292.Which Breakfast Is The Healthiest And Most Popular? — Using Sas Data Analytics

Which Breakfast Is The Healthiest And Most Popular? — Using Sas Data Analytics

/*CREATING A DATASET OF REALWORLD BREAKFAST ITEMS SAS*/

1) Create dataset 

options nocenter;

data work.realbreakfast;

  length ItemID $6 ItemName $40 Cuisine $20 Type $8;

  input ItemID $ ItemName : $40. Cuisine : $20. Type : $8. Calories PriceINR 

        PrepTimeMin;

  datalines;

B001 Idli SouthIndian Veg 120 30 12

B002 Dosa SouthIndian Veg 220 45 15

B003 Poha Maharashtrian Veg 260 35 10

B004 Paratha NorthIndian Veg 350 50 20

B005 EggBhurji Indian NonVeg 200 40 12

B006 BreadJam Continental Veg 180 25 5

B007 Upma SouthIndian Veg 240 30 10

B008 AlooParatha NorthIndian Veg 420 55 22

B009 PuriSabji NorthIndian Veg 380 60 18

B010 Omelette Continental NonVeg 220 35 8

B011 MasalaDosa SouthIndian Veg 260 55 18

B012 OatsPorridge Continental Veg 150 40 7

;

run;

proc print;run;

Output:

Obs	ItemID	ItemName	Cuisine	Type	Calories	PriceINR	PrepTimeMin
1	B001	Idli	SouthIndian	Veg	120	30	12
2	B002	Dosa	SouthIndian	Veg	220	45	15
3	B003	Poha	Maharashtrian	Veg	260	35	10
4	B004	Paratha	NorthIndian	Veg	350	50	20
5	B005	EggBhurji	Indian	NonVeg	200	40	12
6	B006	BreadJam	Continental	Veg	180	25	5
7	B007	Upma	SouthIndian	Veg	240	30	10
8	B008	AlooParatha	NorthIndian	Veg	420	55	22
9	B009	PuriSabji	NorthIndian	Veg	380	60	18
10	B010	Omelette	Continental	NonVeg	220	35	8
11	B011	MasalaDosa	SouthIndian	Veg	260	55	18
12	B012	OatsPorridge	Continental	Veg	150	40	7

 2) Add formatted price and calorie category 

purpose: Create human-friendly categories for calories. 

proc format;

  value calcat

    low - 199 = 'Low'

    200 - 299 = 'Moderate'

    300 - high = 'High';

run;

Log:

NOTE: Format CALCAT has been output.

purpose: create formatted price column for display

purpose: attach calorie category 

Here we already know that Put Function 

Purpose: Converts numeric values to character values.

data work.realbreakfast_fmt;

  set work.realbreakfast;

  PriceINR_f = put(PriceINR, dollar8.);  

  CalorieCat = put(Calories, calcat.);  

run;

proc print;run;

Output:

Obs	ItemID	ItemName	Cuisine	Type	Calories	PriceINR	PrepTimeMin	PriceINR_f	CalorieCat
1	B001	Idli	SouthIndian	Veg	120	30	12	$30	Low
2	B002	Dosa	SouthIndian	Veg	220	45	15	$45	Moderate
3	B003	Poha	Maharashtrian	Veg	260	35	10	$35	Moderate
4	B004	Paratha	NorthIndian	Veg	350	50	20	$50	High
5	B005	EggBhurji	Indian	NonVeg	200	40	12	$40	Moderate
6	B006	BreadJam	Continental	Veg	180	25	5	$25	Low
7	B007	Upma	SouthIndian	Veg	240	30	10	$30	Moderate
8	B008	AlooParatha	NorthIndian	Veg	420	55	22	$55	High
9	B009	PuriSabji	NorthIndian	Veg	380	60	18	$60	High
10	B010	Omelette	Continental	NonVeg	220	35	8	$35	Moderate
11	B011	MasalaDosa	SouthIndian	Veg	260	55	18	$55	Moderate
12	B012	OatsPorridge	Continental	Veg	150	40	7	$40	Low

3) PROC PRINT: show dataset 

purpose: Display the dataset in readable tabular form. 

proc print data=work.realbreakfast_fmt(obs=8) noobs label;

  title "Realworld Breakfast Items - Raw View";

  var ItemID ItemName Cuisine Type Calories CalorieCat PriceINR_f PrepTimeMin;

  label PriceINR_f = "Price (INR)"

        CalorieCat = "Calorie Category";

run;

Output:

Realworld Breakfast Items - Raw View

ItemID	ItemName	Cuisine	Type	Calories	Calorie Category	Price (INR)	PrepTimeMin
B001	Idli	SouthIndian	Veg	120	Low	$30	12
B002	Dosa	SouthIndian	Veg	220	Moderate	$45	15
B003	Poha	Maharashtrian	Veg	260	Moderate	$35	10
B004	Paratha	NorthIndian	Veg	350	High	$50	20
B005	EggBhurji	Indian	NonVeg	200	Moderate	$40	12
B006	BreadJam	Continental	Veg	180	Low	$25	5
B007	Upma	SouthIndian	Veg	240	Moderate	$30	10
B008	AlooParatha	NorthIndian	Veg	420	High	$55	22

4) PROC MEANS: numeric summaries 

purpose: Compute central tendency and spread for numeric variables. 

proc means data=work.realbreakfast n mean std min max maxdec=1;

  var Calories PriceINR PrepTimeMin;

  title "Numeric Summary for Calories, Price and Prep Time";

run;

Output:

Numeric Summary for Calories, Price and Prep Time

The MEANS Procedure

Variable	N	Mean	Std Dev	Minimum	Maximum
Calories PriceINR PrepTimeMin	12 12 12	250.0 41.7 13.1	91.6 11.3 5.5	120.0 25.0 5.0	420.0 60.0 22.0

5) PROC FREQ: categorical distributions 

purpose: Show counts across categorical variables. 

proc freq data=work.realbreakfast_fmt;

  tables Cuisine Type CalorieCat / nocum nopercent;

  title "Frequency counts by Cuisine, Type and Calorie Category";

run;

Output:

Frequency counts by Cuisine, Type and Calorie Category

The FREQ Procedure

Cuisine	Frequency
Continental	3
Indian	1
Maharashtrian	1
NorthIndian	3
SouthIndian	4

Type	Frequency
NonVeg	2
Veg	10

CalorieCat	Frequency
High	3
Low	3
Moderate	6

6) PROC SQL: aggregated summaries and joins 

purpose: Use SQL to group by cuisine and compute aggregated metrics. 

proc sql;

  create table work.by_cuisine as

  select Cuisine,

         count(*) as N,

         mean(Calories) format=8.1 as MeanCalories,

         mean(PriceINR) format=8.1 as MeanPrice,

         mean(PrepTimeMin) format=8.1 as MeanPrep

  from work.realbreakfast

  group by Cuisine

  order by MeanPrice desc;

quit;

proc print;run;

Output:

Obs	Cuisine	N	MeanCalories	MeanPrice	MeanPrep
1	NorthIndian	3	383.3	55.0	20.0
2	Indian	1	200.0	40.0	12.0
3	SouthIndian	4	210.0	40.0	13.8
4	Maharashtrian	1	260.0	35.0	10.0
5	Continental	3	183.3	33.3	6.7

7) PROC SORT: prepare for reporting 

purpose: Sort dataset by descending calories for top-calorie reporting. 

proc sort data=work.realbreakfast out=work.realbreakfast_sorted;

  by descending Calories;

run;

proc print data=work.realbreakfast_sorted;

run;

Output:

Obs	ItemID	ItemName	Cuisine	Type	Calories	PriceINR	PrepTimeMin
1	B008	AlooParatha	NorthIndian	Veg	420	55	22
2	B009	PuriSabji	NorthIndian	Veg	380	60	18
3	B004	Paratha	NorthIndian	Veg	350	50	20
4	B003	Poha	Maharashtrian	Veg	260	35	10
5	B011	MasalaDosa	SouthIndian	Veg	260	55	18
6	B007	Upma	SouthIndian	Veg	240	30	10
7	B002	Dosa	SouthIndian	Veg	220	45	15
8	B010	Omelette	Continental	NonVeg	220	35	8
9	B005	EggBhurji	Indian	NonVeg	200	40	12
10	B006	BreadJam	Continental	Veg	180	25	5
11	B012	OatsPorridge	Continental	Veg	150	40	7
12	B001	Idli	SouthIndian	Veg	120	30	12

8) Macro: summarize by Type (Veg/NonVeg) using PROC MEANS and PROC SQL 

purpose: Macro to generate numeric summary and top-calorie list for a given Type. 

