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sas_university
Obsidian | Level 7
Obs EmpId EmpName EmpLocaton Sex Designation Salary Age1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950
101Tejasreenlrfemaledeveloper2400025
102Aravindtptmalesales2600027
103Mallictcmalemarketing2800023
104Jaganongolemalenontech3000024
105Meghanahydfemaletech3200026
106Praveennlrmaleanalysit3400024
107Likithakadapafemalesap3600027
108Tejeshswaranthapurmalesas3800022
109Tanishatptfemalesales4000028
110Bhanugunturmalemarketing4200046
111Mouryanlrmalesql analysit4400025
112Manohartptmaledeveloper4600027
113Thanvithactcfemalesales4800023
114Chethanongolemalemarketing5000024
115kumarhydmalenontech5200026
116Mouninlrfemaletech5400024
117Jaikadapamaleanalysit5600027
118Karthikanthapurmalesap5800022
119Swarnatptfemalesas6000028
120Sahanagunturfemalesales6200046
121Nagarajnlrmalemarketing6400025
122Kusumatptfemalesql analysit6600027
123Saictcmaledeveloper6800023
124Pawanongolemalesales7000024
125Vamsihydmalemarketing7200026
126Ramyanlrfemalenontech7400024
127Sri vidyakadapafemaletech7600027
128Sravanianthapurfemaleanalysit7800022
129Jayanthitptfemalesap8000028
130hasinigunturfemalesas8200046
131Yeaswanthnlrmalesales8400034
132Naveentptmalemarketing8600065
133Har**bleep**hctcmalesql analysit8800034
134Mounikaongolefemaledeveloper9000089
135Afrozhydfemalesales9200026
136Sureshnlrmalemarketing9400024
137venukadapamalenontech9600027
138Soumyaanthapurfemaletech9800022
139Muralitptmaleanalysit10000028
140kirangunturmalesap10200046
141Rayudunlrmalesas10400025
142Suniltptmalesales10600027
143Rahulctcmalemarketing10800023
144hemaongolefemalesql analysit12200024
145Madhuhydmaledeveloper11200026
145Joshnanlrfemalesales12200024
146Sreekadapamalemarketing11600027
148Pavanianthapurfemalenontech11800022
149Swarooptptmaletech12200028
150Manasagunturfemaleanalysit12200046

This my data

Obs EmpId EmpName EmpLocaton Sex Designation Salary Age1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950
Obs EmpId EmpName EmpLocaton Sex Designation Salary Age1234567891011
144hemaongolefemalesql analysit12200024
145Joshnanlrfemalesales12200024
149Swarooptptmaletech12200028
150Manasagunturfemaleanalysit12200046
148Pavanianthapurfemalenontech11800022
146Sreekadapamalemarketing11600027
145Madhuhydmaledeveloper11200026
143Rahulctcmalemarketing10800023
142Suniltptmalesales10600027
141Rayudunlrmalesas10400025
140kirangunturmalesap10200046

I have data like this 

Obs EmpId EmpName EmpLocaton Sex Designation Salary Age12345
144hemaongolefemalesql analysit12200024
145Joshnanlrfemalesales12200024
149Swarooptptmaletech12200028
150Manasagunturfemaleanalysit12200046
148Pavanianthapurfemalenontech11800022

I got output like this , help me anyone 

3 REPLIES 3
ballardw
Super User

Define what you mean by "top value". Largest single value? Largest sum or mean? Most frequent value? Something else?

For which variable(s)?

Using what groups?

Rick_SAS
SAS Super FREQ

If you want the most popular (=most frequent) categories, you can use the ORDER=FREQ option on PROC FREQ combined with the MAXLEVELS= option on the TABLES statement. For example:

%let TopN = 10;
proc freq data=sashelp.cars ORDER=FREQ;
  tables make / maxlevels=&TopN Plots=FreqPlot;
run;

For more information, see https://blogs.sas.com/content/iml/2018/06/04/top-10-table-bar-chart.html

 

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