311.Which mobile payment apps handle the highest number of transactions, and are they truly the most reliable?A Complete Sas Study

Which mobile payment apps handle the highest number of transactions, and are they truly the most reliable?A Complete Sas Study

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	.

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