Descriptive Analysis I Key.sas

* ============================================================================;
* Lab: Descriptive Analysis I

	* Create a list report
	* Create a frequency report
	* Calculate measures of central tendency
	* Calculate measures of dispersion
	* Graphically describe data
* ============================================================================;

* Example 1. Descriptive Analysis Of 2010 Census Data
* The following data comes from the United States Census Bureau website 
* (http://factfinder2.census.gov).  
* Please read the data into SAS using a DATALINES statement in a DATA step, 
* and create a temporary 
* SAS data set called census;
* ============================================================================;
data census;
	input state $ region $ pop pop65 medage;
	datalines;
	AL South 4779736 657792 37.9
	AK West 710231 54938 33.8
	AZ West 6392017 881831 35.9
	AR South 2915918 419981 37.4
	CA West 37253956 4246514 35.2
	CO West 5029196 549625 36.1
	CT NE 3574097 506559 40.0
	DE South 897934 129277 38.8
	FL South 18801310 3259602 40.7
	GA South 9687653 1032035 35.3
	HI West 1360301 195138 38.6
	ID West 1567582 194668 34.6
	IL NCntrl 12830632 1609213 36.6
	IN NCntrl 6483802 6483802 37.0
	IA NCntrl 3046355 452888 38.1
	KS NCntrl 2853118 376116 36.0
	KY South 4339367 578227 38.1
	LA South 4533372 557857 35.8
	ME NE 1328361 211080 42.7
	MD South 5773552 707642 38.0
	MA NE 6547629 902724 39.1
	MI NCntrl 9883640 1361530 38.9
	MN NCntrl 5303925 683121 37.4
	MS South 2967297 380407 36.0
	MO NCntrl 5988927 838294 37.9
	MT West 989415 146742 39.8
	NE NCntrl 1826341 246677 36.2
	NV West 2700551 324359 36.3
	NH NE 1316470 178268 41.1
	NJ NE 8791894 1185993 39.0
	NM West 2059179 272255 36.7
	NY NE 19378102 2617943 38.0
	NC South 9535483 1234079 37.4
	ND NCntrl 672591 97477 37.0
	OH NCntrl 11536504 1622015 38.8
	OK South 3751351 506714 36.2
	OR West 3831074 533533 38.4
	PA NE 12702379 1959307 40.1
	RI NE 1052567 151881 39.4
	SC South 4625364 631874 37.9
	SD NCntrl 814180 116581 36.9
	TN South 6346105 853462 38.0
	TX South 25145561 2601886 33.6
	UT West 2763885 249462 29.2
	VT NE 625741 91078 41.5
	VA South 8001024 976937 37.5
	WA West 6724540 827677 37.3
	WV South 1852994 297404 41.3
	WI NCntrl 5686986 777314 38.5
	WY West 563626 70090 36.8
	;	
run;




* Task 1. Create a list report containing all the variables in the data set 
* census;
* ============================================================================;
proc print data = census;
run;




* Task 2. Create another list report using the census data set. This time omit 
* the obs column and only display the state abbreviation and total population 
* variables;
* ============================================================================;
proc print data = census noobs;
	var state pop;
run;




* Task 3. Create a frequency report for the variable region in the census data 
* set;
* ============================================================================;
proc freq data = census;
	tables region;
run;




* Task 4. Create a bar graph depicting the number of states in each region;
* ============================================================================;
proc gchart data = census;
	hbar region;
run;




* Example 2. Using The MEANS Procedure
* Porcellini et al. studied 13 HIV-positive patients who were treated with 
* highly active antiretroviral therapy (HAART) for at least 6 months. The CD4 
* T cell counts at baseline for the 13 participants are listed below 
* (Daniel, 2005);
* ============================================================================;
data porcellini;
	input cd4;
	datalines;
	230
	205 
	313 
	207 
	227 
	245 
	173 
	58 
	103 
	181 
	105 
	301 
	169
	;
run;




* Task 2. Request the MEAN, MEDIAN, MODE, Standard Deviation, MIN, and MAX;
* ============================================================================;
proc means data=porcellini mean median mode std min max;
	var cd4;
run;




