196.ANALYSIS OF INDIAN MANGO VARIETIES USING PROC PRINT | PROC MEANS | PROC FREQ | PROC SQL | PROC SORT | PROC REPORT | MACROS IN SAS | DATA MANAGEMENT INSIGHTS | CHARACTER LENGTH HANDLING

ANALYSIS OF INDIAN MANGO VARIETIES USING PROC PRINT | PROC MEANS | PROC FREQ | PROC SQL | PROC SORT | PROC REPORT | MACROS IN SAS | DATA MANAGEMENT INSIGHTS | CHARACTER LENGTH HANDLING

/*A unique dataset project on different types of mangoes*/

Step 1: Create Mangoes Dataset

data mango_varieties;

    length MangoName $20 OriginState $15 HarvestSeason $10 SweetnessLevel $10;

    input MangoName $ OriginState $ HarvestSeason $ AvgWeight_grams SweetnessLevel $ 

          PricePerKg Rating;

    datalines;

Alphonso       Maharashtra   Summer   300   VeryHigh   300  9.5

Dasheri        UttarPradesh  Summer   250   High       150  8.8

Langra         Bihar         Summer   280   High       140  8.6

Banganapalli   AndhraPradesh Summer   350   Medium     120  8.2

Kesar          Gujarat       Summer   260   VeryHigh   250  9.2

Himsagar       WestBengal    Summer   240   High       180  8.9

Totapuri       Karnataka     Summer   400   Low        100  7.5

Neelum         TamilNadu     Summer   220   Medium     130  7.9

Malda          Bihar         Summer   310   High       160  8.7

Raspuri        Karnataka     Summer   270   Medium     140  8.3

Chausa         UttarPradesh  Summer   290   High       155  8.4

Badami         Karnataka     Summer   300   Medium     135  8.1

Mankurad       Goa           Summer   320   High       270  9.0

Mallika        PanIndia      Summer   350   VeryHigh   240  9.1

Sindhura       TamilNadu     Summer   280   Medium     125  7.8

Suvernarekha   AndhraPradesh Summer   260   Medium     110  7.6

Zardalu        Bihar         Summer   230   High       150  8.0

ImamPasand     TamilNadu     Summer   330   VeryHigh   280  9.3

Rajapuri       Gujarat       Summer   370   Low        90   7.2

Amrapali       PanIndia      Summer   270   Medium     145  8.5

;

run;

proc print;run;

Output:

Obs	MangoName	OriginState	HarvestSeason	SweetnessLevel	AvgWeight_grams	PricePerKg	Rating
1	Alphonso	Maharashtra	Summer	VeryHigh	300	300	9.5
2	Dasheri	UttarPradesh	Summer	High	250	150	8.8
3	Langra	Bihar	Summer	High	280	140	8.6
4	Banganapalli	AndhraPradesh	Summer	Medium	350	120	8.2
5	Kesar	Gujarat	Summer	VeryHigh	260	250	9.2
6	Himsagar	WestBengal	Summer	High	240	180	8.9
7	Totapuri	Karnataka	Summer	Low	400	100	7.5
8	Neelum	TamilNadu	Summer	Medium	220	130	7.9
9	Malda	Bihar	Summer	High	310	160	8.7
10	Raspuri	Karnataka	Summer	Medium	270	140	8.3
11	Chausa	UttarPradesh	Summer	High	290	155	8.4
12	Badami	Karnataka	Summer	Medium	300	135	8.1
13	Mankurad	Goa	Summer	High	320	270	9.0
14	Mallika	PanIndia	Summer	VeryHigh	350	240	9.1
15	Sindhura	TamilNadu	Summer	Medium	280	125	7.8
16	Suvernarekha	AndhraPradesh	Summer	Medium	260	110	7.6
17	Zardalu	Bihar	Summer	High	230	150	8.0
18	ImamPasand	TamilNadu	Summer	VeryHigh	330	280	9.3
19	Rajapuri	Gujarat	Summer	Low	370	90	7.2
20	Amrapali	PanIndia	Summer	Medium	270	145	8.5

Step 2: View and Explore the Dataset

proc print data=mango_varieties;

    title "List of Indian Mango Varieties";

run;

Output:

List of Indian Mango Varieties

Obs	MangoName	OriginState	HarvestSeason	SweetnessLevel	AvgWeight_grams	PricePerKg	Rating
1	Alphonso	Maharashtra	Summer	VeryHigh	300	300	9.5
2	Dasheri	UttarPradesh	Summer	High	250	150	8.8
3	Langra	Bihar	Summer	High	280	140	8.6
4	Banganapalli	AndhraPradesh	Summer	Medium	350	120	8.2
5	Kesar	Gujarat	Summer	VeryHigh	260	250	9.2
6	Himsagar	WestBengal	Summer	High	240	180	8.9
7	Totapuri	Karnataka	Summer	Low	400	100	7.5
8	Neelum	TamilNadu	Summer	Medium	220	130	7.9
9	Malda	Bihar	Summer	High	310	160	8.7
10	Raspuri	Karnataka	Summer	Medium	270	140	8.3
11	Chausa	UttarPradesh	Summer	High	290	155	8.4
12	Badami	Karnataka	Summer	Medium	300	135	8.1
13	Mankurad	Goa	Summer	High	320	270	9.0
14	Mallika	PanIndia	Summer	VeryHigh	350	240	9.1
15	Sindhura	TamilNadu	Summer	Medium	280	125	7.8
16	Suvernarekha	AndhraPradesh	Summer	Medium	260	110	7.6
17	Zardalu	Bihar	Summer	High	230	150	8.0
18	ImamPasand	TamilNadu	Summer	VeryHigh	330	280	9.3
19	Rajapuri	Gujarat	Summer	Low	370	90	7.2
20	Amrapali	PanIndia	Summer	Medium	270	145	8.5

proc contents data=mango_varieties;

run;

Output:

The CONTENTS Procedure

Data Set Name	WORK.MANGO_VARIETIES	Observations	20
Member Type	DATA	Variables	7
Engine	V9	Indexes	0
Created	14/09/2015 00:03:34	Observation Length	80
Last Modified	14/09/2015 00:03:34	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	817
Obs in First Data Page	20
Number of Data Set Repairs	0
ExtendObsCounter	YES
Filename	C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary Files\_TD8000_DESKTOP-QFAA4KV_\mango_varieties.sas7bdat
Release Created	9.0401M2
Host Created	X64_8HOME

Alphabetic List of Variables and Attributes
#	Variable	Type	Len
5	AvgWeight_grams	Num	8
3	HarvestSeason	Char	10
1	MangoName	Char	20
2	OriginState	Char	15
6	PricePerKg	Num	8
7	Rating	Num	8
4	SweetnessLevel	Char	10

Step 3: Summary Statistics Using PROC MEANS

proc means data=mango_varieties mean median maxdec=2;

    var AvgWeight_grams PricePerKg Rating;

    title "Summary Statistics for Weight, Price, and Rating";

run;

Output:

Summary Statistics for Weight, Price, and Rating

The MEANS Procedure

Variable	Mean	Median
AvgWeight_grams PricePerKg Rating	294.00 168.50 8.43	285.00 147.50 8.45

Step 4: Frequency Analysis Using PROC FREQ

proc freq data=mango_varieties;

    tables OriginState SweetnessLevel;

    title "Frequency of Mango Origin States and Sweetness Levels";

run;

Output:

Frequency of Mango Origin States and Sweetness Levels

The FREQ Procedure

OriginState	Frequency	Percent	Cumulative Frequency	Cumulative Percent
AndhraPradesh	2	10.00	2	10.00
Bihar	3	15.00	5	25.00
Goa	1	5.00	6	30.00
Gujarat	2	10.00	8	40.00
Karnataka	3	15.00	11	55.00
Maharashtra	1	5.00	12	60.00
PanIndia	2	10.00	14	70.00
TamilNadu	3	15.00	17	85.00
UttarPradesh	2	10.00	19	95.00
WestBengal	1	5.00	20	100.00

SweetnessLevel	Frequency	Percent	Cumulative Frequency	Cumulative Percent
High	7	35.00	7	35.00
Low	2	10.00	9	45.00
Medium	7	35.00	16	80.00
VeryHigh	4	20.00	20	100.00

Step 5: Sorting Data by Rating and Price

proc sort data=mango_varieties out=sorted_mangoes;

    by descending Rating descending PricePerKg;

run;

proc print data=sorted_mangoes;

    title "Mangoes Sorted by Rating and Price";

run;

Output:

Mangoes Sorted by Rating and Price

Obs	MangoName	OriginState	HarvestSeason	SweetnessLevel	AvgWeight_grams	PricePerKg	Rating
1	Alphonso	Maharashtra	Summer	VeryHigh	300	300	9.5
2	ImamPasand	TamilNadu	Summer	VeryHigh	330	280	9.3
3	Kesar	Gujarat	Summer	VeryHigh	260	250	9.2
4	Mallika	PanIndia	Summer	VeryHigh	350	240	9.1
5	Mankurad	Goa	Summer	High	320	270	9.0
6	Himsagar	WestBengal	Summer	High	240	180	8.9
7	Dasheri	UttarPradesh	Summer	High	250	150	8.8
8	Malda	Bihar	Summer	High	310	160	8.7
9	Langra	Bihar	Summer	High	280	140	8.6
10	Amrapali	PanIndia	Summer	Medium	270	145	8.5
11	Chausa	UttarPradesh	Summer	High	290	155	8.4
12	Raspuri	Karnataka	Summer	Medium	270	140	8.3
13	Banganapalli	AndhraPradesh	Summer	Medium	350	120	8.2
14	Badami	Karnataka	Summer	Medium	300	135	8.1
15	Zardalu	Bihar	Summer	High	230	150	8.0
16	Neelum	TamilNadu	Summer	Medium	220	130	7.9
17	Sindhura	TamilNadu	Summer	Medium	280	125	7.8
18	Suvernarekha	AndhraPradesh	Summer	Medium	260	110	7.6
19	Totapuri	Karnataka	Summer	Low	400	100	7.5
20	Rajapuri	Gujarat	Summer	Low	370	90	7.2

Step 6: Grouping with PROC SQL

proc sql;

    select OriginState, 

           count(*) as MangoCount,

           avg(PricePerKg) as AvgPrice format=8.2,

           avg(Rating) as AvgRating format=4.2

    from mango_varieties

    group by OriginState;

quit;

Output:

OriginState	MangoCount	AvgPrice	AvgRating
AndhraPradesh	2	115.00	7.90
Bihar	3	150.00	8.43
Goa	1	270.00	9.00
Gujarat	2	170.00	8.20
Karnataka	3	125.00	7.97
Maharashtra	1	300.00	9.50
PanIndia	2	192.50	8.80
TamilNadu	3	178.33	8.33
UttarPradesh	2	152.50	8.60
WestBengal	1	180.00	8.90

Step 7: Create a Macro for Regional Mango Analysis

%macro region_analysis(state);

    proc sql;

        select MangoName, PricePerKg, Rating

        from mango_varieties

        where OriginState="&state"

        order by Rating desc;

    quit;

%mend;

%region_analysis(Karnataka);

Output:

MangoName	PricePerKg	Rating
Raspuri	140	8.3
Badami	135	8.1
Totapuri	100	7.5

%region_analysis(TamilNadu);

Output:

MangoName	PricePerKg	Rating
ImamPasand	280	9.3
Neelum	130	7.9
Sindhura	125	7.8

%region_analysis(Bihar);

Output:

MangoName	PricePerKg	Rating
Malda	160	8.7
Langra	140	8.6
Zardalu	150	8

Step 8: Custom Report Using PROC REPORT

proc report data=mango_varieties nowd;

    column MangoName OriginState AvgWeight_grams PricePerKg Rating;

    define MangoName / display "Mango Variety";

    define OriginState / group "State";

    define AvgWeight_grams / analysis mean "Avg Weight (g)";

    define PricePerKg / analysis mean "Price (INR/Kg)";

    define Rating / analysis mean "Avg Rating";

    title "Custom Report of Mango Varieties by State";

run;

Output:

Custom Report of Mango Varieties by State

Mango Variety	State	Avg Weight (g)	Price (INR Kg)	Avg Rating
Banganapalli	AndhraPradesh	350	120	8.2
Suvernarekha		260	110	7.6
Langra	Bihar	280	140	8.6
Malda		310	160	8.7
Zardalu		230	150	8
Mankurad	Goa	320	270	9
Kesar	Gujarat	260	250	9.2
Rajapuri		370	90	7.2
Totapuri	Karnataka	400	100	7.5
Raspuri		270	140	8.3
Badami		300	135	8.1
Alphonso	Maharashtra	300	300	9.5
Mallika	PanIndia	350	240	9.1
Amrapali		270	145	8.5
Neelum	TamilNadu	220	130	7.9
Sindhura		280	125	7.8
ImamPasand		330	280	9.3
Dasheri	UttarPradesh	250	150	8.8
Chausa		290	155	8.4
Himsagar	WestBengal	240	180	8.9

Step 9: Create a New Variable - Price Category

data mango_price_category;

    set mango_varieties;

    length PriceCategory $10.;

    if PricePerKg >= 250 then PriceCategory = "Premium";

    else if PricePerKg >= 150 then PriceCategory = "MidRange";

    else PriceCategory = "Budget";

run;

proc print;run;

Output:

