228.GLOBAL ANALYSIS OF INFLUENCERS WORLDWIDE USING PROC FORMAT | PROC PRINT | PROC CONTENTS | PROC MEANS | PROC SUMMARY | PROC FREQ | PROC SORT | PROC SGPLOT | PROC SQL | PROC EXPORT

GLOBAL ANALYSIS OF INFLUENCERS WORLDWIDE USING PROC FORMAT | PROC PRINT | PROC CONTENTS | PROC MEANS | PROC SUMMARY | PROC FREQ | PROC SORT | PROC SGPLOT | PROC SQL | PROC EXPORT | SAS MACROS IN SAS

/*Creating and exploring a “Worldwide Influencers” dataset*/

1.Setting global OPTIONS

OPTIONS

NOCENTER

FULLSTIMER

YEARCUTOFF=1950

ERRORS=3

DKRICOND=ERROR; /* raise error if duplicate key in PROC SQL update */

2.Creating the dataset with DATA‑step functions

Step 1: Define custom format

proc format;

value tierfmt

1 = 'Nano (≤100K)'

2 = 'Micro (100K–999K)'

3 = 'Mid (1M–4.9M)'

4 = 'Macro (5M–9.9M)'

5 = 'Mega (10M+)';

run;

Step 2: Create influencer dataset

data work.influencers_worldwide;

length InfluencerID 8 Name $40 Age 8 Region $20 Country $25 Platform $12

Category $20 Followers_Millions 8 EngagementRate_Pct 8

Avg_Views_K 8 Launch_Y Launch_M Launch_D 8

Birth_Y Birth_M Birth_D 8 Launch_Date Birth_Date 8

Collaboration_Tier 8;

format Launch_Date Birth_Date date9.

EngagementRate_Pct 5.2

Followers_Millions 6.1

Avg_Views_K 6.1

Collaboration_Tier tierfmt.;

input InfluencerID

Name & :$40.

Country & :$25.

Platform & :$12.

Category & :$20.

Followers_Millions

EngagementRate_Pct

Avg_Views_K

Launch_Y Launch_M Launch_D

Birth_Y Birth_M Birth_D;

/* Derived fields */

Region = scan(Country, -1, ' ') || '-' || upcase(substr(Country, 1, 3));

Launch_Date = mdy(Launch_M, Launch_D, Launch_Y);

Birth_Date = mdy(Birth_M, Birth_D, Birth_Y);

Age = intck('YEAR', Birth_Date, today(), 'C');

select;

when (Followers_Millions < 0.1) Collaboration_Tier = 1;

when (Followers_Millions < 1) Collaboration_Tier = 2;

when (Followers_Millions < 5) Collaboration_Tier = 3;

when (Followers_Millions < 10) Collaboration_Tier = 4;

otherwise Collaboration_Tier = 5;

end;

datalines;

