(9) Manipuate the "iris" dataset as follows:

(a) exclude "Sepal.Length" and "Petal.Length" variables from the data set

(b) form a small data set using Species, Petal.Length variables

(c) select the first 50 rows

(d) select rows with Sepal.Length greater than 5.0 and Petal.Length < 1.5

(e) select rows for species setosa or Petal.Width less than 0.4

(f) split the data frame into groups of odd and even case numbers

(g) split the data frame based the value of Species and then use Reduce() function to combine list items into one data frame

(h) split the data frame based the value of Species into a list of sub frames and then find the sub set of each list item based on criteria Sepal.Length > 7 (Hint: apply function(x) {subset(x, Sepal.Length > 7)} to each list item)

(i) split the data frame based the value of Species into a list of sub frames and then fund the mean value of Sepal.Length in each sub data frame

(11) Create three data frames manually as follows: df.1 has customer name, city, zip code, and email address, df.2 has customer name, gender, and birth date, and df.3 contains customer name, hobby, terminal degree, and profession. Make up a few rows of sample data to each data frame. Then merge the three data frames into one using merge() and Reduce() functions, and then save the big data set into a CSV file.

(12) Download three CSV files from https://github.com/jakevdp/data-USstates/ about US states. Load the data from the files into data frames, and then merge them into a big data frame. Save the result as a new CSV file.