Find top values please any one can help

[sas_university]

Obs EmpId EmpName EmpLocaton Sex Designation Salary Age1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950

101	Tejasree	nlr	female	developer	24000	25
102	Aravind	tpt	male	sales	26000	27
103	Malli	ctc	male	marketing	28000	23
104	Jagan	ongole	male	nontech	30000	24
105	Meghana	hyd	female	tech	32000	26
106	Praveen	nlr	male	analysit	34000	24
107	Likitha	kadapa	female	sap	36000	27
108	Tejeshswar	anthapur	male	sas	38000	22
109	Tanisha	tpt	female	sales	40000	28
110	Bhanu	guntur	male	marketing	42000	46
111	Mourya	nlr	male	sql analysit	44000	25
112	Manohar	tpt	male	developer	46000	27
113	Thanvitha	ctc	female	sales	48000	23
114	Chethan	ongole	male	marketing	50000	24
115	kumar	hyd	male	nontech	52000	26
116	Mouni	nlr	female	tech	54000	24
117	Jai	kadapa	male	analysit	56000	27
118	Karthik	anthapur	male	sap	58000	22
119	Swarna	tpt	female	sas	60000	28
120	Sahana	guntur	female	sales	62000	46
121	Nagaraj	nlr	male	marketing	64000	25
122	Kusuma	tpt	female	sql analysit	66000	27
123	Sai	ctc	male	developer	68000	23
124	Pawan	ongole	male	sales	70000	24
125	Vamsi	hyd	male	marketing	72000	26
126	Ramya	nlr	female	nontech	74000	24
127	Sri vidya	kadapa	female	tech	76000	27
128	Sravani	anthapur	female	analysit	78000	22
129	Jayanthi	tpt	female	sap	80000	28
130	hasini	guntur	female	sas	82000	46
131	Yeaswanth	nlr	male	sales	84000	34
132	Naveen	tpt	male	marketing	86000	65
133	Har**bleep**h	ctc	male	sql analysit	88000	34
134	Mounika	ongole	female	developer	90000	89
135	Afroz	hyd	female	sales	92000	26
136	Suresh	nlr	male	marketing	94000	24
137	venu	kadapa	male	nontech	96000	27
138	Soumya	anthapur	female	tech	98000	22
139	Murali	tpt	male	analysit	100000	28
140	kiran	guntur	male	sap	102000	46
141	Rayudu	nlr	male	sas	104000	25
142	Sunil	tpt	male	sales	106000	27
143	Rahul	ctc	male	marketing	108000	23
144	hema	ongole	female	sql analysit	122000	24
145	Madhu	hyd	male	developer	112000	26
145	Joshna	nlr	female	sales	122000	24
146	Sree	kadapa	male	marketing	116000	27
148	Pavani	anthapur	female	nontech	118000	22
149	Swaroop	tpt	male	tech	122000	28
150	Manasa	guntur	female	analysit	122000	46

This my data

Obs EmpId EmpName EmpLocaton Sex Designation Salary Age1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950

Obs EmpId EmpName EmpLocaton Sex Designation Salary Age1234567891011

144	hema	ongole	female	sql analysit	122000	24
145	Joshna	nlr	female	sales	122000	24
149	Swaroop	tpt	male	tech	122000	28
150	Manasa	guntur	female	analysit	122000	46
148	Pavani	anthapur	female	nontech	118000	22
146	Sree	kadapa	male	marketing	116000	27
145	Madhu	hyd	male	developer	112000	26
143	Rahul	ctc	male	marketing	108000	23
142	Sunil	tpt	male	sales	106000	27
141	Rayudu	nlr	male	sas	104000	25
140	kiran	guntur	male	sap	102000	46

I have data like this 

Obs EmpId EmpName EmpLocaton Sex Designation Salary Age12345

144	hema	ongole	female	sql analysit	122000	24
145	Joshna	nlr	female	sales	122000	24
149	Swaroop	tpt	male	tech	122000	28
150	Manasa	guntur	female	analysit	122000	46
148	Pavani	anthapur	female	nontech	118000	22

I got output like this , help me anyone 

[ballardw]

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?

[Reeza]

https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/procstat/procstat_univariate_examples03.htm

[Rick_SAS]

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