252.REAL-WORLD DAL PRICE TREND AND YEAR-WISE COMPARISON IN INDIA (2014–2025) USING PROC SORT | PROC TRANSPOSE | PROC PRINT | PROC MEANS | PROC FREQ | PROC SQL

REAL-WORLD DAL PRICE TREND AND YEAR-WISE COMPARISON IN INDIA (2014–2025) USING PROC SORT | PROC TRANSPOSE | PROC PRINT | PROC MEANS | PROC FREQ | PROC SQL | PROC SUMMARY IN SAS

/*Creating a dataset for different types of dals (pulses) in India from 2014–2025*/

Creation Of The Dataset

option nocenter;

data dal_market_india;

length Dal_Name $20 Main_Producing_State $20 Rainfall_Impact $10 Price_Category $10;

input Dal_ID Year Dal_Name $ Average_Price_Per_Kg Annual_Consumption_Tonnes

Main_Producing_State $ Import_Quantity_Tonnes Export_Quantity_Tonnes Rainfall_Impact $ Price_Category $;

datalines;

1 2014 Toor_Dal 75 450000 Maharashtra 20000 15000 Medium Medium

2 2014 Moong_Dal 90 320000 Rajasthan 15000 10000 Low High

3 2014 Masoor_Dal 65 250000 Madhya_Pradesh 30000 5000 Medium Medium

4 2014 Urad_Dal 85 280000 Andhra_Pradesh 10000 8000 Low High

5 2014 Chana_Dal 60 500000 Maharashtra 25000 20000 Medium Medium

6 2015 Toor_Dal 80 470000 Karnataka 22000 17000 Medium Medium

7 2015 Moong_Dal 95 340000 Rajasthan 18000 12000 Low High

8 2015 Masoor_Dal 70 260000 Uttar_Pradesh 32000 6000 High Medium

9 2015 Urad_Dal 88 290000 Andhra_Pradesh 12000 8500 Low High

10 2015 Chana_Dal 62 510000 Maharashtra 27000 21000 Medium Medium

11 2016 Toor_Dal 120 440000 Maharashtra 25000 18000 Low High

12 2016 Moong_Dal 98 350000 Rajasthan 19000 13000 Low High

13 2016 Masoor_Dal 72 270000 Madhya_Pradesh 33000 7000 High Medium

14 2016 Urad_Dal 95 295000 Andhra_Pradesh 14000 9000 Low High

15 2016 Chana_Dal 68 520000 Maharashtra 29000 22000 Medium Medium

16 2017 Toor_Dal 85 480000 Karnataka 20000 16000 High Medium

17 2017 Moong_Dal 92 360000 Rajasthan 15000 11000 Medium High

18 2017 Masoor_Dal 75 280000 Uttar_Pradesh 34000 7500 High Medium

19 2017 Urad_Dal 90 300000 Andhra_Pradesh 15000 9500 Low High

20 2017 Chana_Dal 65 530000 Maharashtra 28000 23000 Medium Medium

21 2018 Toor_Dal 88 490000 Maharashtra 21000 17000 High Medium

22 2018 Moong_Dal 94 370000 Rajasthan 16000 12000 Medium High

23 2018 Masoor_Dal 78 290000 Madhya_Pradesh 35000 8000 High Medium

24 2018 Urad_Dal 92 310000 Andhra_Pradesh 16000 9700 Low High

25 2018 Chana_Dal 70 540000 Maharashtra 30000 24000 Medium Medium

26 2019 Toor_Dal 95 500000 Karnataka 23000 17500 Low High

27 2019 Moong_Dal 97 380000 Rajasthan 17000 13000 Medium High

28 2019 Masoor_Dal 80 300000 Uttar_Pradesh 36000 8500 High Medium

29 2019 Urad_Dal 96 320000 Andhra_Pradesh 17000 9800 Low High

30 2019 Chana_Dal 72 550000 Maharashtra 31000 25000 Medium Medium

31 2020 Toor_Dal 100 510000 Maharashtra 24000 18000 Low High

32 2020 Moong_Dal 99 390000 Rajasthan 18000 13500 Low High

33 2020 Masoor_Dal 82 310000 Madhya_Pradesh 37000 9000 High Medium

34 2020 Urad_Dal 98 330000 Andhra_Pradesh 18000 9900 Low High

35 2020 Chana_Dal 75 560000 Maharashtra 32000 26000 Medium Medium

;

run;

proc print;run;

