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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233.ANALYZING BEST BIKES WORLDWIDE USING PROC CONTENTS | PROC PRINT | PROC SORT | PROC MEANS | PROC FREQ | PROC UNIVARIATE | PROC SQL | PROC REPORT | PROC TABULATE | PROC SGPLOT | MACROS IN SAS

on

ANALYZING BEST BIKES WORLDWIDE USING PROC CONTENTS | PROC PRINT | PROC SORT | PROC MEANS | PROC FREQ | PROC UNIVARIATE | PROC SQL | PROC REPORT | PROC TABULATE | PROC SGPLOT | MACROS IN SAS

/*Creating A Dataset Of Best Bikes  Worldwide */

1.Option settings

options nocenter fullstimer yearcutoff=1950 errors=3 dkricond=error;        

                

2.User‑defined formats

proc format;

   value $typefmt

      'Motorcycle' = 'MOTOR‑CYCLE'

      'Bicycle'    = 'BICYCLE (Pedal / e‑assist)';

   value engsize

      low‑<200   = 'A: <200 cc'

      200‑<600   = 'B: 200‑599 cc'

      600‑<1000  = 'C: 600‑999 cc'

      1000‑high  = 'D: ≥1000 cc';

run;


3.DATA step: build the core table

data work.best_bikes;

   infile datalines dsd dlm='|' truncover;

   length Brand $15 Model $30 Segment $15 Country_Origin $12 Type $10;

   input

      BikeID            : 8.

      Brand             : $15.

      Model             : $30.

      Type              : $10.

      Segment           : $15.

      Engine_CC         : 8.

      Power_HP          : 8.

      Torque_Nm         : 8.

      Weight_kg         : 8.

      Top_Speed_kmh     : ?? 8.

      Price_USD         : comma8.

      Country_Origin    : $12.

      Rating            : 4.1

      Year_Released     : 4.

      Units_Sold_2024   : comma8.;


   if Power_HP>0 and Weight_kg>0 then HP_per_kg = round(Power_HP/Weight_kg,0.001);

   Model_Age = year(today()) - Year_Released;

   format Price_USD comma10.

          HP_per_kg 6.3

          Rating    3.1

          Type      $typefmt.

          Engine_CC engsize.;

datalines;

1|Honda|CBR1000RR‑R Fireblade SP|Motorcycle|Supersport|1000|214|113|201|322|28000|Japan|9.5|2024|3,500

2|Yamaha|YZF‑R1M|Motorcycle|Supersport|998|200|112|200|299|26000|Japan|9.4|2023|4,200

3|Ducati|Panigale V4 S|Motorcycle|Supersport|1103|214|124|198|306|32000|Italy|9.6|2024|3,700

4|Kawasaki|Ninja H2R|Motorcycle|Hyperbike|998|310|165|216|400|55000|Japan|9.7|2022|800

5|BMW|S1000RR|Motorcycle|Supersport|999|205|113|197|299|25000|Germany|9.3|2024|4,100

6|KTM|1290 Super Duke R|Motorcycle|Naked|1301|180|140|189|289|19000|Austria|9.2|2023|2,900

7|Triumph|Street Triple 765 RS|Motorcycle|Naked|765|128|80|166|241|15000|UK|9.0|2023|3,300

8|Harley‑Davidson|Pan America 1250|Motorcycle|Adventure|1252|150|128|239|225|18000|USA|8.8|2024|2,500

9|Royal Enfield|Himalayan 450|Motorcycle|Adventure|452|40|45|199|143|6,500|India|8.4|2024|50,000

10|Bajaj|Pulsar N250|Motorcycle|Street|249|25|21|162|132|2,300|India|8.2|2023|120,000

11|Giant|TCR Advanced SL 0 Disk|Bicycle|Road|0|0|0|6.7|‑‑|11,200|Taiwan|9.1|2024|1,500

12|Specialized|S‑Works Tarmac SL8|Bicycle|Road|0|0|0|6.6|‑‑|14,500|USA|9.5|2024|2,000

13|Trek|Madone SLR 9|Bicycle|Road|0|0|0|7.1|‑‑|13,000|USA|9.4|2024|1,800

14|Cervélo|Caledonia‑5|Bicycle|Endurance|0|0|0|7.8|‑‑|10,500|Canada|9.0|2023|1,600

15|Santa Cruz|V10|Bicycle|Mountain|0|0|0|15.3|‑‑|10,500|USA|8.9|2022|1,000

16|Canyon|Spectral Mullet|Bicycle|Mountain|0|0|0|14.2|‑‑|6,400|Germany|8.8|2023|2,100

