Turn In: A copy of the first 50 lines or so of the converted text file.

Transcription

1 STAT 325: Final, Take home Spring 2012 Points: 100 pts Name: We will begin by working with the Fools Five dataset. The Fools Five is a large event held each year in Lewiston, MN. The main event is the running event and data from this event will be investigated here. Download the Fools Five 8k Results for 2012 file from our course website or the Fools Five website ( SAS cannot read a PDF file in; however, there are free easy to use software packages that will convert PDF files to text file (which we know can be read into SAS). 1. Go to and convert the pdf file containing the Fools Five Results for Save a copy of the converted text file. (4 pts) Turn In: A copy of the first 50 lines or so of the converted text file. 2. Consider the first few observations in this converted text file. Answer the following: i. Why might the page breaks (the long horizontal lines) be problematic when reading this data in SAS? Discuss. (2 pts) ii. Does it appear that the information for the 1 st place finisher, Elliot Heath, could be read into SAS successfully? Discuss. (2 pts) iii. Does it appear that the information for the 2 nd place finisher, Garrett Heath, could be read into SAS successfully? Discuss. (2 pts) iv. Notice that the widths of some of the columns in the PDF version are too narrow and thus some of the names and cities have been placed on two lines. If the width of these columns were properly adjusted (before making the PDF file), do you think the free Convert pdftotext program would work better? Explain your reasoning. (2 pts) 3. Download and open a copy of the Excel version of the cleaned data. Suppose your colleague decides to use the PROC IMPORT wizard to read in this dataset. Do you think this will work? Explain your reasoning. (4 pts) 1

2 4. Use the following code to read in the Text version of this dataset that was provided on our course website. DATA WORK.FOOLS5; INFILE 'D:\DATA\SAS\Fools5.txt' FIRSTOBS=16; INPUT Place / No / Name & $23. / Age / Sex $ / City & $16. / State $ / ChipTime TIME7. / GunTime TIME7. / Pace TIME7. / SexPlace / SexTotal / DivPlace / DivTotal ; a. Run a PROC CONTENTS for the data. (2 pts) Turn In: A copy of the PROC CONTENTS output. b. What is the purpose of the / in front of each variable in the INPUT statement? Does this code work with the /? Discuss. (2 pts) c. What is the purpose of the & in front of the in format statement for Name (i.e. Name & $23.)? If you don t know Google It! I just recently learned about this myself. (3 pts) 5. There are two different times recorded in this dataset, Gun Time and Chip Time. a. Use Google to identify the difference between Gun and Chip Time in a road race such as Fools Five. (2 pts) b. Similar to date variables, SAS stores time variable as the number of seconds from midnight. For example, 1:00:00 is considered 1:00:00AM and is stored by SAS as 3600 because there are 3600 seconds in an hour. Create a new variable called, GunChipDiff, which simply calculates the difference between the Chip Time and Gun Time. Verify that SAS correctly computed the difference between these two times for at least one observation in your dataset. (2 pt) GunChipDiff = GunTime - ChipTime; 2

3 c. Create a list (i.e. SAS printout) of the ten runners who have the largest difference between their Gun Time and Chip Time. What do these most extreme values imply about how these individuals started the race? (3 pts) Hint: I used the DESCENDING option in my PROC SORT statement to get the data in the appropriate order. Turn In: A print out of the ten runners who have the largest difference between their Chip and Gun Time. Assuming I did this correctly, your output should look as follows. d. A race director wants to ensure nobody s Chip Time is greater their Gun Time as this would mean they got a head start in the race. Verify for the race director that none of the Chip Time values are greater than the Gun Time. (2 pts) 6. Consider the following code. LAG Function DATA FOOLS5_1; SET FOOLS5; TimeLag = LAG(ChipTime); TimeDiff = ChipTime - TimeLag; DIF Function DATA FOOLS5_1; SET FOOLS5; TimeDiff = DIF(ChipTime); a. Run the code using the LAG function. What is the purpose the LAG function? What does the variable TimeDiff tell us about the race? (3 pts) Turn In: A printout of the first 10 observations with the variable TimeLag and TimeDiff printed. b. Consider the code using the LAG function. This code is redundant and assigning the result from LAG(ChipTime) to a variable is not necessary. Rewrite the LAG function code that eliminates this redundancy. (2 pts) 3

