239.Can SAS Reveal Which Office Environments Are Truly Productive And Which Are Not?

Can SAS Reveal Which Office Environments Are Truly Productive And Which Are Not? 

 /*Creating a dataset named Office_Data*/

1.DATA ENTRY (DATALINES)

options nocenter;

data Office_Data;

  length Name $25 Department $15 Role $20 Gender $1 

         Office_Location $15 Work_Mode $10 Laptop_Brand $15 Shift $6;

  input Employee_ID Name $ Department $ Role $ Gender $ Age 

        Experience_Years Salary Office_Location $ Work_Mode $ 

        Leaves_Taken Performance_Rating Laptop_Brand $ 

        Projects_Handled Internet_Speed Commute_Distance_km 

        Shift $ Coffee_Intake_Per_Day;

  datalines;

1   Rakesh_Kumar      IT         Developer        M 28 5 65000 Hyderabad   Hybrid   5 4 Dell         8 50 20 Day   2

2   Sneha_Mehta       HR         HR_Manager       F 35 10 85000 Bangalore  Onsite   3 5 HP           15 0 15 Day   1

3   Arjun_Verma       Finance    Analyst          M 30 6 70000 Mumbai      Hybrid   4 3 Lenovo       6 40 18 Night 3

4   Priya_Sharma      IT         QA_Tester        F 27 4 60000 Chennai     Remote   6 4 Dell         7 60 0  Day   2

5   Nikhil_Desai      Admin      Facility_Head    M 45 20 90000 Pune       Onsite   2 5 Asus         20 0 25 Day   4

6   Aarti_Patil       IT         Developer        F 24 2 50000 Hyderabad   Remote   7 3 HP           5 80 0  Day   1

7   Sunil_Gupta       HR         Recruiter        M 29 5 55000 Bangalore   Hybrid   5 4 Dell         8 40 12 Day   2

8   Kiran_Rao         IT         Data_Engineer    F 32 8 72000 Mumbai      Hybrid   4 4 Lenovo       10 35 14 Night 2

9   Rohit_Jain        Finance    Accountant       M 38 12 80000 Delhi      Onsite   2 5 Asus         12 0 30 Day   1

10  Deepa_Menon       HR         HR_Executive     F 26 3 53000 Hyderabad   Remote   6 3 HP           6 70 0  Day   3

11  Ravi_Kanth        Admin      Support_Staff    M 40 15 48000 Chennai     Onsite  3 4 Dell         9 0 20 Night 2

12  Shruti_Nair       IT         Developer        F 25 3 58000 Bangalore   Hybrid   5 3 Lenovo       7 50 10 Day   2

13  Manish_Tiwari     Finance    Risk_Analyst     M 34 7 75000 Delhi       Hybrid   3 4 HP           11 45 12 Night 3

14  Komal_Sharma      IT         QA_Tester        F 29 6 62000 Pune        Remote   4 4 Asus         8 65 0  Day   2

15  Harsha_Reddy      IT         Team_Lead        M 36 10 90000 Hyderabad  Onsite   2 5 Dell         18 0 20 Day   2

16  Swathi_Jose       HR         Trainer          F 31 8 67000 Mumbai      Remote   5 4 Lenovo       9 50 0  Day   1

17  Varun_Yadav       Admin      Admin_Assistant  M 33 7 52000 Chennai     Onsite   3 3 HP           10 0 18 Day   2

18  Lavanya_Kapoor    IT         Architect        F 39 15 100000 Delhi     Remote   1 5 Dell         25 90 0  Day   3

19  Tarun_Malhotra    Finance    Analyst          M 28 4 68000 Bangalore   Hybrid   6 3 Lenovo       6 55 17 Night 3

20  Meena_Singh       HR         HR_Manager       F 41 17 87000 Hyderabad  Onsite   2 5 HP           17 0 22 Day   1

21  Akash_Rana        IT         Intern           M 22 1 35000 Pune        Remote   8 2 Dell         3 85 0  Day   2

22  Ritu_Sen          Finance    Intern           F 23 1 36000 Mumbai      Hybrid   7 2 HP           2 50 15 Day   2

23  Karthik_Rao       IT         Developer        M 27 3 59000 Chennai     Hybrid   5 3 Asus         6 55 10 Day   2

24  Ramesh_Babu       Admin      Security_Lead    M 50 25 75000 Hyderabad  Onsite   2 5 Lenovo       20 0 30 Night 4

25  Neha_Sood         HR         Recruiter        F 30 5 56000 Pune        Remote   4 4 Dell         7 60 0  Day   2

26  Abhinav_Singh     Finance    Manager          M 37 12 95000 Delhi      Onsite   3 5 HP           15 0 25 Day   2

;

run;

proc print;run;

