My data set is in this format as mentioned below:
NEWID
Age
H_PERS
Income
OCCU
FAMTYPE
REGION
Metro(Yes/No)
Exp_alcohol
population sample-(This is the weighted population each new id represents) etc.
I would like to generate a summarized view like below:
average expenditure value (This should be sum of (exp_alcohol/population sample))
% of population sample across Region Metro and each demographic variable
Please help me with your ideas.
Since I can't see your data set and your description was not very clear, I'm going to guess that you have data that looks something like this and you would like add some new variables that summarizes your data...
data alcohol;
input NEWID Age H_PERS Income OCCU $ FAMTYPE $ REGION $ Metro $
Exp_alcohol population_sample;
datalines;
1234 32 4 65000 abc m CA Yes 2 4
5678 23 5 35000 xyz s WA Yes 3 6
9923 34 3 49000 def d OR No 3 9
8844 26 4 54000 gdp m CA No 1 5
;
run;
data summar;
set alcohol;
retain TotalAvg_expend metro_count total_pop;
Divide = exp_alcohol/population_sample;
TotalAvg_expend + Divide;
total_pop + population_sample;
if metro = 'Yes' then metro_count + population_sample;
percent_metro = (metro_count/total_pop)*100;
drop NEWID Age H_PERS Income OCCU FAMTYPE REGION Divide;
run;
Output:
Exp_ population_ TotalAvg_ metro_ total_ percent_
Metro alcohol sample expend count pop metro
Yes 2 4 0.50000 4 4 100.000
Yes 3 6 1.00000 10 10 100.000
No 3 9 1.33333 10 19 52.632
No 1 5 1.53333 10 24 41.667
Related
Please find the dataset below:
ID amt order_type no_of_order
1 200 em 6
1 300 on 5
2 600 em 10
Output desired:
ID amt order_type no_of_order
1 500 on 11
2 600 em 10
based on the highest amount i need to pick the order_type.
How can this be achieved in sas code
Sounds like you want to get the sum of the two numeric variables for each value of ID and also select one value for ORDER_TYPE. You appear to want to take the value of ORDER_TYPE which had the largest AMT. Here is simply way using PROC SUMMARY.
data have;
input ID amt order_type $ no_of_order;
cards;
1 200 em 6
1 300 on 5
2 600 em 10
;
proc summary data=have ;
by id;
var amt no_of_order;
output out=want sum= idgroup(max(amt) out[1] (order_type)=);
run;
Results:
no_of_ order_
Obs ID _TYPE_ _FREQ_ amt order type
1 1 0 2 500 11 on
2 2 0 1 600 10 em
I have an unbalanced panel dataset of the following form (simplified):
data have;
input ID YEAR EARN LAG_EARN;
datalines;
1 1960 450 .
1 1961 310 450
1 1962 529 310
2 1978 10 .
2 1979 15 10
2 1980 8 15
2 1981 10 8
2 1982 15 10
2 1983 8 15
2 1984 10 8
3 1972 1000 .
3 1973 1599 1000
3 1974 1599 1599
;
run;
I now want to estimate the following model for each ID:
proc reg;
by ID;
EARN = LAG_EARN;
run;
However, I want to do this for rolling windows of some size. Say for example for windows of size 2. The window should only contain non-empty observations. For example, in the case of firm A, the window is applicable from 1961 onwards and thus only one time (since only one year follows after 1961 and the window is supposed to be of size 2).
Finally, I want to get a table with year columns and firm rows. The table should indicate the following: The regression model (with window size 2) has been performed one time for firm A. The quantity of available years, has only allowed one estimation of this model. Put differently, in 1962 the coefficient of the regression model has a value of X based on the 2 year prior window. Applying the same logic to the other two firms, one can get the following table. "X" representing the respective estimated coefficient value in certain year for firm A/B/C based on the 2-year window and "n" indicating the non-existence of such a value:
data want;
input ID 1962 1974 1980 1981 1982 1983 1984;
datalines;
1 X n n n n n n
2 n n X X X X X
3 n X n n n n n
;
run;
I do not know how to execute this. Furthermore, I would like to create a macro that allows me to estimate different rolling window models while still creating analogous output dataframes. I would appreciate any help with it, since I have been struggling quite some time now.
