315.WORLD CUISINES DATASET CREATION AND NUTRIENT ANALYSIS USING PROC SQL | PROC FORMAT | PROC PRINT | PROC MEANS | PROC STDIZE | PROC SGPANEL WITH MACROS AND DATE FORMAT INTEGRATION

WORLD CUISINES DATASET CREATION AND NUTRIENT ANALYSIS USING PROC SQL | PROC FORMAT | PROC PRINT | PROC MEANS | PROC STDIZE | PROC SGPANEL WITH MACROS AND DATE FORMAT INTEGRATION

OPTIONS NOCENTER;

1. PROC FORMAT – custom formats for spiciness & Veg/NonVeg 

proc format;

  value spicefmt

    1 = 'Very Mild'

    2 = 'Mild'

    3 = 'Medium'

    4 = 'Spicy'

    5 = 'Very Spicy';

NOTE:

NOTE: Format SPICEFMT has been output.

  value $vegf

    'Veg'    = 'Vegetarian'

    'NonVeg' = 'Non-Vegetarian';

run;

NOTE:

NOTE: Format $VEGF has been output.

%let analysis_date = %sysfunc(today());

NOTE:

2. PROC SQL – create the WORLD_CUISINE dataset           

Includes a SAS date variable MENU_DATE with DATE9.    

proc sql;

  create table work.world_cuisine as

  select "Paneer Tikka" as Dish_Name length=30,

         "India" as Country length=20,

         280 as Calories,

         18  as Protein,

         16  as Fat,

         4   as Spiciness_Level,

         "Veg" as Veg_or_NonVeg length=7,

         "15JAN2025"d as Menu_Date format=date9.

  from sashelp.class(obs=1)

  union all

  select "Sushi",

         "Japan",

         200,

         16,

         5,

         2,

         "NonVeg",

         "10FEB2025"d

  from sashelp.class(obs=1)

  union all

  select "Tacos al Pastor",

         "Mexico",

         450,

         20,

         22,

         4,

         "NonVeg",

         "05MAR2025"d

  from sashelp.class(obs=1)

  union all

  select "Margherita Pizza",

         "Italy",

         320,

         14,

         12,

         2,

         "Veg",

         "20JAN2025"d

  from sashelp.class(obs=1)

  union all

  select "Falafel Wrap",

         "Lebanon",

         380,

         13,

         18,

         3,

         "Veg",

         "28FEB2025"d

  from sashelp.class(obs=1)

  union all

  select "Bibimbap",

         "South Korea",

         500,

         18,

         15,

         3,

         "NonVeg",

         "03APR2025"d

  from sashelp.class(obs=1)

  union all

  select "Chicken Biryani",

         "India",

         550,

         24,

         20,

         5,

         "NonVeg",

         "12FEB2025"d

  from sashelp.class(obs=1)

  union all

  select "Greek Salad",

         "Greece",

         220,

         8,

         14,

         1,

         "Veg",

         "25MAR2025"d

  from sashelp.class(obs=1)

  union all

  select "Pad Thai",

         "Thailand",

         420,

         17,

         14,

         4,

         "NonVeg",

         "18APR2025"d

  from sashelp.class(obs=1)

  union all

  select "Shakshuka",

         "Israel",

         300,

         14,

         16,

         3,

         "Veg",

         "30JAN2025"d

  from sashelp.class(obs=1)

  union all

  select "Poutine",

         "Canada",

         650,

         18,

         42,

         1,

         "NonVeg",

         "07MAR2025"d

  from sashelp.class(obs=1)

  union all

  select "Hummus Platter",

         "Middle East",

         260,

         10,

         12,

         2,

         "Veg",

         "14APR2025"d

  from sashelp.class(obs=1)

  ;

quit;

proc print data=work.world_cuisine;

  format Spiciness_Level spicefmt.

         Veg_or_NonVeg   $vegf.

