SAS Ron Cody Solutions for even Number problems from Chapter 7 to 15

Ayapparaj / Praxis Business School 1 Chapter 7

Chapter 7

Performing Conditional Processing

/* 2. Using the SAS data set Hosp, use PROC PRINT to list observations for

Subject values of 5, 100, 150, and 200. Do this twice, once using OR

operators and onceusing the IN operator. Note: Subject is a numeric

variable */

data a15009.hospques2;

set a15009.hosp;

where Subject = 5 or Subject = 100 or Subject = 150 or Subject = 200;

*using or function in where statement to give the condition given;

run;

/* OR */


set a15009.hosp;

where Subject in(5,100,150,200);

*using or function in where statement to give the condition given;

run;

proc print data=a15009.hospques22;

run;

/*4. Using the Sales data set, create a new, temporary SAS data set

containing Region and TotalSales plus a new variable called Weight with

values of 1.5 for the North Region, 1.7 for the South Region, and 2.0 for

the West and East Regions. Use a SELECT statement to do this */

data a15009.salesques4;

set a15009.sales (keep = TotalSales Region);

*dataset is in the blog folder uploaded in Dropbox;

select;

*using select statement for giving the conditions and values associated

each condition;

when (Region = 'North') Weight = 1.5;

when (Region = 'South') Weight = 1.7;

when (Region = 'East') Weight = 2.0;

when (Region = 'West') Weight = 2.0;

otherwise;

end;

run;


proc print data=a15009.Salesques4;

run;

/*6. Using the Sales data set, list all the observations where Region is

North and Quantity is less than 60. Include in this list any observations

where the customer name (Customer) is Pet's are Us */

data a15009.salesques6;

set a15009.sales;

where Region = 'North' and Quantity < 60;

*using where statement to specify condition for region and quantity;

run;

proc print data=a15009.Salesques6;

run;

Chapter 8

Performing Iterative Processing: Looping

/*2. Run the program here to create a temporary SAS data set (MonthSales):

data monthsales;

input month sales @@;

---add your line(s) here---

datalines;

1 4000 2 5000 3 . 4 5500 5 5000 6 6000 7 6500 8 4500

9 5100 10 5700 11 6500 12 7500

;

Ayapparaj / Praxis Business School 3 Performing Iterative Processing: Looping

Modify this program so that a new variable, SumSales, representing Sales to

date, is added to the data set. Be sure that the missing value for Sales in

month 3 does not result in a missing value for SumSales */

data a15009.monthsales;

input month sales @@;

sumsales+sales;

*using sum function above for both sum of sales and sum of sales;

retain sumsales 0;

*using retain function initializing sumsales variable to 0;

datalines;

1 4000 2 5000 3 . 4 5500 5 5000 6 6000

7 6500 8 4500 9 5100 10 5700 11 6500 12 7500

;

proc print data=a15009.monthsales;

run;

/*4. Count the number of missing values for the variables A, B, and C in

the Missing data set. Add the cumulative number of missing values to each

observation (use variable names MissA, MissB, and MissC). Use the MISSING

function to test for the missing values */

data a15009.missingdata;

input X $ Y Z A;

if missing(X) then misscounterX+1;

if missing(Y) then misscounterY+1;

if missing(Z) then misscounterZ+1;

if missing(A) then misscounterA+1;

*using sum function for finding number of missing values in each variable;

datalines;

M 56 68 89

F 33 60 71

M 45 91 .

F 35 35 68

M . 71 81

M 50 68 71

. 23 60 46

M 65 72 103

. 35 65 67

M 15 71 75

;

proc print data=a15009.missingdata;run;


/*6. Repeat Problem 5, except have the range of N go from 5 to 100 by 5 */

data a15009.logy2;

do N=5 to 100 by 5;

*using do loop to initialize N variable and assign values from 5 to 100 in

increments of 5;

LogN=LOG(N);

output;

end;

run;

proc print data=a15009.logy2;run;

*8. Use an iterative DO loop to plot the following equation:

Logit = log(p / (1 – p))

Use values of p from 0 to 1 (with a point at every .05). Using the

following GPLOT

statements will produce a very nice plot. (If you do not have SAS/GRAPH

software, use PROC PLOT to plot your points).

