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

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206.ANALYZING BANANA VARIETIES PRODUCTION PRICE REGIONS HEALTH BENEFITS WEIGHT USING PROC MEANS | PROC SQL | PROC MACRO | PROC FREQ | PROC SORT | PROC FORMAT | IN SAS FOR ADVANCED AGRICULTURAL DATA INSIGHTS AND DECISION MAKING

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ANALYZING BANANA VARIETIES PRODUCTION PRICE REGIONS HEALTH BENEFITS WEIGHT USING PROC MEANS | PROC SQL | PROC MACRO | PROC FREQ | PROC SORT | PROC FORMAT | IN SAS FOR ADVANCED AGRICULTURAL DATA INSIGHTS AND DECISION MAKING

/*A unique dataset about different types of bananas*/

Step 1: Creating the Dataset

data Banana_Types;

    length Banana_ID 8 Variety $20 Origin $15 Color $10 Health_Benefit $30;

infile datalines dsd dlm="" truncover;

    input Banana_ID Variety $ Origin $ Color $ Avg_Weight_grams 

          Price_Per_Kg_USD Production_Tonnes_per_Year Health_Benefit :$&30.;

    datalines;

1 Cavendish India Yellow 120 0.45 900000 "Rich in Potassium"

2 Nendran Kerala Yellow 150 0.60 500000 "Aids Digestion"

3 Robusta TamilNadu Green 130 0.40 700000 "Energy Booster"

4 Rasthali Karnataka Yellow 100 0.70 300000 "Good for Skin"

5 Poovan Andhra White 90 0.35 200000 "Iron Rich"

6 Red_Banana Maharashtra Red 160 1.20 150000 "High Antioxidants"

7 Yelakki Karnataka Yellow 80 0.50 120000 "Kid-Friendly"

8 Monthan TamilNadu Green 180 0.55 250000 "Fiber Rich"

9 Karpooravalli Kerala Yellow 140 0.65 300000 "Soothes Ulcers"

10 Saba Philippines Green 200 0.80 800000 "Good for Heart"

11 Manzano Ecuador Yellow 100 0.95 100000 "Apple Taste"

12 Lady_Finger Australia Yellow 85 1.10 75000 "Low Calorie"

13 Blue_Java Hawaii Blue 120 1.50 50000 "Ice Cream Flavor"

14 Plantain Africa Green 300 0.70 600000 "Starch Rich"

15 Mysore_Banana TamilNadu Yellow 95 0.60 250000 "Helps Digestion"

16 "Pisang Raja" Indonesia Yellow 110 0.85 200000 "Vitamin C Source"

17 Praying_Hands Thailand Yellow 200 1.00 180000 "Symbolic Fruit"

;

run;

proc print;run;

Output:

Obs Banana_ID Variety Origin Color Health_Benefit Avg_Weight_grams Price_Per_Kg_USD Production_Tonnes_per_Year
1 1 Cavendish India Yellow Rich in Potassium 120 0.45 900000
2 2 Nendran Kerala Yellow Aids Digestion 150 0.60 500000
3 3 Robusta TamilNadu Green Energy Booster 130 0.40 700000
4 4 Rasthali Karnataka Yellow Good for Skin 100 0.70 300000
5 5 Poovan Andhra White Iron Rich 90 0.35 200000
6 6 Red_Banana Maharashtra Red High Antioxidants 160 1.20 150000
7 7 Yelakki Karnataka Yellow Kid-Friendly 80 0.50 120000
8 8 Monthan TamilNadu Green Fiber Rich 180 0.55 250000
9 9 Karpooravalli Kerala Yellow Soothes Ulcers 140 0.65 300000
10 10 Saba Philippines Green Good for Heart 200 0.80 800000
11 11 Manzano Ecuador Yellow Apple Taste 100 0.95 100000
12 12 Lady_Finger Australia Yellow Low Calorie 85 1.10 75000
13 13 Blue_Java Hawaii Blue Ice Cream Flavor 120 1.50 50000
14 14 Plantain Africa Green Starch Rich 300 0.70 600000
15 15 Mysore_Banana TamilNadu Yellow Helps Digestion 95 0.60 250000
16 16 Pisang Raja Indonesia Yellow Vitamin C Source 110 0.85 200000
17 17 Praying_Hands Thailand Yellow Symbolic Fruit 200 1.00 180000


