186.ANALYZING A UNIQUE WATER BOTTLES DATASET IN SAS | USING PROC PRINT | PROC SORT | PROC MEANS | PROC FREQ | PROC SQL | PROC FORMAT | PROC REPORT | PROC GCHART | PROC UNIVARIATE | PROC SGPLOT | PROC CONTENTS | PROC DATASETS | PROC TRANSPOSE | PROC TABULATE FOR COMPREHENSIVE DATA EXPLORATION | SUMMARY STATISTICS | GRAPHICAL VISUALIZATION | AND SQL-BASED INSIGHTS

ANALYZING A UNIQUE WATER BOTTLES DATASET IN SAS | USING PROC PRINT | PROC SORT | PROC MEANS | PROC FREQ | PROC SQL | PROC FORMAT | PROC REPORT | PROC GCHART | PROC UNIVARIATE | PROC SGPLOT | PROC CONTENTS | PROC DATASETS | PROC TRANSPOSE | PROC TABULATE FOR COMPREHENSIVE DATA EXPLORATION | SUMMARY STATISTICS | GRAPHICAL VISUALIZATION | AND SQL-BASED INSIGHTS

/*A detailed SAS project focused on a dataset involving different types of water bottles.*/

/*Creating a unique water bottle dataset*/

Step 1: Data Preparation in SAS

data water_bottles;

    infile datalines dlm=' ' dsd truncover;

    input Bottle_ID 

          Brand :$20. 

          Material :$20. 

          Capacity_ml 

          Color :$15. 

          Insulated :$3. 

          Price 

          Stock 

          Country :$20.;

    datalines;

1 AquaPure Plastic 1000 Blue Yes 10.99 150 USA

2 ChillMate Stainless_Steel 750 Black Yes 15.50 75 Germany

3 EcoJug Glass 500 Transparent No 12.00 200 India

4 HydroBoost Plastic 2000 Green Yes 14.25 100 USA

5 Glacier Stainless_Steel 1000 Silver Yes 18.99 50 Japan

6 ChillMate Aluminum 750 Red No 11.49 30 China

7 AquaPure Glass 600 Blue No 13.75 120 India

8 HydroBoost Plastic 1500 Orange Yes 9.99 85 USA

9 EcoJug Stainless_Steel 500 White Yes 16.49 60 Germany

10 Glacier Plastic 2000 Grey No 8.99 45 China

11 AquaPure Aluminum 1000 Yellow No 10.50 95 India

12 EcoJug Plastic 600 Green Yes 9.75 40 USA

13 ChillMate Glass 750 Black No 14.20 25 Japan

14 HydroBoost Aluminum 500 Blue No 10.00 65 Germany

15 Glacier Stainless_Steel 2000 Silver Yes 22.99 15 China

16 AquaPure Plastic 1500 White No 7.99 210 India

17 ChillMate Plastic 750 Red No 8.49 120 USA

18 EcoJug Glass 1000 Transparent No 15.49 90 India

19 HydroBoost Plastic 500 Black Yes 6.99 200 USA

20 Glacier Aluminum 1000 Grey Yes 13.00 80 Japan

;

run;

Step 2: Basic PROC Exploration

2.1:To View the Data

proc print data=water_bottles noobs label;

    title "Water Bottles Dataset";

run;

Output:

Water Bottles Dataset

Bottle_ID	Brand	Material	Capacity_ml	Color	Insulated	Price	Stock	Country
1	AquaPure	Plastic	1000	Blue	Yes	10.99	150	USA
2	ChillMate	Stainless_Steel	750	Black	Yes	15.50	75	Germany
3	EcoJug	Glass	500	Transparent	No	12.00	200	India
4	HydroBoost	Plastic	2000	Green	Yes	14.25	100	USA
5	Glacier	Stainless_Steel	1000	Silver	Yes	18.99	50	Japan
6	ChillMate	Aluminum	750	Red	No	11.49	30	China
7	AquaPure	Glass	600	Blue	No	13.75	120	India
8	HydroBoost	Plastic	1500	Orange	Yes	9.99	85	USA
9	EcoJug	Stainless_Steel	500	White	Yes	16.49	60	Germany
10	Glacier	Plastic	2000	Grey	No	8.99	45	China
11	AquaPure	Aluminum	1000	Yellow	No	10.50	95	India
12	EcoJug	Plastic	600	Green	Yes	9.75	40	USA
13	ChillMate	Glass	750	Black	No	14.20	25	Japan
14	HydroBoost	Aluminum	500	Blue	No	10.00	65	Germany
15	Glacier	Stainless_Steel	2000	Silver	Yes	22.99	15	China
16	AquaPure	Plastic	1500	White	No	7.99	210	India
17	ChillMate	Plastic	750	Red	No	8.49	120	USA
18	EcoJug	Glass	1000	Transparent	No	15.49	90	India
19	HydroBoost	Plastic	500	Black	Yes	6.99	200	USA
20	Glacier	Aluminum	1000	Grey	Yes	13.00	80	Japan

 2.2:Dataset Structure

 proc contents data=water_bottles;

    title "Dataset Structure for Water Bottles";

run;

Output:

Dataset Structure for Water Bottles

                                                                              The CONTENTS Procedure

Data Set Name	WORK.WATER_BOTTLES	Observations	20
Member Type	DATA	Variables	9
Engine	V9	Indexes	0
Created	14/09/2015 00:14:33	Observation Length	112
Last Modified	14/09/2015 00:14:33	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	584
Obs in First Data Page	20
Number of Data Set Repairs	0
ExtendObsCounter	YES
Filename	C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary Files\_TD10640_DESKTOP-QFAA4KV_\water_bottles.sas7bdat
Release Created	9.0401M2
Host Created	X64_8HOME

Alphabetic List of Variables and Attributes
#	Variable	Type	Len
1	Bottle_ID	Num	8
2	Brand	Char	20
4	Capacity_ml	Num	8
5	Color	Char	15
9	Country	Char	20
6	Insulated	Char	3
3	Material	Char	20
7	Price	Num	8
8	Stock	Num	8

 2.3: PROC SORT and PROC FREQ

 proc sort data=water_bottles out=sorted_material;

    by Material;

run;

proc freq data=sorted_material;

    tables Material;

    title "Frequency of Bottle Materials";

run;

Output:

Frequency of Bottle Materials

                                                               The FREQ Procedure

Material	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Aluminum	4	20.00	4	20.00
Glass	4	20.00	8	40.00
Plastic	8	40.00	16	80.00
Stainless_Steel	4	20.00	20	100.00

Step 3: Aggregated Analysis

 3.1: Summary Statistics by Material

proc means data=water_bottles n mean min max maxdec=2;

    class Material;

    var Price Capacity_ml;

    title "Summary Stats by Bottle Material";

run;

Output:

Summary Stats by Bottle Material

                                                               The MEANS Procedure

Material	N Obs	Variable	N	Mean	Minimum	Maximum
Aluminum	4	Price Capacity_ml	4 4	11.25 812.50	10.00 500.00	13.00 1000.00
Glass	4	Price Capacity_ml	4 4	13.86 712.50	12.00 500.00	15.49 1000.00
Plastic	8	Price Capacity_ml	8 8	9.68 1231.25	6.99 500.00	14.25 2000.00
Stainless_Steel	4	Price Capacity_ml	4 4	18.49 1062.50	15.50 500.00	22.99 2000.00

Step 4: Using PROC SQL for Advanced Queries

 4.1: Average Price by Brand

proc sql;

    title "Average Price of Bottles by Brand";

    select Brand, mean(Price) as Avg_Price format=dollar8.2

    from water_bottles

    group by Brand

    order by Avg_Price desc;

quit;

Output:

Average Price of Bottles by Brand

Brand	Avg_Price
Glacier	$15.99
EcoJug	$13.43
ChillMate	$12.42
AquaPure	$10.81
HydroBoost	$10.31