         Menu_Date       date9.;

  title "World Cuisine Dataset as of %sysfunc(putn(&analysis_date.,date9.))";

run;

OUTPUT:

World Cuisine Dataset as of 21NOV2025

Obs	Dish_Name	Country	Calories	Protein	Fat	Spiciness_Level	Veg_or_NonVeg	Menu_Date
1	Paneer Tikka	India	280	18	16	Spicy	Vegetarian	15JAN2025
2	Sushi	Japan	200	16	5	Mild	Non-Vegetarian	10FEB2025
3	Tacos al Pastor	Mexico	450	20	22	Spicy	Non-Vegetarian	05MAR2025
4	Margherita Pizza	Italy	320	14	12	Mild	Vegetarian	20JAN2025
5	Falafel Wrap	Lebanon	380	13	18	Medium	Vegetarian	28FEB2025
6	Bibimbap	South Korea	500	18	15	Medium	Non-Vegetarian	03APR2025
7	Chicken Biryani	India	550	24	20	Very Spicy	Non-Vegetarian	12FEB2025
8	Greek Salad	Greece	220	8	14	Very Mild	Vegetarian	25MAR2025
9	Pad Thai	Thailand	420	17	14	Spicy	Non-Vegetarian	18APR2025
10	Shakshuka	Israel	300	14	16	Medium	Vegetarian	30JAN2025
11	Poutine	Canada	650	18	42	Very Mild	Non-Vegetarian	07MAR2025
12	Hummus Platter	Middle East	260	10	12	Mild	Vegetarian	14APR2025

3. Macro – nutrient categorization

Creates protein and fat category variables    

%macro nutrient_category(input_ds=, output_ds=);

data &output_ds.;

  set &input_ds.;

  length Protein_Cat Fat_Cat $20.;

  /* Protein-based category */

  if Protein < 10 then Protein_Cat = "Low Protein";

  else if 10 <= Protein < 20 then Protein_Cat = "Moderate Protein";

  else Protein_Cat = "High Protein";

  /* Fat-based category */

  if Fat < 10 then Fat_Cat = "Low Fat";

  else if 10 <= Fat < 20 then Fat_Cat = "Moderate Fat";

  else Fat_Cat = "High Fat";

  /*Date-based filter flag using DATE statement logic */

  if Menu_Date <= &analysis_date. then Available_Flag = 1;

  else Available_Flag = 0;

run;

%mend nutrient_category;

%nutrient_category(input_ds=work.world_cuisine, output_ds=work.cuisine_with_cat);

OUTPUT:

Obs	Dish_Name	Country	Calories	Protein	Fat	Spiciness_Level	Veg_or_NonVeg	Menu_Date	Protein_Cat	Fat_Cat	Available_Flag
1	Paneer Tikka	India	280	18	16	4	Veg	15JAN2025	Moderate Protein	Moderate Fat	1
2	Sushi	Japan	200	16	5	2	NonVeg	10FEB2025	Moderate Protein	Low Fat	1
3	Tacos al Pastor	Mexico	450	20	22	4	NonVeg	05MAR2025	High Protein	High Fat	1
4	Margherita Pizza	Italy	320	14	12	2	Veg	20JAN2025	Moderate Protein	Moderate Fat	1
5	Falafel Wrap	Lebanon	380	13	18	3	Veg	28FEB2025	Moderate Protein	Moderate Fat	1
6	Bibimbap	South Korea	500	18	15	3	NonVeg	03APR2025	Moderate Protein	Moderate Fat	1
7	Chicken Biryani	India	550	24	20	5	NonVeg	12FEB2025	High Protein	High Fat	1
8	Greek Salad	Greece	220	8	14	1	Veg	25MAR2025	Low Protein	Moderate Fat	1
9	Pad Thai	Thailand	420	17	14	4	NonVeg	18APR2025	Moderate Protein	Moderate Fat	1
10	Shakshuka	Israel	300	14	16	3	Veg	30JAN2025	Moderate Protein	Moderate Fat	1
11	Poutine	Canada	650	18	42	1	NonVeg	07MAR2025	Moderate Protein	High Fat	1
12	Hummus Platter	Middle East	260	10	12	2	Veg	14APR2025	Moderate Protein	Moderate Fat	1