goptions reset=all

ftext='arial'

htext=1.0

ftitle='arial/bo'

htitle=1.5

colors=(black);

symbol v=none i=sm;

title "Logit Plot";

proc gplot data=logitplot;

plot Logit * p;


run;

quit;*/

data a15009.logitplot;

do p=0 to 1 by 0.05;

*using do loop to initialize p variable with values from 0 to 1 increasing

by 0.05;

Logit=LOG(p/(1-p));

output;

end;

run;

goptions reset=all ftext='arial' htext=1.0 ftitle='arial/bo' htitle=1.5

colors=(black);

symbol v=none i=sm;

title "Logit Plot";

proc gplot data=a15009.logitplot;

*using gplot procedure to make plotting;

plot Logit * p;

run;

quit;

/*10. You are testing three speed-reading methods (A, B, and C) by randomly

assigning 10 subjects to each of the three methods. You are given the

results as three lines of reading speeds, each line representing the

results from each of the three methods,

respectively. Here are the results:

250 255 256 300 244 268 301 322 256 333

267 275 256 320 250 340 345 290 280 300

350 350 340 290 377 401 380 310 299 399

Create a temporary SAS data set from these three lines of data. Each

observation should contain Method (A, B, or C), and Score. There should be

30 observations in this data set. Use a DO loop to create the Method

variable and remember to use a single trailing @ in your INPUT statement.

Provide a listing of this data set using PROC PRINT */

data a15009.reading;

do Method = 'MethodA','MethodB','MethodC';

*using do loop to initialize method variable with three values;

do SNo=1 to 10;

input score @;

output;end;end;

datalines;

250 255 256 300 244 268 301 322 256 333

267 275 256 320 250 340 345 290 280 300


350 350 340 290 377 401 380 310 299 399

;

proc print data=a15009.reading noobs;

var Method score;

run;

/* 12. You place money in a fund that returns a compound interest of 4.25%

annually. You deposit $1,000 every year. How many years will it take to

reach $30,000? Do not use compound interest formulas. Rather, use “brute

force” methods with DO WHILE or DO UNTIL statements to solve this problem

*/

data a15009.inte;

interest = 0.0425;*initializing the interest variable;

total=1000; *initializing the total valuable;

do year = 1 to 100 by 1 until (total ge 30000);

*specifying values for year and condition for total to stop the loop when

the

value reaches 30000;

total=total+interest*total;

output;

end;

format total dollar11.2; *specifying format for total variable;

run;

proc print data=a15009.inte;run;


/*14. Generate a table of integers and squares starting at 1 and ending

when the square value is greater than 100. Use either a DO UNTIL or DO

WHILE statement to accomplish this*/

*using DO UNTIL;

data a15009.square;

do Integers = 1 to 100 until (squares ge 100);

*using do until taking values from 1 to 100 and

specifying the condition for squares variable to

stop the loop when it reaches 100;

Squares = Integers * integers;

output;end;run;

proc print data=a15009.square;run;

*using IF STMT;

data a15009.square;

do Integers = 1 to 100 by 1;

Squares = Integers * integers;

if Squares gt 100 then leave;

output;end;run;

proc print data=a15009.square;run;

Ayapparaj / Praxis Business School 8 Chapter 9 Working with Dates

Chapter 9 Working with Dates

/* 2. Using the following lines of data, create a temporary SAS data set

called ThreeDates. Each line of data contains three dates, the first two in

the form mm/dd/yyyy descenders and the last in the form ddmmmyyyy. Name the

three date variables Date1, Date2, and Date3. Format all three using the

MMDDYY10. format. Include in your data set the number of years from Date1

to Date2 (Year12) and the number of years from Date2 to Date3 (Year23).

Round these values to the nearest year. Here are the lines of data (note

that the columns do not line up):

01/03/1950 01/03/1960 03Jan1970

05/15/2000 05/15/2002 15May2003

10/10/1998 11/12/2000 25Dec2005 */

*loading the values as a separate data set in permanent library;

data a15009.three;

input @1 Date1 mmddyy10.

@12 Date2 mmddyy10.

