311.MOBILE PAYMENT APPLICATIONS DATASET ANALYSIS USING DATA STEP | PROC PRINT | PROC SORT | PROC MEANS | PROC FREQ | PROC FORMAT | PROC SQL | SAS MACRO

MOBILE PAYMENT APPLICATIONS DATASET ANALYSIS USING DATA STEP | PROC PRINT | PROC SORT | PROC MEANS | PROC FREQ | PROC FORMAT | PROC SQL | SAS MACRO

options nocenter validvarname=any;

1. CREATING MOBILE PAYMENTS DATASET USING DATA STEP

data work.mobile_payments;

    infile datalines dlm=',' dsd truncover;

    length App_Name $20 Country $15 Security_Level $10;

    input App_Name $

          Country $

          Users_Millions

          Transactions_Per_Day

          Security_Level $

          Avg_Transaction_Value;

datalines;

GooglePay,India,120,80,High,18.5

PhonePe,India,130,92,High,17.4

Paytm,India,90,65,Medium,10.2

AmazonPay,India,55,28,High,22.1

BHIM,India,25,12,High,14.5

PayPal,USA,420,35,High,45.0

Venmo,USA,90,18,Medium,32.5

CashApp,USA,70,22,Medium,28.3

Alipay,China,900,320,High,55.8

WeChatPay,China,850,310,High,48.9

GrabPay,Singapore,30,10,Medium,19.0

M-Pesa,Kenya,50,22,High,6.5

;

run;

proc print data=work.mobile_payments;

    title "RAW DATASET: MOBILE PAYMENT APPLICATIONS";

run;

OUTPUT:

RAW DATASET: MOBILE PAYMENT APPLICATIONS

Obs	App_Name	Country	Security_Level	Users_Millions	Transactions_Per_Day	Avg_Transaction_Value
1	GooglePay	India	High	120	80	18.5
2	PhonePe	India	High	130	92	17.4
3	Paytm	India	Medium	90	65	10.2
4	AmazonPay	India	High	55	28	22.1
5	BHIM	India	High	25	12	14.5
6	PayPal	USA	High	420	35	45.0
7	Venmo	USA	Medium	90	18	32.5
8	CashApp	USA	Medium	70	22	28.3
9	Alipay	China	High	900	320	55.8
10	WeChatPay	China	High	850	310	48.9
11	GrabPay	Singapore	Medium	30	10	19.0
12	M-Pesa	Kenya	High	50	22	6.5

2. PROC SORT – SORT APPS BY USERS

proc sort data=work.mobile_payments out=work.sorted_apps;

    by descending Users_Millions;

run;

proc print data=work.sorted_apps;

    title "APPS SORTED BY NUMBER OF USERS";

run;

OUTPUT:

APPS SORTED BY NUMBER OF USERS

Obs	App_Name	Country	Security_Level	Users_Millions	Transactions_Per_Day	Avg_Transaction_Value
1	Alipay	China	High	900	320	55.8
2	WeChatPay	China	High	850	310	48.9
3	PayPal	USA	High	420	35	45.0
4	PhonePe	India	High	130	92	17.4
5	GooglePay	India	High	120	80	18.5
6	Paytm	India	Medium	90	65	10.2
7	Venmo	USA	Medium	90	18	32.5
8	CashApp	USA	Medium	70	22	28.3
9	AmazonPay	India	High	55	28	22.1
10	M-Pesa	Kenya	High	50	22	6.5
11	GrabPay	Singapore	Medium	30	10	19.0
12	BHIM	India	High	25	12	14.5

3. PROC MEANS – STATISTICAL ANALYSIS

proc means data=work.mobile_payments mean min max stddev;

    var Users_Millions Transactions_Per_Day Avg_Transaction_Value;

    title "STATISTICAL SUMMARY OF MOBILE PAYMENT APPS";

run;

OUTPUT:

STATISTICAL SUMMARY OF MOBILE PAYMENT APPS

The MEANS Procedure

Variable	Mean	Minimum	Maximum	Std Dev
Users_Millions Transactions_Per_Day Avg_Transaction_Value	235.8333333 84.5000000 26.5583333	25.0000000 10.0000000 6.5000000	900.0000000 320.0000000 55.8000000	316.3630921 110.9672351 15.8796873