Output:

Obs	Dal_Name	Main_Producing_State	Rainfall_Impact	Price_Category	Dal_ID	Year	Average_Price_Per_Kg	Annual_Consumption_Tonnes	Import_Quantity_Tonnes	Export_Quantity_Tonnes
1	Toor_Dal	Maharashtra	Medium	Medium	1	2014	75	450000	20000	15000
2	Moong_Dal	Rajasthan	Low	High	2	2014	90	320000	15000	10000
3	Masoor_Dal	Madhya_Pradesh	Medium	Medium	3	2014	65	250000	30000	5000
4	Urad_Dal	Andhra_Pradesh	Low	High	4	2014	85	280000	10000	8000
5	Chana_Dal	Maharashtra	Medium	Medium	5	2014	60	500000	25000	20000
6	Toor_Dal	Karnataka	Medium	Medium	6	2015	80	470000	22000	17000
7	Moong_Dal	Rajasthan	Low	High	7	2015	95	340000	18000	12000
8	Masoor_Dal	Uttar_Pradesh	High	Medium	8	2015	70	260000	32000	6000
9	Urad_Dal	Andhra_Pradesh	Low	High	9	2015	88	290000	12000	8500
10	Chana_Dal	Maharashtra	Medium	Medium	10	2015	62	510000	27000	21000
11	Toor_Dal	Maharashtra	Low	High	11	2016	120	440000	25000	18000
12	Moong_Dal	Rajasthan	Low	High	12	2016	98	350000	19000	13000
13	Masoor_Dal	Madhya_Pradesh	High	Medium	13	2016	72	270000	33000	7000
14	Urad_Dal	Andhra_Pradesh	Low	High	14	2016	95	295000	14000	9000
15	Chana_Dal	Maharashtra	Medium	Medium	15	2016	68	520000	29000	22000
16	Toor_Dal	Karnataka	High	Medium	16	2017	85	480000	20000	16000
17	Moong_Dal	Rajasthan	Medium	High	17	2017	92	360000	15000	11000
18	Masoor_Dal	Uttar_Pradesh	High	Medium	18	2017	75	280000	34000	7500
19	Urad_Dal	Andhra_Pradesh	Low	High	19	2017	90	300000	15000	9500
20	Chana_Dal	Maharashtra	Medium	Medium	20	2017	65	530000	28000	23000
21	Toor_Dal	Maharashtra	High	Medium	21	2018	88	490000	21000	17000
22	Moong_Dal	Rajasthan	Medium	High	22	2018	94	370000	16000	12000
23	Masoor_Dal	Madhya_Pradesh	High	Medium	23	2018	78	290000	35000	8000
24	Urad_Dal	Andhra_Pradesh	Low	High	24	2018	92	310000	16000	9700
25	Chana_Dal	Maharashtra	Medium	Medium	25	2018	70	540000	30000	24000
26	Toor_Dal	Karnataka	Low	High	26	2019	95	500000	23000	17500
27	Moong_Dal	Rajasthan	Medium	High	27	2019	97	380000	17000	13000
28	Masoor_Dal	Uttar_Pradesh	High	Medium	28	2019	80	300000	36000	8500
29	Urad_Dal	Andhra_Pradesh	Low	High	29	2019	96	320000	17000	9800
30	Chana_Dal	Maharashtra	Medium	Medium	30	2019	72	550000	31000	25000
31	Toor_Dal	Maharashtra	Low	High	31	2020	100	510000	24000	18000
32	Moong_Dal	Rajasthan	Low	High	32	2020	99	390000	18000	13500
33	Masoor_Dal	Madhya_Pradesh	High	Medium	33	2020	82	310000	37000	9000
34	Urad_Dal	Andhra_Pradesh	Low	High	34	2020	98	330000	18000	9900
35	Chana_Dal	Maharashtra	Medium	Medium	35	2020	75	560000	32000	26000

