I have been trying to create a demographic table like below this but I can't seem append the different tables. Please advise on where I can make adjustments in the code.
Group A Group B
chort 1 cohort 2 cohort 3 subtotal cohort 4 cohort 5 cohort 6 subtotal
Age
n
mean
sd
median
min
Gender
n
female
male
Race
n
white
asian
hispanic
black
My Code:
PROC FORMAT;
value content
1=' '
2='Age'
3='Gender'
4='Race'
value sex
1=' n'
2=' female'
3=' male';
value race
1=' n'
2=' white'
3=' asian'
4=' hispanic'
5=' black';
value stat
1=' n'
2=' Mean'
3=' Std. Dev.'
4=' Median'
5=' Minimum';
RUN;
DATA testtest;
SET test.test(keep = id group cohort age gender race);
RUN;
data tottest;
set testtest;
output;
if prxmatch('m/COHORT 1|COHORT 2|COHORT 3/oi', cohort) then do;
cohort='Subtotal';
output;
end;
if prxmatch('m/COHORT 4|COHORT 5|COHORT 6/oi', cohort) then do;
cohort='Subtotal';
output;
end;
run;
data count;
if 0 then set testtest nobs=npats;
call symput('npats',put(npats,1.));
stop;
run;
proc freq data=tottest;
tables cohort /out=patk0 noprint;
tables cohort*sex /out=sex0 noprint;
tables cohort*race /out=race0 noprint;
run;
PROC MEANS DATA = testtest n mean std min median;
class cohort;
VAR age;
RUN;
I know that I would have to transpose it and out it in a report. But before I do that, how do I get the variable out of my proc means, proc freq, etc?
Related
I have the following data
DATA HAVE;
input year dz $8. area;
cards;
2000 stroke 08
2000 stroke 06
2000 stroke 06
;
run;
After using proc freq
proc freq data=have;
table area*dz/ list nocum ;
run;
I get the below output
In this output
I want to delete the 'dz', what can I do to delete this column?
I want a row in the end that gives 'total', what can I do to get a 'total' row?
Thank you!
There must be a better way of doing this, but the following code creates the desired table:
data have;
input year dz $8. area;
cards;
2000 stroke 08
2000 stroke 06
2000 stroke 06
;
run;
ods output List=list;
proc freq data=have;
table area*dz / list;
run;
data stage1;
set list(keep= area frequency percent CumFrequency CumPercent) end=eof;
area_char = put(area,best.-l); /* Convert it to char to add the Total row */
if eof then do;
call symputx("cumFreq", cumfrequency);
call symputx("cumPerc", cumpercent);
end;
drop area;
run;
data want;
retain area frequency percent; /* Put the variables in the desired order */
set stage1(rename=(area_char=area) drop=cumfrequency cumpercent) end=eof;
output;
if eof then do; /* Manually create the Total row */
area = "Total";
Frequency = &cumfreq.;
Percent = &cumperc.;
output;
end;
run;
Output (want table):
You should subset your data with a where clause and use a title statement if a important partitioning variable is to be removed from output. If you didn't subset how would your audience know if a count contained say episodes of stroke and ministroke if ministroke was also in the data.
Compute the frequencies with freq and use a reporting procedure (print, report, tababulate) that summarizes to show a total line.
Example:
data have;
input year dz $ area;
cards;
2000 stroke 08
2000 stroke 06
2000 stroke 06
;
proc freq noprint data=have;
where dz = 'stroke';
table area / out=freqs;
run;
title 'Stroke dz';
title2 'print';
proc print data=freqs noobs label;
var area;
sum count percent;
run;
title2 'report';
proc report data=freqs;
columns area count percent;
define area / display;
define count / analysis;
rbreak after / summarize;
run;
title2 'tabulate';
proc tabulate data=freqs;
class area;
var count percent;
table area all, count percent;
run;
Thank you all for your valuable responses. The following code gives me the desired output in a concise way
proc freq data=HAVE;
tables area / list nocum out=a;
run;
proc sql;
create table b as
select * from a
union
select
'Total' as area,
sum(count) as count,
sum(percent) as percent
FROM a
;
quit;
proc print data=b; run;
I have a dataset in SAS in which the months would be dynamically updated each month. I need to calculate the sum vertically each month and paste the sum below, as shown in the image.
Proc means/ proc summary and proc print are not doing the trick for me.
