(Dataset: gss. Variables: mslm_col.n, mslm_lib.n, mslm_spk.n, wtss.) The gss dataset contains three numerics that gauge tolerance toward "anti-American Muslim clergymen"whether they should be allowed to teach in college (gss$mslm_col.n), whether their books should be removed from the library (gss$mslm_lib.n), and whether they should be allowed to preach hatred of the United States (gss$mslm_spk.n). For each variable, a less tolerant response has a numeric code of 0 and a more tolerant response is coded 1.

• Use cut2 to recode gss$muslim_tol into three defined categories, as follows: Make the lowest code its own category; combine the middle two codes into one category; make the highest code its own category. Make sure to apply cut2's plus-1 rule. You can give the collapsed variable a new name, or you can continue with the name gss$muslim_tol. (If the cut2 run goes badly, you can easily go back and re-create gss$muslim_tol from the three original variables.) Run freq on the newly collapsed variable. Make sure that your cut2 run correctly collapsed the middle two codes and left the lowest and highest codes unchanged. If the collapsed variable is incorrect, review the cut2 procedure for collapsing a numeric into defined groups.
• Define gss$muslim_tol as an ordered factor. Supply these level names: "Low", "Middle", "High". To check your work, run freq on gss$muslim_tol. Run printC on the freq command. Copy/paste from Table.Output.html. Print the frequency distribution.