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Re: proc freq for 4 variables

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Contributor
Posts: 59

Re: proc freq for 4 variables

Dear Friends,

i have small question in proc means  i have 4 variables called Age_group  gender landholding_size and BMI_status so all are class variables.

can i make a table with all these variables.

here is the my example

proc freq data=BMI76;

table (Health_status )* (Age_group1 Gender);

attrib Health_status format=H00001_.;

attrib Age_group1 format=AD0000_.;

by village;

run;

i need result like

Health_status
Status 1Status 2Status 3
maleFemalemaleFemalemaleFemale
Super User
Super User
Posts: 7,942

Re: proc freq for 4 variables

Posted in reply to anilgvdbm

Hi,

Could you provide some test data and example output data.  You talk about means, then provide freq code.  You can try something along the lines of:

data _null_;

  array myvar{4} $200. ("AGE_GROUP","GENDER","LANDHOLDING_SIZE","BMI_STATUS");

  do i=1 to 4;

    call execute('proc freq data='||strip(myvar{i})||';

                    table (Health_status )* ('||strip(myvar{i})||' Gender);

                    attrib Health_status format=H00001_.;

                    attrib Age_group1 format=AD0000_.;

                    by village;

                  run;');

  end;

  call execute('data want; set age_group gender landholding_size bmi_status; run;');

run;

This would generate the four proc freq's, and finally add them all to one dataset.  You can change the table line per your requirements, and also the final call execute to sql join each table if you prefer.

Super Contributor
Posts: 543

Re: proc freq for 4 variables

Posted in reply to anilgvdbm

Hi.

You could also look into PROC TABULATE, rather than PROC FREQ.;

Base SAS(R) 9.2 Procedures Guide

Anca,

Super User
Posts: 11,343

Re: proc freq for 4 variables

Posted in reply to anilgvdbm

Where do you want your landholding_size and BMI_status to appear?

Occasional Contributor
Posts: 7

Re: proc freq for 4 variables

Posted in reply to anilgvdbm

If you want a table with AGE & LANDHOLDING_SIZE as dimension variables and HEALTH_STATUS||GENDER as metric variables (I'm guessing this is where BMI would go?), why not consider using a PROC SQL query on your BMI76 table?

If you can provide a table layout and/or sample data, I think a relatively simple macro could be written to allow for a variable number of metric fields to be used in case you add/drop health statuses.

Contributor
Posts: 59

Re: proc freq for 4 variables

Posted in reply to anilgvdbm

Dear Ballardw,

here my problem is

all are class variables like age_group, gender ,landholding and BMI_status.