17|Scott|Spark RC SL|Bicycle|XC|0|0|0|10.2|‑‑|12,000|Switzerland|9.2|2024|900

18|Yeti|SB160|Bicycle|Enduro|0|0|0|14.7|‑‑|9,500|USA|8.7|2024|800

19|Orbea|Gain M20i|Bicycle|E‑Road|0|0|0|11.3|‑‑|6,300|Spain|8.6|2023|1,000

20|Specialized|Turbo Levo SL Comp|Bicycle|E‑MTB|0|0|0|17.9|‑‑|7,500|USA|8.9|2024|1,200

21|Bajaj|Dominar 400|Motorcycle|Sport‑Touring|373|40|35|182|156|3,000|India|8.3|2024|45,000

22|Suzuki|Hayabusa|Motorcycle|Hyperbike|1340|190|150|264|299|28,000|Japan|9.0|2023|6,000

23|Aprilia|RSV4 Factory|Motorcycle|Supersport|1099|217|125|199|305|34,000|Italy|9.4|2024|2,000

24|MV Agusta|Brutale 1000 RR|Motorcycle|Naked|998|208|117|186|299|35,000|Italy|9.3|2023|900

25|Hero|Splendor Plus|Motorcycle|Commuter|97|8|8|112|96|1,000|India|7.8|2024|280,000

26|KTM|RC 390|Motorcycle|Supersport|373|44|37|172|178|5,100|Austria|8.5|2024|22,000

27|Lectric|XP 3.0|Bicycle|E‑Bike|0|0|0|23.6|‑‑|999|USA|8.0|2024|35,000

28|Brompton|C Line Explore|Bicycle|Folding|0|0|0|11.6|‑‑|1,750|UK|8.2|2023|12,000

29|BMW|R1300GS|Motorcycle|Adventure|1300|145|143|239|225|20,500|Germany|9.1|2025|1,000

30|Ducati|DesertX|Motorcycle|Adventure|937|110|92|223|242|17,000|Italy|8.9|2024|1,900

;

run;

proc print;

run;

Output:

Obs Brand Model Segment Country_Origin Type BikeID Engine_CC Power_HP Torque_Nm Weight_kg Top_Speed_kmh Price_USD Rating Year_Released Units_Sold_2024 HP_per_kg Model_Age
1 Honda CBR1000RR-R Fireblade SP Supersport Japan MOTOR-CYCLE 1 D: =1000 cc 214 113 201.0 322 28,000 9.5 2024 3500 1.065 -9
2 Yamaha YZF-R1M Supersport Japan MOTOR-CYCLE 2 C: 600-999 cc 200 112 200.0 299 26,000 9.4 2023 4200 1.000 -8
3 Ducati Panigale V4 S Supersport Italy MOTOR-CYCLE 3 D: =1000 cc 214 124 198.0 306 32,000 9.6 2024 3700 1.081 -9
4 Kawasaki Ninja H2R Hyperbike Japan MOTOR-CYCLE 4 C: 600-999 cc 310 165 216.0 400 55,000 9.7 2022 800 1.435 -7
5 BMW S1000RR Supersport Germany MOTOR-CYCLE 5 C: 600-999 cc 205 113 197.0 299 25,000 9.3 2024 4100 1.041 -9
6 KTM 1290 Super Duke R Naked Austria MOTOR-CYCLE 6 D: =1000 cc 180 140 189.0 289 19,000 9.2 2023 2900 0.952 -8
7 Triumph Street Triple 765 RS Naked UK MOTOR-CYCLE 7 C: 600-999 cc 128 80 166.0 241 15,000 9.0 2023 3300 0.771 -8
8 Harley-Davidson Pan America 1250 Adventure USA MOTOR-CYCLE 8 D: =1000 cc 150 128 239.0 225 18,000 8.8 2024 2500 0.628 -9
9 Royal Enfield Himalayan 450 Adventure India MOTOR-CYCLE 9 B: 200-599 cc 40 45 199.0 143 6,500 8.4 2024 50000 0.201 -9
10 Bajaj Pulsar N250 Street India MOTOR-CYCLE 10 B: 200-599 cc 25 21 162.0 132 2,300 8.2 2023 120000 0.154 -8
11 Giant TCR Advanced SL 0 Disk Road Taiwan BICYCLE (Pedal / e-assist) 11 A: <200 cc 0 0 6.7 . 11,200 9.1 2024 1500 . -9
12 Specialized S-Works Tarmac SL8 Road USA BICYCLE (Pedal / e-assist) 12 A: <200 cc 0 0 6.6 . 14,500 9.5 2024 2000 . -9
13 Trek Madone SLR 9 Road USA BICYCLE (Pedal / e-assist) 13 A: <200 cc 0 0 7.1 . 13,000 9.4 2024 1800 . -9
14 Cervélo Caledonia-5 Endurance Canada BICYCLE (Pedal / e-assist) 14 A: <200 cc 0 0 7.8 . 10,500 9.0 2023 1600 . -8
15 Santa Cruz V10 Mountain USA BICYCLE (Pedal / e-assist) 15 A: <200 cc 0 0 15.3 . 10,500 8.9 2022 1000 . -7
16 Canyon Spectral Mullet Mountain Germany BICYCLE (Pedal / e-assist) 16 A: <200 cc 0 0 14.2 . 6,400 8.8 2023 2100 . -8
17 Scott Spark RC SL XC Switzerland BICYCLE (Pedal / e-assist) 17 A: <200 cc 0 0 10.2 . 12,000 9.2 2024 900 . -9
18 Yeti SB160 Enduro USA BICYCLE (Pedal / e-assist) 18 A: <200 cc 0 0 14.7 . 9,500 8.7 2024 800 . -9
19 Orbea Gain M20i E-Road Spain BICYCLE (Pedal / e-assist) 19 A: <200 cc 0 0 11.3 . 6,300 8.6 2023 1000 . -8
20 Specialized Turbo Levo SL Comp E-MTB USA BICYCLE (Pedal / e-assist) 20 A: <200 cc 0 0 17.9 . 7,500 8.9 2024 1200 . -9
21 Bajaj Dominar 400 Sport-Touring India MOTOR-CYCLE 21 B: 200-599 cc 40 35 182.0 156 3,000 8.3 2024 45000 0.220 -9
22 Suzuki Hayabusa Hyperbike Japan MOTOR-CYCLE 22 D: =1000 cc 190 150 264.0 299 28,000 9.0 2023 6000 0.720 -8
23 Aprilia RSV4 Factory Supersport Italy MOTOR-CYCLE 23 D: =1000 cc 217 125 199.0 305 34,000 9.4 2024 2000 1.090 -9
24 MV Agusta Brutale 1000 RR Naked Italy MOTOR-CYCLE 24 C: 600-999 cc 208 117 186.0 299 35,000 9.3 2023 900 1.118 -8
25 Hero Splendor Plus Commuter India MOTOR-CYCLE 25 A: <200 cc 8 8 112.0 96 1,000 7.8 2024 280000 0.071 -9
26 KTM RC 390 Supersport Austria MOTOR-CYCLE 26 B: 200-599 cc 44 37 172.0 178 5,100 8.5 2024 22000 0.256 -9
27 Lectric XP 3.0 E-Bike USA BICYCLE (Pedal / e-assist) 27 A: <200 cc 0 0 23.6 . 999 8.0 2024 35000 . -9
28 Brompton C Line Explore Folding UK BICYCLE (Pedal / e-assist) 28 A: <200 cc 0 0 11.6 . 1,750 8.2 2023 12000 . -8
29 BMW R1300GS Adventure Germany MOTOR-CYCLE 29 D: =1000 cc 145 143 239.0 225 20,500 9.1 2025 1000 0.607 -10
30 Ducati DesertX Adventure Italy MOTOR-CYCLE 30 C: 600-999 cc 110 92 223.0 242 17,000 8.9 2024 1900 0.493 -9


4.Quick metadata scan

proc contents data=work.best_bikes varnum;

   title "Structure of BEST_BIKES Dataset";

run;

 Output:

Structure of BEST_BIKES Dataset

The CONTENTS Procedure

Data Set Name WORK.BEST_BIKES Observations 30
Member Type DATA Variables 17
Engine V9 Indexes 0
Created 14/09/2015 00:25:54 Observation Length 184
Last Modified 14/09/2015 00:25:54 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 355
Obs in First Data Page 30
Number of Data Set Repairs 0
ExtendObsCounter YES
Filename C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary Files\_TD11048_DESKTOP-QFAA4KV_\best_bikes.sas7bdat
Release Created 9.0401M2
Host Created X64_8HOME


