260.COMPARING MOBILE SPECIFICATIONS & SALES IN INDIA USING PROC PRINT | PROC SORT | PROC MEANS | PROC FREQ | PROC REPORT | PROC TRANSPOSE | PROC SQL | SAS MACROS

COMPARING MOBILE SPECIFICATIONS & SALES IN INDIA USING PROC PRINT | PROC SORT | PROC MEANS | PROC FREQ | PROC REPORT | PROC TRANSPOSE | PROC SQL | SAS MACROS

 /*Compare various mobiles in India using multiple SAS techniques*/

STEP 1: Creating the MOBILES dataset

options nocenter;

data mobiles;

    length Mobile_ID 8 Brand $20 Model $30 OS $15 Storage_GB 8 RAM_GB 8 

           Camera_MP 8 Battery_mAh 8 Price_INR 8 Launch_Year 8;

    infile datalines dlm=',';

    input Mobile_ID Brand $ Model $ OS $ Storage_GB RAM_GB Camera_MP Battery_mAh Price_INR Launch_Year;

    datalines;

1,Samsung,GalaxyS23,Android,256,8,50,4700,79999,2023

2,Apple,iPhone14,iOS,128,6,48,3279,79999,2022

3,Xiaomi,Mi13Pro,Android,256,12,50,4820,59999,2023

4,OnePlus,OnePlus11,Android,256,16,50,5000,56999,2023

5,Vivo,X90Pro,Android,256,12,50,4870,62999,2023

6,Oppo,FindX6,Android,256,12,50,4820,64999,2023

7,Realme,GTNeo5,Android,256,12,50,5000,38999,2023

8,Motorola,Edge40,Android,256,8,50,4400,29999,2023

9,Nothing,Phone1,Android,128,8,50,4500,31999,2022

10,Poco,F5Pro,Android,256,12,64,5160,39999,2023

11,Apple,iPhone15,iOS,256,8,48,3279,89999,2023

12,Samsung,GalaxyZFlip5,Android,256,8,12,3700,99999,2023

13,Apple,iPhone13,iOS,128,4,12,3227,58999,2021

14,OnePlus,OnePlusNord3,Android,256,12,50,5000,33999,2023

15,Vivo,V27Pro,Android,256,8,50,4600,37999,2023

16,Oppo,Reno10Pro,Android,256,12,64,4600,39999,2023

17,Realme,11ProPlus,Android,256,12,200,5000,29999,2023

18,Motorola,MotoG73,Android,128,8,50,5000,18999,2023

19,Xiaomi,RedmiNote12,Android,128,6,48,5000,16999,2023

20,Nokia,X30,Android,256,8,50,4200,36999,2023

21,Samsung,GalaxyA54,Android,128,8,50,5000,38999,2023

22,Apple,iPhoneSE3,iOS,128,4,12,2018,42999,2022

23,Xiaomi,RedmiK60,Android,256,12,64,5500,45999,2023

24,OnePlus,OnePlus10T,Android,256,16,50,4800,49999,2022

25,Vivo,Y100,Android,128,8,64,4500,24999,2023

;

run;

proc print;run;

Output:

Obs	Mobile_ID	Brand	Model	OS	Storage_GB	RAM_GB	Camera_MP	Battery_mAh	Price_INR	Launch_Year
1	1	Samsung	GalaxyS23	Android	256	8	50	4700	79999	2023
2	2	Apple	iPhone14	iOS	128	6	48	3279	79999	2022
3	3	Xiaomi	Mi13Pro	Android	256	12	50	4820	59999	2023
4	4	OnePlus	OnePlus11	Android	256	16	50	5000	56999	2023
5	5	Vivo	X90Pro	Android	256	12	50	4870	62999	2023
6	6	Oppo	FindX6	Android	256	12	50	4820	64999	2023
7	7	Realme	GTNeo5	Android	256	12	50	5000	38999	2023
8	8	Motorola	Edge40	Android	256	8	50	4400	29999	2023
9	9	Nothing	Phone1	Android	128	8	50	4500	31999	2022
10	10	Poco	F5Pro	Android	256	12	64	5160	39999	2023
11	11	Apple	iPhone15	iOS	256	8	48	3279	89999	2023
12	12	Samsung	GalaxyZFlip5	Android	256	8	12	3700	99999	2023
13	13	Apple	iPhone13	iOS	128	4	12	3227	58999	2021
14	14	OnePlus	OnePlusNord3	Android	256	12	50	5000	33999	2023
15	15	Vivo	V27Pro	Android	256	8	50	4600	37999	2023
16	16	Oppo	Reno10Pro	Android	256	12	64	4600	39999	2023
17	17	Realme	11ProPlus	Android	256	12	200	5000	29999	2023
18	18	Motorola	MotoG73	Android	128	8	50	5000	18999	2023
19	19	Xiaomi	RedmiNote12	Android	128	6	48	5000	16999	2023
20	20	Nokia	X30	Android	256	8	50	4200	36999	2023
21	21	Samsung	GalaxyA54	Android	128	8	50	5000	38999	2023
22	22	Apple	iPhoneSE3	iOS	128	4	12	2018	42999	2022
23	23	Xiaomi	RedmiK60	Android	256	12	64	5500	45999	2023
24	24	OnePlus	OnePlus10T	Android	256	16	50	4800	49999	2022
25	25	Vivo	Y100	Android	128	8	64	4500	24999	2023