so i need output like

table-1

                Health_status

age_group1     age_group2     age_group3

male female     male female     male female

table2

                Health_status

lanholding_class    lanholding_class   lanholding_class

   male female          male female          male female

data is here

GenderLandHoldingClassageBMI Health_statusAge_group1
FemaleMedium4619.2122243
maleLarge3618.0204432
FemaleLabour2221.2084542
FemaleSmall1415.9715821
FemaleSmall1114.6509611
FemaleSmall3618.763742
maleSmall2620.2195542
FemaleLabour7116.2434934
maleLabour5619.2578343
FemaleLabour2118.5015642
FemaleLarge1514.7268311
FemaleMedium1314.6423311
FemaleLabour4616.4474233
maleMedium4118.6628242
maleLabour1113.4309711
FemaleLabour3514.7254212
maleLabour3019.5608242
FemaleLarge5016.7973833
FemaleLabour2317.7498132
FemaleMedium3117.1755132
maleLarge2317.5268432
FemaleMedium4617.0833933
maleMedium3618.36332
FemaleMedium1214.7860811
maleMedium7015.7496124
FemaleMedium5515.7401523
maleMedium4118.3239332
FemaleMedium1717.447231
FemaleMedium1114.0434611
FemaleMedium7015.5032924
maleLabour5920.3274643
FemaleSmall4112.9463612
maleSmall2917.8121232
FemaleSmall1214.7196911
FemaleMedium6116.2325734
maleLarge5116.9297133
FemaleMedium3115.4172922
maleMedium2018.7408742
maleMedium1113.3149611
FemaleMedium5116.7817633
maleSmall3617.8456632
maleLarge3617.8456632
maleLarge3617.8456632
FemaleLabour3618.2418132
maleLabour2616.9063132
maleLabour6115.4481724
FemaleLabour3519.1325742
maleLabour3017.4449232
FemaleLabour4218.7629842
FemaleSmall1817.074331
FemaleLabour1612.2888911
maleLabour1317.666331
maleSmall6614.4738214
FemaleSmall3616.4034932
FemaleMedium1516.7410731
maleMedium1113.8559111
FemaleLarge5819.5787243
maleLarge4717.9670533
FemaleMedium3018.7529142
maleMedium2317.387432
FemaleMedium1615.7010621
FemaleLabour4618.7180443
maleLabour4018.4897732
FemaleLabour1918.9732142
maleLabour1319.4347941
maleLabour1115.2230821
FemaleMedium5117.1594733
maleMedium4116.9733232
maleSmall3618.8078742
maleSmall1416.4689431
FemaleLarge6416.1660334
maleMedium5217.6210633
FemaleLarge6117.1489134
maleLarge5314.5080913
FemaleLarge1613.1672811
FemaleLarge1415.3146321
FemaleMedium4615.7364523
FemaleMedium2216.7412532
FemaleMedium1114.3527811
FemaleLarge5521.387743
maleLarge4220.8214542
FemaleLarge1919.7646942
FemaleSmall5616.5440433
FemaleSmall1717.5816931
FemaleSmall1316.2553931
FemaleLabour3621.0160942
maleLabour2919.9583742
maleLabour1115.6437821
FemaleLarge6818.6487644
maleLarge5519.7513343
FemaleLarge2117.8744232
maleLarge1617.781231
FemaleLabour3618.5290442
maleSmall6116.503734
FemaleSmall4317.7982832
maleSmall4018.0699732
FemaleSmall1716.5569831
maleSmall1615.7827221
FemaleLabour7117.9783434
maleLarge6715.8094324
maleSmall5019.2183543
FemaleSmall1716.983231
FemaleSmall4618.3203133
maleLarge4319.2973942
FemaleLarge1719.1472341
maleLarge1315.222421
FemaleLarge1416.1914731
FemaleLarge4318.1119832
maleSmall4816.3257633
maleMedium4320.5662442
FemaleMedium1315.3977221
FemaleLarge3617.5392332
maleLarge3316.3260432
maleLarge1716.6539831
FemaleLarge2119.6979442
FemaleMedium2318.8751742
maleMedium2119.5751742
FemaleMedium5118.6710743
maleMedium4815.9504623
FemaleLarge5117.738533
maleLarge4815.6174223
FemaleLarge2417.6697832
FemaleLarge1818.2997431
maleLabour5617.3629633
FemaleLarge4117.9094632
maleLarge3917.9667132
maleLarge5315.8683623
FemaleSmall2720.3747842
maleSmall4613.7777813
maleSmall2316.2337632
FemaleSmall5116.0086333
maleSmall4814.105913
FemaleSmall2815.1730322
maleSmall2617.9721632
FemaleSmall1314.5563811
FemaleMedium5126.0173753
maleSmall4719.6950543
FemaleLabour2819.7909742
FemaleLabour2620.2813342
FemaleLabour1617.9453131
maleLabour1417.4309231
maleLabour1216.7317431
maleLabour2021.0130542
maleLabour2620.3466342
maleSmall4618.249233
FemaleSmall2917.1198932
maleSmall2617.8200532
FemaleLarge6118.4276634
maleLarge5820.5695243
FemaleLarge2719.7975142
maleLarge2523.2594542
FemaleLarge2518.8001142
maleLarge2319.7584842
FemaleLarge1916.3201532
FemaleLarge1819.8024241
FemaleLarge1615.2537221
FemaleLarge4918.1263133
maleMedium4620.6697443
FemaleLabour2517.4384432
maleLabour2318.5256442
FemaleLabour2117.5276132
FemaleLabour1616.9550831