@23 Date3 date9.;

format Date1 Date2 Date3 mmddyy10.;

datalines;

01/03/1950 01/03/1960 03Jan1970

05/15/2000 05/15/2002 15May2003

10/10/1998 11/12/2000 25Dec2005

;

*accessing the values from the above dataset using set function

Using yrdif function to calculate difference between date1,date2 and date3

variables and rounding them using round command along with yrdif;

data a15009.threedates;

set a15009..three;

year12=round(yrdif(Date1,Date2,'Actual'));

year23=round(yrdif(Date2,Date3,'Actual'));

run;

proc print data=threedates;

run;

proc print data=a15009.threedates;run;

/* 4. Using the Hosp data set, compute the subject’s ages two ways: as of

January 1, 2006 (call it AgeJan1), and as of today’s date (call it

AgeToday). The variable DOB represents the date of birth. Take the integer

portion of both ages. List the first 10 observations */


set a15009.hosp;

AgeToday=int(yrdif(DOB,today(),'Actual'));


AgeJan1=int(yrdif(DOB,'01Jan2006'd,'Actual'));

*using yrdif to find the difference between DOB and today’s date and int to

get only integer value of the difference;

run;

proc print data=a15009.hospques4;run;

/* 6. Using the Medical data set, compute frequencies for the days of the

week for the date of the visit (VisitDate). Supply a format for the days of

the week and months of the year */

*loading the medical dataset in the permanent library;

data a15009.medical;

input @1 VisitDate mmddyy10. @12 patno $3.;

datalines;

11/29/2003 879

11/30/2003 880

09/04/2003 883

08/28/2003 884

09/04/2003 885

08/26/2003 886

08/31/2003 887

08/25/2003 888

11/16/2003 913

11/15/2003 914

;

run;

data a15009.sevenques6;

set a15009.medical(keep=VisitDate); *taking medical data using set

function;

Days = weekday(VisitDate); *fetching weekday from visitdate variable;

run;

proc format; *providing format for days variable;

value days 1='Sun' 2='Mon' 3='Tue'

4='Wed' 5='Thu' 6='Fri'

7='Sat';

run;


title "Frequencies for Visit Dates";

proc freq data=a15009.sevenques6;

tables Days / nocum nopercent;

format Days days.; run;

/* 8. Using the values for Day, Month, and Year in the raw data below,

create a temporary SAS data set containing a SAS date based on these values

(call it Date) and format this value using the MMDDYY10. format. Here are

the Day, Month, and Year values:

25 12 2005

1 1 1960

21 10 1946 */

*storing the data in the permanent library;

data a15009.dataset;

input Day Month Year;

datalines;

25 12 2005

1 1 1960

21 10 1946

;


set a15009.dataset;

Date = mdy(Month,Day,Year);

*merging the day month year values into mmddyyyy format;

format Date mmddyy10.;

run;

proc print data=a15009.sevenques8;run;

/* 10. Using the Hosp data set, compute the number of months from the

admission date (AdmitDate) and December 31, 2007 (call it MonthsDec). Also,

compute the number of months from the admission date to today's date (call

it MonthsToday). Use a date interval function to solve this problem. List

the first 20 observations for your solution */



set a15009.hosp; *you can find hosp dataset in the blog folder uploaded in

the dropbox;

MonthDec = intck('month',AdmitDate,'31Dec2007'd);

*using intck function to find month difference between admitdate and

31Dec2007;

MonthToday = intck('month',AdmitDate,today());

run;

proc print data=a15009.sevenques10;

run;

/* 12. You want to see each patient in the Medical data set on the same day

of the week 5 weeks after they visited the clinic (the variable name is

VisitDate). Provide a listing of the patient number (Patno), the visit

date, and the date for the return visit */


set a15009.medical;

Followdate=intnx('month',VisitDate,5,'sameday');

*using intcx function to execute the specified condition;

run;

proc print data=a15009.sevenques12;

format Followdate VisitDate date9.;

run;


Chapter 10

Subsetting and Combining SAS Data

Sets

/* 2.Using the SAS data set Hosp, create a temporary SAS data set called

Monday2002, consisting of observations from Hosp where the admission date

(AdmitDate) falls on a Monday and the year is 2002. Include in this new

data set a variable called Age, computed as the person’s age as of the

admission date, rounded to the nearest year */

data a15009.monday2002;

set a15009.hosp;

*you can take hosp dataset from blog folder uploaded in dropbox;

where year(AdmitDate) eq 2002 and

weekday(AdmitDate) eq 2;

*using where statement to specify the condition for AdmitDate

Weekday gives value of Monday as 2 as series starts from 1 for Sunday

Year(admitdate) gives year value of admitdate;