Step 2: Explore the Dataset with PROC PRINT

proc print data=Banana_Types noobs;

    title "Different Types of Bananas - Raw Dataset";

run;

Output:

Different Types of Bananas - Raw Dataset

Banana_ID Variety Origin Color Health_Benefit Avg_Weight_grams Price_Per_Kg_USD Production_Tonnes_per_Year
1 Cavendish India Yellow Rich in Potassium 120 0.45 900000
2 Nendran Kerala Yellow Aids Digestion 150 0.60 500000
3 Robusta TamilNadu Green Energy Booster 130 0.40 700000
4 Rasthali Karnataka Yellow Good for Skin 100 0.70 300000
5 Poovan Andhra White Iron Rich 90 0.35 200000
6 Red_Banana Maharashtra Red High Antioxidants 160 1.20 150000
7 Yelakki Karnataka Yellow Kid-Friendly 80 0.50 120000
8 Monthan TamilNadu Green Fiber Rich 180 0.55 250000
9 Karpooravalli Kerala Yellow Soothes Ulcers 140 0.65 300000
10 Saba Philippines Green Good for Heart 200 0.80 800000
11 Manzano Ecuador Yellow Apple Taste 100 0.95 100000
12 Lady_Finger Australia Yellow Low Calorie 85 1.10 75000
13 Blue_Java Hawaii Blue Ice Cream Flavor 120 1.50 50000
14 Plantain Africa Green Starch Rich 300 0.70 600000
15 Mysore_Banana TamilNadu Yellow Helps Digestion 95 0.60 250000
16 Pisang Raja Indonesia Yellow Vitamin C Source 110 0.85 200000
17 Praying_Hands Thailand Yellow Symbolic Fruit 200 1.00 180000

Step 3: Summary Statistics with PROC MEANS

proc means data=Banana_Types mean min max std;

    var Avg_Weight_grams Price_Per_Kg_USD Production_Tonnes_per_Year;

    title "Statistical Summary of Banana Metrics";

run;

Output:

Statistical Summary of Banana Metrics

The MEANS Procedure

Variable Mean Minimum Maximum Std Dev
Avg_Weight_grams
Price_Per_Kg_USD
Production_Tonnes_per_Year
138.8235294
0.7588235
333823.53
80.0000000
0.3500000
50000.00
300.0000000
1.5000000
900000.00
56.4725988
0.3098565
265515.59

Step 4: Frequency Distribution with PROC FREQ

proc freq data=Banana_Types;

    tables Color Origin;

    title "Frequency Distribution of Banana Color and Origin";

run;

Output:

Frequency Distribution of Banana Color and Origin

The FREQ Procedure

Color Frequency Percent Cumulative
Frequency		 Cumulative
Percent
Blue 1 5.88 1 5.88
Green 4 23.53 5 29.41
Red 1 5.88 6 35.29
White 1 5.88 7 41.18
Yellow 10 58.82 17 100.00

Origin Frequency Percent Cumulative
Frequency		 Cumulative
Percent
Africa 1 5.88 1 5.88
Andhra 1 5.88 2 11.76
Australia 1 5.88 3 17.65
Ecuador 1 5.88 4 23.53
Hawaii 1 5.88 5 29.41
India 1 5.88 6 35.29
Indonesia 1 5.88 7 41.18
Karnataka 2 11.76 9 52.94
Kerala 2 11.76 11 64.71
Maharashtra 1 5.88 12 70.59
Philippines 1 5.88 13 76.47
TamilNadu 3 17.65 16 94.12
Thailand 1 5.88 17 100.00

