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Structuring SAS Data Sets and Working with Multiple Observations per Subject

Timothy Forsyth Ashok Viswanathan Debbie McCullough Donn Garvert. Structuring SAS Data Sets and Working with Multiple Observations per Subject. Why Restructure?. Participant #206. 3.0. 2.5. 2.0. Ave. Positive Mood. 1.5. Ave. Negative Mood. 1.0. 0.5. 0.0. 1. 2. 3. 4. 5. 6. 7.

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Structuring SAS Data Sets and Working with Multiple Observations per Subject

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  1. Timothy Forsyth Ashok Viswanathan Debbie McCullough Donn Garvert Structuring SAS Data Sets and Working with Multiple Observations per Subject

  2. Why Restructure? Participant #206 3.0 2.5 2.0 Ave. Positive Mood 1.5 Ave. Negative Mood 1.0 0.5 0.0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Day Since Injury • Some operations are more convenient when there is one observation per subject • Example: Regression analysis • Some operations are more convenient when there are several observations per subject • Example: Plot individual observations as a time series

  3. Converting Data Set with One Observation per Subject to a Data Set with Several Observations per Subject • First method: Using arrays • Can use arrays to transpose the data set • Gives user more control • Original data:

  4. Converting Data Set with One Observation per Subject to a Data Set with Several Observations per Subject • Transposing the entire data set

  5. Converting a Data Set with Several Observations per Subject to a Data Set with One Observation per Subject • Transposing the entire data set back to original form

  6. Converting a Data Set with Several Observations per Subject to a Data Set with One Observation per Subject • Key commands: • RETAIN: since these variables are not in the dataset rainfall1 we must retain these values as SAS will set to missing if we do not • CALL MISSING: this will set any number of numeric values or character values to missing all at once • In our example, if there was a missing data point somewhere in between month 1 and month 5 for each subject the CALL MISSING command will set these values to missing all at once. We use the OF command so that SAS looks at all 5 values.

  7. PROC TRANSPOSE: Converting a Data Set with One Observation per Subject to a Data Set with Several Observations per Subject • Without adding options to PROC TRANSPOSE we will get a confusing output, but the data will be correct:

  8. PROC TRANSPOSE: Converting a Data Set with One Observation per Subject to a Data Set with Several Observations per Subject • When we add options to PROC TRANSPOSE we can get a cleaner looking data set • Renamed _name_ to “month” • Renamed col1 to “rainfall” • Dropped subjects with missing data

  9. PROC TRANSPOSE: Converting a Data Set with Several Observations per Subject to a Data Set with One Observation per Subject • Transposing the rainfall data back to original form

  10. PROC TRANSPOSE: Converting a Data Set with Several Observations per Subject to a Data Set with One Observation per Subject • PREFIX= option: this was not used in the example, but can be used as an option in this conversion • Useful when your id variable is a number • For example if month was listed as 1, 2, 3, 4, 5 we could use PREFIX=Month • This will result in Month 1, Month 2, …, Month 5

  11. Chapter 24: Multiple Observations per Subject • Data Sets Often have two or more observations per subject (or other groupings) • Patients who have repeated visits to the Doctor’s office or clinic • Sales at a store on a given day. • Inches of rainfall over many months in a given city (Current Example) • This is known as Longitudinal Data • SAS processes data one observation at a time so special techniques are needed to perform calculations across observations • I will cover : • Identifying the first and last observation in a group. • A few different ways to count the number of occurrences of a subject (or other grouping.). In our example we will count, for a given city, the number of months that had their average rainfall recorded.

  12. Goal: Create a Variable That Counts the Number of Times Each City Has Had the Rainfall Recorded in a Five Month Period Listing of SAS Data Set Rainfall Ave Average City Rainfall Temp Sunlight Alameda 5.8 53.6 8.9 Fremont 5.2 52.9 8.7 Fremont 4.5 57.6 9.5 Fremont 3.3 59.7 10.1 Fremont 2.2 60.6 11.0 Fremont 1.8 63.9 11.5 Hayward 1.4 60.4 9.3 Hayward 3.6 57.8 10.1 Hayward 2.0 62.5 10.2 Hayward 4.1 55.2 10.8 Hayward 5.7 53.6 11.2 Oakland 3.1 59.2 9.5 Oakland 3.4 60.1 10.1 Sunnyvale 5.7 54.2 9.4 Sunnyvale 3.4 56.8 9.8 Sunnyvale 4.9 55.2 10.3 Sunnyvale 5.0 52.9 10.7 Three Step process: Step 1) Sort the data first by the grouping variable (City) and then by the counting variable(Rainfall). Step 2) Create First and Last Variables. Step 3) Counting the months of recorded rainfall using either a data step or proc sql.