Output:

Obs	Name	Department	Role	Gender	Office_Location	Work_Mode	Laptop_Brand	Shift	Employee_ID	Age	Experience_Years	Salary	Leaves_Taken	Performance_Rating	Projects_Handled	Internet_Speed	Commute_Distance_km	Coffee_Intake_Per_Day
1	Rakesh_Kumar	IT	Developer	M	Hyderabad	Hybrid	Dell	Day	1	28	5	65000	5	4	8	50	20	2
2	Sneha_Mehta	HR	HR_Manager	F	Bangalore	Onsite	HP	Day	2	35	10	85000	3	5	15	0	15	1
3	Arjun_Verma	Finance	Analyst	M	Mumbai	Hybrid	Lenovo	Night	3	30	6	70000	4	3	6	40	18	3
4	Priya_Sharma	IT	QA_Tester	F	Chennai	Remote	Dell	Day	4	27	4	60000	6	4	7	60	0	2
5	Nikhil_Desai	Admin	Facility_Head	M	Pune	Onsite	Asus	Day	5	45	20	90000	2	5	20	0	25	4
6	Aarti_Patil	IT	Developer	F	Hyderabad	Remote	HP	Day	6	24	2	50000	7	3	5	80	0	1
7	Sunil_Gupta	HR	Recruiter	M	Bangalore	Hybrid	Dell	Day	7	29	5	55000	5	4	8	40	12	2
8	Kiran_Rao	IT	Data_Engineer	F	Mumbai	Hybrid	Lenovo	Night	8	32	8	72000	4	4	10	35	14	2
9	Rohit_Jain	Finance	Accountant	M	Delhi	Onsite	Asus	Day	9	38	12	80000	2	5	12	0	30	1
10	Deepa_Menon	HR	HR_Executive	F	Hyderabad	Remote	HP	Day	10	26	3	53000	6	3	6	70	0	3
11	Ravi_Kanth	Admin	Support_Staff	M	Chennai	Onsite	Dell	Night	11	40	15	48000	3	4	9	0	20	2
12	Shruti_Nair	IT	Developer	F	Bangalore	Hybrid	Lenovo	Day	12	25	3	58000	5	3	7	50	10	2
13	Manish_Tiwari	Finance	Risk_Analyst	M	Delhi	Hybrid	HP	Night	13	34	7	75000	3	4	11	45	12	3
14	Komal_Sharma	IT	QA_Tester	F	Pune	Remote	Asus	Day	14	29	6	62000	4	4	8	65	0	2
15	Harsha_Reddy	IT	Team_Lead	M	Hyderabad	Onsite	Dell	Day	15	36	10	90000	2	5	18	0	20	2
16	Swathi_Jose	HR	Trainer	F	Mumbai	Remote	Lenovo	Day	16	31	8	67000	5	4	9	50	0	1
17	Varun_Yadav	Admin	Admin_Assistant	M	Chennai	Onsite	HP	Day	17	33	7	52000	3	3	10	0	18	2
18	Lavanya_Kapoor	IT	Architect	F	Delhi	Remote	Dell	Day	18	39	15	100000	1	5	25	90	0	3
19	Tarun_Malhotra	Finance	Analyst	M	Bangalore	Hybrid	Lenovo	Night	19	28	4	68000	6	3	6	55	17	3
20	Meena_Singh	HR	HR_Manager	F	Hyderabad	Onsite	HP	Day	20	41	17	87000	2	5	17	0	22	1
21	Akash_Rana	IT	Intern	M	Pune	Remote	Dell	Day	21	22	1	35000	8	2	3	85	0	2
22	Ritu_Sen	Finance	Intern	F	Mumbai	Hybrid	HP	Day	22	23	1	36000	7	2	2	50	15	2
23	Karthik_Rao	IT	Developer	M	Chennai	Hybrid	Asus	Day	23	27	3	59000	5	3	6	55	10	2
24	Ramesh_Babu	Admin	Security_Lead	M	Hyderabad	Onsite	Lenovo	Night	24	50	25	75000	2	5	20	0	30	4
25	Neha_Sood	HR	Recruiter	F	Pune	Remote	Dell	Day	25	30	5	56000	4	4	7	60	0	2
26	Abhinav_Singh	Finance	Manager	M	Delhi	Onsite	HP	Day	26	37	12	95000	3	5	15	0	25	2

2.PROC PRINT – VIEWING RAW DATA

proc print data=Office_Data noobs label;

  title "Initial View of Office Data";

run;

Output:

Initial View of Office Data

Name	Department	Role	Gender	Office_Location	Work_Mode	Laptop_Brand	Shift	Employee_ID	Age	Experience_Years	Salary	Leaves_Taken	Performance_Rating	Projects_Handled	Internet_Speed	Commute_Distance_km	Coffee_Intake_Per_Day
Rakesh_Kumar	IT	Developer	M	Hyderabad	Hybrid	Dell	Day	1	28	5	65000	5	4	8	50	20	2
Sneha_Mehta	HR	HR_Manager	F	Bangalore	Onsite	HP	Day	2	35	10	85000	3	5	15	0	15	1
Arjun_Verma	Finance	Analyst	M	Mumbai	Hybrid	Lenovo	Night	3	30	6	70000	4	3	6	40	18	3
Priya_Sharma	IT	QA_Tester	F	Chennai	Remote	Dell	Day	4	27	4	60000	6	4	7	60	0	2
Nikhil_Desai	Admin	Facility_Head	M	Pune	Onsite	Asus	Day	5	45	20	90000	2	5	20	0	25	4
Aarti_Patil	IT	Developer	F	Hyderabad	Remote	HP	Day	6	24	2	50000	7	3	5	80	0	1
Sunil_Gupta	HR	Recruiter	M	Bangalore	Hybrid	Dell	Day	7	29	5	55000	5	4	8	40	12	2
Kiran_Rao	IT	Data_Engineer	F	Mumbai	Hybrid	Lenovo	Night	8	32	8	72000	4	4	10	35	14	2
Rohit_Jain	Finance	Accountant	M	Delhi	Onsite	Asus	Day	9	38	12	80000	2	5	12	0	30	1
Deepa_Menon	HR	HR_Executive	F	Hyderabad	Remote	HP	Day	10	26	3	53000	6	3	6	70	0	3
Ravi_Kanth	Admin	Support_Staff	M	Chennai	Onsite	Dell	Night	11	40	15	48000	3	4	9	0	20	2
Shruti_Nair	IT	Developer	F	Bangalore	Hybrid	Lenovo	Day	12	25	3	58000	5	3	7	50	10	2
Manish_Tiwari	Finance	Risk_Analyst	M	Delhi	Hybrid	HP	Night	13	34	7	75000	3	4	11	45	12	3
Komal_Sharma	IT	QA_Tester	F	Pune	Remote	Asus	Day	14	29	6	62000	4	4	8	65	0	2
Harsha_Reddy	IT	Team_Lead	M	Hyderabad	Onsite	Dell	Day	15	36	10	90000	2	5	18	0	20	2
Swathi_Jose	HR	Trainer	F	Mumbai	Remote	Lenovo	Day	16	31	8	67000	5	4	9	50	0	1
Varun_Yadav	Admin	Admin_Assistant	M	Chennai	Onsite	HP	Day	17	33	7	52000	3	3	10	0	18	2
Lavanya_Kapoor	IT	Architect	F	Delhi	Remote	Dell	Day	18	39	15	100000	1	5	25	90	0	3
Tarun_Malhotra	Finance	Analyst	M	Bangalore	Hybrid	Lenovo	Night	19	28	4	68000	6	3	6	55	17	3
Meena_Singh	HR	HR_Manager	F	Hyderabad	Onsite	HP	Day	20	41	17	87000	2	5	17	0	22	1
Akash_Rana	IT	Intern	M	Pune	Remote	Dell	Day	21	22	1	35000	8	2	3	85	0	2
Ritu_Sen	Finance	Intern	F	Mumbai	Hybrid	HP	Day	22	23	1	36000	7	2	2	50	15	2
Karthik_Rao	IT	Developer	M	Chennai	Hybrid	Asus	Day	23	27	3	59000	5	3	6	55	10	2
Ramesh_Babu	Admin	Security_Lead	M	Hyderabad	Onsite	Lenovo	Night	24	50	25	75000	2	5	20	0	30	4
Neha_Sood	HR	Recruiter	F	Pune	Remote	Dell	Day	25	30	5	56000	4	4	7	60	0	2
Abhinav_Singh	Finance	Manager	M	Delhi	Onsite	HP	Day	26	37	12	95000	3	5	15	0	25	2

3.PROC SORT – ORGANIZING THE DATA

Sort by Department and then by Salary (descending)

proc sort data=Office_Data out=Sorted_Data;

  by Department descending Salary;

run;

proc print;run;

Output:

Obs	Name	Department	Role	Gender	Office_Location	Work_Mode	Laptop_Brand	Shift	Employee_ID	Age	Experience_Years	Salary	Leaves_Taken	Performance_Rating	Projects_Handled	Internet_Speed	Commute_Distance_km	Coffee_Intake_Per_Day
1	Nikhil_Desai	Admin	Facility_Head	M	Pune	Onsite	Asus	Day	5	45	20	90000	2	5	20	0	25	4
2	Ramesh_Babu	Admin	Security_Lead	M	Hyderabad	Onsite	Lenovo	Night	24	50	25	75000	2	5	20	0	30	4
3	Varun_Yadav	Admin	Admin_Assistant	M	Chennai	Onsite	HP	Day	17	33	7	52000	3	3	10	0	18	2
4	Ravi_Kanth	Admin	Support_Staff	M	Chennai	Onsite	Dell	Night	11	40	15	48000	3	4	9	0	20	2
5	Abhinav_Singh	Finance	Manager	M	Delhi	Onsite	HP	Day	26	37	12	95000	3	5	15	0	25	2
6	Rohit_Jain	Finance	Accountant	M	Delhi	Onsite	Asus	Day	9	38	12	80000	2	5	12	0	30	1
7	Manish_Tiwari	Finance	Risk_Analyst	M	Delhi	Hybrid	HP	Night	13	34	7	75000	3	4	11	45	12	3
8	Arjun_Verma	Finance	Analyst	M	Mumbai	Hybrid	Lenovo	Night	3	30	6	70000	4	3	6	40	18	3
9	Tarun_Malhotra	Finance	Analyst	M	Bangalore	Hybrid	Lenovo	Night	19	28	4	68000	6	3	6	55	17	3
10	Ritu_Sen	Finance	Intern	F	Mumbai	Hybrid	HP	Day	22	23	1	36000	7	2	2	50	15	2
11	Meena_Singh	HR	HR_Manager	F	Hyderabad	Onsite	HP	Day	20	41	17	87000	2	5	17	0	22	1
12	Sneha_Mehta	HR	HR_Manager	F	Bangalore	Onsite	HP	Day	2	35	10	85000	3	5	15	0	15	1
13	Swathi_Jose	HR	Trainer	F	Mumbai	Remote	Lenovo	Day	16	31	8	67000	5	4	9	50	0	1
14	Neha_Sood	HR	Recruiter	F	Pune	Remote	Dell	Day	25	30	5	56000	4	4	7	60	0	2
15	Sunil_Gupta	HR	Recruiter	M	Bangalore	Hybrid	Dell	Day	7	29	5	55000	5	4	8	40	12	2
16	Deepa_Menon	HR	HR_Executive	F	Hyderabad	Remote	HP	Day	10	26	3	53000	6	3	6	70	0	3
17	Lavanya_Kapoor	IT	Architect	F	Delhi	Remote	Dell	Day	18	39	15	100000	1	5	25	90	0	3
18	Harsha_Reddy	IT	Team_Lead	M	Hyderabad	Onsite	Dell	Day	15	36	10	90000	2	5	18	0	20	2
19	Kiran_Rao	IT	Data_Engineer	F	Mumbai	Hybrid	Lenovo	Night	8	32	8	72000	4	4	10	35	14	2
20	Rakesh_Kumar	IT	Developer	M	Hyderabad	Hybrid	Dell	Day	1	28	5	65000	5	4	8	50	20	2
21	Komal_Sharma	IT	QA_Tester	F	Pune	Remote	Asus	Day	14	29	6	62000	4	4	8	65	0	2
22	Priya_Sharma	IT	QA_Tester	F	Chennai	Remote	Dell	Day	4	27	4	60000	6	4	7	60	0	2
23	Karthik_Rao	IT	Developer	M	Chennai	Hybrid	Asus	Day	23	27	3	59000	5	3	6	55	10	2
24	Shruti_Nair	IT	Developer	F	Bangalore	Hybrid	Lenovo	Day	12	25	3	58000	5	3	7	50	10	2
25	Aarti_Patil	IT	Developer	F	Hyderabad	Remote	HP	Day	6	24	2	50000	7	3	5	80	0	1
26	Akash_Rana	IT	Intern	M	Pune	Remote	Dell	Day	21	22	1	35000	8	2	3	85	0	2

4.PROC FREQ – CATEGORICAL DISTRIBUTIONS

proc freq data=Office_Data;

  tables Department Work_Mode Shift Gender Laptop_Brand;

  title "Frequency Distribution of Office Variables";

run;

Output:

Frequency Distribution of Office Variables

The FREQ Procedure

Department	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Admin	4	15.38	4	15.38
Finance	6	23.08	10	38.46
HR	6	23.08	16	61.54
IT	10	38.46	26	100.00

Work_Mode	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Hybrid	9	34.62	9	34.62
Onsite	9	34.62	18	69.23
Remote	8	30.77	26	100.00