1. PROC PRINT – Display Dataset

proc print data=dal_market_india;

title "Real-World Dal Price & Consumption Dataset (2014–2025)";

run;

Output:

Real-World Dal Price & Consumption Dataset (2014–2025)


Obs	Dal_Name	Main_Producing_State	Rainfall_Impact	Price_Category	Dal_ID	Year	Average_Price_Per_Kg	Annual_Consumption_Tonnes	Import_Quantity_Tonnes	Export_Quantity_Tonnes
1	Toor_Dal	Maharashtra	Medium	Medium	1	2014	75	450000	20000	15000
2	Moong_Dal	Rajasthan	Low	High	2	2014	90	320000	15000	10000
3	Masoor_Dal	Madhya_Pradesh	Medium	Medium	3	2014	65	250000	30000	5000
4	Urad_Dal	Andhra_Pradesh	Low	High	4	2014	85	280000	10000	8000
5	Chana_Dal	Maharashtra	Medium	Medium	5	2014	60	500000	25000	20000
6	Toor_Dal	Karnataka	Medium	Medium	6	2015	80	470000	22000	17000
7	Moong_Dal	Rajasthan	Low	High	7	2015	95	340000	18000	12000
8	Masoor_Dal	Uttar_Pradesh	High	Medium	8	2015	70	260000	32000	6000
9	Urad_Dal	Andhra_Pradesh	Low	High	9	2015	88	290000	12000	8500
10	Chana_Dal	Maharashtra	Medium	Medium	10	2015	62	510000	27000	21000
11	Toor_Dal	Maharashtra	Low	High	11	2016	120	440000	25000	18000
12	Moong_Dal	Rajasthan	Low	High	12	2016	98	350000	19000	13000
13	Masoor_Dal	Madhya_Pradesh	High	Medium	13	2016	72	270000	33000	7000
14	Urad_Dal	Andhra_Pradesh	Low	High	14	2016	95	295000	14000	9000
15	Chana_Dal	Maharashtra	Medium	Medium	15	2016	68	520000	29000	22000
16	Toor_Dal	Karnataka	High	Medium	16	2017	85	480000	20000	16000
17	Moong_Dal	Rajasthan	Medium	High	17	2017	92	360000	15000	11000
18	Masoor_Dal	Uttar_Pradesh	High	Medium	18	2017	75	280000	34000	7500
19	Urad_Dal	Andhra_Pradesh	Low	High	19	2017	90	300000	15000	9500
20	Chana_Dal	Maharashtra	Medium	Medium	20	2017	65	530000	28000	23000
21	Toor_Dal	Maharashtra	High	Medium	21	2018	88	490000	21000	17000
22	Moong_Dal	Rajasthan	Medium	High	22	2018	94	370000	16000	12000
23	Masoor_Dal	Madhya_Pradesh	High	Medium	23	2018	78	290000	35000	8000
24	Urad_Dal	Andhra_Pradesh	Low	High	24	2018	92	310000	16000	9700
25	Chana_Dal	Maharashtra	Medium	Medium	25	2018	70	540000	30000	24000
26	Toor_Dal	Karnataka	Low	High	26	2019	95	500000	23000	17500
27	Moong_Dal	Rajasthan	Medium	High	27	2019	97	380000	17000	13000
28	Masoor_Dal	Uttar_Pradesh	High	Medium	28	2019	80	300000	36000	8500
29	Urad_Dal	Andhra_Pradesh	Low	High	29	2019	96	320000	17000	9800
30	Chana_Dal	Maharashtra	Medium	Medium	30	2019	72	550000	31000	25000
31	Toor_Dal	Maharashtra	Low	High	31	2020	100	510000	24000	18000
32	Moong_Dal	Rajasthan	Low	High	32	2020	99	390000	18000	13500
33	Masoor_Dal	Madhya_Pradesh	High	Medium	33	2020	82	310000	37000	9000
34	Urad_Dal	Andhra_Pradesh	Low	High	34	2020	98	330000	18000	9900
35	Chana_Dal	Maharashtra	Medium	Medium	35	2020	75	560000	32000	26000