  13. Step One: proc sort. Sort by Grouping Variable and Then by Counting Variable city Rainfall Alameda 5.8 Fremont 5.2 Fremont 4.5 Fremont 3.3 Fremont 1.8 Hayward 1.4 Hayward 3.6 Hayward 2.0 Hayward 4.1 Hayward 5.7 Oakland 3.1 Oakland 3.4 Sunnyvale 5.7 Sunnyvale 3.4 Sunnyvale 4.9 Sunnyvale 5.0 city Rainfall Alameda 5.8 Fremont 1.8 Fremont 2.2 Fremont 3.3 Fremont 4.5 Fremont 5.2 Hayward 1.4 Hayward 2.0 Hayward 3.6 Hayward 4.1 Hayward 5.7 Oakland 3.1 Oakland 3.4 Sunnyvale 3.4 Sunnyvale 4.9 Sunnyvale 5.0 Sunnyvale 5.7 procsortdata=rainfall5; by city rainfall; run; procprintdata=rainfall5; var city rainfall; run; Dataset Rainfall has been sorted first by city and second by rainfall

  14. Step Two: Create First. and Last. Variables city Rainfall Alameda 5.8 Fremont 1.8 Fremont 2.2 Fremont 3.3 Fremont 4.5 Fremont 5.2 Hayward 1.4 Hayward 2.0 Hayward 3.6 Hayward 4.1 Hayward 5.7 Oakland 3.1 Oakland 3.4 Sunnyvale 3.4 Sunnyvale 4.9 Sunnyvale 5.0 Sunnyvale 5.7 Listing of First_City city Rainfall Fremont 1.8 Hayward 1.4 Oakland 3.1 Sunnyvale 3.4 datarainfall_lastrainfall_first; set rainfall5; by city; if last.city thenoutputrainfall_last; elseif first.city thenoutputrainfall_first; run; procprintdata=rainfall_first; run; Using the set dataset; by var(city); creates two temporary SAS variables, first.var and last.var. These two are logical variables; They equal 1 if true and 0 if false. In our case we have generated variables first.city and last.city.

  15. Logical Variables first.var and last.var city Rainfall Alameda 5.8 Fremont 1.8 Fremont 2.2 Fremont 3.3 Fremont 4.5 Fremont 5.2 Hayward 1.4 Hayward 2.0 Hayward 3.6 Hayward 4.1 Hayward 5.7 Oakland 3.1 Oakland 3.4 Sunnyvale 3.4 Sunnyvale 4.9 Sunnyvale 5.0 Sunnyvale 5.7 datarainfall_lastrainfall_first; set rainfall5; by city; put city= rainfall= first.city= last.city=; if last.city thenoutputrainfall_last; elseif first.city thenoutputrainfall_first; run; city=Alameda Rainfall=5.8 FIRST.city=1 LAST.city=1 city=Fremont Rainfall=1.8 FIRST.city=1 LAST.city=0 city=Fremont Rainfall=2.2 FIRST.city=0 LAST.city=0 city=Fremont Rainfall=3.3 FIRST.city=0 LAST.city=0 city=Fremont Rainfall=4.5 FIRST.city=0 LAST.city=0 city=Fremont Rainfall=5.2 FIRST.city=0 LAST.city=1 city=Hayward Rainfall=1.4 FIRST.city=1 LAST.city=0 city=Hayward Rainfall=2 FIRST.city=0 LAST.city=0 Contents from the log Observation 1 for Alameda is both the first and the last variable so first.city =1(true )and Last.city=1(true). Obs 1 for Fremont first.city = 1 last.city =0, etc., etc.

