160.Exploring Renewable Energy Projects with PROC CONTENTS | PROC PRINT | PROC MEANS | PROC FREQ | PROC SGPLOT | PROC SQL | PROC DATASETS IN SAS

Exploring Renewable Energy Projects with PROC CONTENTS | PROC PRINT | PROC MEANS | PROC FREQ | PROC SGPLOT | PROC SQL | PROC DATASETS IN SAS

 /*Creating a unique dataset centered around "Global Renewable Energy Projects"*/

1. Dataset Creation: Global Renewable Energy Projects

1.1. Defining the Dataset Structure

/*We'll define a dataset named Renewable_Projects with the following variables:*/

Project_ID: Unique identifier for each project.

Project_Name: Name of the renewable energy project.

Country: Country where the project is located.

Region: Geographical region (e.g., Asia, Europe).

Energy_Type: Type of renewable energy (e.g., Solar, Wind, Hydro).

Capacity_MW: Installed capacity in megawatts.

Investment_USD_Million: Total investment in USD millions.

Start_Year: Year the project commenced operations.

Status: Current status (e.g., Operational, Under Construction).

Operator: Company or entity operating the project.​

1.2. Sample Data Creation

/*We'll create a sample of 10 records for illustration.​*/

data Renewable_Projects;

    input Project_ID $ Project_Name $ Country $ Region $ Energy_Type $ 

          Capacity_MW Investment_USD_Million Start_Year Status $ Operator $;

    datalines;

P001 SolarOne India Asia Solar 100 150 2015 Operational SunEnergy

P002 WindMax Germany Europe Wind 200 300 2012 Operational WindCorp

P003 HydroFlow Brazil SouthAmerica Hydro 500 600 2010 Operational HydroWorks

P004 SunRise USA NorthAmerica Solar 150 200 2018 Operational SolarTech

P005 WindyPlains Australia Oceania Wind 250 350 2016 Operational WindMasters

P006 RiverPower Canada NorthAmerica Hydro 400 500 2011 Operational HydroCanada

P007 DesertSun UAE Asia Solar 120 180 2019 Operational SunDesert

P008 MountainWind China Asia Wind 300 400 2013 Operational WindChina

P009 AquaEnergy Norway Europe Hydro 350 450 2014 Operational AquaNor

P010 SolarVista Kenya Africa Solar 80 100 2020 Operational SolarAfrica

;

run;

proc print;run;

Output:

Obs	Project_ID	Project_Name	Country	Region	Energy_Type	Capacity_MW	Investment_USD_Million	Start_Year	Status	Operator
1	P001	SolarOne	India	Asia	Solar	100	150	2015	Operatio	SunEnerg
2	P002	WindMax	Germany	Europe	Wind	200	300	2012	Operatio	WindCorp
3	P003	HydroFlo	Brazil	SouthAme	Hydro	500	600	2010	Operatio	HydroWor
4	P004	SunRise	USA	NorthAme	Solar	150	200	2018	Operatio	SolarTec
5	P005	WindyPla	Australi	Oceania	Wind	250	350	2016	Operatio	WindMast
6	P006	RiverPow	Canada	NorthAme	Hydro	400	500	2011	Operatio	HydroCan
7	P007	DesertSu	UAE	Asia	Solar	120	180	2019	Operatio	SunDeser
8	P008	Mountain	China	Asia	Wind	300	400	2013	Operatio	WindChin
9	P009	AquaEner	Norway	Europe	Hydro	350	450	2014	Operatio	AquaNor
10	P010	SolarVis	Kenya	Africa	Solar	80	100	2020	Operatio	SolarAfr

2. Data Exploration and Management

2.1. Viewing the Dataset Structure

/*Use PROC CONTENTS to view the structure and attributes of the dataset.*/

proc contents data=Renewable_Projects;

run;

Output:

                                                                  The CONTENTS Procedure

Data Set Name	WORK.RENEWABLE_PROJECTS	Observations	10
Member Type	DATA	Variables	10
Engine	V9	Indexes	0
Created	14/09/2015 00:03:27	Observation Length	80
Last Modified	14/09/2015 00:03:27	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	10
Number of Data Set Repairs	0
ExtendObsCounter	YES
Filename	C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary Files\_TD860_DESKTOP-QFAA4KV_\renewable_projects.sas7bdat
Release Created	9.0401M2
Host Created	X64_8HOME

Alphabetic List of Variables and Attributes
#	Variable	Type	Len
6	Capacity_MW	Num	8
3	Country	Char	8
5	Energy_Type	Char	8
7	Investment_USD_Million	Num	8
10	Operator	Char	8
1	Project_ID	Char	8
2	Project_Name	Char	8
4	Region	Char	8
8	Start_Year	Num	8
9	Status	Char	8