FemaleMedium4116.4769932
maleLarge6114.7416214
FemaleLarge1416.6377331
FemaleLarge4920.7742543
maleLarge3715.3240422
FemaleLarge1917.9599232
FemaleLarge5117.8689533
maleLarge3917.8147732
FemaleLarge1212.4826411
maleLarge3616.7195832
FemaleMedium1416.3536831
FemaleLarge6616.06834
maleLarge8612.9498614
maleLarge6317.7891134
FemaleLarge4115.9720322
maleLarge3818.8242942
FemaleLarge5620.5314443
maleSmall5316.3845433
maleSmall4618.4098833
FemaleSmall3319.5883642
maleSmall3117.3816532
FemaleSmall1517.0979831
FemaleLarge2618.3287432
maleLarge2318.4089432
maleLarge4615.8542723
FemaleLarge1817.2557831
FemaleMedium3616.2785532
maleSmall3319.3916642
FemaleSmall4616.5333
maleSmall4019.0329242
FemaleSmall2017.6010432
maleSmall1819.6401441
FemaleSmall3817.73932
maleSmall2818.0547632
FemaleSmall1717.7004131
FemaleLabour4014.7774412
maleLabour2818.5126842
FemaleSmall5515.7303323
maleSmall5020.3238343
FemaleSmall2218.7159542
FemaleSmall2018.7239642
FemaleSmall1818.096631
maleSmall1720.3269741
FemaleLabour4515.8481522
FemaleLabour5522.5075143
maleLabour3018.8456442
FemaleSmall3716.7698132
maleSmall2918.5676642
maleSmall1411.8873111
FemaleSmall1213.4466611
FemaleLabour3417.9690532
maleLabour2817.511632
FemaleLarge2517.4361732
FemaleLarge6016.0213233
maleLarge5018.0328333
maleLarge1816.256631
maleLarge1515.9646221
FemaleLarge2118.2926832
FemaleSmall3819.5408642
maleSmall3320.8371942
FemaleSmall1717.3002731
FemaleSmall1312.8745711
FemaleSmall5417.8392733
maleSmall4714.3758213
FemaleSmall2518.8939442
FemaleSmall3519.2371142
maleSmall2718.4302432
FemaleSmall1213.1379311
FemaleSmall1918.0617432
FemaleSmall3518.6934642
maleSmall3022.2333842
maleSmall13011
maleSmall1121.2934341
FemaleSmall1818.3683631
maleSmall4022.2662642
maleMedium6518.909244
maleSmall6518.909244
maleSmall6518.909244
FemaleSmall4019.9324442
maleSmall3517.2327532
FemaleSmall1511.3520411
FemaleLarge4018.6592642
maleLarge3217.9019932
maleLarge1819.2009641
FemaleSmall7020.3630644
maleSmall5525.406653
FemaleSmall3518.2086732
maleSmall2616.6196432
FemaleSmall1214.3321411
FemaleSmall1717.9354131
maleLabour3520.1781142
FemaleSmall6518.6694944
maleLabour6016.9432533
FemaleLabour2216.2078832
maleLabour1718.6745541
FemaleSmall6017.2588633
FemaleMedium3519.374642
maleLabour2817.9263532
FemaleLabour1115.5819821
FemaleMedium2816.6077332
maleMedium2216.1111432
FemaleMedium5517.1513233
FemaleMedium1518.1011331
FemaleMedium6017.9816733
maleLarge56013
FemaleLarge2620.9635242
maleLarge2220.1749642
FemaleSmall7016.3754734
maleSmall6014.5239113
maleSmall5018.9740343
FemaleSmall6519.662344
maleSmall5019.4695343
FemaleSmall3016.7036532
maleSmall2119.0447142
FemaleSmall2215.8854222
FemaleSmall1915.0071422
FemaleSmall1618.2169531
FemaleSmall3617.5679932
maleSmall3016.4339532
FemaleSmall1818.7836741
maleSmall4519.6999342
maleSmall1719.974241
maleSmall1517.2602431
FemaleMedium4023.1451942
maleMedium3521.8277442
FemaleSmall2618.2050132
maleSmall1317.3517231
FemaleSmall2219.3281442
FemaleSmall1815.8954321
FemaleLarge4616.7456933
maleMedium4120.0777742
maleMedium1318.7687641
FemaleSmall5115.9920923
maleSmall4119.448542
FemaleSmall1920.3693342
FemaleMedium4117.435132
maleMedium3118.9668542
FemaleSmall4617.9909633
maleLabour4117.6417732
FemaleLabour1716.4383831
FemaleSmall3619.9232642
maleSmall3117.8983432
FemaleSmall3119.572742
maleLabour2617.7111132
FemaleMedium5120.4613143
maleLabour4619.4054943
maleLabour1516.328931
FemaleLabour1315.7153921
FemaleLabour1113.8948911
FemaleSmall5621.4421543
maleSmall4818.3430333
FemaleSmall1819.0609341
FemaleSmall1214.6854311
FemaleLarge2618.7193842
maleLarge5116.2337733
maleLarge2117.8708132
FemaleMedium3918.9075442
maleSmall2924.0719342
FemaleSmall1214.26111
FemaleMedium3616.8286432
maleSmall5119.2658943
FemaleMedium3325.392752
maleMedium5118.7413443
maleMedium2916.4346932
FemaleMedium5117.5979933
maleMedium4121.9477242
FemaleMedium2119.9410642
maleMedium1720.2933741
FemaleMedium1516.4214331
FemaleMedium1117.0094431
FemaleSmall3118.5636642
maleSmall5116.3981833
maleMedium2322.7044642
FemaleLabour2417.911932
FemaleMedium3825.6402852
FemaleLabour6616.9692634
maleLabour3421.0633642
FemaleLabour1720.7407541
maleLabour1416.3749231