Age = round(yrdif(DOB,AdmitDate,'Actual'));

*using yrdif function to find difference between DOB and AdmitDate;

run;

title "Listing of MONDAY2002";

proc print data=a15009.monday2002;

run;

/* 4. Using the SAS data set Bicycles, create two temporary SAS data sets

as follows: Mountain_USA consists of all observations from Bicycles where

State is Uttar Pradesh and Model is Mountain. Road_France consists of all

Ayapparaj / Praxis Business School 13 Subsetting and Combining SAS Data Sets

observations from Bicycles where State is Maharastra and Model is Road

Bike. Print these two data sets */

data a15009.Mountain_USA a15009.Road_France;

set a15009.Bicycles;

*bicycle dataset is available in the blog folder uploaded in dropbox;

if State="Uttar Pradesh" and Model="Mountain Bike" then output

a15009.Mountain_USA;

else if State="Maharastra" and Model="Road Bike" then output

a15009.Road_France;

run;

*introducing two new datasets as a15009.Mountain_USA a15009.Road_France and

saving the observations to both the datasets based on the conditions given;

proc print data= a15009.Mountain_USA;run;

proc print data= a15009.Road_France;run;

/*6. Repeat Problem 5, except this time sort Inventory and NewProducts

first (create two temporary SAS data sets for the sorted observations).

Next, create a new, temporary SAS data set (Updated) by interleaving the

two temporary, sorted SAS data sets. Print out the result.*/

*sorting inventory dataset by model variable;

proc sort data=a15009.inventory out=a15009.inventory;

by Model;

run;

*sorting newproducts dataset by model variable;

proc sort data=a15009.newproducts out=a15009.newproducts;

by Model;

run;

*merging all the rows of both the datasets into a single dataset updated;

data a15009.updated;

set a15009.inventory a15009.newproducts;

by Model;

run;

title "Listing of UPDATED";

proc print data=a15009.updated;

run;


/* 8. Run the program here to create a SAS data set called Markup:

data markup;

input manuf : $10. Markup;

datalines;

Cannondale 1.05

Trek 1.07

;

Combine this data set with the Bicycles data set so that each observation

in the Bicycles data set now has a markup value of 1.05 or 1.07, depending

on whether the bicycle is made by Cannondale or Trek. In this new data set

(call it Markup_Prices),create a new variable (NewTotal) computed as

TotalCost times Markup */

*combining both datasets using manuf variable;

data a15009.combi;

merge a15009.bicycles (rename=(Manuf=manuf)) a15009.markup2;

by manuf;

newtotal=sum(unitcost); run;

proc print data=a15009.combi;run;

data a15009.markup2;

input manuf : $10. Markup;

datalines;

Atlas 1.05

Hero 1.07

;

*sorting markup2 data by manuf variable;

proc sort data=a15009.markup2;

by manuf;

run;

*sorting markup2 data by manuf variable here the thing to note is

manufacturer is the label name not variable name;

proc sort data=a15009.Bicycles;

by Manuf;

run;


/*10 Using the Purchase and Inventory data sets, provide a list of all

Models (and the Price) that were not purchased*/

*sorting the inventory dataset by Model Variable;


by Model;

run;

*sorting the purchase dataset by Model Variable;

proc sort data=a15009.purchase out=a15009.purchase;

by Model;

run;

*merging two datasets by Model variable

using "IN=" to filter the datsets to find model that were not purchased

along with the proce;

data a15009.notpurchased;

merge a15009.inventory(in=InInventory)a15009.purchase(in=InPurchase);

by Model;

if InInventory and not InPurchase;

keep Model Price;

run;

title "Listing of NOT_BOUGHT";

proc print data=a15009.notpurchased noobs;

run;

/*12 You want to merge two SAS data sets, Demographic and Survey1, based on

an identifier. In Demographic, this identifier is called ID; in Survey1,

the identifier is called Subj. Both are character variables.*/

*you can find both demographictwo and survey1 dataset in the blog folder

uploaded in dropbox;

proc sort data=a15009.demographictwo out=a15009.demographictwo;

by ID;


run;

proc sort data=a15009.survey1 out=a15009.survey1;

by Subj;

run;

data a15009.combine12ten;

merge a15009.demographictwo a15009.survey1 (rename=(Subj = ID));

by ID;

run;

proc print data=a15009.combine12ten ;

run;