Variables in Creation Order
# Variable Type Len Format
1 Brand Char 15  
2 Model Char 30  
3 Segment Char 15  
4 Country_Origin Char 12  
5 Type Char 10 $TYPEFMT.
6 BikeID Num 8  
7 Engine_CC Num 8 ENGSIZE.
8 Power_HP Num 8  
9 Torque_Nm Num 8  
10 Weight_kg Num 8  
11 Top_Speed_kmh Num 8  
12 Price_USD Num 8 COMMA10.
13 Rating Num 8 3.1
14 Year_Released Num 8  
15 Units_Sold_2024 Num 8  
16 HP_per_kg Num 8 6.3
17 Model_Age Num 8  


5.Snapshot listing

proc print data=work.best_bikes (obs=10) label;

   var Brand Model Type Price_USD Rating;

   title "First 10 Bikes — Sanity Check";

run;

Output:

First 10 Bikes — Sanity Check

Obs Brand Model Type Price_USD Rating
1 Honda CBR1000RR-R Fireblade SP MOTOR-CYCLE 28,000 9.5
2 Yamaha YZF-R1M MOTOR-CYCLE 26,000 9.4
3 Ducati Panigale V4 S MOTOR-CYCLE 32,000 9.6
4 Kawasaki Ninja H2R MOTOR-CYCLE 55,000 9.7
5 BMW S1000RR MOTOR-CYCLE 25,000 9.3
6 KTM 1290 Super Duke R MOTOR-CYCLE 19,000 9.2
7 Triumph Street Triple 765 RS MOTOR-CYCLE 15,000 9.0
8 Harley-Davidson Pan America 1250 MOTOR-CYCLE 18,000 8.8
9 Royal Enfield Himalayan 450 MOTOR-CYCLE 6,500 8.4
10 Bajaj Pulsar N250 MOTOR-CYCLE 2,300 8.2


6.Sorting & descriptive stats

proc sort data=work.best_bikes out=price_ranked;

   by descending Price_USD;

run;

proc print;run;

Output:

Obs Brand Model Segment Country_Origin Type BikeID Engine_CC Power_HP Torque_Nm Weight_kg Top_Speed_kmh Price_USD Rating Year_Released Units_Sold_2024 HP_per_kg Model_Age
1 Kawasaki Ninja H2R Hyperbike Japan MOTOR-CYCLE 4 C: 600-999 cc 310 165 216.0 400 55,000 9.7 2022 800 1.435 -7
2 MV Agusta Brutale 1000 RR Naked Italy MOTOR-CYCLE 24 C: 600-999 cc 208 117 186.0 299 35,000 9.3 2023 900 1.118 -8
3 Aprilia RSV4 Factory Supersport Italy MOTOR-CYCLE 23 D: =1000 cc 217 125 199.0 305 34,000 9.4 2024 2000 1.090 -9
4 Ducati Panigale V4 S Supersport Italy MOTOR-CYCLE 3 D: =1000 cc 214 124 198.0 306 32,000 9.6 2024 3700 1.081 -9
5 Honda CBR1000RR-R Fireblade SP Supersport Japan MOTOR-CYCLE 1 D: =1000 cc 214 113 201.0 322 28,000 9.5 2024 3500 1.065 -9
6 Suzuki Hayabusa Hyperbike Japan MOTOR-CYCLE 22 D: =1000 cc 190 150 264.0 299 28,000 9.0 2023 6000 0.720 -8
7 Yamaha YZF-R1M Supersport Japan MOTOR-CYCLE 2 C: 600-999 cc 200 112 200.0 299 26,000 9.4 2023 4200 1.000 -8
8 BMW S1000RR Supersport Germany MOTOR-CYCLE 5 C: 600-999 cc 205 113 197.0 299 25,000 9.3 2024 4100 1.041 -9
9 BMW R1300GS Adventure Germany MOTOR-CYCLE 29 D: =1000 cc 145 143 239.0 225 20,500 9.1 2025 1000 0.607 -10
10 KTM 1290 Super Duke R Naked Austria MOTOR-CYCLE 6 D: =1000 cc 180 140 189.0 289 19,000 9.2 2023 2900 0.952 -8
11 Harley-Davidson Pan America 1250 Adventure USA MOTOR-CYCLE 8 D: =1000 cc 150 128 239.0 225 18,000 8.8 2024 2500 0.628 -9
12 Ducati DesertX Adventure Italy MOTOR-CYCLE 30 C: 600-999 cc 110 92 223.0 242 17,000 8.9 2024 1900 0.493 -9
13 Triumph Street Triple 765 RS Naked UK MOTOR-CYCLE 7 C: 600-999 cc 128 80 166.0 241 15,000 9.0 2023 3300 0.771 -8
14 Specialized S-Works Tarmac SL8 Road USA BICYCLE (Pedal / e-assist) 12 A: <200 cc 0 0 6.6 . 14,500 9.5 2024 2000 . -9
15 Trek Madone SLR 9 Road USA BICYCLE (Pedal / e-assist) 13 A: <200 cc 0 0 7.1 . 13,000 9.4 2024 1800 . -9
16 Scott Spark RC SL XC Switzerland BICYCLE (Pedal / e-assist) 17 A: <200 cc 0 0 10.2 . 12,000 9.2 2024 900 . -9
17 Giant TCR Advanced SL 0 Disk Road Taiwan BICYCLE (Pedal / e-assist) 11 A: <200 cc 0 0 6.7 . 11,200 9.1 2024 1500 . -9
18 Cervélo Caledonia-5 Endurance Canada BICYCLE (Pedal / e-assist) 14 A: <200 cc 0 0 7.8 . 10,500 9.0 2023 1600 . -8
19 Santa Cruz V10 Mountain USA BICYCLE (Pedal / e-assist) 15 A: <200 cc 0 0 15.3 . 10,500 8.9 2022 1000 . -7
20 Yeti SB160 Enduro USA BICYCLE (Pedal / e-assist) 18 A: <200 cc 0 0 14.7 . 9,500 8.7 2024 800 . -9
21 Specialized Turbo Levo SL Comp E-MTB USA BICYCLE (Pedal / e-assist) 20 A: <200 cc 0 0 17.9 . 7,500 8.9 2024 1200 . -9
22 Royal Enfield Himalayan 450 Adventure India MOTOR-CYCLE 9 B: 200-599 cc 40 45 199.0 143 6,500 8.4 2024 50000 0.201 -9
23 Canyon Spectral Mullet Mountain Germany BICYCLE (Pedal / e-assist) 16 A: <200 cc 0 0 14.2 . 6,400 8.8 2023 2100 . -8
24 Orbea Gain M20i E-Road Spain BICYCLE (Pedal / e-assist) 19 A: <200 cc 0 0 11.3 . 6,300 8.6 2023 1000 . -8
25 KTM RC 390 Supersport Austria MOTOR-CYCLE 26 B: 200-599 cc 44 37 172.0 178 5,100 8.5 2024 22000 0.256 -9
26 Bajaj Dominar 400 Sport-Touring India MOTOR-CYCLE 21 B: 200-599 cc 40 35 182.0 156 3,000 8.3 2024 45000 0.220 -9
27 Bajaj Pulsar N250 Street India MOTOR-CYCLE 10 B: 200-599 cc 25 21 162.0 132 2,300 8.2 2023 120000 0.154 -8
28 Brompton C Line Explore Folding UK BICYCLE (Pedal / e-assist) 28 A: <200 cc 0 0 11.6 . 1,750 8.2 2023 12000 . -8
29 Hero Splendor Plus Commuter India MOTOR-CYCLE 25 A: <200 cc 8 8 112.0 96 1,000 7.8 2024 280000 0.071 -9
30 Lectric XP 3.0 E-Bike USA BICYCLE (Pedal / e-assist) 27 A: <200 cc 0 0 23.6 . 999 8.0 2024 35000 . -9


proc means data=work.best_bikes

           n nmiss min p25 median mean p75 max std

           maxdec=1;

   class Type;

   var Price_USD Engine_CC Power_HP Weight_kg;

   title "Descriptive Statistics by Bike Type";

run;

Output:

Descriptive Statistics by Bike Type

The MEANS Procedure

Type N Obs Variable N N Miss Minimum 25th Pctl Median Mean 75th Pctl Maximum Std Dev
BICYCLE (Pedal / e-assist) 12
Price_USD
Engine_CC
Power_HP
Weight_kg
12
12
12
12
0
0
0
0
999.0
0.0
0.0
6.6
6350.0
0.0
0.0
7.5
10000.0
0.0
0.0
11.5
8679.1
0.0
0.0
12.3
11600.0
0.0
0.0
15.0
14500.0
0.0
0.0
23.6
4226.4
0.0
0.0
5.2
MOTOR-CYCLE 18
Price_USD
Engine_CC
Power_HP
Weight_kg
18
18
18
18
0
0
0
0
1000.0
97.0
8.0
112.0
6500.0
452.0
44.0
182.0
19750.0
998.0
165.0
198.5
20577.8
868.6
146.0
196.9
28000.0
1103.0
208.0
216.0
55000.0
1340.0
310.0
264.0
14100.0
390.6
85.0
33.9