4 c. Run the code provided which uses the DIF function? What does the DIF function do for us? Is the outcome of interest here (i.e. TimeDiff) the same as the outcome when the LAG function? Why might the DIF function approach be considered more efficient? Discuss. (3 pts) 7. Consider the Awards file that is provided on our course website. Next, we will use our data to verify the awards and other outcomes from this race. To begin, suppose race organizers have decided to recognize the following runners. Top 10 Overall Finisher for Females Top 10 Overall Finisher for Males Top 3 Finishers for each Age Group / Sex combination o Sex: F=Femal, M=Male o Age Groups: 1 14, 15 19, 20 24, 25 29, 30 34, 35 39, 40 44, 45 49, 50 54, 55 59, 60 64, 65 69, 70+ a. Run the following code. Verify that this code correctly identifies the Top 10 Finishers for Females and Males. (2 pts). Turn In: A print out of the output from this code. SAS Code to Produce Top 10 Finishers for Females and Males DATA Female Male; SET FOOLS5; IF Sex = 'F' THEN OUTPUT Female; IF Sex = 'M' THEN OUTPUT Male; LABEL ChipTime='Race Time'; title 'Printout of Sex=F, Overall'; PROC SORT DATA=Female; DATA Female; SET Female; PROC PRINT DATA=Female LABEL NOOBS; WHERE Place <= 10; title 'Printout of Sex=M, Overall'; PROC SORT DATA=Male; DATA Male; SET Male; SAS Code with Line Numbers (see part b.) 1. DATA Female Male; 2. SET FOOLS5; 3. IF Sex = 'F' THEN OUTPUT Female; 4. IF Sex = 'M' THEN OUTPUT Male; 5. LABEL ChipTime='Race Time'; title 'Printout of Sex=F, Overall'; 8. PROC SORT DATA=Female; DATA Female; 12. SET Female; PROC PRINT DATA=Female LABEL NOOBS; WHERE Place <= 10; title 'Printout of Sex=M, Overall'; 21. PROC SORT DATA=Male; DATA Male; 25. SET Male; 26. 4

