232.ANALYZING CRICKET WORLD CUP STATISTICS USING PROC PRINT | PROC SORT | PROC MEANS | PROC SUMMARY | PROC FREQ | PROC TABULATE | PROC REPORT | PROC FORMAT | PROC DATASETS | PROC SGPLOT | PROC SQL

ANALYZING CRICKET WORLD CUP STATISTICS USING PROC PRINT | PROC SORT | PROC MEANS | PROC SUMMARY | PROC FREQ | PROC TABULATE | PROC REPORT | PROC FORMAT | PROC DATASETS | PROC SGPLOT | PROC SQL | MACROS AND FUNCTIONS IN SAS

/*Creating a Overall  Cricket  World Cup analytic dataset*/

1. Project set‑up

options nocenter

fullstimer

symbolgen

msglevel=i

yearcutoff=1920

dkricond=error;

2. Building the base table with DATA step + INFORMATS

proc format;

value $iso2fmt

36 ='AUS'

356 ='IND'

554 ='NZL'

826 ='ENG'

710 ='RSA'

124 ='CAN'

250 ='FRA'

752 ='SWE'

840 ='USA'

other='---';

value $contfmt

36,554,710 = 'OCEANIA/AFRICA'

356 = 'ASIA'

826 = 'EUROPE'

124,250,752,840 = 'AMERICAS/EUR';

run;

data cricket_wc (label="Overall Cricket World Cup Statistics");

infile datalines dlm='|' dsd truncover;

length Edition 8 Year 8 Host TitleTeam RunnerUp Matches Runs Wkts 8

AveRR 8 CrowdM 8;

input Edition Year Host TitleTeam RunnerUp Matches Runs Wkts AveRR CrowdM;

RptYear = year(mdy(1,1,Year));

ContCode = Host;

/* Apply formats to numeric variables */

format Host iso2fmt.

TitleTeam iso2fmt.

RunnerUp iso2fmt.

Host ContCode contfmt.;

datalines;

1|1975|826|36|124|15|7254|523|4.11|0.40

2|1979|826|36|356|15|7602|544|4.01|0.46

3|1983|356|356|36|27|10649|714|4.24|0.56

4|1987|356|36|826|27|10809|768|4.48|0.69

5|1992|554|36|826|39|10911|835|4.54|0.75

6|1996|356|36|710|37|11220|846|4.52|0.72

7|1999|826|36|826|42|13469|867|4.78|0.84

8|2003|710|36|356|54|17371|1091|4.76|0.92

9|2007|124|840|752|51|21289|1267|5.04|1.21

10|2011|356|356|826|49|25863|1416|5.03|1.25

11|2015|36|36|554|49|27896|1544|5.07|1.24

12|2019|826|826|36|48|27251|1467|5.06|1.15

13|2023|356|356|840|48|28088|1517|5.27|1.38

14|2027|710|710|840|56|29872|1634|5.33|1.42

15|2031|554|554|356|56|31456|1712|5.40|1.48

16|2035|840|840|36|60|32911|1794|5.47|1.55

17|2039|250|250|36|64|34522|1863|5.55|1.63

18|2043|356|36|554|64|36211|1945|5.60|1.70

19|2047|36|36|356|64|37988|2037|5.66|1.78

20|2051|826|36|554|64|39812|2131|5.72|1.86

21|2055|840|840|356|66|41689|2228|5.79|1.94

22|2059|554|554|710|66|43623|2326|5.85|2.02

23|2063|710|710|554|66|45617|2427|5.92|2.11

24|2067|250|250|356|68|47675|2531|5.98|2.20

25|2071|356|356|826|68|49802|2637|6.04|2.29

26|2075|36|36|356|70|52004|2746|6.11|2.38

27|2079|356|36|554|70|54286|2860|6.17|2.47

28|2083|826|36|554|70|56652|2976|6.24|2.56

29|2087|840|840|710|72|59002|3095|6.31|2.66

30|2091|554|554|356|72|61440|3216|6.37|2.76

31|2095|710|710|840|74|63968|3340|6.44|2.86

32|2099|356|356|36|74|66588|3466|6.51|2.97

;

run;

proc print;run;

Output:

Obs	Edition	Year	Host	TitleTeam	RunnerUp	Matches	Runs	Wkts	AveRR	CrowdM	RptYear	ContCode
1	1	1975	EUROPE	AUS	CAN	15	7254	523	4.11	0.40	1975	EUROPE
2	2	1979	EUROPE	AUS	IND	15	7602	544	4.01	0.46	1979	EUROPE
3	3	1983	ASIA	IND	AUS	27	10649	714	4.24	0.56	1983	ASIA
4	4	1987	ASIA	AUS	ENG	27	10809	768	4.48	0.69	1987	ASIA
5	5	1992	OCEANIA/AFRICA	AUS	ENG	39	10911	835	4.54	0.75	1992	OCEANIA/AFRICA
6	6	1996	ASIA	AUS	RSA	37	11220	846	4.52	0.72	1996	ASIA
7	7	1999	EUROPE	AUS	ENG	42	13469	867	4.78	0.84	1999	EUROPE
8	8	2003	OCEANIA/AFRICA	AUS	IND	54	17371	1091	4.76	0.92	2003	OCEANIA/AFRICA
9	9	2007	AMERICAS/EUR	USA	SWE	51	21289	1267	5.04	1.21	2007	AMERICAS/EUR
10	10	2011	ASIA	IND	ENG	49	25863	1416	5.03	1.25	2011	ASIA
11	11	2015	OCEANIA/AFRICA	AUS	NZL	49	27896	1544	5.07	1.24	2015	OCEANIA/AFRICA
12	12	2019	EUROPE	ENG	AUS	48	27251	1467	5.06	1.15	2019	EUROPE
13	13	2023	ASIA	IND	USA	48	28088	1517	5.27	1.38	2023	ASIA
14	14	2027	OCEANIA/AFRICA	RSA	USA	56	29872	1634	5.33	1.42	2027	OCEANIA/AFRICA
15	15	2031	OCEANIA/AFRICA	NZL	IND	56	31456	1712	5.40	1.48	2031	OCEANIA/AFRICA
16	16	2035	AMERICAS/EUR	USA	AUS	60	32911	1794	5.47	1.55	2035	AMERICAS/EUR
17	17	2039	AMERICAS/EUR	FRA	AUS	64	34522	1863	5.55	1.63	2039	AMERICAS/EUR
18	18	2043	ASIA	AUS	NZL	64	36211	1945	5.60	1.70	2043	ASIA
19	19	2047	OCEANIA/AFRICA	AUS	IND	64	37988	2037	5.66	1.78	2047	OCEANIA/AFRICA
20	20	2051	EUROPE	AUS	NZL	64	39812	2131	5.72	1.86	2051	EUROPE
21	21	2055	AMERICAS/EUR	USA	IND	66	41689	2228	5.79	1.94	2055	AMERICAS/EUR
22	22	2059	OCEANIA/AFRICA	NZL	RSA	66	43623	2326	5.85	2.02	2059	OCEANIA/AFRICA
23	23	2063	OCEANIA/AFRICA	RSA	NZL	66	45617	2427	5.92	2.11	2063	OCEANIA/AFRICA
24	24	2067	AMERICAS/EUR	FRA	IND	68	47675	2531	5.98	2.20	2067	AMERICAS/EUR
25	25	2071	ASIA	IND	ENG	68	49802	2637	6.04	2.29	2071	ASIA
26	26	2075	OCEANIA/AFRICA	AUS	IND	70	52004	2746	6.11	2.38	2075	OCEANIA/AFRICA
27	27	2079	ASIA	AUS	NZL	70	54286	2860	6.17	2.47	2079	ASIA
28	28	2083	EUROPE	AUS	NZL	70	56652	2976	6.24	2.56	2083	EUROPE
29	29	2087	AMERICAS/EUR	USA	RSA	72	59002	3095	6.31	2.66	2087	AMERICAS/EUR
30	30	2091	OCEANIA/AFRICA	NZL	IND	72	61440	3216	6.37	2.76	2091	OCEANIA/AFRICA
31	31	2095	OCEANIA/AFRICA	RSA	USA	74	63968	3340	6.44	2.86	2095	OCEANIA/AFRICA
32	32	2099	ASIA	IND	AUS	74	66588	3466	6.51	2.97	2099	ASIA

3.Quick sanity checks

title "First and last 3 rows to confirm ranges";

proc print data=cricket_wc (obs=3);

run;

Output:

First and last 3 rows to confirm ranges

Obs	Edition	Year	Host	TitleTeam	RunnerUp	Matches	Runs	Wkts	AveRR	CrowdM	RptYear	ContCode
1	1	1975	EUROPE	AUS	CAN	15	7254	523	4.11	0.40	1975	EUROPE
2	2	1979	EUROPE	AUS	IND	15	7602	544	4.01	0.46	1979	EUROPE
3	3	1983	ASIA	IND	AUS	27	10649	714	4.24	0.56	1983	ASIA

proc print data=cricket_wc (firstobs=30);

run;

title;

Output:

First and last 3 rows to confirm ranges

Obs	Edition	Year	Host	TitleTeam	RunnerUp	Matches	Runs	Wkts	AveRR	CrowdM	RptYear	ContCode
30	30	2091	OCEANIA/AFRICA	NZL	IND	72	61440	3216	6.37	2.76	2091	OCEANIA/AFRICA
31	31	2095	OCEANIA/AFRICA	RSA	USA	74	63968	3340	6.44	2.86	2095	OCEANIA/AFRICA
32	32	2099	ASIA	IND	AUS	74	66588	3466	6.51	2.97	2099	ASIA

4. PROC SQL exploration

proc sql outobs=5;

title "Top 5 Cricket World Cups by Ticketed Attendance";

select Edition, Year,

CrowdM format=comma6.2 label="Spectators (M)",

CrowdM * 1000000 as PplFlyToHost format=comma12.

from cricket_wc

order by CrowdM desc;

quit;

title;

Output:

Top 5 Cricket World Cups by Ticketed Attendance

Edition	Year	Spectators (M)	PplFlyToHost
32	2099	2.97	2,970,000
31	2095	2.86	2,860,000
30	2091	2.76	2,760,000
29	2087	2.66	2,660,000
28	2083	2.56	2,560,000

5.PROC SORT → PROC MEANS → PROC SUMMARY

proc sort data=cricket_wc out=wc_sorted;

by Host;

run;

proc print;run;

Output:

Obs	Edition	Year	Host	TitleTeam	RunnerUp	Matches	Runs	Wkts	AveRR	CrowdM	RptYear	ContCode
1	11	2015	OCEANIA/AFRICA	AUS	NZL	49	27896	1544	5.07	1.24	2015	OCEANIA/AFRICA
2	19	2047	OCEANIA/AFRICA	AUS	IND	64	37988	2037	5.66	1.78	2047	OCEANIA/AFRICA
3	26	2075	OCEANIA/AFRICA	AUS	IND	70	52004	2746	6.11	2.38	2075	OCEANIA/AFRICA
4	9	2007	AMERICAS/EUR	USA	SWE	51	21289	1267	5.04	1.21	2007	AMERICAS/EUR
5	17	2039	AMERICAS/EUR	FRA	AUS	64	34522	1863	5.55	1.63	2039	AMERICAS/EUR
6	24	2067	AMERICAS/EUR	FRA	IND	68	47675	2531	5.98	2.20	2067	AMERICAS/EUR
7	3	1983	ASIA	IND	AUS	27	10649	714	4.24	0.56	1983	ASIA
8	4	1987	ASIA	AUS	ENG	27	10809	768	4.48	0.69	1987	ASIA
9	6	1996	ASIA	AUS	RSA	37	11220	846	4.52	0.72	1996	ASIA
10	10	2011	ASIA	IND	ENG	49	25863	1416	5.03	1.25	2011	ASIA
11	13	2023	ASIA	IND	USA	48	28088	1517	5.27	1.38	2023	ASIA
12	18	2043	ASIA	AUS	NZL	64	36211	1945	5.60	1.70	2043	ASIA
13	25	2071	ASIA	IND	ENG	68	49802	2637	6.04	2.29	2071	ASIA
14	27	2079	ASIA	AUS	NZL	70	54286	2860	6.17	2.47	2079	ASIA
15	32	2099	ASIA	IND	AUS	74	66588	3466	6.51	2.97	2099	ASIA
16	5	1992	OCEANIA/AFRICA	AUS	ENG	39	10911	835	4.54	0.75	1992	OCEANIA/AFRICA
17	15	2031	OCEANIA/AFRICA	NZL	IND	56	31456	1712	5.40	1.48	2031	OCEANIA/AFRICA
18	22	2059	OCEANIA/AFRICA	NZL	RSA	66	43623	2326	5.85	2.02	2059	OCEANIA/AFRICA
19	30	2091	OCEANIA/AFRICA	NZL	IND	72	61440	3216	6.37	2.76	2091	OCEANIA/AFRICA
20	8	2003	OCEANIA/AFRICA	AUS	IND	54	17371	1091	4.76	0.92	2003	OCEANIA/AFRICA
21	14	2027	OCEANIA/AFRICA	RSA	USA	56	29872	1634	5.33	1.42	2027	OCEANIA/AFRICA
22	23	2063	OCEANIA/AFRICA	RSA	NZL	66	45617	2427	5.92	2.11	2063	OCEANIA/AFRICA
23	31	2095	OCEANIA/AFRICA	RSA	USA	74	63968	3340	6.44	2.86	2095	OCEANIA/AFRICA
24	1	1975	EUROPE	AUS	CAN	15	7254	523	4.11	0.40	1975	EUROPE
25	2	1979	EUROPE	AUS	IND	15	7602	544	4.01	0.46	1979	EUROPE
26	7	1999	EUROPE	AUS	ENG	42	13469	867	4.78	0.84	1999	EUROPE
27	12	2019	EUROPE	ENG	AUS	48	27251	1467	5.06	1.15	2019	EUROPE
28	20	2051	EUROPE	AUS	NZL	64	39812	2131	5.72	1.86	2051	EUROPE
29	28	2083	EUROPE	AUS	NZL	70	56652	2976	6.24	2.56	2083	EUROPE
30	16	2035	AMERICAS/EUR	USA	AUS	60	32911	1794	5.47	1.55	2035	AMERICAS/EUR
31	21	2055	AMERICAS/EUR	USA	IND	66	41689	2228	5.79	1.94	2055	AMERICAS/EUR
32	29	2087	AMERICAS/EUR	USA	RSA	72	59002	3095	6.31	2.66	2087	AMERICAS/EUR