7.Frequency & univariate exploration

proc freq data=work.best_bikes;

   tables Type*Segment / nocum nopercent;

   format Engine_CC engsize.;

   title "Cross‑tab of Segment within Type";

run;

Output:

Cross-tab of Segment within Type

The FREQ Procedure

Frequency
Row Pct
Col Pct
Table of Type by Segment
Type Segment
Adventure Commuter E-Bike E-MTB E-Road Endurance Enduro Folding Hyperbike Mountain Naked Road Sport-Touring Street Supersport XC Total
BICYCLE (Pedal / e-assist)
0
0.00
0.00
0
0.00
0.00
1
8.33
100.00
1
8.33
100.00
1
8.33
100.00
1
8.33
100.00
1
8.33
100.00
1
8.33
100.00
0
0.00
0.00
2
16.67
100.00
0
0.00
0.00
3
25.00
100.00
0
0.00
0.00
0
0.00
0.00
0
0.00
0.00
1
8.33
100.00
12
 
 
MOTOR-CYCLE
4
22.22
100.00
1
5.56
100.00
0
0.00
0.00
0
0.00
0.00
0
0.00
0.00
0
0.00
0.00
0
0.00
0.00
0
0.00
0.00
2
11.11
100.00
0
0.00
0.00
3
16.67
100.00
0
0.00
0.00
1
5.56
100.00
1
5.56
100.00
6
33.33
100.00
0
0.00
0.00
18
 
 
Total
4
1
1
1
1
1
1
1
2
2
3
3
1
1
6
1
30


proc univariate data=work.best_bikes noprint;

   var HP_per_kg;

   histogram HP_per_kg / midpoints=0 to 1 by 0.05;

   inset mean std p5 p95 / position=ne;

   title "Distribution of Power‑to‑Weight Ratio";

run;

Log:

WARNING: The MIDPOINTS= list was extended to accommodate the data.

NOTE: PROCEDURE UNIVARIATE used (Total process time):

      real time           3.17 seconds

      user cpu time       0.40 seconds

      system cpu time     0.37 seconds

      memory              20491.43k

      OS Memory           39764.00k

      Timestamp           14/09/2015 12:29:24 AM

      Step Count                        20  Switch Count  0


8.PROC SQL summary

proc sql;

   create table work.type_summary as

   select Type,

          count(*)             as n_models,

          mean(Price_USD)      as avg_price format=comma10.,

          mean(HP_per_kg)      as avg_hp_wt format=6.3

   from   work.best_bikes

   group  by Type;

quit;

proc print;run;

Output:

Obs Type n_models avg_price avg_hp_wt
1 BICYCLE (Pedal / e-assist) 12 8,679 .
2 MOTOR-CYCLE 18 20,578 0.717


9.Reusable macros

%macro topN(metric=Price_USD, N=5);

   %let metric=%upcase(&metric);

   title "Top &N Bikes by &metric";

   proc sql outobs=&N;

      select BikeID, Brand, Model, &metric format=comma10.

      from   work.best_bikes

      order  by &metric desc;

   quit;

%mend;


%topN(metric=Rating, N=7);

Output:

Top 7 Bikes by RATING

BikeID Brand Model Rating
4 Kawasaki Ninja H2R 10
3 Ducati Panigale V4 S 10
1 Honda CBR1000RR-R Fireblade SP 10
12 Specialized S-Works Tarmac SL8 10
23 Aprilia RSV4 Factory 9
13 Trek Madone SLR 9 9
2 Yamaha YZF-R1M 9


%macro add_inr(rate=83);

   data work.best_bikes_inr;

      set work.best_bikes;

      Price_INR = round(Price_USD * &rate, 0.01);

      format Price_INR comma12.;

   run;

  proc print;run;

%mend;


%add_inr(rate=83.04);

Output:

Obs Brand Model Segment Country_Origin Type BikeID Engine_CC Power_HP Torque_Nm Weight_kg Top_Speed_kmh Price_USD Rating Year_Released Units_Sold_2024 HP_per_kg Model_Age Price_INR
1 Honda CBR1000RR-R Fireblade SP Supersport Japan MOTOR-CYCLE 1 D: =1000 cc 214 113 201.0 322 28,000 9.5 2024 3500 1.065 -9 2,325,120
2 Yamaha YZF-R1M Supersport Japan MOTOR-CYCLE 2 C: 600-999 cc 200 112 200.0 299 26,000 9.4 2023 4200 1.000 -8 2,159,040
3 Ducati Panigale V4 S Supersport Italy MOTOR-CYCLE 3 D: =1000 cc 214 124 198.0 306 32,000 9.6 2024 3700 1.081 -9 2,657,280
4 Kawasaki Ninja H2R Hyperbike Japan MOTOR-CYCLE 4 C: 600-999 cc 310 165 216.0 400 55,000 9.7 2022 800 1.435 -7 4,567,200
5 BMW S1000RR Supersport Germany MOTOR-CYCLE 5 C: 600-999 cc 205 113 197.0 299 25,000 9.3 2024 4100 1.041 -9 2,076,000
6 KTM 1290 Super Duke R Naked Austria MOTOR-CYCLE 6 D: =1000 cc 180 140 189.0 289 19,000 9.2 2023 2900 0.952 -8 1,577,760
7 Triumph Street Triple 765 RS Naked UK MOTOR-CYCLE 7 C: 600-999 cc 128 80 166.0 241 15,000 9.0 2023 3300 0.771 -8 1,245,600
8 Harley-Davidson Pan America 1250 Adventure USA MOTOR-CYCLE 8 D: =1000 cc 150 128 239.0 225 18,000 8.8 2024 2500 0.628 -9 1,494,720
9 Royal Enfield Himalayan 450 Adventure India MOTOR-CYCLE 9 B: 200-599 cc 40 45 199.0 143 6,500 8.4 2024 50000 0.201 -9 539,760
10 Bajaj Pulsar N250 Street India MOTOR-CYCLE 10 B: 200-599 cc 25 21 162.0 132 2,300 8.2 2023 120000 0.154 -8 190,992
11 Giant TCR Advanced SL 0 Disk Road Taiwan BICYCLE (Pedal / e-assist) 11 A: <200 cc 0 0 6.7 . 11,200 9.1 2024 1500 . -9 930,048
12 Specialized S-Works Tarmac SL8 Road USA BICYCLE (Pedal / e-assist) 12 A: <200 cc 0 0 6.6 . 14,500 9.5 2024 2000 . -9 1,204,080
13 Trek Madone SLR 9 Road USA BICYCLE (Pedal / e-assist) 13 A: <200 cc 0 0 7.1 . 13,000 9.4 2024 1800 . -9 1,079,520
14 Cervélo Caledonia-5 Endurance Canada BICYCLE (Pedal / e-assist) 14 A: <200 cc 0 0 7.8 . 10,500 9.0 2023 1600 . -8 871,920
15 Santa Cruz V10 Mountain USA BICYCLE (Pedal / e-assist) 15 A: <200 cc 0 0 15.3 . 10,500 8.9 2022 1000 . -7 871,920
16 Canyon Spectral Mullet Mountain Germany BICYCLE (Pedal / e-assist) 16 A: <200 cc 0 0 14.2 . 6,400 8.8 2023 2100 . -8 531,456
17 Scott Spark RC SL XC Switzerland BICYCLE (Pedal / e-assist) 17 A: <200 cc 0 0 10.2 . 12,000 9.2 2024 900 . -9 996,480
18 Yeti SB160 Enduro USA BICYCLE (Pedal / e-assist) 18 A: <200 cc 0 0 14.7 . 9,500 8.7 2024 800 . -9 788,880
19 Orbea Gain M20i E-Road Spain BICYCLE (Pedal / e-assist) 19 A: <200 cc 0 0 11.3 . 6,300 8.6 2023 1000 . -8 523,152
20 Specialized Turbo Levo SL Comp E-MTB USA BICYCLE (Pedal / e-assist) 20 A: <200 cc 0 0 17.9 . 7,500 8.9 2024 1200 . -9 622,800
21 Bajaj Dominar 400 Sport-Touring India MOTOR-CYCLE 21 B: 200-599 cc 40 35 182.0 156 3,000 8.3 2024 45000 0.220 -9 249,120
22 Suzuki Hayabusa Hyperbike Japan MOTOR-CYCLE 22 D: =1000 cc 190 150 264.0 299 28,000 9.0 2023 6000 0.720 -8 2,325,120
23 Aprilia RSV4 Factory Supersport Italy MOTOR-CYCLE 23 D: =1000 cc 217 125 199.0 305 34,000 9.4 2024 2000 1.090 -9 2,823,360
24 MV Agusta Brutale 1000 RR Naked Italy MOTOR-CYCLE 24 C: 600-999 cc 208 117 186.0 299 35,000 9.3 2023 900 1.118 -8 2,906,400
25 Hero Splendor Plus Commuter India MOTOR-CYCLE 25 A: <200 cc 8 8 112.0 96 1,000 7.8 2024 280000 0.071 -9 83,040
26 KTM RC 390 Supersport Austria MOTOR-CYCLE 26 B: 200-599 cc 44 37 172.0 178 5,100 8.5 2024 22000 0.256 -9 423,504
27 Lectric XP 3.0 E-Bike USA BICYCLE (Pedal / e-assist) 27 A: <200 cc 0 0 23.6 . 999 8.0 2024 35000 . -9 82,957
28 Brompton C Line Explore Folding UK BICYCLE (Pedal / e-assist) 28 A: <200 cc 0 0 11.6 . 1,750 8.2 2023 12000 . -8 145,320
29 BMW R1300GS Adventure Germany MOTOR-CYCLE 29 D: =1000 cc 145 143 239.0 225 20,500 9.1 2025 1000 0.607 -10 1,702,320
30 Ducati DesertX Adventure Italy MOTOR-CYCLE 30 C: 600-999 cc 110 92 223.0 242 17,000 8.9 2024 1900 0.493 -9 1,411,680