1 Addison Rae United States TIKTOK Beauty 88.9 4.7 1300 2019 7 14 2000 10 6

2 Khaby Lame Italy TIKTOK Comedy 162 7.5 2000 2020 3 15 2000 3 9

3 Huda Kattan UAE INSTAGRAM Beauty 52 3.2 900 2010 7 10 1983 10 2

4 Zach King United States YOUTUBE Illusions 25 8.1 750 2008 1 15 1990 2 4

5 PewDiePie Sweden YOUTUBE Gaming 111 3.1 3000 2010 4 29 1989 10 24

6 Charli D’Amelio United States TIKTOK Dance 152 5.2 2500 2019 6 30 2004 5 1

7 Dude Perfect United States YOUTUBE Sports 62 4.0 2800 2009 3 16 1988 5 16

8 NikkieTutorials Netherlands INSTAGRAM Beauty 19.0 4.5 550 2012 6 24 1994 3 2

9 Lionel Messi Argentina INSTAGRAM Sports 504 2.7 7200 2013 5 1 1987 6 24

10 Cristiano Ronaldo Portugal INSTAGRAM Sports 630 2.8 8200 2012 2 14 1985 2 5

11 MrBeast United States YOUTUBE Philanthropy 274 6.2 5000 2012 2 19 1998 5 7

12 Marie Kondo Japan INSTAGRAM Lifestyle 8.7 4.1 200 2015 1 1 1984 10 9

13 Jay Shetty United Kingdom INSTAGRAM Education 16.0 5.8 400 2016 3 5 1987 9 6

14 Yolanda Gampp Canada YOUTUBE Baking 5.4 9.0 220 2015 5 12 1977 7 21

15 Kayla Itsines Australia INSTAGRAM Fitness 16.6 3.9 380 2012 3 24 1991 5 21

16 TravelMad World India INSTAGRAM Travel 2.3 6.5 120 2018 8 9 1992 8 10

17 Najmul Cooking Bangladesh FACEBOOK Cooking 3.1 10.2 95 2019 11 2 1985 11 5

18 Nuseir Yassin Israel FACEBOOK Travel 20 6.1 600 2016 4 9 1992 2 9

19 Macarena Achaga Argentina TIKTOK Comedy 4.8 9.3 160 2021 1 1 1992 3 5

20 TechGirlNikki Kenya YOUTUBE Tech 1.2 8.0 87 2020 9 18 1998 12 12

;

run;

Step 3: Print dataset to verify

proc print data=work.influencers_worldwide; run;

Output:

Obs	InfluencerID	Name	Age	Region	Country	Platform	Category	Followers_Millions	EngagementRate_Pct	Avg_Views_K	Launch_Y	Launch_M	Launch_D	Birth_Y	Birth_M	Birth_D	Launch_Date	Birth_Date	Collaboration_Tier
1	1	Addison Rae	14	States-UNI	United States	TIKTOK	Beauty	88.9	4.70	1300.0	2019	7	14	2000	10	6	14JUL2019	06OCT2000	Mega (10M+)
2	2	Khaby Lame	15	Italy-ITA	Italy	TIKTOK	Comedy	162.0	7.50	2000.0	2020	3	15	2000	3	9	15MAR2020	09MAR2000	Mega (10M+)
3	3	Huda Kattan	31	UAE-UAE	UAE	INSTAGRAM	Beauty	52.0	3.20	900.0	2010	7	10	1983	10	2	10JUL2010	02OCT1983	Mega (10M+)
4	4	Zach King	25	States-UNI	United States	YOUTUBE	Illusions	25.0	8.10	750.0	2008	1	15	1990	2	4	15JAN2008	04FEB1990	Mega (10M+)
5	5	PewDiePie	25	Sweden-SWE	Sweden	YOUTUBE	Gaming	111.0	3.10	3000.0	2010	4	29	1989	10	24	29APR2010	24OCT1989	Mega (10M+)
6	6	Charli D’Amelio	11	States-UNI	United States	TIKTOK	Dance	152.0	5.20	2500.0	2019	6	30	2004	5	1	30JUN2019	01MAY2004	Mega (10M+)
7	7	Dude Perfect	27	States-UNI	United States	YOUTUBE	Sports	62.0	4.00	2800.0	2009	3	16	1988	5	16	16MAR2009	16MAY1988	Mega (10M+)
8	8	NikkieTutorials	21	Netherlands-NET	Netherlands	INSTAGRAM	Beauty	19.0	4.50	550.0	2012	6	24	1994	3	2	24JUN2012	02MAR1994	Mega (10M+)
9	9	Lionel Messi	28	Argentina-ARG	Argentina	INSTAGRAM	Sports	504.0	2.70	7200.0	2013	5	1	1987	6	24	01MAY2013	24JUN1987	Mega (10M+)
10	10	Cristiano Ronaldo	30	Portugal-POR	Portugal	INSTAGRAM	Sports	630.0	2.80	8200.0	2012	2	14	1985	2	5	14FEB2012	05FEB1985	Mega (10M+)
11	11	MrBeast	17	States-UNI	United States	YOUTUBE	Philanthropy	274.0	6.20	5000.0	2012	2	19	1998	5	7	19FEB2012	07MAY1998	Mega (10M+)
12	12	Marie Kondo	30	Japan-JAP	Japan	INSTAGRAM	Lifestyle	8.7	4.10	200.0	2015	1	1	1984	10	9	01JAN2015	09OCT1984	Macro (5M–9.9M)
13	13	Jay Shetty	28	Kingdom-UNI	United Kingdom	INSTAGRAM	Education	16.0	5.80	400.0	2016	3	5	1987	9	6	05MAR2016	06SEP1987	Mega (10M+)
14	14	Yolanda Gampp	38	Canada-CAN	Canada	YOUTUBE	Baking	5.4	9.00	220.0	2015	5	12	1977	7	21	12MAY2015	21JUL1977	Macro (5M–9.9M)
15	15	Kayla Itsines	24	Australia-AUS	Australia	INSTAGRAM	Fitness	16.6	3.90	380.0	2012	3	24	1991	5	21	24MAR2012	21MAY1991	Mega (10M+)
16	16	TravelMad World	23	India-IND	India	INSTAGRAM	Travel	2.3	6.50	120.0	2018	8	9	1992	8	10	09AUG2018	10AUG1992	Mid (1M–4.9M)
17	17	Najmul Cooking	29	Bangladesh-BAN	Bangladesh	FACEBOOK	Cooking	3.1	10.20	95.0	2019	11	2	1985	11	5	02NOV2019	05NOV1985	Mid (1M–4.9M)
18	18	Nuseir Yassin	23	Israel-ISR	Israel	FACEBOOK	Travel	20.0	6.10	600.0	2016	4	9	1992	2	9	09APR2016	09FEB1992	Mega (10M+)
19	19	Macarena Achaga	23	Argentina-ARG	Argentina	TIKTOK	Comedy	4.8	9.30	160.0	2021	1	1	1992	3	5	01JAN2021	05MAR1992	Mid (1M–4.9M)
20	20	TechGirlNikki	16	Kenya-KEN	Kenya	YOUTUBE	Tech	1.2	8.00	87.0	2020	9	18	1998	12	12	18SEP2020	12DEC1998	Mid (1M–4.9M)