proc means data=wc_sorted n sum mean maxdec=2;

var Runs Wkts AveRR CrowdM;

class Host;

title "Aggregated Metrics by Host Country (ISO Code)";

run;

Output:

Aggregated Metrics by Host Country (ISO Code)

The MEANS Procedure

OCEANIA/AFRICA

Runs
Wkts
AveRR
CrowdM

422146.00

22908.00

61.45

19.72

38376.91

2082.55

5.59

1.79

AMERICAS/EUR

Runs
Wkts
AveRR
CrowdM

237088.00

12778.00

34.14

11.19

39514.67

2129.67

5.69

1.87

ASIA

Runs
Wkts
AveRR
CrowdM

293516.00

16169.00

47.86

14.03

32612.89

1796.56

5.32

1.56

EUROPE

Runs
Wkts
AveRR
CrowdM

152040.00

8508.00

29.92

7.27

25340.00

1418.00

4.99

1.21

data wc_sorted;

set cricket_wc;

TitlesSoFar = 1; /* This means: for each time a team won, count as 1 */

run;

proc print;run;

Output:

Obs	Edition	Year	Host	TitleTeam	RunnerUp	Matches	Runs	Wkts	AveRR	CrowdM	RptYear	ContCode	TitlesSoFar
1	1	1975	EUROPE	AUS	CAN	15	7254	523	4.11	0.40	1975	EUROPE	1
2	2	1979	EUROPE	AUS	IND	15	7602	544	4.01	0.46	1979	EUROPE	1
3	3	1983	ASIA	IND	AUS	27	10649	714	4.24	0.56	1983	ASIA	1
4	4	1987	ASIA	AUS	ENG	27	10809	768	4.48	0.69	1987	ASIA	1
5	5	1992	OCEANIA/AFRICA	AUS	ENG	39	10911	835	4.54	0.75	1992	OCEANIA/AFRICA	1
6	6	1996	ASIA	AUS	RSA	37	11220	846	4.52	0.72	1996	ASIA	1
7	7	1999	EUROPE	AUS	ENG	42	13469	867	4.78	0.84	1999	EUROPE	1
8	8	2003	OCEANIA/AFRICA	AUS	IND	54	17371	1091	4.76	0.92	2003	OCEANIA/AFRICA	1
9	9	2007	AMERICAS/EUR	USA	SWE	51	21289	1267	5.04	1.21	2007	AMERICAS/EUR	1
10	10	2011	ASIA	IND	ENG	49	25863	1416	5.03	1.25	2011	ASIA	1
11	11	2015	OCEANIA/AFRICA	AUS	NZL	49	27896	1544	5.07	1.24	2015	OCEANIA/AFRICA	1
12	12	2019	EUROPE	ENG	AUS	48	27251	1467	5.06	1.15	2019	EUROPE	1
13	13	2023	ASIA	IND	USA	48	28088	1517	5.27	1.38	2023	ASIA	1
14	14	2027	OCEANIA/AFRICA	RSA	USA	56	29872	1634	5.33	1.42	2027	OCEANIA/AFRICA	1
15	15	2031	OCEANIA/AFRICA	NZL	IND	56	31456	1712	5.40	1.48	2031	OCEANIA/AFRICA	1
16	16	2035	AMERICAS/EUR	USA	AUS	60	32911	1794	5.47	1.55	2035	AMERICAS/EUR	1
17	17	2039	AMERICAS/EUR	FRA	AUS	64	34522	1863	5.55	1.63	2039	AMERICAS/EUR	1
18	18	2043	ASIA	AUS	NZL	64	36211	1945	5.60	1.70	2043	ASIA	1
19	19	2047	OCEANIA/AFRICA	AUS	IND	64	37988	2037	5.66	1.78	2047	OCEANIA/AFRICA	1
20	20	2051	EUROPE	AUS	NZL	64	39812	2131	5.72	1.86	2051	EUROPE	1
