age nr.employed          job  marital           education housing default emp.var.rate cons.price.idx cons.conf.idx
1   30      5099.1  blue-collar  married            basic.9y     yes      no         -1.8         92.893         -46.2
2   39      5191.0     services   single         high.school      no      no          1.1         93.994         -36.4
3   25      5228.1     services  married         high.school     yes      no          1.4         94.465         -41.8
4   38      5228.1     services  married            basic.9y unknown      no          1.4         94.465         -41.8
5   47      5195.8       admin.  married   university.degree     yes      no         -0.1         93.200         -42.0
6   32      4963.6     services   single   university.degree      no      no         -1.1         94.199         -37.5
7   32      4963.6       admin.   single   university.degree     yes      no         -1.1         94.199         -37.5
8   41      5195.8 entrepreneur  married   university.degree     yes unknown         -0.1         93.200         -42.0
9   31      5195.8     services divorced professional.course      no      no         -0.1         93.200         -42.0
10  35      5191.0  blue-collar  married            basic.9y      no unknown          1.1         93.994         -36.4

 

Create the following variables:

• cons.ratio that is the ratio of cons.price.idx on cons.conf.idx. You can store the result in a vector called cons.ratio in order to check whether your computation is correct.
• prev.nr.employed containing the amount of employees in the previous quarter. You will need the variable emp.var.rate for this which is the percentage change in nr.employed compared to the previous quarter (i.e. -3.4 means that 3.4% of the employees from the previous quarter left). You can store the result in a vector called prev.nr.employed in order to check whether your computation is correct.

 

Store the result with all the old variables and the two new variables in a data frame called survey_newvars.