Obs	MangoName	OriginState	HarvestSeason	SweetnessLevel	AvgWeight_grams	PricePerKg	Rating	PriceCategory
1	Alphonso	Maharashtra	Summer	VeryHigh	300	300	9.5	Premium
2	Dasheri	UttarPradesh	Summer	High	250	150	8.8	MidRange
3	Langra	Bihar	Summer	High	280	140	8.6	Budget
4	Banganapalli	AndhraPradesh	Summer	Medium	350	120	8.2	Budget
5	Kesar	Gujarat	Summer	VeryHigh	260	250	9.2	Premium
6	Himsagar	WestBengal	Summer	High	240	180	8.9	MidRange
7	Totapuri	Karnataka	Summer	Low	400	100	7.5	Budget
8	Neelum	TamilNadu	Summer	Medium	220	130	7.9	Budget
9	Malda	Bihar	Summer	High	310	160	8.7	MidRange
10	Raspuri	Karnataka	Summer	Medium	270	140	8.3	Budget
11	Chausa	UttarPradesh	Summer	High	290	155	8.4	MidRange
12	Badami	Karnataka	Summer	Medium	300	135	8.1	Budget
13	Mankurad	Goa	Summer	High	320	270	9.0	Premium
14	Mallika	PanIndia	Summer	VeryHigh	350	240	9.1	MidRange
15	Sindhura	TamilNadu	Summer	Medium	280	125	7.8	Budget
16	Suvernarekha	AndhraPradesh	Summer	Medium	260	110	7.6	Budget
17	Zardalu	Bihar	Summer	High	230	150	8.0	MidRange
18	ImamPasand	TamilNadu	Summer	VeryHigh	330	280	9.3	Premium
19	Rajapuri	Gujarat	Summer	Low	370	90	7.2	Budget
20	Amrapali	PanIndia	Summer	Medium	270	145	8.5	Budget

proc freq data=mango_price_category;

    tables PriceCategory;

    title "Frequency of Mangoes by Price Category";

run;

Output:

Frequency of Mangoes by Price Category

The FREQ Procedure

PriceCategory	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Budget	10	50.00	10	50.00
MidRange	6	30.00	16	80.00
Premium	4	20.00	20	100.00

Step 10: Macro to Compare Any Two Mango Varieties

%macro compare_mango(mango1, mango2);

    proc sql;

        select MangoName, OriginState, AvgWeight_grams, PricePerKg, Rating

        from mango_varieties

        where MangoName in ("&mango1", "&mango2");

    quit;

%mend;

%compare_mango(Alphonso, Kesar);

Output:

 

MangoName	OriginState	AvgWeight_grams	PricePerKg	Rating
Alphonso	Maharashtra	300	300	9.5
Kesar	Gujarat	260	250	9.2

%compare_mango(Langra, Dasheri);

Output:

MangoName	OriginState	AvgWeight_grams	PricePerKg	Rating
Dasheri	UttarPradesh	250	150	8.8
Langra	Bihar	280	140	8.6

Step 11: Filter and Export Selected Data

data high_rated_mangoes;

    set mango_varieties;

    if Rating >= 9.0;

run;

proc print data=high_rated_mangoes;

    title "Mangoes with Rating 9.0 and Above";

run;

Output:

Mangoes with Rating 9.0 and Above

Obs	MangoName	OriginState	HarvestSeason	SweetnessLevel	AvgWeight_grams	PricePerKg	Rating
1	Alphonso	Maharashtra	Summer	VeryHigh	300	300	9.5
2	Kesar	Gujarat	Summer	VeryHigh	260	250	9.2
3	Mankurad	Goa	Summer	High	320	270	9.0
4	Mallika	PanIndia	Summer	VeryHigh	350	240	9.1
5	ImamPasand	TamilNadu	Summer	VeryHigh	330	280	9.3

proc export data=high_rated_mangoes

    outfile="HighRatedMangoes.csv"

    dbms=csv replace;

run;

Log:

NOTE: The file 'HighRatedMangoes.csv' is:

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

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

      Last Modified=31 May 2025 17:35:14,

      Create Time=31 May 2025 17:35:14

NOTE: 6 records were written to the file 'HighRatedMangoes.csv'.

      The minimum record length was 34.

      The maximum record length was 84.

NOTE: There were 5 observations read from the data set WORK.HIGH_RATED_MANGOES.

NOTE: DATA statement used (Total process time):

      real time           0.12 seconds

      cpu time            0.04 seconds

5 records created in HighRatedMangoes.csv from HIGH_RATED_MANGOES.