3.Peeking under the hood: PROC CONTENTS

PROC CONTENTS DATA=work.influencers_worldwide VARNUM;

TITLE "Structural metadata for INFLUENCERS_WORLDWIDE";

RUN;

Output:

Structural metadata for INFLUENCERS_WORLDWIDE

The CONTENTS Procedure

Data Set Name	WORK.INFLUENCERS_WORLDWIDE	Observations	20
Member Type	DATA	Variables	19
Engine	V9	Indexes	0
Created	14/09/2015 00:33:34	Observation Length	232
Last Modified	14/09/2015 00:33:34	Deleted Observations	0
Protection		Compressed	NO
Data Set Type		Sorted	NO
Label
Data Representation	WINDOWS_64
Encoding	wlatin1 Western (Windows)

Engine/Host Dependent Information
Data Set Page Size	65536
Number of Data Set Pages	1
First Data Page	1
Max Obs per Page	282
Obs in First Data Page	20
Number of Data Set Repairs	0
ExtendObsCounter	YES
Filename	C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary Files\_TD15916_DESKTOP-QFAA4KV_\influencers_worldwide.sas7bdat
Release Created	9.0401M2
Host Created	X64_8HOME

Variables in Creation Order
#	Variable	Type	Len	Format
1	InfluencerID	Num	8
2	Name	Char	40
3	Age	Num	8
4	Region	Char	20
5	Country	Char	25
6	Platform	Char	12
7	Category	Char	20
8	Followers_Millions	Num	8	6.1
9	EngagementRate_Pct	Num	8	5.2
10	Avg_Views_K	Num	8	6.1
11	Launch_Y	Num	8
12	Launch_M	Num	8
13	Launch_D	Num	8
14	Birth_Y	Num	8
15	Birth_M	Num	8
16	Birth_D	Num	8
17	Launch_Date	Num	8	DATE9.
18	Birth_Date	Num	8	DATE9.
19	Collaboration_Tier	Num	8	TIERFMT.