2. PROC SORT – Sort Data by Dal_Name & Year

proc sort data=dal_market_india out=sorted_dal;

by Dal_Name Year;

run;

proc print data=sorted_dal;

title "According To The Dal Name and Year";

run;

Output:

According To The Dal Name and Year


Obs	Dal_Name	Main_Producing_State	Rainfall_Impact	Price_Category	Dal_ID	Year	Average_Price_Per_Kg	Annual_Consumption_Tonnes	Import_Quantity_Tonnes	Export_Quantity_Tonnes
1	Chana_Dal	Maharashtra	Medium	Medium	5	2014	60	500000	25000	20000
2	Chana_Dal	Maharashtra	Medium	Medium	10	2015	62	510000	27000	21000
3	Chana_Dal	Maharashtra	Medium	Medium	15	2016	68	520000	29000	22000
4	Chana_Dal	Maharashtra	Medium	Medium	20	2017	65	530000	28000	23000
5	Chana_Dal	Maharashtra	Medium	Medium	25	2018	70	540000	30000	24000
6	Chana_Dal	Maharashtra	Medium	Medium	30	2019	72	550000	31000	25000
7	Chana_Dal	Maharashtra	Medium	Medium	35	2020	75	560000	32000	26000
8	Masoor_Dal	Madhya_Pradesh	Medium	Medium	3	2014	65	250000	30000	5000
9	Masoor_Dal	Uttar_Pradesh	High	Medium	8	2015	70	260000	32000	6000
10	Masoor_Dal	Madhya_Pradesh	High	Medium	13	2016	72	270000	33000	7000
11	Masoor_Dal	Uttar_Pradesh	High	Medium	18	2017	75	280000	34000	7500
12	Masoor_Dal	Madhya_Pradesh	High	Medium	23	2018	78	290000	35000	8000
13	Masoor_Dal	Uttar_Pradesh	High	Medium	28	2019	80	300000	36000	8500
14	Masoor_Dal	Madhya_Pradesh	High	Medium	33	2020	82	310000	37000	9000
15	Moong_Dal	Rajasthan	Low	High	2	2014	90	320000	15000	10000
16	Moong_Dal	Rajasthan	Low	High	7	2015	95	340000	18000	12000
17	Moong_Dal	Rajasthan	Low	High	12	2016	98	350000	19000	13000
18	Moong_Dal	Rajasthan	Medium	High	17	2017	92	360000	15000	11000
19	Moong_Dal	Rajasthan	Medium	High	22	2018	94	370000	16000	12000
20	Moong_Dal	Rajasthan	Medium	High	27	2019	97	380000	17000	13000
21	Moong_Dal	Rajasthan	Low	High	32	2020	99	390000	18000	13500
22	Toor_Dal	Maharashtra	Medium	Medium	1	2014	75	450000	20000	15000
23	Toor_Dal	Karnataka	Medium	Medium	6	2015	80	470000	22000	17000
24	Toor_Dal	Maharashtra	Low	High	11	2016	120	440000	25000	18000
25	Toor_Dal	Karnataka	High	Medium	16	2017	85	480000	20000	16000
26	Toor_Dal	Maharashtra	High	Medium	21	2018	88	490000	21000	17000
27	Toor_Dal	Karnataka	Low	High	26	2019	95	500000	23000	17500
28	Toor_Dal	Maharashtra	Low	High	31	2020	100	510000	24000	18000
29	Urad_Dal	Andhra_Pradesh	Low	High	4	2014	85	280000	10000	8000
30	Urad_Dal	Andhra_Pradesh	Low	High	9	2015	88	290000	12000	8500
31	Urad_Dal	Andhra_Pradesh	Low	High	14	2016	95	295000	14000	9000
32	Urad_Dal	Andhra_Pradesh	Low	High	19	2017	90	300000	15000	9500
33	Urad_Dal	Andhra_Pradesh	Low	High	24	2018	92	310000	16000	9700
34	Urad_Dal	Andhra_Pradesh	Low	High	29	2019	96	320000	17000	9800
35	Urad_Dal	Andhra_Pradesh	Low	High	34	2020	98	330000	18000	9900