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

NOTE: PROCEDURE EXPORT used (Total process time):

      real time           2.31 seconds

      cpu time            0.29 seconds

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195.INDIAN CUISINES DATA ANALYSIS USING DATA STEP | LENGTH | PROC PRINT | PROC SORT | PROC FREQ | PROC MEANS | PROC SQL | PROC UNIVARIATE | MACROS | DERIVED VARIABLES | EXPORT IN SAS FOR CULINARY INSIGHTS

INDIAN CUISINES DATA ANALYSIS USING DATA STEP | LENGTH | PROC PRINT | PROC SORT | PROC FREQ | PROC MEANS | PROC SQL | PROC UNIVARIATE | MACROS | DERIVED VARIABLES | EXPORT IN SAS FOR CULINARY INSIGHTS

/*Creating A unique dataset on different types of Indian cuisines.*/

Step 1: Creating the Dataset – Indian Cuisines

A dataset called Indian_Cuisines using DATALINES in SAS. It includes:

Cuisine_ID: Unique identifier

Cuisine_Name: Name of the cuisine

Region: Region of origin

Spice_Level: Low, Medium, High

Main_Ingredient: Primary ingredient

Veg_NonVeg: Type of dish

Popularity_Score: Numeric rating from 1 to 10

Preparation_Time: Time in minutes

data Indian_Cuisines;

    length Cuisine_Name $20 Region $15 Spice_Level $10 

           Main_Ingredient $15 Veg_NonVeg $10;

    input Cuisine_ID Cuisine_Name $ Region $ Spice_Level $ 

          Main_Ingredient $ Veg_NonVeg $ Popularity_Score Preparation_Time;

    datalines;

1 Biryani South High Rice NonVeg 9 60

2 ButterChicken North Medium Chicken NonVeg 8 50

3 Dosa South Medium Rice Veg 7 30

4 RoganJosh North High Lamb NonVeg 8 55

5 Pakhala East Low Rice Veg 6 20

6 LittiChokha East Medium Wheat Veg 7 35

7 Idli South Low Rice Veg 8 25

8 PavBhaji West Medium MixedVeg Veg 9 40

9 Poha West Low Rice Veg 7 15

10 CholeBhature North Medium Chickpeas Veg 9 45

11 HyderabadiHaleem South High Meat NonVeg 8 70

12 Sambhar South Medium Lentils Veg 8 35

13 DalTadka North Low Lentils Veg 7 30

14 MacherJhol East Medium Fish NonVeg 6 40

15 Vindaloo West High Pork NonVeg 7 50

16 RajmaChawal North Medium KidneyBeans Veg 8 35

17 FishCurry East High Fish NonVeg 7 45

18 Khandvi West Low Gramflour Veg 6 20

19 AlooParatha North Medium Potato Veg 8 30

20 Khichdi PanIndia Low Lentils Veg 9 25

;

run;

proc print;run;

Output:

Obs	Cuisine_Name	Region	Spice_Level	Main_Ingredient	Veg_NonVeg	Cuisine_ID	Popularity_Score	Preparation_Time
1	Biryani	South	High	Rice	NonVeg	1	9	60
2	ButterChicken	North	Medium	Chicken	NonVeg	2	8	50
3	Dosa	South	Medium	Rice	Veg	3	7	30
4	RoganJosh	North	High	Lamb	NonVeg	4	8	55
5	Pakhala	East	Low	Rice	Veg	5	6	20
6	LittiChokha	East	Medium	Wheat	Veg	6	7	35
7	Idli	South	Low	Rice	Veg	7	8	25
8	PavBhaji	West	Medium	MixedVeg	Veg	8	9	40
9	Poha	West	Low	Rice	Veg	9	7	15
10	CholeBhature	North	Medium	Chickpeas	Veg	10	9	45
11	HyderabadiHaleem	South	High	Meat	NonVeg	11	8	70
12	Sambhar	South	Medium	Lentils	Veg	12	8	35
13	DalTadka	North	Low	Lentils	Veg	13	7	30
14	MacherJhol	East	Medium	Fish	NonVeg	14	6	40
15	Vindaloo	West	High	Pork	NonVeg	15	7	50
16	RajmaChawal	North	Medium	KidneyBeans	Veg	16	8	35
17	FishCurry	East	High	Fish	NonVeg	17	7	45
18	Khandvi	West	Low	Gramflour	Veg	18	6	20
19	AlooParatha	North	Medium	Potato	Veg	19	8	30
20	Khichdi	PanIndia	Low	Lentils	Veg	20	9	25

Step 2: Using Basic PROC Steps

a) PROC PRINT

proc print data=Indian_Cuisines;

    title "All Indian Cuisines";

run;

Output:

All Indian Cuisines

Obs	Cuisine_Name	Region	Spice_Level	Main_Ingredient	Veg_NonVeg	Cuisine_ID	Popularity_Score	Preparation_Time
1	Biryani	South	High	Rice	NonVeg	1	9	60
2	ButterChicken	North	Medium	Chicken	NonVeg	2	8	50
3	Dosa	South	Medium	Rice	Veg	3	7	30
4	RoganJosh	North	High	Lamb	NonVeg	4	8	55
5	Pakhala	East	Low	Rice	Veg	5	6	20
6	LittiChokha	East	Medium	Wheat	Veg	6	7	35
7	Idli	South	Low	Rice	Veg	7	8	25
8	PavBhaji	West	Medium	MixedVeg	Veg	8	9	40
9	Poha	West	Low	Rice	Veg	9	7	15
10	CholeBhature	North	Medium	Chickpeas	Veg	10	9	45
11	HyderabadiHaleem	South	High	Meat	NonVeg	11	8	70
12	Sambhar	South	Medium	Lentils	Veg	12	8	35
13	DalTadka	North	Low	Lentils	Veg	13	7	30
14	MacherJhol	East	Medium	Fish	NonVeg	14	6	40
15	Vindaloo	West	High	Pork	NonVeg	15	7	50
16	RajmaChawal	North	Medium	KidneyBeans	Veg	16	8	35
17	FishCurry	East	High	Fish	NonVeg	17	7	45
18	Khandvi	West	Low	Gramflour	Veg	18	6	20
19	AlooParatha	North	Medium	Potato	Veg	19	8	30
20	Khichdi	PanIndia	Low	Lentils	Veg	20	9	25

b) PROC SORT – Sort by Region and Spice Level

proc sort data=Indian_Cuisines out=Sorted_Cuisines;

    by Region Spice_Level;

run;

proc print;run;

Output:

Obs	Cuisine_Name	Region	Spice_Level	Main_Ingredient	Veg_NonVeg	Cuisine_ID	Popularity_Score	Preparation_Time
1	FishCurry	East	High	Fish	NonVeg	17	7	45
2	Pakhala	East	Low	Rice	Veg	5	6	20
3	LittiChokha	East	Medium	Wheat	Veg	6	7	35
4	MacherJhol	East	Medium	Fish	NonVeg	14	6	40
5	RoganJosh	North	High	Lamb	NonVeg	4	8	55
6	DalTadka	North	Low	Lentils	Veg	13	7	30
7	ButterChicken	North	Medium	Chicken	NonVeg	2	8	50
8	CholeBhature	North	Medium	Chickpeas	Veg	10	9	45
9	RajmaChawal	North	Medium	KidneyBeans	Veg	16	8	35
10	AlooParatha	North	Medium	Potato	Veg	19	8	30
11	Khichdi	PanIndia	Low	Lentils	Veg	20	9	25
12	Biryani	South	High	Rice	NonVeg	1	9	60
13	HyderabadiHaleem	South	High	Meat	NonVeg	11	8	70
14	Idli	South	Low	Rice	Veg	7	8	25
15	Dosa	South	Medium	Rice	Veg	3	7	30
16	Sambhar	South	Medium	Lentils	Veg	12	8	35
17	Vindaloo	West	High	Pork	NonVeg	15	7	50
18	Poha	West	Low	Rice	Veg	9	7	15
19	Khandvi	West	Low	Gramflour	Veg	18	6	20
20	PavBhaji	West	Medium	MixedVeg	Veg	8	9	40

c) PROC FREQ – Frequency of Spice Levels

proc freq data=Indian_Cuisines;

    tables Spice_Level;

    title "Frequency of Spice Levels";

run;

Output:

Frequency of Spice Levels

The FREQ Procedure

Spice_Level	Frequency	Percent	Cumulative Frequency	Cumulative Percent
High	5	25.00	5	25.00
Low	6	30.00	11	55.00
Medium	9	45.00	20	100.00

d) PROC MEANS – Average Preparation Time by Veg/Non-Veg

proc means data=Indian_Cuisines noprint;

    class Veg_NonVeg;

    var Preparation_Time;

    output out=PrepTimeAvg mean=Avg_Prep_Time;

run;

proc print data=PrepTimeAvg;

    title "Average Preparation Time by Veg/Non-Veg";

run;

Output:

Average Preparation Time by Veg/Non-Veg

Obs	Veg_NonVeg	_TYPE_	_FREQ_	Avg_Prep_Time
1		0	20	37.7500
2	NonVeg	1	7	52.8571
3	Veg	1	13	29.6154

Step 3: Using PROC SQL

a) Find Top 5 Most Popular Dishes

proc sql outobs=5;

    select Cuisine_Name, Popularity_Score

    from Indian_Cuisines

    order by Popularity_Score desc;

quit;

Output:

Cuisine_Name	Popularity_Score
PavBhaji	9
Khichdi	9
Biryani	9
CholeBhature	9
Idli	8

b) Count of Veg and Non-Veg Items

proc sql;

    select Veg_NonVeg, count(*) as Count

    from Indian_Cuisines

    group by Veg_NonVeg;

quit;

Output:

Veg_NonVeg	Count
NonVeg	7
Veg	13

c) Average Popularity Score per Region

proc sql;

    select Region, avg(Popularity_Score) as Avg_Score format=4.2

    from Indian_Cuisines

    group by Region;

quit;

Output:

Region	Avg_Score
East	6.50
North	8.00
PanIndia	9.00
South	8.00
West	7.25

Step 4: Advanced Use with Macros

Task: Create summary reports per Region using a Macro

%macro Region_Report(region);

    proc print data=Indian_Cuisines;

        where Region = "&region";

        title "Cuisine Report for Region: &region";

    run;

    proc means data=Indian_Cuisines;

        where Region = "&region";

        var Popularity_Score Preparation_Time;

        title "Average Popularity and Preparation Time for &region";

    run;

%mend;

%Region_Report(North);

Output:

Cuisine Report for Region: North

Obs	Cuisine_Name	Region	Spice_Level	Main_Ingredient	Veg_NonVeg	Cuisine_ID	Popularity_Score	Preparation_Time
2	ButterChicken	North	Medium	Chicken	NonVeg	2	8	50
4	RoganJosh	North	High	Lamb	NonVeg	4	8	55
10	CholeBhature	North	Medium	Chickpeas	Veg	10	9	45
13	DalTadka	North	Low	Lentils	Veg	13	7	30
16	RajmaChawal	North	Medium	KidneyBeans	Veg	16	8	35
19	AlooParatha	North	Medium	Potato	Veg	19	8	30

Average Popularity and Preparation Time for North

The MEANS Procedure

Variable	N	Mean	Std Dev	Minimum	Maximum
Popularity_Score Preparation_Time	6 6	8.0000000 40.8333333	0.6324555 10.6848803	7.0000000 30.0000000	9.0000000 55.0000000

%Region_Report(South);

Output:

Cuisine Report for Region: South

Obs	Cuisine_Name	Region	Spice_Level	Main_Ingredient	Veg_NonVeg	Cuisine_ID	Popularity_Score	Preparation_Time
1	Biryani	South	High	Rice	NonVeg	1	9	60
3	Dosa	South	Medium	Rice	Veg	3	7	30
7	Idli	South	Low	Rice	Veg	7	8	25
11	HyderabadiHaleem	South	High	Meat	NonVeg	11	8	70
12	Sambhar	South	Medium	Lentils	Veg	12	8	35

Average Popularity and Preparation Time for South

The MEANS Procedure

Variable	N	Mean	Std Dev	Minimum	Maximum
Popularity_Score Preparation_Time	5 5	8.0000000 44.0000000	0.7071068 19.8116128	7.0000000 25.0000000	9.0000000 70.0000000

%Region_Report(East);

Output:

Cuisine Report for Region: East

Obs	Cuisine_Name	Region	Spice_Level	Main_Ingredient	Veg_NonVeg	Cuisine_ID	Popularity_Score	Preparation_Time
5	Pakhala	East	Low	Rice	Veg	5	6	20
6	LittiChokha	East	Medium	Wheat	Veg	6	7	35
14	MacherJhol	East	Medium	Fish	NonVeg	14	6	40
17	FishCurry	East	High	Fish	NonVeg	17	7	45

Average Popularity and Preparation Time for East

The MEANS Procedure

Variable	N	Mean	Std Dev	Minimum	Maximum
Popularity_Score Preparation_Time	4 4	6.5000000 35.0000000	0.5773503 10.8012345	6.0000000 20.0000000	7.0000000 45.0000000

%Region_Report(West);

Output:

Cuisine Report for Region: West

Obs	Cuisine_Name	Region	Spice_Level	Main_Ingredient	Veg_NonVeg	Cuisine_ID	Popularity_Score	Preparation_Time
8	PavBhaji	West	Medium	MixedVeg	Veg	8	9	40
9	Poha	West	Low	Rice	Veg	9	7	15
15	Vindaloo	West	High	Pork	NonVeg	15	7	50
18	Khandvi	West	Low	Gramflour	Veg	18	6	20

Average Popularity and Preparation Time for West

The MEANS Procedure

Variable	N	Mean	Std Dev	Minimum	Maximum
Popularity_Score Preparation_Time	4 4	7.2500000 31.2500000	1.2583057 16.5201897	6.0000000 15.0000000	9.0000000 50.0000000

Step 5: Conditional Logic with Macro Parameters

Task: Filter Cuisines Based on Spice Level and Type

%macro Filter_Cuisines(spice, type);

    proc sql;

        select Cuisine_Name, Region, Main_Ingredient, Preparation_Time

        from Indian_Cuisines

        where Spice_Level="&spice" and Veg_NonVeg="&type";

    quit;

%mend;

%Filter_Cuisines(High, NonVeg);