4. PROC FREQ – FREQUENCY OF SECURITY LEVELS

proc freq data=work.mobile_payments;

    tables Security_Level;

    title "FREQUENCY OF SECURITY LEVELS ACROSS APPS";

run;

OUTPUT:

FREQUENCY OF SECURITY LEVELS ACROSS APPS

The FREQ Procedure

Security_Level	Frequency	Percent	Cumulative Frequency	Cumulative Percent
High	8	66.67	8	66.67
Medium	4	33.33	12	100.00

5. PROC FORMAT – CUSTOM FORMATTING FOR SECURITY LEVEL

proc format;

    value $secfmt

        'High'   = 'HIGHLY SECURE'

        'Medium' = 'MODERATELY SECURE'

        'Low'    = 'LOW SECURITY';

run;

LOG:

NOTE: Format $SECFMT has been output.

proc print data=work.mobile_payments;

    format Security_Level $secfmt.;

    title "MOBILE APPS WITH SECURITY LEVEL FORMAT APPLIED";

run;

OUTPUT:

MOBILE APPS WITH SECURITY LEVEL FORMAT APPLIED

Obs	App_Name	Country	Security_Level	Users_Millions	Transactions_Per_Day	Avg_Transaction_Value
1	GooglePay	India	HIGHLY SECURE	120	80	18.5
2	PhonePe	India	HIGHLY SECURE	130	92	17.4
3	Paytm	India	MODERATELY SECURE	90	65	10.2
4	AmazonPay	India	HIGHLY SECURE	55	28	22.1
5	BHIM	India	HIGHLY SECURE	25	12	14.5
6	PayPal	USA	HIGHLY SECURE	420	35	45.0
7	Venmo	USA	MODERATELY SECURE	90	18	32.5
8	CashApp	USA	MODERATELY SECURE	70	22	28.3
9	Alipay	China	HIGHLY SECURE	900	320	55.8
10	WeChatPay	China	HIGHLY SECURE	850	310	48.9
11	GrabPay	Singapore	MODERATELY SECURE	30	10	19.0
12	M-Pesa	Kenya	HIGHLY SECURE	50	22	6.5

6. PROC SQL – CALCULATE TOTAL USERS AND AVG TRANSACTION VALUE

proc sql;

    create table work.summary_sql as

    select 

        Country,

        count(*) as Total_Apps,

        sum(Users_Millions) as Total_Users_M,

        avg(Avg_Transaction_Value) as Avg_Txn_Value

    from work.mobile_payments

    group by Country;

quit;

proc print data=work.summary_sql;

    title "COUNTRY-WISE SUMMARY FROM PROC SQL";

run;

OUTPUT:

COUNTRY-WISE SUMMARY FROM PROC SQL

Obs	Country	Total_Apps	Total_Users_M	Avg_Txn_Value
1	China	2	1750	52.3500
2	India	5	420	16.5400
3	Kenya	1	50	6.5000
4	Singapore	1	30	19.0000
5	USA	3	580	35.2667

7. MACRO – CALCULATE AVERAGE USERS PER SECURITY LEVEL

%macro security_avg(level);

    proc sql;

        select 

            "&level" as Security_Level,

            avg(Users_Millions) as Avg_Users

        from work.mobile_payments

        where Security_Level = "&level";

    quit;

%mend security_avg;

%security_avg(High);

OUTPUT:

Security_Level	Avg_Users
High	318.75

%security_avg(Medium);

OUTPUT:

Security_Level	Avg_Users
Medium	70

%security_avg(Low);  /* No app with Low, returns missing */

OUTPUT:

Security_Level	Avg_Users
Low	.

To Visit My Previous Different Types Of Oils Dataset:Click Here

To Visit My Previous Different Types Of Series 2025 Dataset:Click Here

To Visit My Previous Analyzing Yoga Asanas Worldwide Dataset:Click Here

To Visit My Previous Analyzing Indian Languages Dataset:Click Here  

Follow Us On : 

 

--- FOLLOW OUR BLOG FOR MORE INFORMATION.

--->PLEASE DO COMMENTS AND SHARE OUR BLOG.