3. PROC MEANS – Price & Consumption Statistics

proc means data=dal_market_india mean min max std;

var Average_Price_Per_Kg Annual_Consumption_Tonnes Import_Quantity_Tonnes Export_Quantity_Tonnes;

class Dal_Name;

title "Statistical Summary of Dal Prices & Quantities";

run;

Output:

Statistical Summary of Dal Prices & Quantities

The MEANS Procedure


Dal_Name	N Obs	Variable	Mean	Minimum	Maximum	Std Dev
Chana_Dal	7	Average_Price_Per_Kg Annual_Consumption_Tonnes Import_Quantity_Tonnes Export_Quantity_Tonnes	67.4285714 530000.00 28857.14 23000.00	60.0000000 500000.00 25000.00 20000.00	75.0000000 560000.00 32000.00 26000.00	5.4116277 21602.47 2410.30 2160.25
Masoor_Dal	7	Average_Price_Per_Kg Annual_Consumption_Tonnes Import_Quantity_Tonnes Export_Quantity_Tonnes	74.5714286 280000.00 33857.14 7285.71	65.0000000 250000.00 30000.00 5000.00	82.0000000 310000.00 37000.00 9000.00	5.9960304 21602.47 2410.30 1410.00
Moong_Dal	7	Average_Price_Per_Kg Annual_Consumption_Tonnes Import_Quantity_Tonnes Export_Quantity_Tonnes	95.0000000 358571.43 16857.14 12071.43	90.0000000 320000.00 15000.00 10000.00	99.0000000 390000.00 19000.00 13500.00	3.2659863 24102.95 1573.59 1239.24
Toor_Dal	7	Average_Price_Per_Kg Annual_Consumption_Tonnes Import_Quantity_Tonnes Export_Quantity_Tonnes	91.8571429 477142.86 22142.86 16928.57	75.0000000 440000.00 20000.00 15000.00	120.0000000 510000.00 25000.00 18000.00	15.0269599 25634.80 1951.80 1096.53
Urad_Dal	7	Average_Price_Per_Kg Annual_Consumption_Tonnes Import_Quantity_Tonnes Export_Quantity_Tonnes	92.0000000 303571.43 14571.43 9200.00	85.0000000 280000.00 10000.00 8000.00	98.0000000 330000.00 18000.00 9900.00	4.6547467 17491.49 2820.00 725.7180352

4. PROC FREQ – Frequency of Rainfall Impact

proc freq data=dal_market_india;

tables Rainfall_Impact*Dal_Name / nocol norow nopercent;

title "Rainfall Impact Distribution Across Dal Types";

run;

Output:

Rainfall Impact Distribution Across Dal Types

The FREQ Procedure

Frequency


Table of Rainfall_Impact by Dal_Name
Rainfall_Impact	Dal_Name
Rainfall_Impact	Chana_Dal	Masoor_Dal	Moong_Dal	Toor_Dal	Urad_Dal	Total
High	0	6	0	2	0	8
Low	0	0	4	3	7	14
Medium	7	1	3	2	0	13
Total	7	7	7	7	7	35

5. PROC SQL – Top 3 Most Expensive Dal Years

proc sql outobs=3;

title "Top 3 Most Expensive Dal Prices by Year";

select Dal_Name, Year, Average_Price_Per_Kg

from dal_market_india

order by Average_Price_Per_Kg desc;

quit;

Output:

Top 3 Most Expensive Dal Prices by Year


Dal_Name	Year	Average_Price_Per_Kg
Toor_Dal	2016	120
Toor_Dal	2020	100
Moong_Dal	2020	99

6. PROC SUMMARY – Yearly Aggregation

proc summary data=dal_market_india nway;

class Year;

var Average_Price_Per_Kg Annual_Consumption_Tonnes;

output out=yearly_summary mean=Avg_Price Mean_Consumption;

run;

proc print data=yearly_summary;

title "Yearly Average Price & Consumption of All Dals";

run;

Output:

Yearly Average Price & Consumption of All Dals


Obs	Year	_TYPE_	_FREQ_	Avg_Price	Mean_Consumption
1	2014	1	5	75.0	360000
2	2015	1	5	79.0	374000
3	2016	1	5	90.6	375000
4	2017	1	5	81.4	390000
5	2018	1	5	84.4	400000
6	2019	1	5	88.0	410000
7	2020	1	5	90.8	420000

7. MACRO – Generate Report for a Specific Dal

%macro dal_report(dal=);

proc sql;

title "Detailed Report for &dal (2014–2025)";

select * from dal_market_india

where Dal_Name="&dal"

order by Year;

quit;

%mend;

%dal_report(dal=Toor_Dal);

Output:

Detailed Report for Toor_Dal (2014–2025)


Dal_Name	Main_Producing_State	Rainfall_Impact	Price_Category	Dal_ID	Year	Average_Price_Per_Kg	Annual_Consumption_Tonnes	Import_Quantity_Tonnes	Export_Quantity_Tonnes
Toor_Dal	Maharashtra	Medium	Medium	1	2014	75	450000	20000	15000
Toor_Dal	Karnataka	Medium	Medium	6	2015	80	470000	22000	17000
Toor_Dal	Maharashtra	Low	High	11	2016	120	440000	25000	18000
Toor_Dal	Karnataka	High	Medium	16	2017	85	480000	20000	16000
Toor_Dal	Maharashtra	High	Medium	21	2018	88	490000	21000	17000
Toor_Dal	Karnataka	Low	High	26	2019	95	500000	23000	17500
Toor_Dal	Maharashtra	Low	High	31	2020	100	510000	24000	18000

%dal_report(dal=Moong_Dal);

Output:

Detailed Report for Moong_Dal (2014–2025)


Dal_Name	Main_Producing_State	Rainfall_Impact	Price_Category	Dal_ID	Year	Average_Price_Per_Kg	Annual_Consumption_Tonnes	Import_Quantity_Tonnes	Export_Quantity_Tonnes
Moong_Dal	Rajasthan	Low	High	2	2014	90	320000	15000	10000
Moong_Dal	Rajasthan	Low	High	7	2015	95	340000	18000	12000
Moong_Dal	Rajasthan	Low	High	12	2016	98	350000	19000	13000
Moong_Dal	Rajasthan	Medium	High	17	2017	92	360000	15000	11000
Moong_Dal	Rajasthan	Medium	High	22	2018	94	370000	16000	12000
Moong_Dal	Rajasthan	Medium	High	27	2019	97	380000	17000	13000
Moong_Dal	Rajasthan	Low	High	32	2020	99	390000	18000	13500

8. PROC TRANSPOSE - Comparision Of Prices

proc sort data=dal_market_india out=dal_sorted;

by Dal_Name Year;

run;

proc transpose data=dal_sorted out=dal_price_trend(drop=_NAME_) prefix=Year_;

by Dal_Name; /* Dals in rows */

id Year; /* Years as columns */

var Average_Price_Per_Kg; /* Price values */

run;

proc print data=dal_price_trend;

title "Dal Price Trend (2014–2025) - Years as Columns, Dals as Rows";

run;

Output:

Dal Price Trend (2014–2025) - Years as Columns, Dals as Rows


Obs	Dal_Name	Year_2014	Year_2015	Year_2016	Year_2017	Year_2018	Year_2019	Year_2020
1	Chana_Dal	60	62	68	65	70	72	75
2	Masoor_Dal	65	70	72	75	78	80	82
3	Moong_Dal	90	95	98	92	94	97	99
4	Toor_Dal	75	80	120	85	88	95	100
5	Urad_Dal	85	88	95	90	92	96	98

Sas All

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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

252.REAL-WORLD DAL PRICE TREND AND YEAR-WISE COMPARISON IN INDIA (2014–2025) USING PROC SORT | PROC TRANSPOSE | PROC PRINT | PROC MEANS | PROC FREQ | PROC SQL | PROC SUMMARY IN SAS

REAL-WORLD DAL PRICE TREND AND YEAR-WISE COMPARISON IN INDIA (2014–2025) USING PROC SORT | PROC TRANSPOSE | PROC PRINT | PROC MEANS | PROC FREQ | PROC SQL | PROC SUMMARY IN SAS

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