I was given the following code before:
`%let month = month name;
%put &month.;
data new_totals;
set Final_&month. end=end;
&month._sum + &month._final;
/*feb_sum + &month._final;*/
output;
if end then do;
measure = 'Total';
&month._final = &month._sum;
/*Feb_final = feb_sum;*/
output;
end;
drop &month._sum;
run; `
The problem is this has all the months hardcoded, which i don't want. I am not too familiar with loops or arrays, so need a solution for this, please.
enter image description here
It may be better to use a reporting procedure such as PRINT or REPORT to produce the desired output.
data have;
length group $20;
do group = 'A', 'B', 'C';
array month_totals jan2020 jan2019 feb2020 feb2019 mar2019 apr2019 may2019 jun2019 jul2019 aug2019 sep2019 oct2019 oct2019 nov2019 dec2019;
do over month_totals;
month_totals = 10 + floor(rand('uniform', 60));
end;
output;
end;
run;
ods excel file='data_with_total_row.xlsx';
proc print noobs data=have;
var group ;
sum jan2020--dec2019;
run;
proc report data=have;
columns group jan2020--dec2019;
define group / width=20;
rbreak after / summarize;
compute after;
group = 'Total';
endcomp;
run;
ods excel close;
Data structure
The data sets you are working with are 'difficult' because the date aspect of the data is actually in the metadata, i.e. the column name. An even better approach, in SAS, is too have a categorical data with columns
group (categorical role)
month (categorical role)
total (continuous role)
Such data can be easily filtered with a where clause, and reporting procedures such as REPORT and TABULATE can use the month variable in a class statement.
Example:
data have;
length group $20;
do group = 'A', 'B', 'C';
do _n_ = 0 by 1 until (month >= '01feb2020'd);
month = intnx('month', '01jan2018'd, _n_);
total = 10 + floor(rand('uniform', 60));
output;
end;
end;
format month monyy5.;
run;
proc tabulate data=have;
class group month;
var total;
table
group all='Total'
,
month='' * total='' * sum=''*f=comma9.
;
where intck('month', month, '01feb2020'd) between 0 and 13;
run;
proc report data=have;
column group (month,total);
define group / group;
define month / '' across order=data ;
define total / '' ;
where intck('month', month, '01feb2020'd) between 0 and 13;
run;
Here is a basic way. Borrowed sample data from Richard.
data have;
length group $20;
do group = 'A', 'B';
array months jan2020 jan2019 feb2020 feb2019 mar2019 apr2019 may2019 jun2019 jul2019 aug2019 sep2019 oct2019 oct2019 nov2019 dec2019;
do over months;
months = 10 + floor(rand('uniform', 60, 1));
end;
output;
end;
run;
proc summary data=have;
var _numeric_;
output out=temp(drop=_:) sum=;
run;
data want;
set have temp (in=t);
if t then group='Total';
run;
I need a summation column, however, both retain and lag commando'es are inefficient.
There are number of ways. You could use proc sql or proc means. I've written a way below:
data begin;
length person $3 sallary 5;
input person sallary;
datalines;
a 200
a 300
b 800
c 400
c 500
c 600
;
run;
proc means data=begin noprint;
by person; /*Handle each person as distinct subset*/
output out=Sal_by_person(drop= _type_ _freq_)
sum(sallary)=Total_sallary /*What we calculate and what we call them.*/
;
run;
I have to calculate the correlation and covariance for my daily sales values for an event window. The event window is of 45 day period and my data looks like -
store_id date sales
5927 12-Jan-07 3,714.00
5927 12-Jan-07 3,259.00
5927 14-Jan-07 3,787.00
5927 14-Jan-07 3,480.00
5927 17-Jan-07 3,646.00
5927 17-Jan-07 3,316.00
4978 18-Jan-07 3,530.00
4978 18-Jan-07 3,103.00
4978 18-Jan-07 3,026.00
4978 21-Jan-07 3,448.00
Now, for every store_id, date combination, I need to go back 45 days (there is more data for each combination in my original data set) calculate the correlation between sales and lag(sales) i.e. autocorrelation of degree one. As you can see, the date column is not continuous. So something like (date - 45) is not going to work.
