Task 2. One of the variables that the researchers measured was sense of vitality. The researchers 'normalised' the values according to the typical value for Japanese adults, i.e. 5f]. Consequently, individuals with a measured vitality of greater than if] have an above average sense of vitality, and those below if] have a below average sense of vitality. These who have a value of if] would be consistent with the .lapanese average. Using R, create a new variable {i.e. vector} that transforms the vitality variable to a categorical variable using the following denition of the values for the categorical variable: - if the quantitative value of vitality is less then ll, the new value is I'"belo'iv average\" - if the quantitative value of vitality is ll 5 value at 51, the new value is \"average" - if the quantitative value of vitality is greater than or equal to 51, the new value is \"above average\" You should {i} call the new variable vit_cat and [ii] add it to the act3 datafrarne (Le. so that it is a new column in that dataframe}. Finally, use the ll function, table(} to show the frequency distribution {as counts} for the new variable. Remember, all of this task should be completed using R; ensure that all of the code you used shows in your knitted le. The only code that necessarily should have some output showing is the application of the table[} function. Task 3. When we collect data for a research study, we often have questions we want to address that only apply to a subset of the data. For example, while the act3 dataset represents a sample that is supposed to represent the population of \"adults\