FemaleSmall5117.0707133
maleSmall4119.1586842
FemaleSmall1717.6002731
FemaleSmall1515.3535621
maleSmall1114.6175111
FemaleLarge3620.2943542
maleLarge3123.160542
maleLarge1517.772431
maleLarge1317.3068431
FemaleLabour3325.3122952
maleLabour6113.584814
FemaleLabour1214.6180511
FemaleLarge6118.5366944
maleLarge1620.4941
FemaleLarge3119.4582742
maleLarge2616.4981932
maleLarge1115.4629821
FemaleLarge3321.7198442
FemaleLarge3620.7777742
maleLarge7115.9304924
maleLarge2918.2942532
FemaleLarge1116.4891531
FemaleLarge5119.7131343
FemaleLarge3618.482632
maleLarge3418.0937932
maleLarge2118.5727342
maleLarge2019.3367342
FemaleLarge1721.2311541
FemaleLarge1319.7674641
FemaleMedium5123.8404843
maleMedium4621.9095243
FemaleMedium2621.2097942
FemaleMedium1917.0512932
FemaleMedium1115.8685521
FemaleMedium6115.151224
maleMedium5616.4866733
maleMedium2515.502822
FemaleMedium2417.6579232
FemaleMedium1916.1794832
maleMedium1719.9018641
FemaleMedium2616.3413232
maleMedium2219.790942
FemaleLarge2016.5207132
FemaleLarge1717.8542131
maleLarge1516.0768131
maleLarge1315.2378521
FemaleLabour3015.7682322
maleSmall2518.0332332
FemaleLabour2118.7783442
maleLabour4019.1405542
maleLabour1916.9836632
FemaleSmall5120.0302343
FemaleSmall4018.1253532
maleSmall2818.0213332
FemaleLabour4116.5638532
maleLabour3618.0477332
FemaleLabour1818.2157531
FemaleLabour5520.252843
maleSmall4818.616843
FemaleSmall4620.8569543
maleSmall4120.9787842
FemaleSmall1920.2632342
maleSmall1617.0060331
FemaleSmall4025.0401252
maleSmall3319.9002842
maleSmall1614.6835511
maleSmall1212.9045511
FemaleSmall5322.7536643
maleSmall4118.3141232
maleSmall1216.4415831
FemaleSmall3518.2504132
maleSmall2620.083142
FemaleSmall3816.327432
maleSmall3318.4123232
FemaleSmall5021.3402943
FemaleSmall4619.1199443
maleSmall4022.7914342
FemaleSmall4318.6914142
maleSmall6717.6699634
maleSmall3616.9059332
FemaleMedium4218.4971632
maleMedium3517.8744932
FemaleMedium2018.9160142
maleMedium1421.6594841
FemaleMedium1616.5570831
FemaleMedium1312.9211411
FemaleMedium5017.6463133
maleMedium7014.2417514
maleMedium4518.0003832
FemaleMedium1918.4224932
FemaleMedium5517.1552633
maleMedium5018.2652433
FemaleMedium1817.8000431
maleSmall5119.1249143
maleLarge3516.2330732
FemaleLarge1920.1630742
FemaleLarge1414.821911
FemaleLarge1415.0719321
FemaleLarge5015.3974723
maleLarge5025.9731853
maleLarge2521.2591942
FemaleSmall1716.8158131
FemaleMedium5019.8949543
maleMedium4019.833542
FemaleMedium1719.0304641
FemaleMedium1516.7285131
FemaleLarge4018.8282342
maleLarge3518.1436532
maleLarge1920.3842542
maleLarge1721.9907641
maleLarge1620.6183141
maleLarge1516.6496631
maleLarge1317.47931
FemaleLarge1214.0984811
FemaleLarge3118.7488742
FemaleLarge3517.6799932
maleLarge3020.6224142
maleLarge2518.664942
maleLarge1617.8483331
maleLarge1314.1826411
FemaleLarge2919.5528642
maleLarge2219.4222242
FemaleSmall4222.5126442
maleSmall3720.6131342
FemaleSmall2118.0704332
FemaleSmall1615.5736321
FemaleSmall1414.3922611
FemaleSmall1214.3496711
FemaleLarge3518.3853832
FemaleLarge4020.9643642
maleLarge3517.8324132
maleLarge3019.7470842
maleLarge4014.7647712
FemaleLarge3018.2997432
maleLarge2520.4776742
maleLarge1317.5754531
maleLarge1716.8094131
maleLarge1317.3589331
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maleSmall3015.9445822
maleLarge1416.4294931
maleLarge1213.7363711
FemaleLabour4118.2482232
maleLabour3321.5457842
FemaleLabour1315.7883821
FemaleSmall3621.6015642
maleLabour3124.1608842
FemaleSmall5118.9842243
maleLabour4616.1426533
FemaleLabour2622.1905742
FemaleLabour2120.9317742
FemaleLabour1616.299531
maleLabour2017.0357832
FemaleLabour6119.7794544
FemaleLabour2113.2121712
maleLabour1414.6624511
FemaleLabour1113.7322911
FemaleSmall5517.5407433
maleSmall4620.7595443
maleSmall2619.7721642
FemaleLabour3617.8733332
maleLabour2619.4666742
maleLabour1913.5899612
FemaleSmall3118.6497442
maleSmall2919.1897942
FemaleSmall4619.6205443
maleSmall4115.5110122
FemaleSmall1416.0948431
FemaleSmall1115.6234721
FemaleSmall3120.6652742
maleSmall2618.1464832
maleSmall6121.1849744
FemaleSmall3116.643432
maleSmall2617.3967832
FemaleSmall6626.8150954
maleSmall6116.020434
FemaleSmall2219.2832542
FemaleSmall14011
FemaleLabour4620.2736643