2.2. Viewing the Data

/*Use PROC PRINT to display the dataset.​*/

proc print data=Renewable_Projects;

run;

Output:

Obs	Project_ID	Project_Name	Country	Region	Energy_Type	Capacity_MW	Investment_USD_Million	Start_Year	Status	Operator
1	P001	SolarOne	India	Asia	Solar	100	150	2015	Operatio	SunEnerg
2	P002	WindMax	Germany	Europe	Wind	200	300	2012	Operatio	WindCorp
3	P003	HydroFlo	Brazil	SouthAme	Hydro	500	600	2010	Operatio	HydroWor
4	P004	SunRise	USA	NorthAme	Solar	150	200	2018	Operatio	SolarTec
5	P005	WindyPla	Australi	Oceania	Wind	250	350	2016	Operatio	WindMast
6	P006	RiverPow	Canada	NorthAme	Hydro	400	500	2011	Operatio	HydroCan
7	P007	DesertSu	UAE	Asia	Solar	120	180	2019	Operatio	SunDeser
8	P008	Mountain	China	Asia	Wind	300	400	2013	Operatio	WindChin
9	P009	AquaEner	Norway	Europe	Hydro	350	450	2014	Operatio	AquaNor
10	P010	SolarVis	Kenya	Africa	Solar	80	100	2020	Operatio	SolarAfr

3. Data Manipulation

3.1. Filtering Data

/*Filter projects located in Asia.*/

data Asia_Projects;

    set Renewable_Projects;

    where Region = 'Asia';

run;

proc print;run;

Output:

Obs	Project_ID	Project_Name	Country	Region	Energy_Type	Capacity_MW	Investment_USD_Million	Start_Year	Status	Operator
1	P001	SolarOne	India	Asia	Solar	100	150	2015	Operatio	SunEnerg
2	P007	DesertSu	UAE	Asia	Solar	120	180	2019	Operatio	SunDeser
3	P008	Mountain	China	Asia	Wind	300	400	2013	Operatio	WindChin

3.2. Creating New Variables

/*Calculate the cost per megawatt for each project.*/

data Renewable_Projects;

    set Renewable_Projects;

    Cost_Per_MW = Investment_USD_Million / Capacity_MW;

run;

proc print;run;

Output:

Obs	Project_ID	Project_Name	Country	Region	Energy_Type	Capacity_MW	Investment_USD_Million	Start_Year	Status	Operator	Cost_Per_MW
1	P001	SolarOne	India	Asia	Solar	100	150	2015	Operatio	SunEnerg	1.50000
2	P002	WindMax	Germany	Europe	Wind	200	300	2012	Operatio	WindCorp	1.50000
3	P003	HydroFlo	Brazil	SouthAme	Hydro	500	600	2010	Operatio	HydroWor	1.20000
4	P004	SunRise	USA	NorthAme	Solar	150	200	2018	Operatio	SolarTec	1.33333
5	P005	WindyPla	Australi	Oceania	Wind	250	350	2016	Operatio	WindMast	1.40000
6	P006	RiverPow	Canada	NorthAme	Hydro	400	500	2011	Operatio	HydroCan	1.25000
7	P007	DesertSu	UAE	Asia	Solar	120	180	2019	Operatio	SunDeser	1.50000
8	P008	Mountain	China	Asia	Wind	300	400	2013	Operatio	WindChin	1.33333
9	P009	AquaEner	Norway	Europe	Hydro	350	450	2014	Operatio	AquaNor	1.28571
10	P010	SolarVis	Kenya	Africa	Solar	80	100	2020	Operatio	SolarAfr	1.25000

4. Data Analysis

4.1. Summary Statistics

/*Use PROC MEANS to compute summary statistics for capacity and investment.*/

proc means data=Renewable_Projects mean median min max;

    var Capacity_MW Investment_USD_Million;

run;

Output:

                                                             The MEANS Procedure

Variable	Mean	Median	Minimum	Maximum
Capacity_MW Investment_USD_Million	245.0000000 323.0000000	225.0000000 325.0000000	80.0000000 100.0000000	500.0000000 600.0000000

4.2. Frequency Analysis

/*Use PROC FREQ to analyze the distribution of energy types.*/

proc freq data=Renewable_Projects;

    tables Energy_Type;

run;

Output:

                                                                 The FREQ Procedure

Energy_Type	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Hydro	3	30.00	3	30.00
Solar	4	40.00	7	70.00
Wind	3	30.00	10	100.00

5. Data Visualization

5.1. Bar Chart of Projects by Energy Type

/*Use PROC SGPLOT to create a bar chart showing the number of projects per energy type.*/

proc sgplot data=Renewable_Projects;

    vbar Energy_Type;

    title "Number of Projects by Energy Type";

run;

Log:

NOTE: PROCEDURE SGPLOT used (Total process time):

      real time           2.56 seconds

      cpu time            0.53 seconds

NOTE: Listing image output written to SGPlot1.png.