/*14 Data set Inventory contains two variables: Model (an 8-byte character

variable) and Price (a numeric value). The price of Model M567 has changed

to 25.95 and the price of Model X999 has changed to 35.99. Create a

temporary SAS data set (call it NewPrices) by updating the prices in the

Inventory data set*/

data a15009.modelnew;

input Model $ Price;

datalines;

M567 25.95

X999 35.99

;

*sorting inventory data by model variable;


by Model;

run;

*updating inventory data with modelnew for price for the models;

data a15009.newprices;

update a15009.inventory a15009.modelnew;

by Model;

run;

proc print data=a15009.newprices ;

run;


Chapter 11

Working with Numeric Functions

/* 2. Count the number of missing values for WBC, RBC, and Chol in the

Blood data set. Use the MISSING function to detect missing values */

data a15009.choly;

set a15009.blood;

*blood dataset is present in the blog folder uploaded in dropbox folder;

if missing(Gender) then MissG+1;

if missing(WBC) then MissWBC+1;

if missing(RBC) then MissRBC+1;

if missing(Chol) then MissChol+1;

*using sum function to find the number of missing values in each variable;

run;

proc print data=a15009.choly;run;

/* 4. The SAS data set Psych contains an ID variable, 10 question responses

(Ques1– Ques10), and 5 scores (Score1–Score5). You want to create a new,

temporary SAS data set (Evaluate) containing the following:

a. A variable called QuesAve computed as the mean of Ques1–Ques10. Perform

this computation only if there are seven or more non-missing question

values.

b. If there are no missing Score values, compute the minimum score

(MinScore), the maximum score (MaxScore), and the second highest score

(SecondHighest) */

data a15009.evaluate;

set a15009.psych;

*pysch dataset is present in the blog folder uploaded in dropbox folder;

if n(of Ques1-Ques10) ge 7 then QuesAve=mean(of Ques1-Ques10);

if n(of Score1-Score5) eq 5 then maxscore=max(of Score1-Score5);

if n(of Score1-Score5) eq 5 then Minscore=min(of Score1-Score5);

if n(of Score1-Score5) eq 5 then SecondHighest=largest(2,of Score1-Score5);

*using if then stmt to find max score min score secondhighest of the score

variables;

run;

proc print data=a15009.evaluate;run;

Ayapparaj / Praxis Business School 18 Working with Numeric Functions

/* 6. Write a short DATA _NULL_ step to determine the largest integer you

can score on your computer in 3, 4, 5, 6, and 7 bytes */

data _null_;

set a15009.cons;

put int3= int4= int5= int6= int7= ;

run;

*output will appear in the log window;

/* 8. Create a temporary SAS data set (Random) consisting of 1,000

observations, each with a random integer from 1 to 5. Make sure that all

integers in the range are equally likely. Run PROC FREQ to test this

assumption */

data a15009.random;

do i=1 to 1000;

x=int(rand('uniform')*5)+1 /*OR*/ x=int(ranuni(0)*5+1);output ;end;

*here am using rand function to get random value between 1 and 5;

run;

proc freq data=a15009.random;

tables x/missing;run;

/* 10. Data set Char_Num contains character variables Age and Weight and

numeric variables SS and Zip. Create a new, temporary SAS data set called

Convert with new variables NumAge and NumWeight that are numeric values of

Age and Weight, respectively, and CharSS and CharZip that are character

variables created from SS and Zip. CharSS should contain leading 0s and

dashes in the appropriate places for Social Security numbers and CharZip

should contain leading 0s Hint: The Z5. format includes leading 0s for the

ZIP code */

Ayapparaj / Praxis Business School 19 Working with Numeric Functions

data a15009.convert;

set a15009.char_num;

*char_num dataset is present in the blog folder uploaded in dropbox folder;

NumAge = input(Age,8.);

NumWeight = input(weight,8.);

*converting character variables weight and age into numeric variables;

CharSS = put(SS,ssn11.);

CharZip = put(Zip,z5.);

*converting numeric variables SS and Zip into character variables;

run;

proc print data=a15009.convert;

run;

/* 12. Using the Stocks data set (containing variables Date and Price),

compute daily changes in the prices. Use the statements here to create the

plot.

