> set.seed(42293888)
> x <- (-4):5
> y <- 2 + x + rnorm(length(x))
> org <- data.frame(x = x, y = y, weights = 1:10)
>
> # show data and fit model. Notice the R-squared
> head(org)
   x          y weights
1 -4  0.4963671       1
2 -3 -0.5675720       2
3 -2 -0.3615302       3
4 -1  0.7091697       4
5  0  0.6485203       5
6  1  3.8495979       6
> summary(lm(y ~ x, org, weights = weights))

Call:
lm(formula = y ~ x, data = org, weights = weights)

Weighted Residuals:
    Min      1Q  Median      3Q     Max
-3.1693 -0.4463  0.2017  0.9100  2.9667

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)   1.7368     0.3514   4.942  0.00113 **
x             0.9016     0.1111   8.113 3.95e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.019 on 8 degrees of freedom
Multiple R-squared:  0.8916,    Adjusted R-squared:  0.8781
F-statistic: 65.83 on 1 and 8 DF,  p-value: 3.946e-05

>
> # make redundant data set with redundant rows
> idx <- unlist(mapply(rep, x = 1:nrow(org), times = org$weights))
> org_redundant <- org[idx, ]
> head(org_redundant)
     x          y weights
1   -4  0.4963671       1
2   -3 -0.5675720       2
2.1 -3 -0.5675720       2
3   -2 -0.3615302       3
3.1 -2 -0.3615302       3
3.2 -2 -0.3615302       3
>
> # fit model and notice the same R-squared
> summary(lm(y ~ x, org_redundant))

Call:
lm(formula = y ~ x, data = org_redundant)

Residuals:
     Min       1Q   Median       3Q      Max
-1.19789 -0.29506 -0.05435  0.33131  2.36610

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  1.73680    0.13653   12.72   <2e-16 ***
x            0.90163    0.04318   20.88   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7843 on 53 degrees of freedom
Multiple R-squared:  0.8916,    Adjusted R-squared:  0.8896
F-statistic: 436.1 on 1 and 53 DF,  p-value: < 2.2e-16

>
> # glm gives you the same with family = gaussian()
> # just compute the R^2 from the deviances. See
> #   https://stats.stackexchange.com/a/46358/81865
> fit <- glm(y ~ x, family = gaussian(), org_redundant)
> fit$coefficients
(Intercept)           x
  1.7368017   0.9016347
> 1 - fit$deviance / fit$null.deviance
[1] 0.8916387