STATA 11 for Windows SAMPLE SESSION - Food Security Group ...

Stata 11 Sample Session Section 2 – Restructuring Data Files – Table Lookup & Aggregation
The key word “unmatched” is used and within
parentheses the type of join is specified). There
are four types of joins:
none - all unmatched observations are ignored
(this is the default), i.e. if there is not a matching
observation in both files, the observation is
dropped from the final dataset.
both - unmatched observations from the “master”
(or file that is in memory) and “using” (file that is
not in memory) data are included.
master - unmatched observations from the
“master” data are included but not unmatched
observations from the “using” file.
using - unmatched observations from the “using”
data are included but not unmatched observations
from the “master” file.
4. Cross datasets. In this type of merge, the first
observation in the first file is joined horizontally
with every observation in the second data set. The
second observation in the first file is then joined
with every observation in the second data set and
so on. This type of file combination is rarely
used.
In this tutorial we will use the “merge” and the “joinby”
commands.
With this information in hand, we can now think about
the specific steps we must take to create the file we need
to produce the output we want. Logically, there are three
steps:
1. We need to know how many calories each
household produced for the year. We can generate
a file with this information using data we have
stored in three files—the production file, c-q4.dta,
and two table-lookup files, conver.dta and
calories.dta.
2. We need to know how many adult equivalents are
in each household. We can generate a file with
this information using data from the member file,
c-q1a.dta.
3. We need to combine the results from steps 1 and 2
into one file so we can compute calories produced
per adult equivalent per day.
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