Working with Time Series Data

The following statements transpose the variable CPI in the CPICITY data set shown in a previous example from time series cross-sectional form to a standard form time series data set. (Only a subset of the data shown in the previous example is used here.) Note that the method shown in this example works only for a single variable.

title "Original Data Set";
proc print data=cpicity;
run;

proc sort data=cpicity out=temp;
   by date city;
run;

proc transpose data=temp out=citycpi(drop=_name_);
   var cpi;
   id city;
   by date;
run;

title "Transposed Data Set";
proc print data=citycpi;
run;

The names of the variables in the transposed data sets are taken from the city names in the ID variable CITY. The original and the transposed data sets are shown in Figure 13 and Figure 14.

The following statements transpose the CITYCPI data set back to the original form of the CPICITY data set. The variable _NAME_ is added to the data set to tell PROC TRANSPOSE the name of the variable in which to store the observations in the transposed data set. (If the (DROP=_NAME_ _LABEL_) option were omitted from the first PROC TRANSPOSE step, this would not be necessary. PROC TRANSPOSE assumes ID _NAME_ by default.)

The NAME=CITY option in the PROC TRANSPOSE statement causes PROC TRANSPOSE to store the names of the transposed variables in the variable CITY. Because PROC TRANSPOSE recodes the values of the CITY variable to create valid SAS variable names in the transposed data set, the values of the variable CITY in the retransposed data set are not the same as in the original. The retransposed data set is shown in Figure 15.

data temp;
   set citycpi;
   _name_ = 'CPI';
run;

proc transpose data=temp out=retrans name=city;
   by date;
run;

proc sort data=retrans;
   by city date;
run;

title "Retransposed Data Set";
proc print data=retrans;
run;