can somone help me with code for c and d, i included my code for b. this is done using r

In class, it was mentioned that bootstrapping can be used to estimate the sampling dis- tribution of most statistics, but not all. (a) Give an example of a statistic for which the bootstrap might struggle to provide an accurate sampling distribution. Hint: Some things really need to be safnpled from continuous distributions. (b) Demonstrate your example from part (a). Take 1000 samples of size 25 from a standard normal distribution, calculate your statistic on each one, and create a histogram. A single sample of size 25 from a standard normal can be taken in R with rnorm(25). (e) Now select one of your 1000 samples uniformly at random and take 1000 bootstrap samples. With each bootstrap sample, calculate the statistic and create a histogram. (d) How does your bootstrap distribution differ from the "true" sampling distribution? Give a brief explanation for why the bootstrap fails. 95 data