Output:

Cuisine_Name	Region	Main_Ingredient	Preparation_Time
Biryani	South	Rice	60
RoganJosh	North	Lamb	55
HyderabadiHaleem	South	Meat	70
Vindaloo	West	Pork	50
FishCurry	East	Fish	45

Step 6: Creating a Flag Variable – Fast Food (≤30 mins)

data Fast_Food;

    set Indian_Cuisines;

    if Preparation_Time <= 30 then FastFood="Yes";

    else FastFood="No";

run;

proc print data=Fast_Food;

    title "Cuisines Classified as Fast Food (<=30 mins)";

run;

Output:

Cuisines Classified as Fast Food (<=30 mins)

Obs	Cuisine_Name	Region	Spice_Level	Main_Ingredient	Veg_NonVeg	Cuisine_ID	Popularity_Score	Preparation_Time	FastFood
1	Biryani	South	High	Rice	NonVeg	1	9	60	No
2	ButterChicken	North	Medium	Chicken	NonVeg	2	8	50	No
3	Dosa	South	Medium	Rice	Veg	3	7	30	Yes
4	RoganJosh	North	High	Lamb	NonVeg	4	8	55	No
5	Pakhala	East	Low	Rice	Veg	5	6	20	Yes
6	LittiChokha	East	Medium	Wheat	Veg	6	7	35	No
7	Idli	South	Low	Rice	Veg	7	8	25	Yes
8	PavBhaji	West	Medium	MixedVeg	Veg	8	9	40	No
9	Poha	West	Low	Rice	Veg	9	7	15	Yes
10	CholeBhature	North	Medium	Chickpeas	Veg	10	9	45	No
11	HyderabadiHaleem	South	High	Meat	NonVeg	11	8	70	No
12	Sambhar	South	Medium	Lentils	Veg	12	8	35	No
13	DalTadka	North	Low	Lentils	Veg	13	7	30	Yes
14	MacherJhol	East	Medium	Fish	NonVeg	14	6	40	No
15	Vindaloo	West	High	Pork	NonVeg	15	7	50	No
16	RajmaChawal	North	Medium	KidneyBeans	Veg	16	8	35	No
17	FishCurry	East	High	Fish	NonVeg	17	7	45	No
18	Khandvi	West	Low	Gramflour	Veg	18	6	20	Yes
19	AlooParatha	North	Medium	Potato	Veg	19	8	30	Yes
20	Khichdi	PanIndia	Low	Lentils	Veg	20	9	25	Yes

Step 7: Using PROC UNIVARIATE – Analyze Time Distribution

proc univariate data=Indian_Cuisines;

    var Preparation_Time;

    histogram;

    title "Preparation Time Distribution of Indian Cuisines";

run;

Output:

Preparation Time Distribution of Indian Cuisines

The UNIVARIATE Procedure

Variable: Preparation_Time

Moments
N	20	Sum Weights	20
Mean	37.75	Sum Observations	755
Std Deviation	14.3705657	Variance	206.513158
Skewness	0.50422081	Kurtosis	-0.1578856
Uncorrected SS	32425	Corrected SS	3923.75
Coeff Variation	38.0677236	Std Error Mean	3.21335617

Basic Statistical Measures
Location		Variability
Mean	37.75000	Std Deviation	14.37057
Median	35.00000	Variance	206.51316
Mode	30.00000	Range	55.00000
		Interquartile Range	20.00000

Note: The mode displayed is the smallest of 2 modes with a count of 3.

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	11.74784	Pr > |t|	<.0001
Sign	M	10	Pr >= |M|	<.0001
Signed Rank	S	105	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	70.0
99%	70.0
95%	65.0
90%	57.5
75% Q3	47.5
50% Median	35.0
25% Q1	27.5
10%	20.0
5%	17.5
1%	15.0
0% Min	15.0

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
15	9	50	2
20	18	50	15
20	5	55	4
25	20	60	1
25	7	70	11

Step 8: Creating a Macro for Top N Dishes by Popularity

%macro Top_Dishes(n);

    proc sql outobs=&n;

        select Cuisine_Name, Region, Popularity_Score

        from Indian_Cuisines

        order by Popularity_Score desc;

    quit;

%mend;

%Top_Dishes(3);

Output:

Cuisine_Name	Region	Popularity_Score
PavBhaji	West	9
Khichdi	PanIndia	9
Biryani	South	9

Step 9: Exporting Final Datasets

proc export data=Fast_Food

    outfile="Fast_Food_Cuisines.csv"

    dbms=csv

    replace;

run;

Log:

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

NOTE: PROCEDURE EXPORT used (Total process time):

      real time           1.21 seconds

      cpu time            0.14 seconds

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