Note: If you do not have SAS/GRAPH installed, use PROC PLOT and omit the

GOPTIONS and SYMBOL statements.

goptions reset=all colors=(black) ftext=swiss htitle=1.5;

symbol1 v=dot i=smooth;

title "Plot of Daily Price Differences";

proc gplot data=difference;

plot Diff*Date;

run;

quit; */

data a15009.difference;

set a15009.stocks;

Diff = Dif(Price);

*using dif function to calculate the difference in thr price compared to

the previous value;

run;

goptions reset=all colors=(black) ftext=swiss htitle=1.5;

symbol1 v=dot i=smooth;

title "Plot of Daily Price Differences";

proc gplot data=a15009.difference;

plot Diff * Date;

run;quit;


Chapter 12

Working with Character Functions

/*2 Using the data set Mixed, create a temporary SAS data set (also called

Mixed) with the following new variables:

a. NameLow – Name in lowercase

b. NameProp – Name in proper case

c. (Bonus – difficult) NameHard – Name in proper case without using the

PROPCASE function*/

data a15009.mixed;

set a15009.mixed;

*you can find mixed dataset in the blog folder uploaded in dropbox;

length First Last $ 15 NameHard $ 20;

NameLow = lowcase(Name);

*converting entire word into lower case;

NameProp = propcase(Name);

*making first letter of each work into uppercase;

First = lowcase(scan(Name,1,' '));


Last = lowcase(scan(Name,2,' '));


substr(First,1,1) = upcase(substr(First,1,1));

*converting entire word into upper case;

substr(Last,1,1) = upcase(substr(Last,1,1));

*converting entire word into upper case;

NameHard = catx(' ',First,Last);

*using catx making first letter of each work into uppercase,without using

propcase;

drop First Last;

run;

proc print data=a15009.mixed;

Ayapparaj / Praxis Business School 21 Working with Character Functions

run;

/*4 Data set Names_And_More contains a character variable called Height. As

you can see in the listing in Problem 3, the heights are in feet and

inches. Assume that these units can be in upper- or lowercase and there may

or may not be a period following the units. Create a temporary SAS data set

(Height) that contains a numeric variable (HtInches) that is the height in

inches.*/

data a15009.height;

set a15009.names_and_more(keep = Height);

Height = compress(Height,'INFT.','i');

*using compress function with "i" argument to remove characters and to

ignore cases;

/* Alternative

Height = compress(Height,' ','kd');

*keep digits and blanks;

*/

Feet = input(scan(Height,1,' '),8.);

Inches = input(scan(Height,2,' '),?? 8.);

*using scan function to extract values around the characters from the

variable

1 value before space and 2 for value after two for ;

if missing(Inches) then HtInches = 12*Feet;

else HtInches = 12*Feet + Inches;

drop Feet Inches;

run;

proc print data=a15009.height;

run;

/*6 Data set Study (shown here) contains the character variables Group and

Dose. Create a new, temporary SAS data set (Study) with a variable called

GroupDose by putting these two values together, separated by a dash. The

length of the resulting variable should be 6 (test this using PROC CONTENTS

or the SAS Explorer). Make sure that there are no blanks (except trailing

blanks) in this value. Try this problem two ways: first using one of the

CAT functions, and second without using any CAT functions*/

*Using CAT functions;


data a15009.study;

set a15009.study;

length GroupDose $ 6;

GroupDose = catx('-',Group,Dose);

*here we are using catx to supply "-" as a separator between Group and Dose

variables;

run;

proc print data=a15009.study;

run;

*Without using CAT functions;

data a15009.study;

set a15009.study;

length GroupDose $ 6;

GroupDose = trim(Group) || '-' || Dose;

*using trim function to trim any space around thr values in Group and

Dose and join them and supply "-" in between the two values;

run;


run;

/*8 Notice in the listing of data set Study in Problem 6 that the variable

called Weight contains units (either lbs or kgs). These units are not

always consistent in case and may or may not contain a period. Assume an

upper- or lowercase LB indicates pounds and an upper- or lowercase KG

indicates kilograms. Create a new, temporary SAS data set (Study) with a

numeric variable also called Weight (careful here) that represents weight

in pounds, rounded to the nearest 10th of a pound. Note: 1 kilogram = 2.2

pounds*/

data a15009.study;

set a15009.study(keep=Weight rename=(Weight = WeightUnits));

Weight = input(compress(WeightUnits,,'kd'),8.);