proc print data=work.best_bikes_inr (obs=5);

   var Brand Model Price_USD Price_INR;

   title "USD To INR Check (First 5 Rows)";

run;

Output:

USD To INR Check (First 5 Rows)

Obs Brand Model Price_USD Price_INR
1 Honda CBR1000RR-R Fireblade SP 28,000 2,325,120
2 Yamaha YZF-R1M 26,000 2,159,040
3 Ducati Panigale V4 S 32,000 2,657,280
4 Kawasaki Ninja H2R 55,000 4,567,200
5 BMW S1000RR 25,000 2,076,000


10.Professional‑grade reporting

proc report data=work.type_summary nowd

            headline headskip style(column)=[cellwidth=2in];

   column Type n_models avg_price avg_hp_wt;

   define Type       / group 'Bike Type';

   define n_models   / analysis 'Models';

   define avg_price  / analysis 'Average Price (USD)' format=comma10.;

   define avg_hp_wt  / analysis 'Avg HP per kg';

   title "Executive Summary — Price & Performance by Type";

run;

Output:

Executive Summary — Price & Performance by Type

Bike Type Models Average Price (USD) Avg HP per kg
BICYCLE (Pedal / e-assist) 12 8,679 .
MOTOR-CYCLE 18 20,578 0.717


proc tabulate data=work.best_bikes format=comma10.;

   class Type Segment;

   var   Price_USD;

   table Type*Segment,

         Price_USD*(n mean median p90);

   title "Price Distribution Matrix";

run;

Output:

Price Distribution Matrix

  Price_USD
N Mean Median P90
Type Segment 1 999 999 999
BICYCLE (Pedal / e-assist) E-Bike
E-MTB 1 7,500 7,500 7,500
E-Road 1 6,300 6,300 6,300
Endurance 1 10,500 10,500 10,500
Enduro 1 9,500 9,500 9,500
Folding 1 1,750 1,750 1,750
Mountain 2 8,450 8,450 10,500
Road 3 12,900 13,000 14,500
XC 1 12,000 12,000 12,000
MOTOR-CYCLE Adventure 4 15,500 17,500 20,500
Commuter 1 1,000 1,000 1,000
Hyperbike 2 41,500 41,500 55,000
Naked 3 23,000 19,000 35,000
Sport-Touring 1 3,000 3,000 3,000
Street 1 2,300 2,300 2,300
Supersport 6 25,017 27,000 34,000


11.Visualising relationships

proc sgplot data=work.best_bikes;

   scatter x=Engine_CC y=Power_HP / group=Type;

   reg     x=Engine_CC y=Power_HP / nomarkers;

   where Engine_CC>0;  /* exclude bicycles */

   title "Engine Size vs Horse‑Power (Motorcycles)";

run;

Log:
NOTE: PROCEDURE SGPLOT used (Total process time):
      real time           1.03 seconds
      user cpu time       0.06 seconds
      system cpu time     0.01 seconds
      memory              1802.78k
      OS Memory           40064.00k
      Timestamp           14/09/2015 12:36:05 AM
      Step Count                        31  Switch Count  0

NOTE: Listing image output written to SGPlot1.png.
NOTE: There were 18 observations read from the data set WORK.BEST_BIKES.
      WHERE Engine_CC>0;




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