STEP 2: Creating the SALES dataset

data sales;

    length Sale_ID 8 Mobile_ID 8 Units_Sold 8 Region $15 Quarter $2;

    infile datalines dlm=',';

    input Sale_ID Mobile_ID Units_Sold Region $ Quarter $;

    datalines;

1,1,5000,North,Q1

2,2,4500,North,Q1

3,3,6000,West,Q1

4,4,5500,South,Q1

5,5,4800,East,Q1

6,6,3000,West,Q1

7,7,4200,North,Q1

8,8,2500,East,Q1

9,9,3200,West,Q1

10,10,2800,South,Q1

11,11,4900,North,Q2

12,12,1500,West,Q2

13,13,5200,South,Q2

14,14,3700,East,Q2

15,15,3900,North,Q2

16,16,4100,West,Q2

17,17,3300,South,Q2

18,18,3700,East,Q2

19,19,4200,North,Q2

20,20,2500,South,Q2

21,21,3800,North,Q3

22,22,3000,West,Q3

23,23,2100,South,Q3

24,24,2700,East,Q3

25,25,2300,North,Q3

;

run;

proc print;run;

Output:

Obs	Sale_ID	Mobile_ID	Units_Sold	Region	Quarter
1	1	1	5000	North	Q1
2	2	2	4500	North	Q1
3	3	3	6000	West	Q1
4	4	4	5500	South	Q1
5	5	5	4800	East	Q1
6	6	6	3000	West	Q1
7	7	7	4200	North	Q1
8	8	8	2500	East	Q1
9	9	9	3200	West	Q1
10	10	10	2800	South	Q1
11	11	11	4900	North	Q2
12	12	12	1500	West	Q2
13	13	13	5200	South	Q2
14	14	14	3700	East	Q2
15	15	15	3900	North	Q2
16	16	16	4100	West	Q2
17	17	17	3300	South	Q2
18	18	18	3700	East	Q2
19	19	19	4200	North	Q2
20	20	20	2500	South	Q2
21	21	21	3800	North	Q3
22	22	22	3000	West	Q3
23	23	23	2100	South	Q3
24	24	24	2700	East	Q3
25	25	25	2300	North	Q3

STEP 3: PROC PRINT — Display datasets

proc print data=mobiles;

    title "List of Mobiles in India - Specifications";

run;

Output:

List of Mobiles in India - Specifications

Obs	Mobile_ID	Brand	Model	OS	Storage_GB	RAM_GB	Camera_MP	Battery_mAh	Price_INR	Launch_Year
1	1	Samsung	GalaxyS23	Android	256	8	50	4700	79999	2023
2	2	Apple	iPhone14	iOS	128	6	48	3279	79999	2022
3	3	Xiaomi	Mi13Pro	Android	256	12	50	4820	59999	2023
4	4	OnePlus	OnePlus11	Android	256	16	50	5000	56999	2023
5	5	Vivo	X90Pro	Android	256	12	50	4870	62999	2023
6	6	Oppo	FindX6	Android	256	12	50	4820	64999	2023
7	7	Realme	GTNeo5	Android	256	12	50	5000	38999	2023
8	8	Motorola	Edge40	Android	256	8	50	4400	29999	2023
9	9	Nothing	Phone1	Android	128	8	50	4500	31999	2022
10	10	Poco	F5Pro	Android	256	12	64	5160	39999	2023

proc print data=sales;

    title "Mobile Sales Data in India";

run;