NOTE: There were 10 observations read from the data set WORK.RENEWABLE_PROJECTS.

5.2. Scatter Plot of Investment vs. Capacity

/*Visualize the relationship between investment and capacity.*/

proc sgplot data=Renewable_Projects;

    scatter x=Capacity_MW y=Investment_USD_Million / group=Energy_Type;

    title "Investment vs. Capacity by Energy Type";

run;

Log:

NOTE: PROCEDURE SGPLOT used (Total process time):

      real time           0.75 seconds

      cpu time            0.09 seconds

NOTE: Listing image output written to SGPlot3.png.

NOTE: There were 10 observations read from the data set WORK.RENEWABLE_PROJECTS.

6. Advanced Data Operations

6.1. Sorting Data

/*Sort projects by descending capacity.*/

proc sort data=Renewable_Projects out=Sorted_Projects;

    by descending Capacity_MW;

run;

proc print;run;

Output:

Obs	Project_ID	Project_Name	Country	Region	Energy_Type	Capacity_MW	Investment_USD_Million	Start_Year	Status	Operator	Cost_Per_MW
1	P003	HydroFlo	Brazil	SouthAme	Hydro	500	600	2010	Operatio	HydroWor	1.20000
2	P006	RiverPow	Canada	NorthAme	Hydro	400	500	2011	Operatio	HydroCan	1.25000
3	P009	AquaEner	Norway	Europe	Hydro	350	450	2014	Operatio	AquaNor	1.28571
4	P008	Mountain	China	Asia	Wind	300	400	2013	Operatio	WindChin	1.33333
5	P005	WindyPla	Australi	Oceania	Wind	250	350	2016	Operatio	WindMast	1.40000
6	P002	WindMax	Germany	Europe	Wind	200	300	2012	Operatio	WindCorp	1.50000
7	P004	SunRise	USA	NorthAme	Solar	150	200	2018	Operatio	SolarTec	1.33333
8	P007	DesertSu	UAE	Asia	Solar	120	180	2019	Operatio	SunDeser	1.50000
9	P001	SolarOne	India	Asia	Solar	100	150	2015	Operatio	SunEnerg	1.50000
10	P010	SolarVis	Kenya	Africa	Solar	80	100	2020	Operatio	SolarAfr	1.25000

6.2. Aggregating Data

/*Calculate total capacity and investment by region.​*/

proc sql;

    create table Region_Summary as

    select Region, 

           sum(Capacity_MW) as Total_Capacity,

           sum(Investment_USD_Million) as Total_Investment

    from Renewable_Projects

    group by Region;

quit;

proc print;run;

Output:

Obs	Region	Total_Capacity	Total_Investment
1	Africa	80	100
2	Asia	520	730
3	Europe	550	750
4	NorthAme	550	700
5	Oceania	250	350
6	SouthAme	500	600

7. Creating Indexes for Efficient Access

/*Use PROC DATASETS to create an index on the Country variable for faster access.*/

proc datasets library=work;

    modify Renewable_Projects;

    index create Country;

run;

quit;

Log:

index create Country;

NOTE: Simple index Country has been defined.

62   run;

NOTE: MODIFY was successful for WORK.RENEWABLE_PROJECTS.DATA.

63   quit;

NOTE: PROCEDURE DATASETS used (Total process time):

      real time           0.26 seconds

      cpu time            0.03 seconds

Output:

Directory
Libref	WORK
Engine	V9
Physical Name	C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary Files\_TD860_DESKTOP-QFAA4KV_
Filename	C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary Files\_TD860_DESKTOP-QFAA4KV_

#	Name	Member Type	File Size	Last Modified
1	ASIA_PROJECTS	DATA	131072	22/04/2025 08:54:30
2	REGION_SUMMARY	DATA	131072	22/04/2025 08:58:00
3	RENEWABLE_PROJECTS	DATA	131072	22/04/2025 08:54:47
4	SORTED_PROJECTS	DATA	131072	22/04/2025 08:57:39

PRACTICE AND COMMENT YOUR CODE: 

-->PLEASE FOLLOW OUR BLOG FOR MORE UPDATES.

TO FOLLOW OUR TELEGRAM CHANNEL CLICK HERE