4.Basic prints with PROC PRINT and WHERE‑logic

PROC PRINT DATA=work.influencers_worldwide

NOOBS LABEL SPLIT='*';

WHERE Platform='INSTAGRAM' AND EngagementRate_Pct > 4;

VAR Name Country Followers_Millions EngagementRate_Pct Category;

TITLE "High‑engagement Instagram creators (>4% ER)";

RUN;

Output:

High-engagement Instagram creators (>4% ER)

Name	Country	Followers_Millions	EngagementRate_Pct	Category
NikkieTutorials	Netherlands	19.0	4.50	Beauty
Marie Kondo	Japan	8.7	4.10	Lifestyle
Jay Shetty	United Kingdom	16.0	5.80	Education
TravelMad World	India	2.3	6.50	Travel

5.Descriptive statistics: PROC MEANS and PROC SUMMARY

PROC MEANS DATA=work.influencers_worldwide N MEAN STD MIN MAX MAXDEC=2;

CLASS Platform;

VAR Followers_Millions EngagementRate_Pct Avg_Views_K Age;

TITLE "Platform‑wise central tendency and spread";

RUN;

Output:

Platform-wise central tendency and spread

The MEANS Procedure

FACEBOOK

Followers_Millions
EngagementRate_Pct
Avg_Views_K
Age

11.55

8.15

347.50

26.00

11.95

2.90

357.09

4.24

3.10

6.10

95.00

23.00

20.00

10.20

600.00

29.00

INSTAGRAM

Followers_Millions
EngagementRate_Pct
Avg_Views_K
Age

156.08

4.19

2243.75

26.88

256.27

1.38

3386.44

3.72

2.30

2.70

120.00

21.00

630.00

6.50

8200.00

31.00

TIKTOK

Followers_Millions
EngagementRate_Pct
Avg_Views_K
Age

101.93

6.68

1490.00

15.75

72.39

2.13

1014.10

5.12

4.80

4.70

160.00

11.00

162.00

9.30

2500.00

23.00

YOUTUBE

Followers_Millions
EngagementRate_Pct
Avg_Views_K
Age

79.77

6.40

1976.17

24.67

103.65

2.40

1950.75

7.97

1.20

3.10

87.00

16.00

274.00

9.00

5000.00

38.00

PROC SUMMARY DATA=work.influencers_worldwide NWAY;

CLASS Category Collaboration_Tier;

VAR Followers_Millions;

OUTPUT OUT=work.mean_by_tier

MEAN=Avg_Followers_M;

RUN;

Output:

Obs	Category	Collaboration_Tier	_TYPE_	_FREQ_	Avg_Followers_M
1	Baking	Macro (5M–9.9M)	3	1	5.4
2	Beauty	Mega (10M+)	3	3	53.3
3	Comedy	Mid (1M–4.9M)	3	1	4.8
4	Comedy	Mega (10M+)	3	1	162.0
5	Cooking	Mid (1M–4.9M)	3	1	3.1
6	Dance	Mega (10M+)	3	1	152.0
7	Education	Mega (10M+)	3	1	16.0
8	Fitness	Mega (10M+)	3	1	16.6
9	Gaming	Mega (10M+)	3	1	111.0
10	Illusions	Mega (10M+)	3	1	25.0
11	Lifestyle	Macro (5M–9.9M)	3	1	8.7
12	Philanthropy	Mega (10M+)	3	1	274.0
13	Sports	Mega (10M+)	3	3	398.7
14	Tech	Mid (1M–4.9M)	3	1	1.2
15	Travel	Mid (1M–4.9M)	3	1	2.3
16	Travel	Mega (10M+)	3	1	20.0

6.Quick distributions with PROC FREQ

PROC FREQ DATA=work.influencers_worldwide ORDER=FREQ;

TABLES Platform*Collaboration_Tier / NOCOL NOPERCENT NOROW;

TITLE "Cross‑tab: Platform × Tier (counts only)";

RUN;

Output:

Cross-tab: Platform × Tier (counts only)

The FREQ Procedure

INSTAGRAM

YOUTUBE

TIKTOK

FACEBOOK

Total

7.Sorting for time‑series: PROC SORT

PROC SORT DATA=work.influencers_worldwide

OUT=work.influencers_by_launch

SORTSEQ=LINGUISTIC(Numeric_Collation=ON); /* multi‑lingual safety */

BY Launch_Date;

RUN;

proc print;run;

Output:

Obs	InfluencerID	Name	Age	Region	Country	Platform	Category	Followers_Millions	EngagementRate_Pct	Avg_Views_K	Launch_Y	Launch_M	Launch_D	Birth_Y	Birth_M	Birth_D	Launch_Date	Birth_Date	Collaboration_Tier
1	4	Zach King	25	States-UNI	United States	YOUTUBE	Illusions	25.0	8.10	750.0	2008	1	15	1990	2	4	15JAN2008	04FEB1990	Mega (10M+)
2	7	Dude Perfect	27	States-UNI	United States	YOUTUBE	Sports	62.0	4.00	2800.0	2009	3	16	1988	5	16	16MAR2009	16MAY1988	Mega (10M+)
3	5	PewDiePie	25	Sweden-SWE	Sweden	YOUTUBE	Gaming	111.0	3.10	3000.0	2010	4	29	1989	10	24	29APR2010	24OCT1989	Mega (10M+)
4	3	Huda Kattan	31	UAE-UAE	UAE	INSTAGRAM	Beauty	52.0	3.20	900.0	2010	7	10	1983	10	2	10JUL2010	02OCT1983	Mega (10M+)
5	10	Cristiano Ronaldo	30	Portugal-POR	Portugal	INSTAGRAM	Sports	630.0	2.80	8200.0	2012	2	14	1985	2	5	14FEB2012	05FEB1985	Mega (10M+)
6	11	MrBeast	17	States-UNI	United States	YOUTUBE	Philanthropy	274.0	6.20	5000.0	2012	2	19	1998	5	7	19FEB2012	07MAY1998	Mega (10M+)
7	15	Kayla Itsines	24	Australia-AUS	Australia	INSTAGRAM	Fitness	16.6	3.90	380.0	2012	3	24	1991	5	21	24MAR2012	21MAY1991	Mega (10M+)
8	8	NikkieTutorials	21	Netherlands-NET	Netherlands	INSTAGRAM	Beauty	19.0	4.50	550.0	2012	6	24	1994	3	2	24JUN2012	02MAR1994	Mega (10M+)
9	9	Lionel Messi	28	Argentina-ARG	Argentina	INSTAGRAM	Sports	504.0	2.70	7200.0	2013	5	1	1987	6	24	01MAY2013	24JUN1987	Mega (10M+)
10	12	Marie Kondo	30	Japan-JAP	Japan	INSTAGRAM	Lifestyle	8.7	4.10	200.0	2015	1	1	1984	10	9	01JAN2015	09OCT1984	Macro (5M–9.9M)
11	14	Yolanda Gampp	38	Canada-CAN	Canada	YOUTUBE	Baking	5.4	9.00	220.0	2015	5	12	1977	7	21	12MAY2015	21JUL1977	Macro (5M–9.9M)
12	13	Jay Shetty	28	Kingdom-UNI	United Kingdom	INSTAGRAM	Education	16.0	5.80	400.0	2016	3	5	1987	9	6	05MAR2016	06SEP1987	Mega (10M+)
13	18	Nuseir Yassin	23	Israel-ISR	Israel	FACEBOOK	Travel	20.0	6.10	600.0	2016	4	9	1992	2	9	09APR2016	09FEB1992	Mega (10M+)
14	16	TravelMad World	23	India-IND	India	INSTAGRAM	Travel	2.3	6.50	120.0	2018	8	9	1992	8	10	09AUG2018	10AUG1992	Mid (1M–4.9M)
15	6	Charli D’Amelio	11	States-UNI	United States	TIKTOK	Dance	152.0	5.20	2500.0	2019	6	30	2004	5	1	30JUN2019	01MAY2004	Mega (10M+)
16	1	Addison Rae	14	States-UNI	United States	TIKTOK	Beauty	88.9	4.70	1300.0	2019	7	14	2000	10	6	14JUL2019	06OCT2000	Mega (10M+)
17	17	Najmul Cooking	29	Bangladesh-BAN	Bangladesh	FACEBOOK	Cooking	3.1	10.20	95.0	2019	11	2	1985	11	5	02NOV2019	05NOV1985	Mid (1M–4.9M)
18	2	Khaby Lame	15	Italy-ITA	Italy	TIKTOK	Comedy	162.0	7.50	2000.0	2020	3	15	2000	3	9	15MAR2020	09MAR2000	Mega (10M+)
19	20	TechGirlNikki	16	Kenya-KEN	Kenya	YOUTUBE	Tech	1.2	8.00	87.0	2020	9	18	1998	12	12	18SEP2020	12DEC1998	Mid (1M–4.9M)
20	19	Macarena Achaga	23	Argentina-ARG	Argentina	TIKTOK	Comedy	4.8	9.30	160.0	2021	1	1	1992	3	5	01JAN2021	05MAR1992	Mid (1M–4.9M)