Step 5: Sorting Data with PROC SORT

proc sort data=Banana_Types out=Sorted_Bananas;

    by descending Production_Tonnes_per_Year;

run;

proc print data=Sorted_Bananas;

    title "Banana Types Sorted by Highest Production";

run;

Output:

Banana Types Sorted by Highest Production

Obs Banana_ID Variety Origin Color Health_Benefit Avg_Weight_grams Price_Per_Kg_USD Production_Tonnes_per_Year
1 1 Cavendish India Yellow Rich in Potassium 120 0.45 900000
2 10 Saba Philippines Green Good for Heart 200 0.80 800000
3 3 Robusta TamilNadu Green Energy Booster 130 0.40 700000
4 14 Plantain Africa Green Starch Rich 300 0.70 600000
5 2 Nendran Kerala Yellow Aids Digestion 150 0.60 500000
6 4 Rasthali Karnataka Yellow Good for Skin 100 0.70 300000
7 9 Karpooravalli Kerala Yellow Soothes Ulcers 140 0.65 300000
8 8 Monthan TamilNadu Green Fiber Rich 180 0.55 250000
9 15 Mysore_Banana TamilNadu Yellow Helps Digestion 95 0.60 250000
10 5 Poovan Andhra White Iron Rich 90 0.35 200000
11 16 Pisang Raja Indonesia Yellow Vitamin C Source 110 0.85 200000
12 17 Praying_Hands Thailand Yellow Symbolic Fruit 200 1.00 180000
13 6 Red_Banana Maharashtra Red High Antioxidants 160 1.20 150000
14 7 Yelakki Karnataka Yellow Kid-Friendly 80 0.50 120000
15 11 Manzano Ecuador Yellow Apple Taste 100 0.95 100000
16 12 Lady_Finger Australia Yellow Low Calorie 85 1.10 75000
17 13 Blue_Java Hawaii Blue Ice Cream Flavor 120 1.50 50000

Step 6: SQL Queries using PROC SQL

6.1: Select High Value Bananas

proc sql;

    select Variety, Price_Per_Kg_USD

    from Banana_Types

    where Price_Per_Kg_USD > 1;

quit;

Output:

Variety Price_Per_Kg_USD
Red_Banana 1.2
Lady_Finger 1.1
Blue_Java 1.5

6.2: Group by Origin and Calculate Avg Production

proc sql;

    select Origin, avg(Production_Tonnes_per_Year) as Avg_Production

    from Banana_Types

    group by Origin;

quit;

Output:

Origin Avg_Production
Africa 600000
Andhra 200000
Australia 75000
Ecuador 100000
Hawaii 50000
India 900000
Indonesia 200000
Karnataka 210000
Kerala 400000
Maharashtra 150000
Philippines 800000
TamilNadu 400000
Thailand 180000

6.3: Maximum Priced Banana

proc sql;

    select Variety, Price_Per_Kg_USD

    from Banana_Types

    having Price_Per_Kg_USD = max(Price_Per_Kg_USD);

quit;

Output:

Variety Price_Per_Kg_USD
Blue_Java 1.5

Step 7: Use of PROC FORMAT

proc format;

    value pricefmt

        low -< 0.5 = 'Low'

        0.5 -< 1 = 'Medium'

        1 - high = 'High';

run;


data Banana_Types_Formatted;

    set Banana_Types;

    Price_Category = put(Price_Per_Kg_USD, pricefmt.);

run;

proc print;run;

Output:

Obs Banana_ID Variety Origin Color Health_Benefit Avg_Weight_grams Price_Per_Kg_USD Production_Tonnes_per_Year Price_Category
1 1 Cavendish India Yellow Rich in Potassium 120 0.45 900000 Low
2 2 Nendran Kerala Yellow Aids Digestion 150 0.60 500000 Medium
3 3 Robusta TamilNadu Green Energy Booster 130 0.40 700000 Low
4 4 Rasthali Karnataka Yellow Good for Skin 100 0.70 300000 Medium
5 5 Poovan Andhra White Iron Rich 90 0.35 200000 Low
6 6 Red_Banana Maharashtra Red High Antioxidants 160 1.20 150000 High
7 7 Yelakki Karnataka Yellow Kid-Friendly 80 0.50 120000 Medium
8 8 Monthan TamilNadu Green Fiber Rich 180 0.55 250000 Medium
9 9 Karpooravalli Kerala Yellow Soothes Ulcers 140 0.65 300000 Medium
10 10 Saba Philippines Green Good for Heart 200 0.80 800000 Medium
11 11 Manzano Ecuador Yellow Apple Taste 100 0.95 100000 Medium
12 12 Lady_Finger Australia Yellow Low Calorie 85 1.10 75000 High
13 13 Blue_Java Hawaii Blue Ice Cream Flavor 120 1.50 50000 High
14 14 Plantain Africa Green Starch Rich 300 0.70 600000 Medium
15 15 Mysore_Banana TamilNadu Yellow Helps Digestion 95 0.60 250000 Medium
16 16 Pisang Raja Indonesia Yellow Vitamin C Source 110 0.85 200000 Medium
17 17 Praying_Hands Thailand Yellow Symbolic Fruit 200 1.00 180000 High

proc freq data=Banana_Types_Formatted;

    tables Price_Category;

    title "Price Category Frequency";

run;

Output:

Price Category Frequency

The FREQ Procedure

Price_Category Frequency Percent Cumulative
Frequency		 Cumulative
Percent
High 4 23.53 4 23.53
Low 3 17.65 7 41.18
Medium 10 58.82 17 100.00

Step 8: Create and Use a Macro

Define Macro to Filter Based on Origin

%macro filter_origin(region);

    proc print data=Banana_Types;

        where Origin="&region";

        title "Bananas Originating from &region";

    run;

%mend;


%filter_origin(Kerala)

Output:

Bananas Originating from Kerala

Obs Banana_ID Variety Origin Color Health_Benefit Avg_Weight_grams Price_Per_Kg_USD Production_Tonnes_per_Year
2 2 Nendran Kerala Yellow Aids Digestion 150 0.60 500000
9 9 Karpooravalli Kerala Yellow Soothes Ulcers 140 0.65 300000

%filter_origin(TamilNadu)

Output:

Bananas Originating from TamilNadu

Obs Banana_ID Variety Origin Color Health_Benefit Avg_Weight_grams Price_Per_Kg_USD Production_Tonnes_per_Year
3 3 Robusta TamilNadu Green Energy Booster 130 0.40 700000
8 8 Monthan TamilNadu Green Fiber Rich 180 0.55 250000
15 15 Mysore_Banana TamilNadu Yellow Helps Digestion 95 0.60 250000

Step 9: Macro to Calculate High Production Bananas

%macro high_production(min_production);

    proc sql;

        select Variety, Production_Tonnes_per_Year

        from Banana_Types

        where Production_Tonnes_per_Year > &min_production;

    quit;

%mend;


%high_production(400000)

Output:

Variety Production_Tonnes_per_Year
Cavendish 900000
Nendran 500000
Robusta 700000
Saba 800000
Plantain 600000

Step 10: Create a New Derived Variable – Profit Estimation

data Banana_Types_Profit;

    set Banana_Types;

    Estimated_Profit_Million = (Price_Per_Kg_USD * Production_Tonnes_per_Year) / 1000000;

run;

proc print data=Banana_Types_Profit;

    title "Estimated Profit in Million USD";

run;

Output:

Estimated Profit in Million USD

Obs Banana_ID Variety Origin Color Health_Benefit Avg_Weight_grams Price_Per_Kg_USD Production_Tonnes_per_Year Estimated_Profit_Million
1 1 Cavendish India Yellow Rich in Potassium 120 0.45 900000 0.4050
2 2 Nendran Kerala Yellow Aids Digestion 150 0.60 500000 0.3000
3 3 Robusta TamilNadu Green Energy Booster 130 0.40 700000 0.2800
4 4 Rasthali Karnataka Yellow Good for Skin 100 0.70 300000 0.2100
5 5 Poovan Andhra White Iron Rich 90 0.35 200000 0.0700
6 6 Red_Banana Maharashtra Red High Antioxidants 160 1.20 150000 0.1800
7 7 Yelakki Karnataka Yellow Kid-Friendly 80 0.50 120000 0.0600
8 8 Monthan TamilNadu Green Fiber Rich 180 0.55 250000 0.1375
9 9 Karpooravalli Kerala Yellow Soothes Ulcers 140 0.65 300000 0.1950
10 10 Saba Philippines Green Good for Heart 200 0.80 800000 0.6400
11 11 Manzano Ecuador Yellow Apple Taste 100 0.95 100000 0.0950
12 12 Lady_Finger Australia Yellow Low Calorie 85 1.10 75000 0.0825
13 13 Blue_Java Hawaii Blue Ice Cream Flavor 120 1.50 50000 0.0750
14 14 Plantain Africa Green Starch Rich 300 0.70 600000 0.4200
15 15 Mysore_Banana TamilNadu Yellow Helps Digestion 95 0.60 250000 0.1500
16 16 Pisang Raja Indonesia Yellow Vitamin C Source 110 0.85 200000 0.1700
17 17 Praying_Hands Thailand Yellow Symbolic Fruit 200 1.00 180000 0.1800

Step 11: Data Cleaning – Remove Inconsistent Records

data Cleaned_Bananas;

    set Banana_Types;

    if Price_Per_Kg_USD > 0 and Avg_Weight_grams > 50 then output;

run;

proc print data=Cleaned_Bananas;

    title "Cleaned Banana Dataset with Valid Prices and Weights";

run;

Output:

Cleaned Banana Dataset with Valid Prices and Weights

Obs Banana_ID Variety Origin Color Health_Benefit Avg_Weight_grams Price_Per_Kg_USD Production_Tonnes_per_Year
1 1 Cavendish India Yellow Rich in Potassium 120 0.45 900000
2 2 Nendran Kerala Yellow Aids Digestion 150 0.60 500000
3 3 Robusta TamilNadu Green Energy Booster 130 0.40 700000
4 4 Rasthali Karnataka Yellow Good for Skin 100 0.70 300000
5 5 Poovan Andhra White Iron Rich 90 0.35 200000
6 6 Red_Banana Maharashtra Red High Antioxidants 160 1.20 150000
7 7 Yelakki Karnataka Yellow Kid-Friendly 80 0.50 120000
8 8 Monthan TamilNadu Green Fiber Rich 180 0.55 250000
9 9 Karpooravalli Kerala Yellow Soothes Ulcers 140 0.65 300000
10 10 Saba Philippines Green Good for Heart 200 0.80 800000
11 11 Manzano Ecuador Yellow Apple Taste 100 0.95 100000
12 12 Lady_Finger Australia Yellow Low Calorie 85 1.10 75000
13 13 Blue_Java Hawaii Blue Ice Cream Flavor 120 1.50 50000
14 14 Plantain Africa Green Starch Rich 300 0.70 600000
15 15 Mysore_Banana TamilNadu Yellow Helps Digestion 95 0.60 250000
16 16 Pisang Raja Indonesia Yellow Vitamin C Source 110 0.85 200000
17 17 Praying_Hands Thailand Yellow Symbolic Fruit 200 1.00 180000




To Visit My Previous Concatenate The Variables:Click Here
To Visit My Previous Macro Statements:Click Here
To Visit My Previous Bird Species Dataset:Click Here
To Visit My Previous Assignment Statement:Click Here





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