*using compress(kd)inside input function to keep numerical values alone

from the string

and change if character variables present to numerical;

if find(WeightUnits,'KG','i') then Weight = round(2.2*Weight,.1);

*using find function with "i" argument to remove characters and to ignore

cases;

else if find(WeightUnits,'LB','i') then Weight = round(Weight,.1);

run;


run;


/*10 Data set Errors contains character variables Subj (3 bytes) and

PartNumber (8 bytes). (See the partial listing here.) Create a temporary

SAS data set (Check1) with any observation in Errors that violates either

of the following two rules: first, Subj should contain only digits, and

second, PartNumber should contain only the uppercase letters L and S and

digits. Here is a partial listing of Errors:*/

data a15009.violates_rules;

set a15009.errors;

where notdigit(trim(Subj)) or

verify(trim(PartNumber),'0123456789LS');

*using notdigit to check any invalid character type value present

Here you should use trim function along with notdigit because

Without the TRIM function "not" function used here would

return the position of the first trailing blank in each of the character

values;

run;

proc print data=a15009.violates_rules;

run;

/*12 List the subject number (Subj) for any observations in Errors where

PartNumber contains an upper- or lowercase X or D.*/

proc print data=a15009.errors;

where findc(PartNumber,'XD','i');

*using findc function with argument "i" to find if the variable values

contain any case ;

var Subj PartNumber;


run;

/*14. List all patients in the Medical data set where the word antibiotics

is in the comment field (Comment).*/

title "Observations Involving the word Antibiotics";

proc print data=a15009.medicaltwo;

where findw(Comment,'antibiotics');

*using findw function to find if the comment variable contain the word

"antiboitics" in its values;

run;

<< Medicaltwo dataset >>

/*16 Provide a list, in alphabetical order by last name, of the

observations in the Names_And_More data set. Set the length of the last

name to 15 and remove multiple blanks from Name. Note: The variable Name

contains a first name, one or more spaces, and then a last name.*/

data a15009.names;

set a15009.names_and_more;

length Last $ 15;

Name = compbl(Name);

*using compbl function to compress any blanks values present;

Last = scan(Name,2,' ');

*using scan function to take only second part of the name and store it the

last vsriable;

run;

*sorting the data in names dataset based on last variable values;

proc sort data=a15009.names;

by Last;

run;

proc print data=a15009.names;

Ayapparaj / Praxis Business School 25 Chapter 13 Working with Arrays

id Name;

var Phone Height Mixed;

run;

Chapter 13 Working with Arrays /* 1 Using the SAS data set Survey1, create a new, temporary SAS data set

(Survey1) where the values of the variables Ques1–Ques5 are reversed as

follows: 1 ?? 5; 2 ?? 4; 3 ?? 3; 4 ?? 2; 5 ?? 1.

Note: Ques1–Ques5 are character variables. Accomplish this using an

array.*/

*Data set SURVEY;

proc format library=a15009;

value $gender 'M' = 'Male'

'F' = 'Female'

' ' = 'Not entered'

other = 'Miscoded';

value age low-29 = 'Less than 30'

30-50 = '30 to 50'

51-high = '51+';

value $likert '1' = 'Strongly disagree'

'2' = 'Disagree'

'3' = 'No opinion'

'4' = 'Agree'

'5' = 'Strongly agree';

run;

data a15009.survey12;

set a15009.survey1;

array Ques{5} $ Q1-Q5;

*creating array with 5 values for storing variables from Q1 to Q5;

do i = 1 to 5;

Ques{i} = translate(Ques{i},'54321','12345');

*using do loop to create "i" variable with values from 1 to 5 and to

reverse the question using translate function inside the Ques array;

end;

drop i;

run;

proc print data=a15009.survey12;

run;

/* 2.Redo Problem 1, except use data set Survey2. Note: Ques1–Ques5 are

numeric variables.*/

data a15009.survey22;

set a15009.survey2;

array Ques{5} Q1-Q5;

Ayapparaj / Praxis Business School 26 Chapter 14 Displaying Your Data

do i = 1 to 5;

Ques{i} = 6 - Ques{i};

end;

drop i;

run;

proc print data=a15009.survey22;

run;