 

4.2: Top 5 Most Expensive Bottles

proc sql outobs=5;

    title "Top 5 Expensive Bottles";

    select * from water_bottles

    order by Price desc;

quit;

Output:

Top 5 Expensive Bottles

Bottle_ID	Brand	Material	Capacity_ml	Color	Insulated	Price	Stock	Country
15	Glacier	Stainless_Steel	2000	Silver	Yes	22.99	15	China
5	Glacier	Stainless_Steel	1000	Silver	Yes	18.99	50	Japan
9	EcoJug	Stainless_Steel	500	White	Yes	16.49	60	Germany
2	ChillMate	Stainless_Steel	750	Black	Yes	15.5	75	Germany
18	EcoJug	Glass	1000	Transparent	No	15.49	90	India

4.3: Total Stock Value by Country

proc sql;

    title "Total Stock Value by Country";

    select Country, sum(Stock * Price) as Total_Stock_Value format=dollar10.2

    from water_bottles

    group by Country;

quit;

Output:

Total Stock Value by Country

Country	Total_Stock_Value
China	$1,094.10
Germany	$2,801.90
India	$8,119.50
Japan	$2,344.50
USA	$6,729.45

Step 5: Using SAS Macros for Repeated Analysis

Define Macro

%macro analyze_by_material(mat=);

proc sql;

  title "Analysis for Material: &mat";

  select Brand, Capacity_ml, Price, Stock

  from water_bottles

  where Material="&mat";

quit;

proc means data=water_bottles(where=(Material="&mat")) n mean min max maxdec=2;

  var Price Capacity_ml Stock;

  title "Summary for &mat Bottles";

  run;

%mend;

Call the Macro

%analyze_by_material(mat=Plastic)

Output:

Summary for Plastic Bottles

                                                                The MEANS Procedure

Variable	N	Mean	Minimum	Maximum
Price Capacity_ml Stock	8 8 8	9.68 1231.25 118.75	6.99 500.00 40.00	14.25 2000.00 210.00

%analyze_by_material(mat=Glass)

Output:

Summary for Glass Bottles

                                                                The MEANS Procedure

Variable	N	Mean	Minimum	Maximum
Price Capacity_ml Stock	4 4 4	13.86 712.50 108.75	12.00 500.00 25.00	15.49 1000.00 200.00

%analyze_by_material(mat=Aluminum)

Output:

Summary for Aluminum Bottles

                                                              The MEANS Procedure    

Variable	N	Mean	Minimum	Maximum
Price Capacity_ml Stock	4 4 4	11.25 812.50 67.50	10.00 500.00 30.00	13.00 1000.00 95.00

Step 6: Custom Format and Labeling

proc format;

    value $insfmt 'Yes' = 'Insulated'

                   'No' = 'Not Insulated';

run;

proc print data=water_bottles;

    format Insulated $insfmt.;

    title "Water Bottles with Custom Insulation Labels";

run;

Output:

Water Bottles with Custom Insulation Labels

Obs	Bottle_ID	Brand	Material	Capacity_ml	Color	Insulated	Price	Stock	Country
1	1	AquaPure	Plastic	1000	Blue	Insulated	10.99	150	USA
2	2	ChillMate	Stainless_Steel	750	Black	Insulated	15.50	75	Germany
3	3	EcoJug	Glass	500	Transparent	Not Insulated	12.00	200	India
4	4	HydroBoost	Plastic	2000	Green	Insulated	14.25	100	USA
5	5	Glacier	Stainless_Steel	1000	Silver	Insulated	18.99	50	Japan
6	6	ChillMate	Aluminum	750	Red	Not Insulated	11.49	30	China
7	7	AquaPure	Glass	600	Blue	Not Insulated	13.75	120	India
8	8	HydroBoost	Plastic	1500	Orange	Insulated	9.99	85	USA
9	9	EcoJug	Stainless_Steel	500	White	Insulated	16.49	60	Germany
10	10	Glacier	Plastic	2000	Grey	Not Insulated	8.99	45	China
11	11	AquaPure	Aluminum	1000	Yellow	Not Insulated	10.50	95	India
12	12	EcoJug	Plastic	600	Green	Insulated	9.75	40	USA
13	13	ChillMate	Glass	750	Black	Not Insulated	14.20	25	Japan
14	14	HydroBoost	Aluminum	500	Blue	Not Insulated	10.00	65	Germany
15	15	Glacier	Stainless_Steel	2000	Silver	Insulated	22.99	15	China
16	16	AquaPure	Plastic	1500	White	Not Insulated	7.99	210	India
17	17	ChillMate	Plastic	750	Red	Not Insulated	8.49	120	USA
18	18	EcoJug	Glass	1000	Transparent	Not Insulated	15.49	90	India
19	19	HydroBoost	Plastic	500	Black	Insulated	6.99	200	USA
20	20	Glacier	Aluminum	1000	Grey	Insulated	13.00	80	Japan