4. PROC MEANS – descriptive statistics

By Veg_or_NonVeg                                     

proc means data=work.cuisine_with_cat n mean std min max;

  class Veg_or_NonVeg;

  var Calories Protein Fat;

  format Veg_or_NonVeg $vegf.;

  title "Descriptive Statistics of World Cuisines by Veg/NonVeg";

run;

OUTPUT:

Descriptive Statistics of World Cuisines by Veg/NonVeg

The MEANS Procedure

Veg_or_NonVeg	N Obs	Variable	N	Mean	Std Dev	Minimum	Maximum
Non-Vegetarian	6	Calories Protein Fat	6 6 6	461.6666667 18.8333333 19.6666667	151.7124473 2.8577380 12.4365054	200.0000000 16.0000000 5.0000000	650.0000000 24.0000000 42.0000000
Vegetarian	6	Calories Protein Fat	6 6 6	293.3333333 12.8333333 14.6666667	54.6504041 3.4880749 2.4221203	220.0000000 8.0000000 12.0000000	380.0000000 18.0000000 18.0000000

5. PROC STDIZE – standardize nutrients                    

Create standardized Z-scores for Calories, Protein, Fat

proc stdize data=work.cuisine_with_cat

            out=work.cuisine_std

            method=std;

  var Calories Protein Fat;

run;

OUTPUT:

Obs	Dish_Name	Country	Calories	Protein	Fat	Spiciness_Level	Veg_or_NonVeg	Menu_Date	Protein_Cat	Fat_Cat	Available_Flag
1	Paneer Tikka	India	-0.69736	0.49628	-0.13061	4	Veg	15JAN2025	Moderate Protein	Moderate Fat	1
2	Sushi	Japan	-1.26955	0.03818	-1.36208	2	NonVeg	10FEB2025	Moderate Protein	Low Fat	1
3	Tacos al Pastor	Mexico	0.51855	0.95438	0.54110	4	NonVeg	05MAR2025	High Protein	High Fat	1
4	Margherita Pizza	Italy	-0.41126	-0.41993	-0.57842	2	Veg	20JAN2025	Moderate Protein	Moderate Fat	1
5	Falafel Wrap	Lebanon	0.01788	-0.64898	0.09329	3	Veg	28FEB2025	Moderate Protein	Moderate Fat	1
6	Bibimbap	South Korea	0.87617	0.49628	-0.24256	3	NonVeg	03APR2025	Moderate Protein	Moderate Fat	1
7	Chicken Biryani	India	1.23379	1.87058	0.31720	5	NonVeg	12FEB2025	High Protein	High Fat	1
8	Greek Salad	Greece	-1.12650	-1.79423	-0.35451	1	Veg	25MAR2025	Low Protein	Moderate Fat	1
9	Pad Thai	Thailand	0.30398	0.26723	-0.35451	4	NonVeg	18APR2025	Moderate Protein	Moderate Fat	1
10	Shakshuka	Israel	-0.55431	-0.41993	-0.13061	3	Veg	30JAN2025	Moderate Protein	Moderate Fat	1
11	Poutine	Canada	1.94903	0.49628	2.78014	1	NonVeg	07MAR2025	Moderate Protein	High Fat	1
12	Hummus Platter	Middle East	-0.84041	-1.33613	-0.57842	2	Veg	14APR2025	Moderate Protein	Moderate Fat	1

6. PROC SGPANEL – visualization by Veg_or_NonVeg

proc sgpanel data=work.cuisine_std;

  panelby Veg_or_NonVeg / columns=2;

  scatter x=Protein y=Calories / group=Country;

  rowaxis label="Calories (Standardized)";

  colaxis label="Protein (Standardized)";

  format Veg_or_NonVeg $vegf.;

  title "Calories vs Protein by Veg/NonVeg (Standardized Values)";

run;

OUTPUT:

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