5 PROC PRINT DATA=Male LABEL NOOBS; WHERE Place <= 10; PROC PRINT DATA=Male LABEL NOOBS; WHERE Place <= 10; b. Next, consider the version of the code which contains the line number for each line. i. What is the purpose of Line #3 and Line #4? Explain what SAS is doing here. (3 pts). Again, don t know Google It! ii. What is the purpose of Line #13. How do I know this is a legitimate way of identifying the Place for each runner? Explain. (3 pts) 8. Carefully copy and paste the following code into SAS. This code assumes you have already created the Female and Male datasets from the code above. Run this code in SAS. SAS Code DATA FOOLS5_Version2; SET Female Male; IF Age=. THEN AgeGroup=.; ELSE IF Age < 15 THEN AgeGroup=1; ELSE IF Age > 14 AND Age < 20 THEN AgeGroup = 2; ELSE IF Age > 19 AND Age < 25 THEN AgeGroup = 3; ELSE IF Age > 24 AND Age < 30 THEN AgeGroup = 4; ELSE IF Age > 29 AND Age < 35 THEN AgeGroup = 5; ELSE IF Age > 34 AND Age < 40 THEN AgeGroup = 6; ELSE IF Age > 39 AND Age < 45 THEN AgeGroup = 7; ELSE IF Age > 44 AND Age < 50 THEN AgeGroup = 8; ELSE IF Age > 49 AND Age < 55 THEN AgeGroup = 9; ELSE IF Age > 54 AND Age < 60 THEN AgeGroup = 10; ELSE IF Age > 59 AND Age < 65 THEN AgeGroup = 11; ELSE IF Age > 64 AND Age < 70 THEN AgeGroup = 12; ELSE IF Age > 69 THEN AgeGroup = 13; LABEL AgeGroup='Age Group' ChipTime='Race Time'; DATA FemaleGroup1 FemaleGroup2 FemaleGroup3 FemaleGroup4 FemaleGroup5 FemaleGroup6 FemaleGroup7 FemaleGroup8 FemaleGroup9 FemaleGroup10 FemaleGroup11 FemaleGroup12 FemaleGroup13 MaleGroup1 MaleGroup2 MaleGroup3 MaleGroup4 MaleGroup5 MaleGroup6 MaleGroup7 MaleGroup8 MaleGroup9 MaleGroup10 MaleGroup11 MaleGroup12 MaleGroup13; SET FOOLS5_Version2; IF Sex = 'F' AND AgeGroup = 1 THEN OUTPUT FemaleGroup1; IF Sex = 'F' AND AgeGroup = 2 THEN OUTPUT FemaleGroup2; IF Sex = 'F' AND AgeGroup = 3 THEN OUTPUT FemaleGroup3; IF Sex = 'F' AND AgeGroup = 4 THEN OUTPUT FemaleGroup4; IF Sex = 'F' AND AgeGroup = 5 THEN OUTPUT FemaleGroup5; IF Sex = 'F' AND AgeGroup = 6 THEN OUTPUT FemaleGroup6; IF Sex = 'F' AND AgeGroup = 7 THEN OUTPUT FemaleGroup7; IF Sex = 'F' AND AgeGroup = 8 THEN OUTPUT FemaleGroup8; IF Sex = 'F' AND AgeGroup = 9 THEN OUTPUT FemaleGroup9; IF Sex = 'F' AND AgeGroup = 10 THEN OUTPUT FemaleGroup10; IF Sex = 'F' AND AgeGroup = 11 THEN OUTPUT FemaleGroup11; IF Sex = 'F' AND AgeGroup = 12 THEN OUTPUT FemaleGroup12; IF Sex = 'F' AND AgeGroup = 13 THEN OUTPUT FemaleGroup13; IF Sex = 'M' AND AgeGroup = 1 THEN OUTPUT MaleGroup1; IF Sex = 'M' AND AgeGroup = 2 THEN OUTPUT MaleGroup2; IF Sex = 'M' AND AgeGroup = 3 THEN OUTPUT MaleGroup3; IF Sex = 'M' AND AgeGroup = 4 THEN OUTPUT MaleGroup4; IF Sex = 'M' AND AgeGroup = 5 THEN OUTPUT MaleGroup5; IF Sex = 'M' AND AgeGroup = 6 THEN OUTPUT MaleGroup6; IF Sex = 'M' AND AgeGroup = 7 THEN OUTPUT MaleGroup7; IF Sex = 'M' AND AgeGroup = 8 THEN OUTPUT MaleGroup8; IF Sex = 'M' AND AgeGroup = 9 THEN OUTPUT MaleGroup9; IF Sex = 'M' AND AgeGroup = 10 THEN OUTPUT MaleGroup10; IF Sex = 'M' AND AgeGroup = 11 THEN OUTPUT MaleGroup11; IF Sex = 'M' AND AgeGroup = 12 THEN OUTPUT MaleGroup12; 5

6 IF Sex = 'M' AND AgeGroup = 13 THEN OUTPUT MaleGroup13; title 'Printout of Sex=F, AgeGroup=1-14'; PROC SORT DATA=FemaleGroup1; DATA FemaleGroup1; SET FemaleGroup1; PROC PRINT DATA=FemaleGroup1 LABEL NOOBS; title 'Printout of Sex=F, AgeGroup=15-19'; PROC SORT DATA=FemaleGroup2; DATA FemaleGroup2; SET FemaleGroup2; PROC PRINT DATA=FemaleGroup2 LABEL NOOBS; title 'Printout of Sex=F, AgeGroup=20-24'; PROC SORT DATA=FemaleGroup3; DATA FemaleGroup3; SET FemaleGroup3; PROC PRINT DATA=FemaleGroup3 LABEL NOOBS; title 'Printout of Sex=F, AgeGroup=25-29'; PROC SORT DATA=FemaleGroup4; DATA FemaleGroup4; SET FemaleGroup4; PROC PRINT DATA=FemaleGroup4 LABEL NOOBS; title 'Printout of Sex=F, AgeGroup=30-34'; PROC SORT DATA=FemaleGroup5; DATA FemaleGroup5; SET FemaleGroup5; PROC PRINT DATA=FemaleGroup5 LABEL NOOBS; title 'Printout of Sex=F, AgeGroup=35-39'; PROC SORT DATA=FemaleGroup6; DATA FemaleGroup6; SET FemaleGroup6; PROC PRINT DATA=FemaleGroup6 LABEL NOOBS; 6