Step 7: Creating a Flag Variable

data flagged_bottles;

    set water_bottles;

    if Price > 15 then High_Priced = "Yes";

    else High_Priced = "No";

run;

proc freq data=flagged_bottles;

    tables High_Priced;

    title "High Priced Bottles";

run;

Output:

High Priced Bottles

                                                                The FREQ Procedure

High_Priced	Frequency	Percent	Cumulative Frequency	Cumulative Percent
No	15	75.00	15	75.00
Yes	5	25.00	20	100.00

Step 8: Dataset Split – By Insulation

data insulated non_insulated;

    set water_bottles;

    if Insulated = "Yes" then output insulated;

    else output non_insulated;

run;

proc print data=insulated;

    title "Insulated Bottles";

run;

Output:

Insulated Bottles

Obs	Bottle_ID	Brand	Material	Capacity_ml	Color	Insulated	Price	Stock	Country
1	1	AquaPure	Plastic	1000	Blue	Yes	10.99	150	USA
2	2	ChillMate	Stainless_Steel	750	Black	Yes	15.50	75	Germany
3	4	HydroBoost	Plastic	2000	Green	Yes	14.25	100	USA
4	5	Glacier	Stainless_Steel	1000	Silver	Yes	18.99	50	Japan
5	8	HydroBoost	Plastic	1500	Orange	Yes	9.99	85	USA
6	9	EcoJug	Stainless_Steel	500	White	Yes	16.49	60	Germany
7	12	EcoJug	Plastic	600	Green	Yes	9.75	40	USA
8	15	Glacier	Stainless_Steel	2000	Silver	Yes	22.99	15	China
9	19	HydroBoost	Plastic	500	Black	Yes	6.99	200	USA
10	20	Glacier	Aluminum	1000	Grey	Yes	13.00	80	Japan

proc print data=non_insulated;

    title "Non-Insulated Bottles";

run;

Output:

Non-Insulated Bottles

Obs	Bottle_ID	Brand	Material	Capacity_ml	Color	Insulated	Price	Stock	Country
1	3	EcoJug	Glass	500	Transparent	No	12.00	200	India
2	6	ChillMate	Aluminum	750	Red	No	11.49	30	China
3	7	AquaPure	Glass	600	Blue	No	13.75	120	India
4	10	Glacier	Plastic	2000	Grey	No	8.99	45	China
5	11	AquaPure	Aluminum	1000	Yellow	No	10.50	95	India
6	13	ChillMate	Glass	750	Black	No	14.20	25	Japan
7	14	HydroBoost	Aluminum	500	Blue	No	10.00	65	Germany
8	16	AquaPure	Plastic	1500	White	No	7.99	210	India
9	17	ChillMate	Plastic	750	Red	No	8.49	120	USA
10	18	EcoJug	Glass	1000	Transparent	No	15.49	90	India

Step 9: Exporting the Dataset

proc export data=water_bottles

    outfile="/mnt/data/water_bottles.csv"

    dbms=csv

    replace;

run;

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