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Technical documentation that writes itself
Technical documentation that writes itself
Technical documentation that writes itself
ClusterLoop w factors
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/*library statement*/
libname storage '/folders/myfolders/';
proc print data=storage.census_factors (obs=10);
run;
proc standard data=storage.dmefzip replace out=prefactor;
run;
proc sort data=prefactor;
by zipcode;
run;
options pageno=1;
%macro loop (c);
options mprint ;
%do s=1 %to 10;
%let rand=%eval(100*&c+&s);
proc fastclus data=storage.census_factors out=clus&Rand cluster=clus maxclusters=&c
converge=0 maxiter=100 replace=random random=&Rand;
ods output pseudofstat=fstat&Rand (keep=value);
var factor1--factor5;
title1 "Clusters=&c, Run &s";
run;
title1;
proc freq data=clus&Rand noprint;
tables clus/out=counts&Rand;
where clus>.;
run;
proc summary data=counts&Rand;
var count;
output out=m&Rand min=;
run;
data Stats&Rand;
label count=' ';
merge fstat&rand
m&rand (drop= _type_ _freq_)
;
Iter=&rand;
Clusters=&c;
rename count=minimum value=PseudoF;
run;
proc append base=ClusStatHold data=Stats&Rand;
run;
%end;
options nomprint;
%Mend Loop;
%Macro OuterLoop;
proc datasets library=work;
delete ClusStatHold;
run;
%do clus=4 %to 8;
%Loop (&clus);
%end;
%Mend OuterLoop;
%OuterLoop;
proc ggplot data=ClusStatHold;
plot pseudoF*minimum/haxis=axis1;
symbol value=dot color=blue pointlabel=("#clusters" color=black);
axis offset=(5,5)pct;
title "F by Min for Clusters";
run;
title;
quit;
%let varlist=INCMINDX PRCHHFM PRCRENT PRC55P PRC65P HHMEDAGE PRCUN18 PRC200K OOMEDHVL PRCWHTE;
/*descriptive stats for clusters*/
proc sort data=clus610;
by zipcode;
run;
data cluster_vars;
merge clus610 (keep=zipcode factor1--factor5 clus in=a) prefactor (in=b);
by zipcode;
run;
proc print data=cluster_vars (obs=10);
run;
proc summary data=cluster_vars nway;
class clus;
var &varlist;
output out=ClusStats mean=;
run;
proc summary data=cluster_vars nway;
where clus>.;
var &varlist;
output out=OverallStats mean=;
run;
proc print data=clusStats;run;
proc print data=OverallStats;run;
data stats;
set ClusStats
OverallStats
;
run;
proc print data=stats;run;
ClusterLoop w factors
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/*library statement*/
libname storage '/folders/myfolders/';
proc print data=storage.census_factors (obs=10);
run;
proc standard data=storage.dmefzip replace out=prefactor;
run;
proc sort data=prefactor;
by zipcode;
run;
options pageno=1;
%macro loop (c);
options mprint ;
%do s=1 %to 10;
%let rand=%eval(100*&c+&s);
proc fastclus data=storage.census_factors out=clus&Rand cluster=clus maxclusters=&c
converge=0 maxiter=100 replace=random random=&Rand;
ods output pseudofstat=fstat&Rand (keep=value);
var factor1--factor5;
title1 "Clusters=&c, Run &s";
run;
title1;
proc freq data=clus&Rand noprint;
tables clus/out=counts&Rand;
where clus>.;
run;
proc summary data=counts&Rand;
var count;
output out=m&Rand min=;
run;
data Stats&Rand;
label count=' ';
merge fstat&rand
m&rand (drop= _type_ _freq_)
;
Iter=&rand;
Clusters=&c;
rename count=minimum value=PseudoF;
run;
proc append base=ClusStatHold data=Stats&Rand;
run;
%end;
options nomprint;
%Mend Loop;
%Macro OuterLoop;
proc datasets library=work;
delete ClusStatHold;
run;
%do clus=4 %to 8;
%Loop (&clus);
%end;
%Mend OuterLoop;
%OuterLoop;
proc ggplot data=ClusStatHold;
plot pseudoF*minimum/haxis=axis1;
symbol value=dot color=blue pointlabel=("#clusters" color=black);
axis offset=(5,5)pct;
title "F by Min for Clusters";
run;
title;
quit;
%let varlist=INCMINDX PRCHHFM PRCRENT PRC55P PRC65P HHMEDAGE PRCUN18 PRC200K OOMEDHVL PRCWHTE;
/*descriptive stats for clusters*/
proc sort data=clus610;
by zipcode;
run;
data cluster_vars;
merge clus610 (keep=zipcode factor1--factor5 clus in=a) prefactor (in=b);
by zipcode;
run;
proc print data=cluster_vars (obs=10);
run;
proc summary data=cluster_vars nway;
class clus;
var &varlist;
output out=ClusStats mean=;
run;
proc summary data=cluster_vars nway;
where clus>.;
var &varlist;
output out=OverallStats mean=;
run;
proc print data=clusStats;run;
proc print data=OverallStats;run;
data stats;
set ClusStats
OverallStats
;
run;
proc print data=stats;run;
ClusterLoop w factors
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/*library statement*/
libname storage '/folders/myfolders/';
proc print data=storage.census_factors (obs=10);
run;
proc standard data=storage.dmefzip replace out=prefactor;
run;
proc sort data=prefactor;
by zipcode;
run;
options pageno=1;
%macro loop (c);
options mprint ;
%do s=1 %to 10;
%let rand=%eval(100*&c+&s);
proc fastclus data=storage.census_factors out=clus&Rand cluster=clus maxclusters=&c
converge=0 maxiter=100 replace=random random=&Rand;
ods output pseudofstat=fstat&Rand (keep=value);
var factor1--factor5;
title1 "Clusters=&c, Run &s";
run;
title1;
proc freq data=clus&Rand noprint;
tables clus/out=counts&Rand;
where clus>.;
run;
proc summary data=counts&Rand;
var count;
output out=m&Rand min=;
run;
data Stats&Rand;
label count=' ';
merge fstat&rand