maleLabour4117.363332
FemaleLabour2116.8747332
maleLabour1617.2531731
FemaleMedium4121.6692442
maleLabour3617.7025732
FemaleLabour1517.2935831
FemaleLabour13011
FemaleSmall3317.6308532
maleSmall2918.0984532
FemaleSmall2517.8515632
maleSmall2122.0907942
FemaleSmall3617.6041732
maleLabour2616.6593332
FemaleSmall4320.7810942
maleLabour3917.9627432
maleLabour6317.8912134
FemaleLabour2214.6861912
FemaleLabour1315.5188121
maleSmall5118.9942443
FemaleSmall1314.4750311
FemaleSmall6016.8055833
FemaleMedium5623.6936343
maleMedium5121.1764443
FemaleMedium2920.1166542
maleMedium2620.7065442
FemaleLarge4119.100542
FemaleMedium1916.6122332
maleMedium1314.3597711
maleMedium1123.1929541
FemaleSmall4617.5661933
maleSmall4018.996642
maleSmall1315.1299421
maleSmall1118.5614541
FemaleSmall3620.2858842
maleSmall3118.3617732
maleLabour1315.4029221
maleLabour1215.4930721
FemaleSmall3618.8317942
maleMedium6619.2027844
FemaleMedium2617.5098532
maleMedium3616.4339832
FemaleSmall5117.5241133
maleSmall4119.4054942
FemaleSmall2620.5346642
maleSmall1213.8806211
FemaleSmall1111.8959711
FemaleMedium4320.618342
maleMedium3615.8605922
maleMedium6616.4503134
FemaleMedium1816.6835931
maleMedium1315.4285321
maleMedium1113.6684311
FemaleMedium4616.2219133
maleSmall4119.3751142
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maleMedium2119.2391542
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maleLarge4116.6138532
FemaleSmall1916.0526932
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maleMedium3818.9158742
maleMedium1315.3008521
FemaleMedium3816.6059732
maleLabour2718.6778342
maleLabour5915.012223
maleLabour1415.2174821
FemaleLabour1113.5305711
FemaleLabour1713.8579611
FemaleSmall3318.0082432
maleMedium2919.3115442
maleMedium4620.1823243
maleMedium4717.0244733
maleSmall2521.4549342
FemaleSmall1414.2504711
FemaleSmall4919.2956743
FemaleSmall2919.8436242
FemaleMedium4121.989642
maleSmall3617.9316532
maleSmall6117.2888934
FemaleLarge6520.012844
MaleLabour5120.5775843
MaleSmall4418.7096542
MaleSmall4021.8995842
FemaleSmall4422.5001342
FemaleSmall3819.1232542
FemaleSmall3521.805842
FemaleSmall1819.7530941
FemaleSmall1621.7843641
FemaleSmall1314.0481811
MaleSmall1819.0713541
MaleLabour5818.2953333
FemaleLarge4817.2430333
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FemaleMedium4224.3904142
FemaleMedium2318.6406142
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FemaleMedium2417.0428732
MaleMedium2817.5370832
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FemaleLabour4516.6010232
FemaleLabour1114.7701711
MaleLabour8113.5048314
FemaleLabour7218.9253444
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FemaleLabour4418.2507132
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FemaleLabour2220.9988642
FemaleLabour1819.1743741
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FemaleLarge2920.9703642
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FemaleMedium4017.1627732
MaleMedium2118.7872342
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FemaleMedium3018.2906532
MaleMedium1113.9943211
FemaleMedium7019.6156944
MaleMedium4518.2200932
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FemaleSmall5217.2719433
MaleSmall5217.5315533
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FemaleSmall3723.9460742
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FemaleSmall1517.7777831
FemaleSmall1117.3221231
MaleSmall1618.8677441
FemaleSmall1416.307731
MaleSmall1118.6101641
MaleSmall4119.946842
MaleSmall3421.402942
FemaleSmall3821.0829942
FemaleSmall2019.4074142
MaleSmall12157.793181
FemaleSmall2620.0614142
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MaleMedium3816.6848832
FemaleMedium4926.6311553
MaleMedium2921.7764842
MaleMedium2314.2041912
FemaleMedium2917.6528932
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FemaleMedium2618.2319832
MaleMedium1112.846711
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MaleMedium8015.8201824
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FemaleLabour4218.9207342
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FemaleLabour4320.0266942
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Occasional Contributor
Posts: 7

