, mean = 0, sd = 2)
beta_0 <- 1
beta_1 <- -1
y <- beta_0*x   (beta_1*x - error)

library(tibble)
df <- tibble(x = x, y = y)
df

library(ggplot2)
ggplot(data = df, aes(x = x, y = y))   geom_point() 
labs(title = "y = f(x)")

I get an graph image like this:

I also get a data table like this of different coordinate data:

I would like to now randomly classify my data, such that my table looks like:

Where 1 represents that points membership to the group and -1 representing the point not being affiliated to the group. On the graph this would look like I had blue dots for Group1 membership vs red dots for Group2 membership.

Any help with this would be greatly appreciated.

Thank you.

CodePudding user response：

To do it the way you suggested (with one column for group 1 and one column for group 2), you could do:

library(dplyr)
library(ggplot2)

df %>%
  mutate(group1 = sample(c(-1, 1), n, TRUE),
         group2 = -group1) %>%
  ggplot(aes(x = x, y = y, color = factor(group1)))   
  geom_point()  
  scale_color_brewer('group', palette = 'Set1', 
                     labels = c('Group 1', 'Group 2'))  
  labs(title = "y = f(x)")

However, it seems a bit redundant to me having two mutually exclusive binary columns. You could just have a single column called group which is either group 1 or group 2:

df %>%
  mutate(group = sample(c('Group 1', 'Group 2'), n, TRUE)) %>%
  ggplot(aes(x = x, y = y, color = group))   
  geom_point()  
  scale_color_brewer(palette = 'Set1')  
  labs(title = "y = f(x)"