8.Visual pulse: PROC SGPLOT

PROC SGPLOT DATA=work.influencers_worldwide;

SCATTER X=Followers_Millions Y=EngagementRate_Pct /

GROUP=Platform DATALABEL=Name;

YAXIS LABEL='Engagement Rate (%)';

XAXIS LABEL='Followers (Millions)';

TITLE "Followers vs. Engagement – which megastars defy the reach‑rate paradox?";

RUN;

Log:

NOTE: PROCEDURE SGPLOT used (Total process time):

real time 3.14 seconds

user cpu time 0.21 seconds

system cpu time 0.18 seconds

memory 10975.40k

OS Memory 28792.00k

Timestamp 14/09/2015 12:38:58 AM

Step Count 34 Switch Count 0

NOTE: Listing image output written to SGPlot1.png.

NOTE: There were 20 observations read from the data set WORK.INFLUENCERS_WORLDWIDE.

9.Relational power: PROC SQL for continent averages

PROC SQL;

SELECT CASE

WHEN Country IN ('United States','Canada') THEN 'North America'

WHEN Country IN ('Argentina','Brazil') THEN 'South America'

WHEN Country IN ('Italy','Portugal','Sweden','UAE',

'United Kingdom') THEN 'Europe'

WHEN Country IN ('Kenya') THEN 'Africa'

WHEN Country IN ('Japan','India','Bangladesh','Israel') THEN 'Asia'

ELSE 'Other'

END AS Continent,

COUNT(*) AS Num_Influencers,

ROUND(AVG(Followers_Millions),0.1) AS Avg_Millions_Followers FORMAT=6.1,

ROUND(AVG(EngagementRate_Pct),0.1) AS Avg_Engage_Pct FORMAT=5.1

FROM work.influencers_worldwide

GROUP BY Continent

ORDER BY Avg_Millions_Followers DESC;

QUIT;

Output:

Followers vs. Engagement – which megastars defy the reach-rate paradox?

Continent	Num_Influencers	Avg_Millions_Followers	Avg_Engage_Pct
South America	2	254.4	6.0
Europe	5	194.2	4.5
North America	6	101.2	6.2
Other	2	17.8	4.2
Asia	4	8.5	6.7
Africa	1	1.2	8.0

10.Reusable automation: a macro for regional one‑pagers

%MACRO region_report(rgn);

%PUT NOTE: ===== Generating report for &rgn =====;

TITLE1 "Influencers in &rgn — Descriptive Snapshot";

PROC PRINT DATA=work.influencers_worldwide NOOBS;

WHERE UPCASE(Region) = "%UPCASE(&rgn)";

VAR Name Platform Category Followers_Millions EngagementRate_Pct;

RUN;

PROC MEANS DATA=work.influencers_worldwide MAXDEC=2;

WHERE UPCASE(Region) = "%UPCASE(&rgn)";

VAR Followers_Millions EngagementRate_Pct Avg_Views_K;

TITLE2 "Key statistics for &rgn";

RUN;

PROC FREQ DATA=work.influencers_worldwide;

WHERE UPCASE(Region) = "%UPCASE(&rgn)";

TABLES Category / NOPERCENT NOCOL;

TITLE2 "Category spread for &rgn";

RUN;

%MEND;

%region_report(States-UNI);

Output:

Influencers in States-UNI — Descriptive Snapshot

Name	Platform	Category	Followers_Millions	EngagementRate_Pct
Addison Rae	TIKTOK	Beauty	88.9	4.70
Zach King	YOUTUBE	Illusions	25.0	8.10
Charli D’Amelio	TIKTOK	Dance	152.0	5.20
Dude Perfect	YOUTUBE	Sports	62.0	4.00
MrBeast	YOUTUBE	Philanthropy	274.0	6.20