Re: proc freq for 4 variables

Posted in reply to anilgvdbm

Are you looking for a single PROCEDURE approach, or just code that will create the tables you require?

Super User
Posts: 11,343

Re: proc freq for 4 variables

Posted in reply to anilgvdbm

Still not showing WHERE you want BMI_status to appear. The values don't really look like class variables at this stage unless you have a not documented format that is assigning the value to categories such as under weight, normal, overweight and obese. Do you want BMI_status to appear as each value with a count or what.

Since you started with proc freq something like this might get close:

proc tabulate data=<your data set here>;

class age_group gender landholding  BMI_status; /* if any of your records have missing values you may want the option /missing on this line*/

format Health_status H00001_.  Age_group1 AD0000_.; /* if you have a grouping format for BMI then add it here*/

table BMI_status, /* assumes the rows are going to be BMI*/

        Health_status*age_group* gender *(n colpctn);  /* this will have a column heading for each value of health_status and gender with in each*/

/* if you want a separate table for each value of Health_status*/

table health_status,

        bmi_status,

        age_group*Gender * (n colpctn);

table BMI_status, /* assumes the rows are going to be BMI*/

        Health_status*landholding * gender *(n colpctn);  /* this will have a column heading for each value of health_status and gender with in each*/

/* if you want a separate table for each value of Health_status*/

table health_status,

        bmi_status,

        landholding *Gender * (n colpctn);

run;

If you want other percentages you'll want to look up percentages in proc tabulate.

Regular Contributor
Posts: 217

Re: proc freq for 4 variables

Posted in reply to anilgvdbm

I've created PROC Reports that provided similar results.  Proc Tabulate, which I have never used, might also be the solution you need.

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