Shift	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Day	20	76.92	20	76.92
Night	6	23.08	26	100.00

Gender	Frequency	Percent	Cumulative Frequency	Cumulative Percent
F	12	46.15	12	46.15
M	14	53.85	26	100.00

Laptop_Brand	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Asus	4	15.38	4	15.38
Dell	8	30.77	12	46.15
HP	8	30.77	20	76.92
Lenovo	6	23.08	26	100.00

5.PROC MEANS – NUMERIC SUMMARY

proc means data=Office_Data mean min max std;

  var Age Experience_Years Salary Leaves_Taken Commute_Distance_km Coffee_Intake_Per_Day;

  class Department;

  title "Descriptive Statistics by Department";

run;

Output:

Descriptive Statistics by Department

The MEANS Procedure

Department	N Obs	Variable	Mean	Minimum	Maximum	Std Dev
Admin	4	Age Experience_Years Salary Leaves_Taken Commute_Distance_km Coffee_Intake_Per_Day	42.0000000 16.7500000 66250.00 2.5000000 23.2500000 3.0000000	33.0000000 7.0000000 48000.00 2.0000000 18.0000000 2.0000000	50.0000000 25.0000000 90000.00 3.0000000 30.0000000 4.0000000	7.2571804 7.6757193 19805.30 0.5773503 5.3774219 1.1547005
Finance	6	Age Experience_Years Salary Leaves_Taken Commute_Distance_km Coffee_Intake_Per_Day	31.6666667 7.0000000 70666.67 4.1666667 19.5000000 2.3333333	23.0000000 1.0000000 36000.00 2.0000000 12.0000000 1.0000000	38.0000000 12.0000000 95000.00 7.0000000 30.0000000 3.0000000	5.7503623 4.3817805 19531.17 1.9407902 6.7156534 0.8164966
HR	6	Age Experience_Years Salary Leaves_Taken Commute_Distance_km Coffee_Intake_Per_Day	32.0000000 8.0000000 67166.67 4.1666667 8.1666667 1.6666667	26.0000000 3.0000000 53000.00 2.0000000 0 1.0000000	41.0000000 17.0000000 87000.00 6.0000000 22.0000000 3.0000000	5.2915026 5.0596443 15393.72 1.4719601 9.5166521 0.8164966
IT	10	Age Experience_Years Salary Leaves_Taken Commute_Distance_km Coffee_Intake_Per_Day	28.9000000 5.7000000 65100.00 4.7000000 7.4000000 2.0000000	22.0000000 1.0000000 35000.00 1.0000000 0 1.0000000	39.0000000 15.0000000 100000.00 8.0000000 20.0000000 3.0000000	5.3427001 4.2700507 18687.19 2.1108187 8.4878999 0.4714045

6.PROC UNIVARIATE – DETAILED ANALYSIS

proc univariate data=Office_Data;

  var Salary;

  histogram Salary;

  inset mean median std;

  title "Salary Distribution with Histogram";

run;

Output:

Salary Distribution with Histogram

The UNIVARIATE Procedure

Variable: Salary

Moments
N	26	Sum Weights	26
Mean	67038.4615	Sum Observations	1743000
Std Deviation	17356.2226	Variance	301238462
Skewness	0.11051425	Kurtosis	-0.6024564
Uncorrected SS	1.24379E11	Corrected SS	7530961538
Coeff Variation	25.8899476	Std Error Mean	3403.83529

Basic Statistical Measures
Location		Variability
Mean	67038.46	Std Deviation	17356
Median	66000.00	Variance	301238462
Mode	75000.00	Range	65000
		Interquartile Range	25000

Note: The mode displayed is the smallest of 2 modes with a count of 2.

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	19.69498	Pr > |t|	<.0001
Sign	M	13	Pr >= |M|	<.0001
Signed Rank	S	175.5	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	100000
99%	100000
95%	95000
90%	90000
75% Q3	80000
50% Median	66000
25% Q1	55000
10%	48000
5%	36000
1%	35000
0% Min	35000

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
35000	21	87000	20
36000	22	90000	5
48000	11	90000	15
50000	6	95000	26
52000	17	100000	18

7.PROC SQL – POWERFUL DATA QUERYING

A.Average Salary by Department

proc sql;

  select Department, avg(Salary) as Avg_Salary format=comma10.

  from Office_Data

  group by Department;

quit;

Output:

Department	Avg_Salary
Admin	66,250
Finance	70,667
HR	67,167
IT	65,100

B.Employees With Rating = 5 and Salary > 80000

proc sql;

  select Name, Department, Role, Salary, Performance_Rating

  from Office_Data

  where Performance_Rating = 5 and Salary > 80000;

quit;

Output:

Name	Department	Role	Salary	Performance_Rating
Sneha_Mehta	HR	HR_Manager	85000	5
Nikhil_Desai	Admin	Facility_Head	90000	5
Harsha_Reddy	IT	Team_Lead	90000	5
Lavanya_Kapoor	IT	Architect	100000	5
Meena_Singh	HR	HR_Manager	87000	5
Abhinav_Singh	Finance	Manager	95000	5

8.MACRO – REUSABLE REPORTING

A.Create a macro to generate salary report for any department

%macro SalaryReport(dept);

  proc sql;

    select Name, Role, Salary

    from Office_Data

    where Department = "&dept"

    order by Salary desc;

  quit;

%mend SalaryReport;

%SalaryReport(IT);

Output:

Name	Role	Salary
Lavanya_Kapoor	Architect	100000
Harsha_Reddy	Team_Lead	90000
Kiran_Rao	Data_Engineer	72000
Rakesh_Kumar	Developer	65000
Komal_Sharma	QA_Tester	62000
Priya_Sharma	QA_Tester	60000
Karthik_Rao	Developer	59000
Shruti_Nair	Developer	58000
Aarti_Patil	Developer	50000
Akash_Rana	Intern	35000

%SalaryReport(HR);

Output:

Name	Role	Salary
Meena_Singh	HR_Manager	87000
Sneha_Mehta	HR_Manager	85000
Swathi_Jose	Trainer	67000
Neha_Sood	Recruiter	56000
Sunil_Gupta	Recruiter	55000
Deepa_Menon	HR_Executive	53000

9.CUSTOM FORMAT FOR RATINGS

proc format;

  value ratingfmt

    1 = "Poor"

    2 = "Below Avg"

    3 = "Average"

    4 = "Good"

    5 = "Excellent";

run;

proc freq data=Office_Data;

  tables Performance_Rating;

  format Performance_Rating ratingfmt.;

  title "Performance Rating Distribution (Formatted)";

run;

Output:

Performance Rating Distribution (Formatted)

The FREQ Procedure

Performance_Rating	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Below Avg	2	7.69	2	7.69
Average	7	26.92	9	34.62
Good	9	34.62	18	69.23
Excellent	8	30.77	26	100.00

10.GROUP-WISE SUMMARIZATION

proc sql;

  select Work_Mode, count(*) as Count, avg(Internet_Speed) as Avg_Speed

  from Office_Data

  group by Work_Mode;

quit;

Output:

Work_Mode	Count	Avg_Speed
Hybrid	9	46.66667
Onsite	9	0
Remote	8	70

11.PROC TABULATE – MULTI-DIMENSIONAL SUMMARY

proc tabulate data=Office_Data;

  class Department Work_Mode;

  var Salary;

  table Department, Work_Mode*Salary*(mean std);

  title "Salary Summary by Department and Work Mode";

run;

Output:

Salary Summary by Department and Work Mode

	Work_Mode
	Hybrid		Onsite		Remote
	Salary		Salary		Salary
	Mean	Std	Mean	Std	Mean	Std
Department	.	.	66250.00	19805.30	.	.
Admin
Finance	62250.00	17745.89	87500.00	10606.60	.	.
HR	55000.00	.	86000.00	1414.21	58666.67	7371.11
IT	63500.00	6454.97	90000.00	.	61400.00	24079.04

12.DATA CLEANING EXAMPLE (CASE CONVERSION)

data Cleaned_Office;

  set Office_Data;

  Name_Upper = upcase(Name);

  Department_Proper = propcase(Department);

run;

proc print;run;

Output:

Obs	Name	Department	Role	Gender	Office_Location	Work_Mode	Laptop_Brand	Shift	Employee_ID	Age	Experience_Years	Salary	Leaves_Taken	Performance_Rating	Projects_Handled	Internet_Speed	Commute_Distance_km	Coffee_Intake_Per_Day	Name_Upper	Department_Proper
1	Rakesh_Kumar	IT	Developer	M	Hyderabad	Hybrid	Dell	Day	1	28	5	65000	5	4	8	50	20	2	RAKESH_KUMAR	It
2	Sneha_Mehta	HR	HR_Manager	F	Bangalore	Onsite	HP	Day	2	35	10	85000	3	5	15	0	15	1	SNEHA_MEHTA	Hr
3	Arjun_Verma	Finance	Analyst	M	Mumbai	Hybrid	Lenovo	Night	3	30	6	70000	4	3	6	40	18	3	ARJUN_VERMA	Finance
4	Priya_Sharma	IT	QA_Tester	F	Chennai	Remote	Dell	Day	4	27	4	60000	6	4	7	60	0	2	PRIYA_SHARMA	It
5	Nikhil_Desai	Admin	Facility_Head	M	Pune	Onsite	Asus	Day	5	45	20	90000	2	5	20	0	25	4	NIKHIL_DESAI	Admin
6	Aarti_Patil	IT	Developer	F	Hyderabad	Remote	HP	Day	6	24	2	50000	7	3	5	80	0	1	AARTI_PATIL	It
7	Sunil_Gupta	HR	Recruiter	M	Bangalore	Hybrid	Dell	Day	7	29	5	55000	5	4	8	40	12	2	SUNIL_GUPTA	Hr
8	Kiran_Rao	IT	Data_Engineer	F	Mumbai	Hybrid	Lenovo	Night	8	32	8	72000	4	4	10	35	14	2	KIRAN_RAO	It
9	Rohit_Jain	Finance	Accountant	M	Delhi	Onsite	Asus	Day	9	38	12	80000	2	5	12	0	30	1	ROHIT_JAIN	Finance
10	Deepa_Menon	HR	HR_Executive	F	Hyderabad	Remote	HP	Day	10	26	3	53000	6	3	6	70	0	3	DEEPA_MENON	Hr
11	Ravi_Kanth	Admin	Support_Staff	M	Chennai	Onsite	Dell	Night	11	40	15	48000	3	4	9	0	20	2	RAVI_KANTH	Admin
12	Shruti_Nair	IT	Developer	F	Bangalore	Hybrid	Lenovo	Day	12	25	3	58000	5	3	7	50	10	2	SHRUTI_NAIR	It
13	Manish_Tiwari	Finance	Risk_Analyst	M	Delhi	Hybrid	HP	Night	13	34	7	75000	3	4	11	45	12	3	MANISH_TIWARI	Finance
14	Komal_Sharma	IT	QA_Tester	F	Pune	Remote	Asus	Day	14	29	6	62000	4	4	8	65	0	2	KOMAL_SHARMA	It
15	Harsha_Reddy	IT	Team_Lead	M	Hyderabad	Onsite	Dell	Day	15	36	10	90000	2	5	18	0	20	2	HARSHA_REDDY	It
16	Swathi_Jose	HR	Trainer	F	Mumbai	Remote	Lenovo	Day	16	31	8	67000	5	4	9	50	0	1	SWATHI_JOSE	Hr
17	Varun_Yadav	Admin	Admin_Assistant	M	Chennai	Onsite	HP	Day	17	33	7	52000	3	3	10	0	18	2	VARUN_YADAV	Admin
18	Lavanya_Kapoor	IT	Architect	F	Delhi	Remote	Dell	Day	18	39	15	100000	1	5	25	90	0	3	LAVANYA_KAPOOR	It
19	Tarun_Malhotra	Finance	Analyst	M	Bangalore	Hybrid	Lenovo	Night	19	28	4	68000	6	3	6	55	17	3	TARUN_MALHOTRA	Finance
20	Meena_Singh	HR	HR_Manager	F	Hyderabad	Onsite	HP	Day	20	41	17	87000	2	5	17	0	22	1	MEENA_SINGH	Hr
21	Akash_Rana	IT	Intern	M	Pune	Remote	Dell	Day	21	22	1	35000	8	2	3	85	0	2	AKASH_RANA	It
22	Ritu_Sen	Finance	Intern	F	Mumbai	Hybrid	HP	Day	22	23	1	36000	7	2	2	50	15	2	RITU_SEN	Finance
23	Karthik_Rao	IT	Developer	M	Chennai	Hybrid	Asus	Day	23	27	3	59000	5	3	6	55	10	2	KARTHIK_RAO	It
24	Ramesh_Babu	Admin	Security_Lead	M	Hyderabad	Onsite	Lenovo	Night	24	50	25	75000	2	5	20	0	30	4	RAMESH_BABU	Admin
25	Neha_Sood	HR	Recruiter	F	Pune	Remote	Dell	Day	25	30	5	56000	4	4	7	60	0	2	NEHA_SOOD	Hr
26	Abhinav_Singh	Finance	Manager	M	Delhi	Onsite	HP	Day	26	37	12	95000	3	5	15	0	25	2	ABHINAV_SINGH	Finance

13.CONDITIONAL LOGIC – FLAGGING

data Flagged;

  retain Name Department Role Gender Office_Location Work_Mode Laptop_Brand Shift 

         Employee_ID Age Experience_Years Salary Leaves_Taken Performance_Rating 

         Projects_Handled Internet_Speed Commute_Distance_km Coffee_Intake_Per_Day ;

  length Leave_Status $15;

  set Office_Data;

  if Leaves_Taken > 5 then Leave_Status = "High";

  else Leave_Status = "Normal";

run;

proc print;run;

