PU/DSS/OTR 
Using  _n, you can create a unique identifier for each case in your data, type 
Check the results in the data editor, ‘idall’ is equal to ‘id’ 
Using  _N   you can also create a variable with the total number of cases in your 
dataset: 
Check the results in the data editor: 
Indexing: creating ids 
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PU/DSS/OTR 
We can create ids by categories. For example by major. 
Check the results in the data editor: 
First we have to sort the data by the variable on 
which we are basing the id (major in this case). 
Then we use the command by to tell Stata that we 
are using major as the base variable (notice the 
colon). 
Then we use browse to check the two variables. 
Indexing: creating ids by categories 
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PU/DSS/OTR 
----- You can create lagged values with _n .  
----- You can create forward values with _n: 
NOTE: Notice the square brackets 
For times series see: http://dss.princeton.edu/training/TS101.pdf  
A more advance alternative to create lags uses the “L” operand within a time series 
setting (tsset command must be specified first): 
You can also use the “F” operand (with tsset) 
Indexing: lag and forward values 
gen lag1_year=year[_n-1] 
gen lag2_year=year[_n-2] 
tsset year 
time variable:  year, 1980 to 2009 
delta:  1 unit 
gen l1_year=L1.year 
gen l2_year=L2.year 
gen for1_year=year[_n+1] 
gen for2_year=year[_n+2] 
gen f1_year=F1.year 
gen f2_year=F2.year 
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PU/DSS/OTR 
Combining _n and _N  you can create a countdown variable. 
Check the results in the data editor: 
Check the results in the data editor: 
You can create a variable based on one value of another variable. For example, 
create a variable with the highest SAT value in the sample. 
NOTE: You could get the same result without sorting by using 
egen and the max function 
Indexing: countdown and specific values 
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PU/DSS/OTR 
After 
Before 
sort var1 var2 … 
gsort is another command to sort data. The difference between gsort and 
sort is that with gsort you can sort in ascending or descending order, while 
with sort you can sort only in ascending order. Use +/- to indicate whether you 
want to sort in ascending/descending order. Here are some examples: 
Sorting 
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PU/DSS/OTR 
Deleting variables 
Use drop to delete variables and keep to keep them 
After 
Before 
Notice the dash between ‘total’ and ‘readnews2’, you can use this format to indicate a list so you 
do not have to type in the name of all the variables
Or 
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PU/DSS/OTR 
Deleting cases (selectively) 
You can drop cases selectively using the conditional “if”, for example 
drop if var1==1  /*This will drop observations (rows) 
where gender =1*/ 
drop if age>40 /*This will drop observation where  
age>40*/ 
Alternatively, you can keep options you want 
keep if var1==1 
keep if age<40 
keep if country==7 | country==13 
keep if state==“New York” | state==“New Jersey” 
| = “or”, & = “and” 
For more details type help keep or help drop. 
PU/DSS/OTR 
Merge/Append 
Please check this document: 
http://dss.princeton.edu/training/Merge101.pdf 
PU/DSS/OTR 
Merging fuzzy text (reclink) 
RECLINK - Matching fuzzy text. Reclink stands for ‘record linkage’. It is a program written by Michael Blasnik to merge imperfect 
string variables. For example 
Reclink helps you to merge the two databases by using a matching algorithm for these types of variables. Since it is a user 
created program, you may need to install it by typing  ssc install reclink. Once installed you can type help reclink 
for details 
As in merge, the merging variables must have the same name: state, university, city, name, etc. Both the master and the using 
files should have an id variable identifying each observation.  
Note: the name of ids must be different, for example id1 (id master) and id2 (id using). Sort both files by the matching (merging) 
variables. The basic sytax is: 
reclink var1 var2 var3 … using myusingdata, gen(myscore) idm(id1) idu(id2) 
The variable myscore indicates the strength of the match; a perfect match will have a score of 1. Description (from reclink help 
pages): 
“reclink uses record linkage methods to match observations between two datasets where no perfect key fields exist -- 
essentially a fuzzy merge.  reclink allows for user-defined matching and non-matching weights for each variable and 
employs a bigram string comparator to assess imperfect string matches. 
The master and using datasets must each have a variable that uniquely identifies observations.  Two new variables are 
created, one to hold the matching score (scaled 0-1) and one for the merge variable.  In addition, all of the 
matching variables from the using dataset are brought into the master dataset (with newly prefixed names) to allow 
for manual review of matches.” 
Data1 
Data2 
Princeton University 
Princeton U 
PU/DSS/OTR 
Graphs: scatterplot 
Scatterplots are good to explore possible relationships or patterns between variables and to identify outliers. Use the command 
scatter
(sometimes adding 
twoway
is useful when adding more graphs). The format  is 
scatter y x
. Below we check the relationship 
between SAT scores and age. For more details type  
help scatter
twoway scatter sat age 
twoway scatter sat agemlabel(last
twoway scatter sat age, mlabel(last) || 
lfit sat age, yline(30) xline(1800) 
twoway scatter sat age, mlabel(last) || 
lfit sat age  
1400
1600
1800
2000
2200
2400
SAT
20
25
30
35
40
Age
DOE01
DOE02
DOE03
DOE04
DOE05
DOE06
DOE07
DOE08
DOE09
DOE10
DOE11
DOE12
DOE13
DOE14
DOE15
DOE16
DOE17
DOE18
DOE19
DOE20
DOE21
DOE22
DOE23
DOE24
DOE25
DOE26
DOE27
DOE28
DOE29
DOE30
1400
1600
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2200
2400
SAT
20
25
30
35
40
Age
DOE01
DOE02
DOE03
DOE04
DOE05
DOE06
DOE07
DOE08
DOE09
DOE10
DOE11
DOE12
DOE13
DOE14
DOE15
DOE16
DOE17
DOE18
DOE19
DOE20
DOE21
DOE22
DOE23
DOE24
DOE25
DOE26
DOE27
DOE28
DOE29
DOE30
1400
1600
1800
2000
2200
2400
20
25
30
35
40
Age
SAT
Fitted values
DOE01
DOE02
DOE03
DOE04
DOE05
DOE06
DOE07
DOE08
DOE09
DOE10
DOE11
DOE12
DOE13
DOE14
DOE15
DOE16
DOE17
DOE18
DOE19
DOE20
DOE21
DOE22
DOE23
DOE24
DOE25
DOE26
DOE27
DOE28
DOE29
DOE30
1400
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SAT
Fitted values
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