Perform the following steps:

1. Validate whether the dataset, x (generated with the rnorm function), is distributed normally with a one-sample Kolmogorov-Smirnov test:

        > x = rnorm(50)
        > ks.test(x,"pnorm")
        Output:
         One-sample Kolmogorov-Smirnov test
        data: x
        D = 0.1698, p-value = 0.0994
        alternative hypothesis: two-sided

2. Next, you can generate uniformly distributed sample data:

    > set.seed(3) 
    > x = runif(n=20, min=0, max=20)
    > y = runif(n=20, min=0, max=20)

3. We first plot the ecdf of two generated data samples:

         > plot(ecdf(x), do.points = FALSE, verticals=T, xlim=c(0, 20))
         > lines(ecdf(y), lty=3, do.points = FALSE, verticals=T)

4. Finally, we apply a two-sample Kolmogorov-Smirnov test on two groups of data:

        > ks.test(x,y)
        Output:
         Two-sample Kolmogorov-Smirnov test
        data: x and y
        D = 0.3, p-value = 0.3356
        alternative hypothesis: two-sided

This recipe will create a sample data and perform the Kolmogorov-Smirnov test. Perform the following in R:

> g1 = sample(50:100, 30, replace=TRUE)
> g2 = sample(50:100, 30, replace=TRUE)
> plot.ecdf(g1, verticals=TRUE, do.points=FALSE, col="red")
> lines(ecdf(g2), verticals=TRUE, do.points=FALSE, col="blue")

> ks.test(g1,g2)
Output:
 Two-sample Kolmogorov-Smirnov test
data: g1 and g2
D = 0.2, p-value = 0.586
alternative hypothesis: two-sided