* Example 3. Using The MEANS Procedure And SGPLOT Procedure
* Thilothammal et al. designed a study to determine the efficacy of BCG 
* (bacillus Calmette-Guérin) vaccine in preventing tuberculosis meningitis. 
* Among the data collected on each subject was a measure of nutritional status 
* (actual weight expressed as a percentage of expected weight for actual 
* height). The nutritional status values of the 107 cases studied are listed 
* below (Daniel,2005);
* ============================================================================;
data thilothmmal;
	input status @@;
	datalines;
	73.3 54.6 82.4 76.5 72.2 73.6 74.0 80.5 71.0 56.8 80.6 100.0 79.6 67.3 50.4 66.0 83.0 72.3 
	55.7 64.1 66.3 50.9 71.0 76.5 99.6 79.3 76.9 96.0 64.8 74.0 72.6 80.7 109.0 68.6 73.8 74.0 
	72.7 65.9 73.3 84.4 73.2 70.0 72.8 73.6 70.0 77.4 76.4 66.3 50.5 72.0 97.5 130.0 68.1 86.4 
	70.0 73.0 59.7 89.6 76.9 74.6 67.7 91.9 55.0 90.9 70.5 88.2 70.5 74.0 55.5 80.0 76.9 78.1 
	63.4 58.8 92.3 100.0 84.0 71.4 84.6 123.7 93.7 76.9 79.6 45.6 92.5 65.6 61.3 64.5 72.7 77.5 
	76.9 80.2 76.9 88.7 78.1 60.6 59.0 84.7 78.2 72.4 68.3 67.5 76.9 82.6 85.4 65.7 65.9
	;
run;




* Task 2. Request the MEAN, MEDIAN, MODE, Standard Deviation, MIN, and MAX;
* ============================================================================;
proc means data=thilothmmal mean median mode std min max;
	var status;
run;




* Task 3. Create a histogram of these values;
* ============================================================================;
proc sgplot data=thilothmmal;
	histogram status;
	density status;
run;

proc univariate data=thilothmmal plot;
	var status;
run;




* Example 4. Analyze Class Survey Data
* The data below was collected from students in a class. It contains eight 
* variables: id, height, weight, male (coded as 1 if the student is male and 0 
* if the student is female), bach5300 (coded as 1 if the student took BACH5300 
* and 0 if the student did not take BACH5300), bios5300 (coded as 1 if the 
* student took BIOS5300 and 0 if the student did not take BIOS5300), epid5300 
* (coded as 1 if the student took EPID5300 and 0 if the student did not take 
* EPID5300), and gpa;
* ============================================================================;
data classsurv;
	input id height weight male bach5300 bios5300 epid5300 gpa;
	datalines;
	1 170 185 1 1 1 1 3.6
	2 175 162 1 1 1 1 3.7
	3 231 180 1 1 1 1 3.8
	4 189 190 1 1 0 1 3.8
	5 164 175 1 1 0 1 .
	6 178 178 1 1 0 0 3.78
	7 . 192 1 1 1 0 3.87
	8 184 178 1 1 1 0 3.99
	9 186 169 1 1 1 0 3.98
	10 174 130 1 1 0 0 4
	11 165 140 0 1 0 1 2.8
	12 155 125 0 1 1 . 3.56
	13 158 126 0 1 1 1 .	
	14 156 138 0 1 1 1 2.9
	15 168 116 0 1 1 1 3.5
	16 145 114 0 1 0 1 3.4
	17 158 135 0 1 1 0 3.3
	18 110 141 0 1 0 0 3.8
	19 153 137 0 1 0 0 3.4
	20 165 129 0 1 0 0 3.6
	;
run;




* Task 2. Request the MEAN, MEDIAN, MODE, Standard Deviation, MIN, and MAX for 
* all variables in the data set;
* ============================================================================;
proc means data = classsurv n mean median mode std min max;
run;




* Task 3. Create a boxplot of the variable height. Make sure to label any 
* outliers with the variable id;
* ============================================================================;
proc sgplot data = classsurv;
	vbox height / datalabel = id;
run;