I have gotten till this part -
data ds1;
set ds;
by store_id;
LAG_SALE = lag(sales);
IF FIRST.store_idTHEN DO;
LAG_SALE = .;
END;
run;
For calculating correlation and covariances -
proc corr data=ds1 outp=Corr
by store_id date;
cov; /** include covariances **/
var sales lag_sale;
run;
But how do I insert the event window for each date, store_id combination? My final output should look something like this -
id date corr cov
5927 12-Jan-07 ... ...
5927 14-Jan-07 ... ...
Here is what I've come up with:
First I convert the date to a SAS date, which is the number of days since Jan. 1 1960:
data ds;
set ds (rename=(date=old_date));
date = input(old_date, date11.);
drop old_date;
run;
Then compute lag_sale (I am using the same calculation you used in the question, but make sure this is what you want to do. For some observations the lag sale is the previous recorded date, but for some it is the same store_id and date, just a different observation.):
proc sort data=ds; by store_id; run;
data ds;
set ds;
by store_id;
lag_sale = lag(sales);
if first.store_id then lag_sale = .;
run;
Then set up the final data set:
data final;
length store_id 8 date 8 cov 8 corr 8;
if _n_ = 0;
run;
Then create a macro which takes a store_id and date and runs proc corr. The first part of the macro selects only the data with that store_id and within the past 45 days of the date. Then it runs proc corr. Then it formats proc corr how you want it and appends the results to the "final" data set.
%macro corr(store_id, date);
data ds2;
set ds;
where store_id = &store_id and %eval(&date-45) <= date <=&date
and lag_sale ne .;
run;
proc corr noprint data=ds2 cov outp=corr;
by store_id;
var sales lag_sale;
run;
data corr2;
set corr;
where _type_ in ('CORR', 'COV') and _name_ = 'sales';
retain cov;
date = &date;
if _type_ = 'COV' then cov = lag_sale;
else do;
corr = lag_sale;
output;
end;
keep store_id date corr cov;
run;
proc append base=final data=corr2 force; run;
%mend corr;
Finally run the macro for each store_id/date combination.
proc sort data=ds out=ds3 nodupkey;
by store_id date;
run;
data _null_;
set ds3;
call execute('%corr('||store_id||','||date||');');
run;
proc sort data=final;
by store_id date;
run;
County...AgeGrp...Population
A.............1..........200
A.............2..........100
A.............3..........100
A............All.........400
B.............1..........200
So, I have a list of counties and I'd like to find the under 18 population as a percent of the population for each county, so as an example from the table above I'd like to add only the population of agegrp 1 and 2 and divide by the 'all' population. In this case it would be 300/400. I'm wondering if this can be done for every county.
Let's call your SAS data set "HAVE" and say it has two character variables (County and AgeGrp) and one numeric variable (Population). And let's say you always have one observation in your data set for a each County with AgeGrp='All' on which the value of Population is the total for the county.
To be safe, let's sort the data set by County and process it in another data step to, creating a new data set named "WANT" with new variables for the county population (TOT_POP), the sum of the two Age Group values you want (TOT_GRP) and calculate the proportion (AgeGrpPct):
proc sort data=HAVE;
by County;
run;
data WANT;
retain TOT_POP TOT_GRP 0;
set HAVE;
by County;
if first.County then do;
TOT_POP = 0;
TOT_GRP = 0;
end;
if AgeGrp in ('1','2') then TOT_GRP + Population;
else if AgeGrp = 'All' then TOT_POP = Population;
if last.County;
AgeGrpPct = TOT_GRP / TOT_POP;
keep County TOT_POP TOT_GRP AgeGrpPct;
output;
run;
Notice that the observation containing AgeGrp='All' is not really needed; you could just as well have created another variable to collect a running total for all age groups.
If you want a procedural approach, create a format for the under 18's, then use PROC FREQ to calculate the percentage. It is necessary to exclude the 'All' values from the dataset with this method (it's generally bad practice to include summary rows in the source data).
PROC TABULATE could also be used for this.
data have;
input County $ AgeGrp $ Population;
datalines;
A 1 200
A 2 100
A 3 100
A All 400
B 1 200
B 2 300
B 3 500
B All 1000
;
run;
proc format;
value $age_fmt '1','2' = '<18'
other = '18+';
run;
proc sort data=have;
by county;
run;
proc freq data=have (where=(agegrp ne 'All')) noprint;
by county;
table agegrp / out=want (drop=COUNT where=(agegrp in ('1','2')));
format agegrp $age_fmt.;
weight population;
run;