%macro summary_by_type(type=);

  %put NOTE: Starting summary for &type;

  proc means data=work.realbreakfast(where=(upcase(Type)=upcase("&type"))) n mean std min max maxdec=1;

    var Calories PriceINR PrepTimeMin;

    title "Summary for &type Items";

  run;

  proc sql;

    create table work.top_&type as

    select ItemID, ItemName, Calories, PriceINR, PrepTimeMin

    from work.realbreakfast

    where upcase(Type)=upcase("&type")

    order by Calories desc;

  quit;

%mend;

%summary_by_type(type=Veg);

Log:

NOTE: Starting summary for Veg

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

WHERE UPCASE(Type)='VEG';

Output:

Summary for Veg Items

The MEANS Procedure

Variable	N	Mean	Std Dev	Minimum	Maximum
Calories PriceINR PrepTimeMin	10 10 10	258.0 42.5 13.7	99.0 12.3 5.8	120.0 25.0 5.0	420.0 60.0 22.0

%summary_by_type(type=NonVeg);

Log:

NOTE: Starting summary for NonVeg

NOTE: There were 2 observations read from the data set WORK.REALBREAKFAST.

WHERE UPCASE(Type)='NONVEG';

Output:

Summary for NonVeg Items

The MEANS Procedure

Variable	N	Mean	Std Dev	Minimum	Maximum
Calories PriceINR PrepTimeMin	2 2 2	210.0 37.5 10.0	14.1 3.5 2.8	200.0 35.0 8.0	220.0 40.0 12.0

9) PROC REPORT: fancy printable report 

purpose: Produce a formatted, report-style listing for presentation. 

proc report data=work.realbreakfast_fmtnowd headline spacing=1;

  column ItemID ItemName Cuisine Type Calories PriceINR_f PrepTimeMin CalorieCat;

  define ItemID / group 'ID' width=6;

  define ItemName / display 'Breakfast Item' width=30;

  define Cuisine / group 'Cuisine';

  define Type / group 'Type';

  define Calories / analysis mean 'Calories' format=8.;

  define PriceINR_f / display 'Price (INR)';

  define PrepTimeMin / analysis mean 'Prep Time (min)';

  define CalorieCat / display 'Cal Cat';

  title "Formatted Breakfast Items Report";

run;

Output:

Formatted Breakfast Items Report

ID	Breakfast Item	Cuisine	Type	Calories	Price (INR)	Prep Time (min)	Cal Cat
B001	Idli	SouthIndian	Veg	120	$30	12	Low
B002	Dosa	SouthIndian	Veg	220	$45	15	Moderate
B003	Poha	Maharashtrian	Veg	260	$35	10	Moderate
B004	Paratha	NorthIndian	Veg	350	$50	20	High
B005	EggBhurji	Indian	NonVeg	200	$40	12	Moderate
B006	BreadJam	Continental	Veg	180	$25	5	Low
B007	Upma	SouthIndian	Veg	240	$30	10	Moderate
B008	AlooParatha	NorthIndian	Veg	420	$55	22	High
B009	PuriSabji	NorthIndian	Veg	380	$60	18	High
B010	Omelette	Continental	NonVeg	220	$35	8	Moderate
B011	MasalaDosa	SouthIndian	Veg	260	$55	18	Moderate
B012	OatsPorridge	Continental	Veg	150	$40	7	Low

10) PROC SQL: select items with calories > 300 (high calorie items) 

purpose: Create a table of high-calorie breakfast items. 

proc sql;

  create table work.high_cal as

  select * from work.realbreakfast

  where Calories > 300

  order by Calories desc;

quit;

proc print;run;

Output:

Obs	ItemID	ItemName	Cuisine	Type	Calories	PriceINR	PrepTimeMin
1	B008	AlooParatha	NorthIndian	Veg	420	55	22
2	B009	PuriSabji	NorthIndian	Veg	380	60	18
3	B004	Paratha	NorthIndian	Veg	350	50	20

11) Simple QC checks: duplicate ItemID and missing values 

purpose: Basic QC checks to identify duplicates and missing numeric values. 

proc sql;

  select 'DUPLICATE_ITEMIDS' as CheckType, ItemID, count(*) as CNT

  from work.realbreakfast

  group by ItemID

  having count(*) > 1;

quit;

Log:

NOTE: No rows were selected.

proc means data=work.realbreakfast n nmiss;

  var Calories PriceINR PrepTimeMin;

  title "Missing-value check for numeric fields";

run;

Output:

Missing-value check for numeric fields

The MEANS Procedure

Variable	N	N Miss
Calories PriceINR PrepTimeMin	12 12 12	0 0 0

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