7 title 'Printout of Sex=F, AgeGroup=40-44'; PROC SORT DATA=FemaleGroup7; DATA FemaleGroup7; SET FemaleGroup7; PROC PRINT DATA=FemaleGroup7 LABEL NOOBS; title 'Printout of Sex=F, AgeGroup=45-49'; PROC SORT DATA=FemaleGroup8; DATA FemaleGroup8; SET FemaleGroup8; PROC PRINT DATA=FemaleGroup8 LABEL NOOBS; title 'Printout of Sex=F, AgeGroup=50-54'; PROC SORT DATA=FemaleGroup9; DATA FemaleGroup9; SET FemaleGroup9; PROC PRINT DATA=FemaleGroup9 LABEL NOOBS; title 'Printout of Sex=F, AgeGroup=55-59'; PROC SORT DATA=FemaleGroup10; DATA FemaleGroup10; SET FemaleGroup10; PROC PRINT DATA=FemaleGroup10 LABEL NOOBS; title 'Printout of Sex=F, AgeGroup=60-64'; PROC SORT DATA=FemaleGroup11; DATA FemaleGroup11; SET FemaleGroup11; PROC PRINT DATA=FemaleGroup11 LABEL NOOBS; title 'Printout of Sex=F, AgeGroup=65-69'; PROC SORT DATA=FemaleGroup12; DATA FemaleGroup12; SET FemaleGroup12; PROC PRINT DATA=FemaleGroup12 LABEL NOOBS; 7

8 title 'Printout of Sex=F, AgeGroup= 70+'; PROC SORT DATA=FemaleGroup13; DATA FemaleGroup13; SET FemaleGroup13; PROC PRINT DATA=FemaleGroup13 LABEL NOOBS; a. What does this code produce? Explain. (2 pts) Turn In: A print out of the output from this code. b. What is the purpose of creating the dataset FOOLS5_Version2? Explain what this dataset contains that the Female and Male datasets do not contain. (2 pts) c. What is the purpose of the LABEL line in the DATA STEP for the FOOLS5_Version2 dataset? (2 pts) Hint: Remove the LABEL command in one of the PROC PRINT statements or run a PROC CONTENTS to help figure this out. d. Change the WHERE Place = 1 statement in each of the PROC PRINT statements so that the Top 3 Finishers for each Age Group are printed out. (3 pts) Comment: These individuals will be identified as the Ribbon Winners for each Age Group. Turn In: A print out of the Ribbon Winners for each Age Group. e. The output produced by our SAS code is *not* the same that is posted on the Fools Five website. For example, consider the top finishers for Age Group: Awards PDF File: SAS Printout From what I can tell, it appears that anybody that was a Top 10 Finisher cannot be considered a top finisher in their respective age group. For example, from the Awards document posted on their website, Fools Five identifies Lizzy Taggart as winner of this 8

9 Age Group and I assume this is because Tori Tyler was a Top 10 Overall Finisher for Females. Assuming this is the case, make modifications to the code provided to correctly identify the Ribbon Winners in each Age Group. Carefully explain the modifications were made. Verify that your modifications indeed work. (4 pts) Hint: I made modifications to the FOOLS5_Version2 dataset using an IF statement that involved the variable Place. Turn In: A discussion of the modifications made to the code. I do *not* need a printout of all the code. 9. Consider the following SAS code. You should determine the appropriate PROC SORT code and the appropriate PROC PRINT code to produce a *.html file that can easily be viewed on the web. The ODS statements provided will create the desired *.html files. DATA Results_Overall; SET Female Male; IF Place > 10 THEN DELETE; Partial SAS code for this problem DATA Results_AgeGroups; SET FemaleGroup1 FemaleGroup2 FemaleGroup3 FemaleGroup4 FemaleGroup5 FemaleGroup6 FemaleGroup7 FemaleGroup8 FemaleGroup9 FemaleGroup10 FemaleGroup11 FemaleGroup12 FemaleGroup13; IF Place > 3 THEN DELETE; PROC FORMAT; VALUE FAgeGroup 1 = 'Age: 1-14' 2 = 'Age: 15-19' 3 = 'Age: 20-24' 4 = 'Age: 25-29' 5 = 'Age: 30-34' 6 = 'Age: 35-39' 7 = 'Age: 40-44' 8 = 'Age: 45-49' 9 = 'Age: 50-54' 10 = 'Age: 55-59' 11 = 'Age: 60-64' 12 = 'Age: 65-69' 13 = 'Age: 70+ ' ; <PROC SORT CODE MISSING HERE> ODS HTML FILE='D:\DATA\SAS\Fools5_Awards.html'; title 'Top 10 Finishers'; PROC PRINT DATA=Results_Overall LABEL NOOBS; <PROC PRINT CODE MISSING HERE> title 'Ribbon Winners for Females by Age Group'; 9

