Artificial time series data 

x <- round(rnorm(1:12),1)

ts(x,start=2000,
   frequency=4)

     Qtr1 Qtr2 Qtr3 Qtr4
2000  0.3  1.0 -0.1  0.9
2001  1.8  0.0 -0.7  1.6
2002  0.2 -0.9 -0.8 -0.1

print(ts(x,start=2000,
       frequency=12))

      Jan  Feb  Mar  Apr  May  Jun  Jul  Aug  Sep  Oct  Nov  Dec
2000  0.3  1.0 -0.1  0.9  1.8  0.0 -0.7  1.6  0.2 -0.9 -0.8 -0.1

The data file “unemployment.csv” used below consists of data originally downloaded from the OECD Database website.

unemployment <- read.csv("unemployment.csv")
# This is of course incorrect, but demonstrates how monthly multivariate time 
# series can be constructed from scratch.
ts(unemployment[2:5],
   start = 1970,
   frequency=4)

        Germany France Italy  Netherlands
1970 Q1  0.557   2.477  4.000  0.868     
1970 Q2  0.689   2.712  4.001  1.213     
1970 Q3  0.912   2.806  4.711  2.114     
1970 Q4  1.000   2.690  4.691  2.151     
1971 Q1  2.132   2.853  3.942  2.624     
1971 Q2  3.965   4.028  4.312  3.772     
1971 Q3  3.934   4.406  4.925  4.067     
1971 Q4  3.822   4.938  5.261  3.916     
1972 Q1  3.661   5.191  5.313  3.827     
1972 Q2  3.192   5.833  5.658  3.648     
1972 Q3  3.190   6.246  5.574  4.015     
1972 Q4  4.505   7.396  6.269  5.818     
1973 Q1  6.441   8.041  6.918  8.519     
1973 Q2  7.921   8.253  7.694 10.987     
1973 Q3  7.932   9.660  8.504 10.604     
1973 Q4  8.002  10.234  8.611  9.191     
1974 Q1  7.661  10.373  9.896  8.394     
1974 Q2  7.611  10.479 10.248  7.982     
1974 Q3  7.598   9.975 10.451  7.785     
1974 Q4  6.863   9.348 10.214  6.917     
1975 Q1  6.203   8.866  9.127  5.965     
1975 Q2  6.653   9.439  8.593  5.469     
1975 Q3  7.672  10.358  8.835  5.401     
1975 Q4  8.849  11.704 10.239  6.546     
1976 Q1  9.560  12.225 11.291  7.587     
1976 Q2  9.386  11.544 11.983  7.109     
1976 Q3 10.318  12.291 12.092  6.654     
1976 Q4 11.412  12.438 12.251  5.590     
1977 Q1 11.530  11.910 11.997  5.096     
1977 Q2 11.118  11.279 11.762  4.802