/* 4.Data set Survey2 has five numeric variables (Q1–Q5), each with values

of 1, 2, 3, 4, or 5. You want to determine for each subject (observation)

if they responded with a 5 on any of the five questions. This is easily

done using the OR or the IN operators. However, for this question, use an

array to check each of the five questions. Set variable (ANY5) equal to Yes

if any of the five questions is a 5 and No otherwise.*/

data a15009.any5;

set a15009.survey2;

array Ques{5} Q1-Q5;

Any5 = 'No ';

do i = 1 to 5;

if Ques{i} = 5 then do;

Any5 = 'Yes';

leave;

end;

end;

drop i;

run;

proc print data=a15009.any5;

run;

Chapter 14 Displaying Your Data /*1 List the first 10 observations in data set Blood. Include only the

variables Subject,WBC (white blood cell), RBC (red blood cell), and Chol.

Label the last three variables “White Blood Cells,” “Red Blood Cells,” and

“Cholesterol,” respectively. Omit the Obs column, and place Subject in the

first column. Be sure the column headings are the variable labels, not the

variable names.*/

proc print data=a15009.blood (obs=10) label;

Ayapparaj / Praxis Business School 27 Chapter 14 Displaying Your Data

id Subject;

var WBC RBC Chol;

label WBC = 'White Blood Cells'

RBC = 'Red Blood Cells'

Chol = 'Cholesterol';

run;

/*2 Using the data set Sales, create the report shown here:*/

proc sort data=a15009.sales out=a15009.sales;

by Region;

run;

proc print data=a15009.sales;

by Region;

id Region;

var Quantity TotalSales;

sumby Region;

run;

Ayapparaj / Praxis Business School 28 Chapter 15 Creating Customized Reports

/*4.List the first five observations from data set Blood. Print only

variables Subject, Gender, and BloodType. Omit the Obs column.*/

proc print data=a15009.blood(obs=5) noobs;

var Subject Gender BloodType;

run;

Chapter 15 Creating Customized Reports

/*2 Using the Blood data set, produce a summary report showing the average

WBC and RBC count for each value of Gender as well as an overall average.

Your report should look like this:*/

proc report data=a15009.blood nowd headline;

column Gender WBC RBC;

define Gender / group width=6;


define WBC / analysis mean "Average WBC"

width=7 format=comma6.0;

define RBC / analysis mean "Average RBC"

width=7 format=5.2;

rbreak after / dol summarize;

run;

quit;

/*4 Using the SAS data set BloodPressure, compute a new variable in your

report. This variable (Hypertensive) is defined as Yes for females

(Gender=F) if the SBP is greater than 138 or the DBP is greater than 88 and

No otherwise. For males (Gender=M), Hypertensive is defined as Yes if the

SBP is over 140 or the DBP is over 90 and No otherwise. Your report should

look like this:*/

*Data set BLOODPRESSURE;

proc report data=a15009.bloodpressure nowd;

column Gender SBP DBP Hypertensive;

define Gender / Group width=6;

define SBP / display width=5;

define DBP / display width=5;

define Hypertensive / computed "Hypertensive?" width=13;

compute Hypertensive / character length=3;

if Gender = 'F' and (SBP gt 138 or DBP gt 88)

then Hypertensive = 'Yes';

else Hypertensive='No';

if Gender = 'M' and

(SBP gt 140 or DBP gt 90)

then Hypertensive = 'Yes';

else Hypertensive = 'No';

endcomp;

run;

quit;


/*6 Using the SAS data set BloodPressure, produce a report showing Gender,

Age, SBP, and DBP. Order the report in Gender and Age order as shown

here:*/

proc report data=a15009.bloodpressure nowd;

column Gender Age SBP DBP;

define Gender / order width=6;

define Age / order width=5;

define SBP / display "Systolic Blood Pressure" width=8;

define DBP / display "Diastolic Blood Pressure" width=9;

run;

quit;

/*8 Using the data set Blood, produce a report like the one here. The

numbers in the table are the average WBC and RBC counts for each

combination of blood type and gender.*/

proc report data=a15009.bloodnew nowd headline;

column BloodType Gender,WBC Gender,RBC;

define BloodType / group 'Blood Type' width=5;

define Gender / across width=8 '-Gender-';

define WBC / analysis mean format=comma8.;

define RBC / analysis mean format=8.2;

run;

quit;

Date post:	12-Apr-2017
Category:	Data & Analytics
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SAS Ron Cody Solutions for even Number problems from Chapter 7 to 15

Data & Analytics