Try this macro. This will only output if there are non-missing values of lags that you specify.
%macro lag(data=, out=, window=);
data _want_;
set &data.;
by ID;
LAG_EARN = lag&window.(earn);
if(first.ID) then call missing(lag_earn);
if(NOT missing(lag_earn));
run;
proc sort data=_want_;
by year id;
run;
proc transpose data=_want_
out=&out.(drop=_NAME_);
by ID notsorted;
id year;
var lag_earn;
run;
proc sort data=&out.;
by id;
run;
%mend;
%lag(data=have, out=want, window=1);
This is my code:
DATA sales;
INFILE 'D:\Users\...\Desktop\Onions.dat';
INPUT VisitingTeam $ 1-20 ConcessionSales 21-24 BleacherSales 25-28
OurHits 29-31 TheirHits 32-34 OurRuns 35-37 TheirRuns 38-40;
PROC PRINT DATA = sales;
TITLE 'SAS Data Set Sales';
RUN;
This is the data, but the spacing may be incorrect.
Columbia Peaches 35 67 1 10 2 1
Plains Peanuts 210 . 2 5 0 2
Gilroy Garlics 151035 12 11 7 6
Sacramento Tomatoes 124 85 15 4 9 1
;
I need to add or delete a blank column at the 19th
column. Can someone help?
Just open the dataset and then look at what the variable name is. Then do:
Data Want (drop=varible_name_you_are_dropping); /*This is your output dataset*/
Set have; /*this is your dataset you have*/
Run;
I want to keep only the row with the highest rank1 for each team. If there is a tie, I want the row with the higher rank2. And then the higher rank3.
For example,
data test;
input name $ team $ rank1 rank2 rank3 country $
datalines;
Bob A 5 6 5 US
Joe A 8 2 6 UK
Dav B 9 7 2 GER
Jim B 9 4 4 FRA
Bob C 3 4 1 FRA
Dan D 5 2 7 GER
Ike D 5 2 7 US
Jay D 5 2 8 UK
run;
I want:
Joe A 8 2 6 UK
Dav B 9 7 2 GER
Bob C 3 4 1 FRA
Jay D 5 2 8 UK
What is the most efficient way to do this? The dataset I'm working with is pretty big and is not sorted. I tried the below code but the sorts take forever to run. And the second sort sorts already sorted data. What if most teams only appear once in the dataset? Is it faster to split into duplicates and non-duplicates, sort only the duplicates and then append?
proc sort data=test;
by team descending rank1 descending rank2 descending rank3;
run;
proc sort data=test nodupkey;
by team;
run;
You can do that with PROC SUMMARY. Not sure about performance compared to what you are already doing.
proc summary data=test nway;
class team;
output out=ranked(drop=_:) idgroup(max(rank:) out(name rank: country)=);
run;
data1 is data from 1990 and it looks like
Panelkey Region income
1 9 30
2 1 20
4 2 40
data2 is data from 2000 and it looks like
Panelkey Region income
3 2 40
2 1 30
1 1 20
I want to add a column of where each person lived in 1990.
Panelkey Region income Region1990
3 2 40 .
2 1 30 1
1 1 20 9
How can I do this on Stata?
The following code will deal with panels that live in multiple regions in the same year by choosing the region with larger income. This would make sense if income was proportional to fraction of the year spent in a region. Same income ties will be broken arbitrarily using the highest region's value. Other types of aggregation might make sense (take a look at the -collapse- command).
Note that I tweaked your data by inserting second rows for the last observation in each year:
clear
input Panelkey Region income
1 9 30
2 1 20
4 2 40
4 10 80
end
rename (Region income) =1990
bysort Panelkey (income Region): keep if _n==_N
isid Panelkey
save "data1990.dta", replace
clear
input Panelkey Region income
3 2 40
2 1 30
1 1 20
1 9 20
end
bysort Panelkey (income Region): keep if _n==_N
isid Panelkey
merge 1:1 Panelkey using "data1990.dta", keep(match master) nogen
list, clean noobs