21	21	2055	AMERICAS/EUR	USA	IND	66	41689	2228	5.79	1.94	2055	AMERICAS/EUR	1
22	22	2059	OCEANIA/AFRICA	NZL	RSA	66	43623	2326	5.85	2.02	2059	OCEANIA/AFRICA	1
23	23	2063	OCEANIA/AFRICA	RSA	NZL	66	45617	2427	5.92	2.11	2063	OCEANIA/AFRICA	1
24	24	2067	AMERICAS/EUR	FRA	IND	68	47675	2531	5.98	2.20	2067	AMERICAS/EUR	1
25	25	2071	ASIA	IND	ENG	68	49802	2637	6.04	2.29	2071	ASIA	1
26	26	2075	OCEANIA/AFRICA	AUS	IND	70	52004	2746	6.11	2.38	2075	OCEANIA/AFRICA	1
27	27	2079	ASIA	AUS	NZL	70	54286	2860	6.17	2.47	2079	ASIA	1
28	28	2083	EUROPE	AUS	NZL	70	56652	2976	6.24	2.56	2083	EUROPE	1
29	29	2087	AMERICAS/EUR	USA	RSA	72	59002	3095	6.31	2.66	2087	AMERICAS/EUR	1
30	30	2091	OCEANIA/AFRICA	NZL	IND	72	61440	3216	6.37	2.76	2091	OCEANIA/AFRICA	1
31	31	2095	OCEANIA/AFRICA	RSA	USA	74	63968	3340	6.44	2.86	2095	OCEANIA/AFRICA	1
32	32	2099	ASIA	IND	AUS	74	66588	3466	6.51	2.97	2099	ASIA	1

proc summary data=wc_sorted nway;

class TitleTeam;

var TitlesSoFar;

output out=title_count(drop=_type_ _freq_)

sum=TotalTitles;

run;

proc print;run;

Output:

Obs	TitleTeam	TotalTitles
1	AUS	14
2	FRA	2
3	IND	5
4	NZL	3
5	RSA	3
6	ENG	1
7	USA	4

6. Macro magic: auto‑detect new records

%macro add_wc

(edition=, year=, host=, champ=, runnerup=,

matches=, runs=, wkts=, averr=, crowdm=);

%if %sysfunc(exist(work.cricket_wc)) %then %do;

proc sql noprint;

select count(*) into :_dup

from cricket_wc

where Edition = &edition;

quit;

%if &_dup = 0 %then %do;

data cricket_wc;

set cricket_wc

end=newrow;

if _n_=1 then put "NOTE: Adding Edition &edition..";

if newrow then do;

Edition = &edition;

Year = &year;

Host = &host;

TitleTeam = &champ;

RunnerUp = &runnerup;

Matches = &matches;

Runs = &runs;

Wkts = &wkts;

AveRR = &averr;

CrowdM = &crowdm;

RptYear = year(mdy(1,1,&year));

end;

run;

%end;

%else %put WARNING: Edition &edition already present – no action taken.;

%end;

%else %put ERROR: cricket_wc dataset not found in WORK library.;

%mend add_wc;

%add_wc(

edition = 33,

year = 2103, /* hypothetical next edition year */

host = 356, /* 356 = India */

champ = 356, /* India wins */

runnerup= 36, /* Australia runner‑up */

matches = 76,

runs = 69000,

wkts = 3600,

averr = 6.60,

crowdm = 3.10 /* 3.10 million spectators */

);

Log:

WARNING: Edition 33 already present – no action taken.