  16. Step Three: Use Data Step to Count the Number of Months of Recorded Rainfall city Rainfall Alameda 5.8 Fremont 1.8 Fremont 2.2 Fremont 3.3 Fremont 4.5 Fremont 5.2 Hayward 1.4 Hayward 2.0 Hayward 3.6 Hayward 4.1 Hayward 5.7 Oakland 3.1 Oakland 3.4 Sunnyvale 3.4 Sunnyvale 4.9 Sunnyvale 5.0 Sunnyvale 5.7 datamonths_of_rec_rainfall; set rainfall5; by city; if first.city thenN_months_rec = 0; N_months_rec +1; if last.city thenoutput; run; title'Listing of Counts'; procprintdata=months_of_rec_rainfalllabelnoobs; labelN_months_rec = '# of Months Recorded'; var City Rainfall N_months_rec;; run; Listing of Counts # of Months City Rainfall Recorded Alameda 5.8 1 Fremont 5.2 5 Hayward 5.7 5 Oakland 3.4 2 Sunnyvale 5.7 4 Prediction(actual) ; Alameda 1(1) , Fremont 5(5), Hayward 5(5) Oakland 2(2), Sunnyvale 4(4).

  17. A Look at the Previous SAS Code datamonths_of_rec_rainfall; set rainfall5; by city; if first.city thenN_months_rec= 0; 1) N_months_rec +1; 2) if last.city thenoutput; 3) run; title'Listing of Counts'; procprintdata=months_of_rec_rainfalllabelnoobs; labelN_months_rec = '# of Months Recorded'; var City Rainfall N_months_rec;; run; 1) Initialize the counter at zero 2) Sum Statement 3) Conditional Statement

  18. Counting the Number of Months with Recorded Rainfall Using proc sql city Rainfall Alameda 5.8 Fremont 1.8 Fremont 2.2 Fremont 3.3 Fremont 4.5 Fremont 5.2 Hayward 1.4 Hayward 2.0 Hayward 3.6 Hayward 4.1 Hayward 5.7 Oakland 3.1 Oakland 3.4 Sunnyvale 3.4 Sunnyvale 4.9 Sunnyvale 5.0 Sunnyvale 5.7 procsql; createtableMonths_Rec_Rainas select city, count(city) asmonths_rec_rain from rainfall5 group by city; quit; months_rec_ city rain Alameda 1 Fremont 5 Hayward 5 Oakland 2 Sunnyvale 4 The proc sql gives the same result as the data step.

  19. Summary Chapter 24: Part One It was shown, via a three step process, how to create a variable to count the number of occurrences of a grouping variable. The example shown dealt with the number of months of recorded rainfall in a given city. These techniques have utility in the medical or clinical setting.

  20. Preparing the Data – Converting Data Set to Longitudinal Data – An Alternative Method SAS CODE SAS output Output: Average Obs city month Rain AveTempSunlight 1 Hayward 1 1.4 60.4 9.3 2 Hayward 2 3.6 57.8 10.1 3 Hayward 3 2.0 62.5 10.2 4 Hayward 4 4.1 55.2 10.8 5 Hayward 5 5.7 53.6 11.2 6 Oakland 1 3.1 59.2 9.5 7 Oakland 2 3.4 60.1 10.1 8 Alameda 1 5.8 53.6 8.9 9 Sunnyval 1 5.7 54.2 9.4 10 Sunnyval 2 3.4 56.8 9.8 11 Sunnyval 3 4.9 55.2 10.3 12 Sunnyval 4 5.0 52.9 10.7 13 Fremont 1 5.2 52.9 8.7 14 Fremont 2 4.5 57.6 9.5 15 Fremont 3 3.3 59.7 10.1 16 Fremont 4 2.2 60.6 11.0 17 Fremont 5 1.8 63.9 11.5 /*eliminate missing values*/ /*create longitudinal data*/ data rainfall1; set rainfall; array rainfall_array{5} month_1-month_5; array temp_array{5} temp_1-temp_5; array hours_array{5} hours_1-hours_5; do month = 1 to 5; if missing(rainfall_array{month}) then leave; Rain = rainfall_array{month}; AveTemp= temp_array{month}; AverageSunlight = hours_array{month}; Output; end; keep city rain month AveTemAverageSunlight; run; procprint data=rainfall1; run;