m&rand (drop= _type_ _freq_)
;
Iter=&rand;
Clusters=&c;
rename count=minimum value=PseudoF;
run;
proc append base=ClusStatHold data=Stats&Rand;
run;
%end;
options nomprint;
%Mend Loop;
%Macro OuterLoop;
proc datasets library=work;
delete ClusStatHold;
run;
%do clus=4 %to 8;
%Loop (&clus);
%end;
%Mend OuterLoop;
%OuterLoop;
proc ggplot data=ClusStatHold;
plot pseudoF*minimum/haxis=axis1;
symbol value=dot color=blue pointlabel=("#clusters" color=black);
axis offset=(5,5)pct;
title "F by Min for Clusters";
run;
title;
quit;
%let varlist=INCMINDX PRCHHFM PRCRENT PRC55P PRC65P HHMEDAGE PRCUN18 PRC200K OOMEDHVL PRCWHTE;
/*descriptive stats for clusters*/
proc sort data=clus610;
by zipcode;
run;
data cluster_vars;
merge clus610 (keep=zipcode factor1--factor5 clus in=a) prefactor (in=b);
by zipcode;
run;
proc print data=cluster_vars (obs=10);
run;
proc summary data=cluster_vars nway;
class clus;
var &varlist;
output out=ClusStats mean=;
run;
proc summary data=cluster_vars nway;
where clus>.;
var &varlist;
output out=OverallStats mean=;
run;
proc print data=clusStats;run;
proc print data=OverallStats;run;
data stats;
set ClusStats
OverallStats
;
run;
proc print data=stats;run;
archiva.ai
Clustering Analysis and Descriptive Statistics for Census Data
Introduction
This code is designed to perform clustering analysis on a census dataset and calculate descriptive statistics for each cluster and overall. It is useful for data preprocessing and targeted marketing strategies.
Code Contents
Set library location for storage
Print first 10 observations of census factors dataset
Standardize data and output to new dataset
Sort data by zipcode
Set option for page number
Create macro to loop through a specified number of clusters
Set option for macro to print
Loop through specified number of clusters
Create outer loop to run through multiple number of clusters
Delete previous dataset
Merge datasets and calculate descriptive statistics for each cluster
Print first 10 observations of merged dataset
Calculate overall descriptive statistics for all clusters
Merge overall and cluster statistics
Print merged dataset
View code
archiva.ai
Clustering Analysis and Descriptive Statistics for Census Data
Introduction
This code is designed to perform clustering analysis on a census dataset and calculate descriptive statistics for each cluster and overall. It is useful for data preprocessing and targeted marketing strategies.
Code Contents
Set library location for storage
Print first 10 observations of census factors dataset
Standardize data and output to new dataset
Sort data by zipcode
Set option for page number
Create macro to loop through a specified number of clusters
Set option for macro to print
Loop through specified number of clusters
Create outer loop to run through multiple number of clusters
Delete previous dataset
Merge datasets and calculate descriptive statistics for each cluster
Print first 10 observations of merged dataset
Calculate overall descriptive statistics for all clusters
Merge overall and cluster statistics
Print merged dataset
View code
archiva.ai
Clustering Analysis and Descriptive Statistics for Census Data
Introduction
This code is designed to perform clustering analysis on a census dataset and calculate descriptive statistics for each cluster and overall. It is useful for data preprocessing and targeted marketing strategies.
Code Contents
Set library location for storage
Print first 10 observations of census factors dataset
Standardize data and output to new dataset
Sort data by zipcode
Set option for page number
Create macro to loop through a specified number of clusters
Set option for macro to print
Loop through specified number of clusters
Create outer loop to run through multiple number of clusters
Delete previous dataset
Merge datasets and calculate descriptive statistics for each cluster
Print first 10 observations of merged dataset
Calculate overall descriptive statistics for all clusters
Merge overall and cluster statistics
Print merged dataset
View code
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Simply connect your SAS codebase to Archiva. Watch your documentation update automatically with every commit, ensuring it's always in sync with your code.
AI-powered analysis
Archiva's AI analyzes your code line-by-line, writing detailed technical documentation for every change.
Deep code understanding
Leveraging data lineage, code statistics, and your system's specifics, Archiva generates accurate technical documentation.
Interactive exploration
Ask Archiva anything about your code and data connections..
Connect & Stay updated
Simply connect your SAS codebase to Archiva. Watch your documentation update automatically with every commit, ensuring it's always in sync with your code.
AI-powered analysis
Archiva's AI analyzes your code line-by-line, writing detailed technical documentation for every change.
Deep code understanding
Leveraging data lineage, code statistics, and your system's specifics, Archiva generates accurate technical documentation.
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