Output:

Mobile Sales Data in India

Obs	Sale_ID	Mobile_ID	Units_Sold	Region	Quarter
1	1	1	5000	North	Q1
2	2	2	4500	North	Q1
3	3	3	6000	West	Q1
4	4	4	5500	South	Q1
5	5	5	4800	East	Q1
6	6	6	3000	West	Q1
7	7	7	4200	North	Q1
8	8	8	2500	East	Q1
9	9	9	3200	West	Q1
10	10	10	2800	South	Q1

STEP 4: PROC SORT — Sort mobiles by price (descending)

proc sort data=mobiles out=mobiles_sorted;

    by descending Price_INR;

run;

title "Mobiles Sorted by Price (Highest First)";

proc print data=mobiles_sorted;

run;

 Output:

Mobiles Sorted by Price (Highest First)

Obs	Mobile_ID	Brand	Model	OS	Storage_GB	RAM_GB	Camera_MP	Battery_mAh	Price_INR	Launch_Year
1	12	Samsung	GalaxyZFlip5	Android	256	8	12	3700	99999	2023
2	11	Apple	iPhone15	iOS	256	8	48	3279	89999	2023
3	1	Samsung	GalaxyS23	Android	256	8	50	4700	79999	2023
4	2	Apple	iPhone14	iOS	128	6	48	3279	79999	2022
5	6	Oppo	FindX6	Android	256	12	50	4820	64999	2023
6	5	Vivo	X90Pro	Android	256	12	50	4870	62999	2023
7	3	Xiaomi	Mi13Pro	Android	256	12	50	4820	59999	2023
8	13	Apple	iPhone13	iOS	128	4	12	3227	58999	2021
9	4	OnePlus	OnePlus11	Android	256	16	50	5000	56999	2023
10	24	OnePlus	OnePlus10T	Android	256	16	50	4800	49999	2022
11	23	Xiaomi	RedmiK60	Android	256	12	64	5500	45999	2023
12	22	Apple	iPhoneSE3	iOS	128	4	12	2018	42999	2022
13	10	Poco	F5Pro	Android	256	12	64	5160	39999	2023
14	16	Oppo	Reno10Pro	Android	256	12	64	4600	39999	2023
15	7	Realme	GTNeo5	Android	256	12	50	5000	38999	2023
16	21	Samsung	GalaxyA54	Android	128	8	50	5000	38999	2023
17	15	Vivo	V27Pro	Android	256	8	50	4600	37999	2023
18	20	Nokia	X30	Android	256	8	50	4200	36999	2023
19	14	OnePlus	OnePlusNord3	Android	256	12	50	5000	33999	2023
20	9	Nothing	Phone1	Android	128	8	50	4500	31999	2022
21	8	Motorola	Edge40	Android	256	8	50	4400	29999	2023
22	17	Realme	11ProPlus	Android	256	12	200	5000	29999	2023
23	25	Vivo	Y100	Android	128	8	64	4500	24999	2023
24	18	Motorola	MotoG73	Android	128	8	50	5000	18999	2023
25	19	Xiaomi	RedmiNote12	Android	128	6	48	5000	16999	2023

STEP 5: PROC MEANS — Price and battery statistics

proc means data=mobiles mean min max;

    var Price_INR Battery_mAh;

run;

Output:

The MEANS Procedure

Variable	Mean	Minimum	Maximum
Price_INR Battery_mAh	48519.00 4478.92	16999.00 2018.00	99999.00 5500.00-0

STEP 6: PROC FREQ — OS type count

proc freq data=mobiles;

    tables OS;

run;

Output:

The FREQ Procedure

OS	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Android	21	84.00	21	84.00
iOS	4	16.00	25	100.00

STEP 7: PROC REPORT — Brand-wise price and storage summary

proc report data=mobiles nowd;

    column Brand Price_INR Storage_GB;

    define Brand / group;

    define Price_INR / mean;

    define Storage_GB / mean;

run;

Output:

Brand	Price_INR	Storage_GB
Apple	67999	160
Motorola	24499	192
Nokia	36999	256
Nothing	31999	128
OnePlus	46999	256
Oppo	52499	256
Poco	39999	256
Realme	34499	256
Samsung	72999	213.33333
Vivo	41999	213.33333
Xiaomi	40999	213.33333

STEP 8: PROC TRANSPOSE — Reshape data for Year-wise analysis

proc transpose data=mobiles out=mobiles_transposed prefix=Mobile_;

    by Launch_Year;

    var Price_INR;

run;

proc print data=mobiles_transposed;run;

Output:

Obs	Launch_Year	_NAME_	Mobile_1	Mobile_2	Mobile_3	Mobile_4	Mobile_5	Mobile_6	Mobile_7	Mobile_8
1	2023	Price_INR	79999	.	.	.	.	.	.	.
2	2022	Price_INR	79999	.	.	.	.	.	.	.
3	2023	Price_INR	59999	56999	62999	64999	38999	29999	.	.
4	2022	Price_INR	31999	.	.	.	.	.	.	.
5	2023	Price_INR	39999	89999	99999	.	.	.	.	.
6	2021	Price_INR	58999	.	.	.	.	.	.	.
7	2023	Price_INR	33999	37999	39999	29999	18999	16999	36999	38999
8	2022	Price_INR	42999	.	.	.	.	.	.	.
9	2023	Price_INR	45999	.	.	.	.	.	.	.
10	2022	Price_INR	49999	.	.	.	.	.	.	.
11	2023	Price_INR	24999	.	.	.	.	.	.	.

STEP 9: PROC SQL — Join mobiles & sales for revenue

proc sql;

    create table mobile_revenue as

    select m.Brand, m.Model, m.Price_INR, s.Units_Sold,

           (m.Price_INR * s.Units_Sold) as Revenue

    from mobiles m

    join sales s

    on m.Mobile_ID = s.Mobile_ID;

quit;

title "Mobile Revenue Calculation";

proc print data=mobile_revenue;

run;

Output:

Mobile Revenue Calculation

Obs	Brand	Model	Price_INR	Units_Sold	Revenue
1	Samsung	GalaxyS23	79999	5000	399995000
2	Apple	iPhone14	79999	4500	359995500
3	Xiaomi	Mi13Pro	59999	6000	359994000
4	OnePlus	OnePlus11	56999	5500	313494500
5	Vivo	X90Pro	62999	4800	302395200
6	Oppo	FindX6	64999	3000	194997000
7	Realme	GTNeo5	38999	4200	163795800
8	Motorola	Edge40	29999	2500	74997500
9	Nothing	Phone1	31999	3200	102396800
10	Poco	F5Pro	39999	2800	111997200
11	Apple	iPhone15	89999	4900	440995100
12	Samsung	GalaxyZFlip5	99999	1500	149998500
13	Apple	iPhone13	58999	5200	306794800
14	OnePlus	OnePlusNord3	33999	3700	125796300
15	Vivo	V27Pro	37999	3900	148196100
16	Oppo	Reno10Pro	39999	4100	163995900
17	Realme	11ProPlus	29999	3300	98996700
18	Motorola	MotoG73	18999	3700	70296300
19	Xiaomi	RedmiNote12	16999	4200	71395800
20	Nokia	X30	36999	2500	92497500
21	Samsung	GalaxyA54	38999	3800	148196200
22	Apple	iPhoneSE3	42999	3000	128997000
23	Xiaomi	RedmiK60	45999	2100	96597900
24	OnePlus	OnePlus10T	49999	2700	134997300
25	Vivo	Y100	24999	2300	57497700

STEP 10: SAS Macro — Top Selling Mobiles by Units

%macro top_sellers(threshold);

    proc sql;

        select Brand, Model, Units_Sold

        from mobile_revenue

        where Units_Sold > &threshold

        order by Units_Sold desc;

    quit;

%mend;

%top_sellers(4000);

Output:

Brand	Model	Units_Sold
Xiaomi	Mi13Pro	6000
OnePlus	OnePlus11	5500
Apple	iPhone13	5200
Samsung	GalaxyS23	5000
Apple	iPhone15	4900
Vivo	X90Pro	4800
Apple	iPhone14	4500
Realme	GTNeo5	4200
Xiaomi	RedmiNote12	4200
Oppo	Reno10Pro	4100

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