  21. Counting the Number of Months That Data Has Been Recorded for Each City A few points to note: code procmeans data=rainfall1 nwaynoprint; class city; output out=counts (rename=(_freq_ = N_Recorded) drop = _type_); run; procprint data=counts; run; Things to notice: nway nprint rename • A proc freq approach can be used in addition to a proc means approach • We present here the proc means approach • Both ways can give us the same result/output

  22. Output The SAS System 14:46 Sunday, May 30, 2010 13   Average Obscity N_Recorded_STAT_ month Rain AveTemp Sunlight 1 Alameda 1 N 1.00000 1.00000 1.0000 1.0000 2 Alameda 1 MIN 1.00000 5.80000 53.6000 8.9000 3 Alameda 1 MAX 1.00000 5.80000 53.6000 8.9000 4 Alameda 1 MEAN 1.00000 5.80000 53.6000 8.9000 5 Alameda 1 STD . . . . 6 Fremont 5 N 5.00000 5.00000 5.0000 5.0000 7 Fremont 5 MIN 1.00000 1.80000 52.9000 8.7000 8 Fremont 5 MAX 5.00000 5.20000 63.9000 11.5000 9 Fremont 5 MEAN 3.00000 3.40000 58.9400 10.1600 10 Fremont 5 STD 1.58114 1.45430 4.0685 1.1261 11 Hayward 5 N 5.00000 5.00000 5.0000 5.0000 12 Hayward 5 MIN 1.00000 1.40000 53.6000 9.3000 13 Hayward 5 MAX 5.00000 5.70000 62.5000 11.2000 14 Hayward 5 MEAN 3.00000 3.36000 57.9000 10.3200 15 Hayward 5 STD 1.58114 1.71552 3.6469 0.7259 16 Oakland 2 N 2.00000 2.00000 2.0000 2.0000 17 Oakland 2 MIN 1.00000 3.10000 59.2000 9.5000 18 Oakland 2 MAX 2.00000 3.40000 60.1000 10.1000 19 Oakland 2 MEAN 1.50000 3.25000 59.6500 9.8000 20 Oakland 2 STD 0.70711 0.21213 0.6364 0.4243 21 Sunnyval4 N 4.00000 4.00000 4.0000 4.0000 22 Sunnyval 4 MIN 1.00000 3.40000 52.9000 9.4000 23 Sunnyval 4 MAX 4.00000 5.70000 56.8000 10.7000 24 Sunnyval 4 MEAN 2.50000 4.75000 54.7750 10.0500 25 Sunnyval 4 STD 1.29099 0.96782 1.6460 0.5686

  23. Counting the Differences Between Values in the Longitudinal Data Set • We would like to see the differences in values by city and by month for the following variables: • The average rainfall recorded (given by ‘rain’) • The average temperature recorded (given by ‘avetemp’) • The average sunlight recorded (given by ‘avesunlight’)

  24. Preparing data for handling differences being analyzed procsort data=rainfall1 out=rainfall1; by city month; run; data last; set rainfall1; by city; put city=month=first.city = last.city=; if last.city; run;

  25. Logfile for the code generated by first.city and last.city log file: NOTE: There were 17 observations read from the data set WORK.RAINFALL1. NOTE: The data set WORK.RAINFALL1 has 17 observations and 5 variables. NOTE: PROCEDURE SORT used (Total process time): real time 0.01 seconds cpu time 0.00 seconds 61 62 data last; 63 set rainfall1; 64 by city; 65 put city=month=first.city = last.city=; 66 if last.city; 67 run; city=Alameda month=1 FIRST.city=1 LAST.city=1 city=Fremont month=1 FIRST.city=1 LAST.city=0 city=Fremont month=2 FIRST.city=0 LAST.city=0 city=Fremont month=3 FIRST.city=0 LAST.city=0 city=Fremont month=4 FIRST.city=0 LAST.city=0 city=Fremont month=5 FIRST.city=0 LAST.city=1 city=Hayward month=1 FIRST.city=1 LAST.city=0 city=Hayward month=2 FIRST.city=0 LAST.city=0 city=Hayward month=3 FIRST.city=0 LAST.city=0 city=Hayward month=4 FIRST.city=0 LAST.city=0 city=Hayward month=5 FIRST.city=0 LAST.city=1 city=Oakland month=1 FIRST.city=1 LAST.city=0 city=Oakland month=2 FIRST.city=0 LAST.city=1 city=Sunnyval month=1 FIRST.city=1 LAST.city=0 city=Sunnyval month=2 FIRST.city=0 LAST.city=0 city=Sunnyval month=3 FIRST.city=0 LAST.city=0 city=Sunnyval month=4 FIRST.city=0 LAST.city=1 NOTE: There were 17 observations read from the data set WORK.RAINFALL1. NOTE: The data set WORK.LAST has 5 observations and 5 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds

  26. Capturing Differences Generated From Observed Values by City and Month data difference; set rainfall1; by city; if first.city and last.city then delete; Diff_Rain = rain - lag(rain); Diff_AveTemp = AveTemp - lag(AveTemp); Diff_AverageSunlight = AverageSunlight - lag(AverageSunlight); if not first.city then output; run; procprint data=difference; run;

  27. A Complete Set of Differences Generated – Resulting Output The SAS System 14:46 Sunday, May 30, 2010 5 Ave Average Diff_ Diff_ Diff_ Average • Obscity month Rain Temp Sunlight Rain AveTemp Sunlight • 1 Fremont 2 4.5 57.6 9.5 -0.7 4.7 0.8 • 2 Fremont 3 3.3 59.7 10.1 -1.2 2.1 0.6 • 3 Fremont 4 2.2 60.6 11.0 -1.1 0.9 0.9 • 4 Fremont 5 1.8 63.9 11.5 -0.4 3.3 0.5 • 5 Hayward 2 3.6 57.8 10.1 2.2 -2.6 0.8 • 6 Hayward 3 2.0 62.5 10.2 -1.6 4.7 0.1 • 7 Hayward 4 4.1 55.2 10.8 2.1 -7.3 0.6 • 8 Hayward 5 5.7 53.6 11.2 1.6 -1.6 0.4 • 9 Oakland 2 3.4 60.1 10.1 0.3 0.9 0.6 • 10 Sunnyval2 3.4 56.8 9.8 -2.3 2.6 0.4 • 11 Sunnyval 3 4.9 55.2 10.3 1.5 -1.6 0.5 • 12 Sunnyval4 5.0 52.9 10.7 0.1 -2.3 0.4

  28. Capturing the Differences of the First and Last Observations for a Given City datafirst_last; set rainfall1; by city; if first.city and last.city then delete; if first.city or last.city then do; diff_rain = rain - lag(rain); diff_temp = avetemp - lag(avetemp); diff_sunlight = averagesunlight - lag(averagesunlight); end; if last.city then output; run; procprint data=first_last; run;

  29. Capturing the Differences of the First and Last Observations for a Given City Ave Average Average diff_ diff_ diff_ Obs city month Rain Temp Sunlight rain temp Sunlight 1 Fremont 5 1.8 63.9 11.5 -3.4 11.0 2.8 2 Hayward 5 5.7 53.6 11.2 4.3 -6.8 1.9 3 Oakland 2 3.4 60.1 10.1 0.3 0.9 0.6 4 Sunnyval4 5.0 52.9 10.7 -0.7 -1.3 1.3

  30. Using RETAIN statement is one of the best ways to “remember” values from previous observations Variables that do not come from SAS data sets are set to a missing value during each iteration of the DATA step A RETAIN statement allows you to tell SAS not to do this The RETAIN Statement

  31. Using a RETAIN Statement to Compute Difference Between First and Last Observation Need to sort by the ID, or which ever variable you are grouping by! The RETAIN statement ensures these variables are not set back to missing values during iteration. The Variables named within the RETAIN Statement are not replaced with missing values during the iterations. data new_data_set; set old_data_set; byID; if first.ID and last.ID then delete; Retain First_Var_1 First_Var_2 … First_Var_3; If first .ID then do; First_Var_1 = Var_1 First_Var_2 = Var_2 … First_Var_n = Var_n If last.ID then do; Diff_Var_1 = Var_1 - First_Var_1; Diff_Var_2 = Var_2 - First_Var_2; … Diff_Var_n = Var_n - First_Var_n; End; Drop Frist_: ; Run;

  32. What Happens? The RETAIN statement ensures your variables are not set back to missing values. When processing first observation, the retained variables are set to respective variable values. The last iteration subtracts the retained first values from the respective last variable values.