Output:

Obs	Name	Department	Role	Gender	Office_Location	Work_Mode	Laptop_Brand	Shift	Employee_ID	Age	Experience_Years	Salary	Leaves_Taken	Performance_Rating	Projects_Handled	Internet_Speed	Commute_Distance_km	Coffee_Intake_Per_Day	Leave_Status
1	Rakesh_Kumar	IT	Developer	M	Hyderabad	Hybrid	Dell	Day	1	28	5	65000	5	4	8	50	20	2	Normal
2	Sneha_Mehta	HR	HR_Manager	F	Bangalore	Onsite	HP	Day	2	35	10	85000	3	5	15	0	15	1	Normal
3	Arjun_Verma	Finance	Analyst	M	Mumbai	Hybrid	Lenovo	Night	3	30	6	70000	4	3	6	40	18	3	Normal
4	Priya_Sharma	IT	QA_Tester	F	Chennai	Remote	Dell	Day	4	27	4	60000	6	4	7	60	0	2	High
5	Nikhil_Desai	Admin	Facility_Head	M	Pune	Onsite	Asus	Day	5	45	20	90000	2	5	20	0	25	4	Normal
6	Aarti_Patil	IT	Developer	F	Hyderabad	Remote	HP	Day	6	24	2	50000	7	3	5	80	0	1	High
7	Sunil_Gupta	HR	Recruiter	M	Bangalore	Hybrid	Dell	Day	7	29	5	55000	5	4	8	40	12	2	Normal
8	Kiran_Rao	IT	Data_Engineer	F	Mumbai	Hybrid	Lenovo	Night	8	32	8	72000	4	4	10	35	14	2	Normal
9	Rohit_Jain	Finance	Accountant	M	Delhi	Onsite	Asus	Day	9	38	12	80000	2	5	12	0	30	1	Normal
10	Deepa_Menon	HR	HR_Executive	F	Hyderabad	Remote	HP	Day	10	26	3	53000	6	3	6	70	0	3	High
11	Ravi_Kanth	Admin	Support_Staff	M	Chennai	Onsite	Dell	Night	11	40	15	48000	3	4	9	0	20	2	Normal
12	Shruti_Nair	IT	Developer	F	Bangalore	Hybrid	Lenovo	Day	12	25	3	58000	5	3	7	50	10	2	Normal
13	Manish_Tiwari	Finance	Risk_Analyst	M	Delhi	Hybrid	HP	Night	13	34	7	75000	3	4	11	45	12	3	Normal
14	Komal_Sharma	IT	QA_Tester	F	Pune	Remote	Asus	Day	14	29	6	62000	4	4	8	65	0	2	Normal
15	Harsha_Reddy	IT	Team_Lead	M	Hyderabad	Onsite	Dell	Day	15	36	10	90000	2	5	18	0	20	2	Normal
16	Swathi_Jose	HR	Trainer	F	Mumbai	Remote	Lenovo	Day	16	31	8	67000	5	4	9	50	0	1	Normal
17	Varun_Yadav	Admin	Admin_Assistant	M	Chennai	Onsite	HP	Day	17	33	7	52000	3	3	10	0	18	2	Normal
18	Lavanya_Kapoor	IT	Architect	F	Delhi	Remote	Dell	Day	18	39	15	100000	1	5	25	90	0	3	Normal
19	Tarun_Malhotra	Finance	Analyst	M	Bangalore	Hybrid	Lenovo	Night	19	28	4	68000	6	3	6	55	17	3	High
20	Meena_Singh	HR	HR_Manager	F	Hyderabad	Onsite	HP	Day	20	41	17	87000	2	5	17	0	22	1	Normal
21	Akash_Rana	IT	Intern	M	Pune	Remote	Dell	Day	21	22	1	35000	8	2	3	85	0	2	High
22	Ritu_Sen	Finance	Intern	F	Mumbai	Hybrid	HP	Day	22	23	1	36000	7	2	2	50	15	2	High
23	Karthik_Rao	IT	Developer	M	Chennai	Hybrid	Asus	Day	23	27	3	59000	5	3	6	55	10	2	Normal
24	Ramesh_Babu	Admin	Security_Lead	M	Hyderabad	Onsite	Lenovo	Night	24	50	25	75000	2	5	20	0	30	4	Normal
25	Neha_Sood	HR	Recruiter	F	Pune	Remote	Dell	Day	25	30	5	56000	4	4	7	60	0	2	Normal
26	Abhinav_Singh	Finance	Manager	M	Delhi	Onsite	HP	Day	26	37	12	95000	3	5	15	0	25	2	Normal

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