6. One way of manipulating a large dataset is to take a random sample and construct numerical and graphical summaries of the sample. Use the following code to construct a random sample that consists of 10% of the original number of transcripts; the sampling is done without replacement, such that a single transcript cannot be chosen more than once. Using the set. seed() function allows for pseudo-random sampling; that is, a random sam- ple that is reproducible. Replace xxxx in the function with four numbers of your choice, then run the code to create transcript. sample, a vector of transcript lengths. set. seed(xxxx) sample. size = 0.1 * nrow(coding . mrna) transcript . sample = sample(coding . mrna$transcript_length, size = sample. size, replace = FALSE) Now with transcript. sample, calculate the number of transcripts in the dataset, the five- number summary, and draw a histogram and boxplot. Does the sample data more closely resemble the complete version of the data or the trimmed version from Question 4