1. Introduction to Stata II
2016
Stefan Dahlberg
Dep. of Political Science
University of Gothenburg

2. Data management II – creating and changing variables
We want to group this variabel into fewer categorys of countries with low or high degrees of disproportional electoral systems

3. Data management II – creating and changing variables
There are (as usuall) several ways to do this and here is the long way using the generate command:
Use excercise2.dta
1, .tab fh_pr
2, create a new variable
. gen fh_pr_cat=fh_pr
3, recode the new values of the new variable
.recode fh_pr_cat (1/2=1) (2/5=2) (6/7=3)
.tab fh_pr_cat

4. Data management II – creating and changing variables
Let’s have a quick look at our new variable:
. des fh_pr_cat
. sum fh_pr_cat
. tab fh_pr_cat

5. Data management II – creating and changing variables
And here’s another way to do it using the genenrate and the replace commands:
(we make a new var with even fewer categories)
1, create a new variable
. gen fh_pr_cat2=.
2, replace the values of the new variable
.replace fh_pr_cat2 =1 if fh_pr_cat==2
.replace fh_pr_cat2 =2 if fh_pr_cat==1 | fh_pr_cat==3

6. Data management II – creating and changing variables
Let’s have a quick look at our new variable:
. des disp_cat2
. sum disp_cat2
. tab disp_cat2
To inspect that the codings are okay use tab
.tab disp_cat disp_cat2
. drop disp_cat2

7. Data management II – creating and changing variables
Or a combination of them both using the recode and gen commands simultaniously
Recode into a new variable
. recode fh_pr_cat (1/2=1) (2/5=2) (6/7=3), gen(fh_pr_cat3)
.tab fh_pr_cat3

8. Data management II – creating and changing variables
Now, use the quickest method to create a new variable of a fh_ipolity2 with four categorys based on the percentile values…

9. Data management II – creating and changing variables
Solution:
.sum fh_ipolity2, d
.recode fh_ipolity2 (min/ 1.83 =1) (1.83 /5.21=2) (5.21/8.91=3) (8.91/max=4), gen(fh_ipolity2_4cat)
.tab fh_ipolity2_4cat

10. Data management II – creating and changing variables
Lets say that we are interested in the interaction effect between political rights and GDP per capita!
.gen int_fhxgdp= fh_pr*mad_gdppc
.tab int_fhxgdp
Now, we briefly examine the new variable in relation to the others
. sum fh_pr mad_gdppc int_fhxgdp

11. Data management II – creating and changing variables
Creating a variable for deviations from the mean
.sum fh_cl
.gen fh_cl_dev=fh_cl-3.86
.tab fh_cl_dev

12. Data management II – creating and changing variables
Try to only list/tab countries with large deviations from the mean

13. Data management II – creating and changing variables
Now, try to only list/tab countries with large deviations from the mean
.tab fh_cl_dev
.list cname_year if fh_cl_dev>3.86
.tab cname_year if fh_cl_dev>3.86

14. Data management II – creating and changing variables
Many times the absolute deviation is a better operationalization
.sum fh_cl
.gen fh_cl_dev=abs(fh_cl-3.86)
.tab fh_cl_dev

15. Data management II – creating and changing variables
Introducing the egen command
egen = extensions to generate
Egen contains many ways to create new variables, mostly as a function of another variable
To see everything it can do, type help egen

16. Data management II – creating and changing variables
The egen command
egen = extensions to generate
General syntax
. egen new varname = function(argument), [options]
For example: varname, numlist or expression

17. Data management II – creating and changing variables
The egen command includes several functions
A few examples of useful egen functions:
std
fill
mean
min / max
cut
Any many many more…

18. Data management II The egen command
If we want standardized variables:
.egen var_z=std(varX)
For example:
.egen mad_pop_z=std(mad_pop)
.tab mad_pop_z

19. Data management II The egen command
Or if we want to create an id variable:
. egen id=fill(1 2 3)
.order id
.browse
To find information about the numlist, type .help numlist

20. Data management II The egen command
Or if we want to center a variable around its mean:
.egen temp=mean(bl_asyt25 )
.gen bl_asyt25 _c=bl_asyt25 -temp
.drop temp
sometimes used for interactions

21. Data management II The egen command
For more complex variables we can do this separately for different groups via the by-statement using the by or bysort statment.
The command is then executed separately for each category of the variable you specify
. [by varname:] command varlist [in] [if], [options]

22. Data management II The egen command
A solution (among others)
. bysort countrycode: egen temp = min(fh_cl)
. gen mad_rs = mad_gdppc – temp
. drop temp
. tab mad_rs
. list cname mad_rs
Gen x=

23. Data management II – creating and changing variables
If you have an ordinal variabel and want to turn into a set of dummy-variables
.tab x, gen(x_dummy)
For example
.tab year, gen(year_dummy)

24. Data management II - Creating and changing variables
A somewhat faster method:
.recode fh_cl (1=1) (2/max=0), gen(fh_cl1)
Even quicker
.tab fh_cl, gen(fh_cldum)

25. Data management II
After these excercises we have alot of unwanted variables in our data-set
Which variables do we want to keep
.keep var1-var_n
Which variables do we want to drop
.drop var1-var_n

26. Data management II
Keep and drop commands does also work for observations, remember?
.keep if var1==2/4
.drop if var1!=1
.drop if var1<.

27. Summing up III What have we learnt?
Generating new variables
Changing and recoding variables
Using temporary commands/programs
Simple two-way cross-tabulations
Creating dummy variables