  33. Example:Computing Differences Between the First and Last Observation in a BY Group Using RETAIN Statement data first_last; set rainfall; by City; if first.city and last.city then delete; retain First_Rainfall First_Temp First_Hours; if first.City then do; First_Rainfall = Rainfall; First_Temp = Temp; First_Hours = Hours; end; if last.City then do; Diff_Rainfall = Rainfall - First_Rainfall; Diff_Temp = Temp - First_Temp; Diff_Hours = Hours - First_Hours; output; end; drop First_: ; run; proc sort data=rainfall; by City Month; run; data last; set rainfall; by City; put City= Month= First.City= Last.City=; if last.City; run;

  34. Our Output Displaying the RETAIN Statement City Month Rainfall Temp Hours Diff_ Rainfall Diff_ Temp Diff_Hours Fremont 5 1.8 63.9 11.5 -3.4 11.0 2.8 Hayward 5 5.7 53.6 11.2 4.3 -6.8 1.9 Oakland 5 3.6 60.3 10.8 0.5 1.1 1.3 Sunnyvale 4 5.0 52.9 10.7 -0.7 -1.3 1.3

  35. How does RETAIN compare to LAG? Output from LAG statement: city month Rain Temp Sunlight diff_raindiff_tempdiff_Sunlight Fremont 5 1.8 63.9 11.5 -3.4 11.0 2.8 Hayward 5 5.7 53.6 11.2 4.3 -6.8 1.9 Oakland 2 3.4 60.1 10.1 0.3 0.9 0.6 Sunnyvale 4 5.0 52.9 10.7 -0.7 -1.3 1.3 Output from RETAIN statement City Month Rainfall Temp Hours Diff_ Rainfall Diff_ Temp Diff_Hours Fremont 5 1.8 63.9 11.5 -3.4 11.0 2.8 Hayward 5 5.7 53.6 11.2 4.3 -6.8 1.9 Oakland 5 3.6 60.3 10.8 0.5 1.1 1.3 Sunnyvale 4 5.0 52.9 10.7 -0.7 -1.3 1.3

  36. Using Retained Variable to “Remember” a Previous Value Suppose you want to know if a certain variable value is the maximum of all of you observations The RETAIN Statement allows us to easily find this while preserving a variable’s value from previous iteration

  37. Example:Finding the Maximum Rainfall, Temperature and Hours of Daylight of all Observations data Maximums; Set rainfall; Retain Max_RainfallMax_TempMax_Hours; Max_Rainfall = Max(Max_Rainfall, Rainfall); Max_Temp = Max(Max_Temp, Temp); Max_Hours = Max(Max_Hours, Hours); run; title "Displaying the RETAIN Statement- Finding Maximums“; proc print data=Maximums noobs; run;

  38. Our Output Displaying the RETAIN Statement- Finding Maximums city Month Rainfall Temp Hours Max_RainfallMax_TempMax_Hours Alameda 1 5.8 53.6 8.9 5.8 53.6 8.9 Fremont 1 5.2 52.9 8.7 5.8 53.6 8.9 Fremont 2 4.5 57.6 9.5 5.8 57.6 9.5 Fremont 3 3.3 59.7 10.1 5.8 59.7 10.1 Fremont 4 2.2 60.6 11.0 5.8 60.6 11.0 Fremont 5 1.8 63.9 11.5 5.8 63.9 11.5 Hayward 1 1.4 60.4 9.3 5.8 63.9 11.5 Hayward 2 3.6 57.8 10.1 5.8 63.9 11.5 Hayward 3 2.0 62.5 10.2 5.8 63.9 11.5 Hayward 4 4.1 55.2 10.8 5.8 63.9 11.5 Hayward 5 5.7 53.6 11.2 5.8 63.9 11.5 Oakland 1 3.1 59.2 9.5 5.8 63.9 11.5 Oakland 2 3.4 60.1 10.1 5.8 63.9 11.5 Oakland 5 3.6 60.3 10.8 5.8 63.9 11.5 Sunnyvale 1 5.7 54.2 9.4 5.8 63.9 11.5 Sunnyvale 2 3.4 56.8 9.8 5.8 63.9 11.5 Sunnyvale 3 4.9 55.2 10.3 5.8 63.9 11.5 Sunnyvale 4 5.0 52.9 10.7 5.8 63.9 11.5

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