10 PROC PRINT DATA=Results_AgeGroups NOOBS; <PROC PRINT CODE MISSING HERE> ODS HTML CLOSE; a. Add the appropriate PROC SORT and PROC PRINT code to obtain the desired output. (4 pts) Comment: Example output has been provided on our course website (i.e. my copy of my *.html file can be used as a guide I hope it is correct!). b. Consider the SET statement in the creation of the Results_AgeGroups dataset. What is this SET statement doing? Explain. (2 pts) c. What is the purpose of the PROC FORMAT statement? You should use FAgeGroup. Format in your code. (2 pts) 10. Suppose your colleague is the Race Director and likes how summarized all this information so efficiently. What code would have to change in order to summarize the data from Fools Five 2013? You can assume the format of the race (i.e. age groups, declaration of winners, etc.) will remain the same. Discuss. (4 pts) 10

11 Next, we will consider the BoxScore dataset that contains information from the 2012 NCAA Men s Basketball championship game between Kansas and Kentucky. The following the official boxscore from this game. (Link: Use PROC IMPORT to read in the Boxscore.xlsx dataset. Run a PROC CONTENTS on the Boxscore dataset to verify it was read in correctly. I have provided my version here for your reference. (2 pts) 11

12 The following code assumes this initial dataset is named Boxscore. The INDEX function is being used here to identify whether or not the KansasItem (or KentuckyItem) contains the word Foul. SAS Code to compute the total number of fouls for both teams. DATA Fouls; SET Boxscore; KansasFoul = 0; IF INDEX(KansasItem,"Foul")>0 THEN KansasFoul = 1; KentuckyFoul=0; IF INDEX(KentuckyItem,"Foul")>0 THEN KentuckyFoul=1; title 'Number of Fouls'; PROC MEANS DATA=Fouls SUM MAXDEC=0 NONOBS; VAR KansasFoul KentuckyFoul; Output from PROC MEANS 1. DATA Fouls; 2. SET Boxscore; 3. KansasFoul = 0; 4. IF INDEX(KansasItem,"Foul")>0 THEN KansasFoul = 1; 5. KentuckyFoul=0; 6. IF INDEX(KentuckyItem,"Foul")>0 THEN KentuckyFoul=1; title 'Number of Fouls'; 9. PROC MEANS DATA=Fouls SUM MAXDEC=0 NONOBS; 10. VAR KansasFoul KentuckyFoul; 11. PROC MEANS output matches the boxscore for PF (ie. personal foul) a. Line #3 and Line #4 are redundant. Remove Line #3 and fix Line #4 to fix this redundancy. Rerun the code to ensure it works. (3 pts) Turn In: You new Line #4 and a printout of the result from PROC MEANS. b. Modify the above code to obtain the number of personal fouls in each half for each team. (3 pts) Turn In: The result from PROC MEANS. c. Modify the above code to compute the one of the following. (3 pts) a. Number of Assists (denoted AST in the boxscore) b. Number of Steals (denoted STL in the boxsore) c. Number of Blocks (denoted BLK in the boxscore) Turn In: The result from PROC MEANS. 12

13 d. Repeat part c., but give a breakdown of what you computed for each half for each team. (2 pts) Turn In: The result from PROC MEANS. e. Modify the above code to compute the one of the following. (4 pts) a. The number of free throws made and attempted (denoted as FTM A in the boxscore) b. The number of three pointers made and attempted (denoted as 3PM A in the boxscore) c. The number of field goals made and attempted (denoted as FGM A in the boxscore). Turn In: The result from PROC MEANS. 12. Your friend believes that the task of computing the above summaries would be much easier if the KansasItems and KentuckyItems were put into a single column, named Boxscore Item, instead of having them in two separate columns. a. Consider the code that you have written above for any one of your summaries. How would you modify your code if the KansasItem and KentuckyItem were put into a single column? (4 pts) Comment: You do *not* need to run or turn in your modified code. A discussion explaining the modifications is sufficient here. b. If a single Boxscore Item column is created, will it be easier, harder, or about the same in terms of coding difficulty to obtain summaries for each half separately? Explain. (2 pts) c. If a single Boxscore Item column is created, how are you going to keep track of each team? Recall, it is important that the PROC MEANS output give a summary for each team. Discuss. (4 pts) 13