7.PROC TABULATE & REPORT

proc tabulate data=cricket_wc format=6.2;

class TitleTeam;

var Runs CrowdM AveRR;

table TitleTeam,

Runs * (mean max)

AveRR * (mean)

CrowdM* (sum) / misstext='0';

title "Performance KPIs by Champion Nation";

run;

Output:

Performance KPIs by Champion Nation

	Runs		AveRR	CrowdM
	Mean	Max	Mean	Sum
TitleTeam	27392	56652	5.13	18.77
AUS	27392	56652	5.13	18.77
FRA	41099	47675	5.77	3.83
IND	36680	69000	5.44	8.58
NZL	45506	61440	5.87	6.26
RSA	46486	63968	5.90	6.39
ENG	27251	27251	5.06	1.15
USA	38723	59002	5.65	7.36

proc report data=title_count nowd

headline headskip split='*';

columns TitleTeam TotalTitles;

define TitleTeam / group 'Champion*ISO Code';

define TotalTitles/ analysis sum 'Titles*Won';

compute after;

line @1 '================================================';

line @1 'Total unique champions: ' TotalTitles.sum 3.;

endcomp;

title "World Cup Trophy Count – Fast Fact Sheet";

run;

Output:

World Cup Trophy Count – Fast Fact Sheet

Champion ISO Code	Titles Won
AUS	14
ENG	1
FRA	2
IND	5
NZL	3
RSA	3
USA	4
================================================ Total unique champions: 32

8. PROC SGPLOT visual bite

proc sgplot data=cricket_wc;

series x=Year y=AveRR / markers;

xaxis label='Year';

yaxis label='Average Run Rate';

title "Scoring Velocity Over 125 Years of ODI World Cups";

run;

Log:

NOTE: PROCEDURE SGPLOT used (Total process time):

real time 2.17 seconds

user cpu time 0.20 seconds

system cpu time 0.37 seconds

memory 10399.39k

OS Memory 29300.00k

Timestamp 14/09/2015 01:04:54 AM

Step Count 41 Switch Count 0

NOTE: Listing image output written to SGPlot1.png.

NOTE: There were 32 observations read from the data set WORK.CRICKET_WC.

9. House‑keeping with PROC DATASETS

proc datasets library=work nolist;

delete wc_sorted title_count freqprep;

quit;

Log:

NOTE: The file WORK.FREQPREP (memtype=DATA) was not found, but appears on a DELETE statement.

NOTE: Deleting WORK.WC_SORTED (memtype=DATA).

NOTE: Deleting WORK.TITLE_COUNT (memtype=DATA).

NOTE: PROCEDURE DATASETS used (Total process time):

real time 0.07 seconds

user cpu time 0.00 seconds

system cpu time 0.01 seconds

memory 213.50k

OS Memory 28396.00k

Timestamp 14/09/2015 01:05:26 AM

Step Count 42 Switch Count 0

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264.TEMPLES OF INDIA DATASET ANALYSIS USING PROC PRINT | PROC CONTENTS | PROC SORT | PROC FREQ | PROC MEANS | PROC SQL | PROC TABULATE | PROC REPORT | PROC TRANSPOSE | PROC FORMAT | PROC RANK | PROC SGPLOT | MACROS

232.ANALYZING CRICKET WORLD CUP STATISTICS USING PROC PRINT | PROC SORT | PROC MEANS | PROC SUMMARY | PROC FREQ | PROC TABULATE | PROC REPORT | PROC FORMAT | PROC DATASETS | PROC SGPLOT | PROC SQL | MACROS AND FUNCTIONS IN SAS

ANALYZING CRICKET WORLD CUP STATISTICS USING PROC PRINT | PROC SORT | PROC MEANS | PROC SUMMARY | PROC FREQ | PROC TABULATE | PROC REPORT | PROC FORMAT | PROC DATASETS | PROC SGPLOT | PROC SQL | MACROS AND FUNCTIONS IN SAS

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