Influencers in States-UNI — Descriptive Snapshot

Key statistics for States-UNI

The MEANS Procedure

Followers_Millions
EngagementRate_Pct
Avg_Views_K

120.38

5.64

2470.00

97.59

1.59

1646.06

25.00

4.00

750.00

274.00

8.10

5000.00

Influencers in States-UNI — Descriptive Snapshot

Category spread for States-UNI

The FREQ Procedure

Category	Frequency	Cumulative Frequency
Beauty	1	1
Dance	1	2
Illusions	1	3
Philanthropy	1	4
Sports	1	5

%region_report(Argentina-ARG);

Output:

Influencers in Argentina-ARG — Descriptive Snapshot

Name	Platform	Category	Followers_Millions	EngagementRate_Pct
Lionel Messi	INSTAGRAM	Sports	504.0	2.70
Macarena Achaga	TIKTOK	Comedy	4.8	9.30

Influencers in Argentina-ARG — Descriptive Snapshot

Key statistics for Argentina-ARG

The MEANS Procedure

Followers_Millions
EngagementRate_Pct
Avg_Views_K

254.40

6.00

3680.00

352.99

4.67

4978.03

4.80

2.70

160.00

504.00

9.30

7200.00

Influencers in Argentina-ARG — Descriptive Snapshot

Category spread for Argentina-ARG

The FREQ Procedure

Category	Frequency	Cumulative Frequency
Comedy	1	1
Sports	1	2

11.Exporting

PROC EXPORT DATA=work.mean_by_tier

OUTFILE="influencer_mean_by_tier.csv"

DBMS=CSV REPLACE;

RUN;

Log:

NOTE: The file 'influencer_mean_by_tier.csv' is:

Filename=C:\sas folder\SASFoundation\9.4\influencer_mean_by_tier.csv,

RECFM=V,LRECL=32767,File Size (bytes)=0,

Last Modified=02 July 2025 17:13:54,

Create Time=02 July 2025 17:13:54

NOTE: 17 records were written to the file 'influencer_mean_by_tier.csv'.

The minimum record length was 26.

The maximum record length was 57.

NOTE: There were 16 observations read from the data set WORK.MEAN_BY_TIER.

NOTE: DATA statement used (Total process time):

real time 0.13 seconds

user cpu time 0.00 seconds

system cpu time 0.06 seconds

memory 8720.25k

OS Memory 35316.00k

Timestamp 14/09/2015 12:45:56 AM

Step Count 45 Switch Count 0

16 records created in influencer_mean_by_tier.csv from WORK.MEAN_BY_TIER.

NOTE: "influencer_mean_by_tier.csv" file was successfully created.

NOTE: PROCEDURE EXPORT used (Total process time):

real time 2.50 seconds

user cpu time 0.09 seconds

system cpu time 0.15 seconds

memory 8720.25k

OS Memory 35316.00k

Timestamp 14/09/2015 12:45:56 AM

Step Count 45 Switch Count 7

To Visit My Previous Book Store Dataset:Click Here

To Visit My Previous Festival Dataset:Click Here

To Visit My Previous SQL Joins:Click Here

To Visit My Previous Sas Interview Questions-1:Click Here

--->PRACTICE AND COMMENT YOUR OUTPUT:

--->PLEASE FOLLOW OUR BLOG FOR MORE UPDATES.

PLEASE FOLLOW OUR TELEGRAM CHANNEL CLICK HERE

PLEASE FOLLOW OUR FACEBOOK PAGE CLICK HERE

PLEASE FOLLOW OUR INSTAGRAM PAGE CLICK HERE

Sas All

Search This Blog

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

228.GLOBAL ANALYSIS OF INFLUENCERS WORLDWIDE USING PROC FORMAT | PROC PRINT | PROC CONTENTS | PROC MEANS | PROC SUMMARY | PROC FREQ | PROC SORT | PROC SGPLOT | PROC SQL | PROC EXPORT | SAS MACROS IN SAS

GLOBAL ANALYSIS OF INFLUENCERS WORLDWIDE USING PROC FORMAT | PROC PRINT | PROC CONTENTS | PROC MEANS | PROC SUMMARY | PROC FREQ | PROC SORT | PROC SGPLOT | PROC SQL | PROC EXPORT | SAS MACROS IN SAS

Comments

Post a Comment