Chapter 2 (was Ch. 8) (September 21, 2008; 4:54 p.m.) Constructing a data set for analysis: reading, combining, managing and manipulating data sets

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1 Chapter 2 (was Ch. 8) (September 21, :54 p.m.) Constructing a data set for analysis: reading, combining, managing and manipulating data sets 2. Constructing a data set for analysis: reading, combining, managing and manipulating data sets 1. Temporary versus permanent status of data sets LIBNAME 2. Reading data into a SAS data set 2.1 Reading data directly as part of a program anyone for datalines? 2.2 Reading data sets that were saved as text infile can be your friend 2.3 Sometimes variables are in particular columns or in particular formats 2.4 Reading comma-separated values text files with, delimiters 2.5 Reading Excel spreadsheets directly 2.6 Reading SPSS data files the little (SPSS) engine that could 3. Writing out a file or constructing a simple report 4. Concatenating data sets/adding observations SET 5. Merging data sets/adding variables MERGE 6 Database processing with PROC SQL 7. Next steps and a summary Exercises 2.1. Temporary versus permanent status of data sets LIBNAME Display 2.1: Defining a library and referencing a data set within that library libname myfiles "\\muserver2\users\b\baileraj\public.www\classes\sta402\data" ods rtf bodytitle proc means data=myfiles.nitrofen maxdec=1 min q1 median q3 max class conc var total brood1 brood2 brood3 ods rtf close Display 2.2: Concentration-specific five-number summary for brood counts conc N Obs 0 10 total brood1 brood2 brood3 Minimum Lower Quartile Median Upper Quartile Maximum

2 Lower Upper conc N Obs Minimum Quartile Median Quartile Maximum total brood1 brood2 brood total brood1 brood2 brood total brood1 brood2 brood total brood1 brood2 brood Display 2.3: Example code that generates a temporary data set data junk do kk = 0 to 10 by 1 x = kk y = 3 + 2*kk + rannor(0) output end proc plot data=junk * or equivalently, data=work.junk plot y*x proc reg data=junk model y=x 2.2. Reading data into a SAS data set Common starting point for a statistical collaborator or consultant: meet with a colleague or client who is interested in securing assistance in exploring relationships among a collection of variables, testing for treatment differences, or other really fun stuff. Critical question: do you have your data in an electronic format? The answer to this question varies across disciplines. Colleagues from the biological and medical sciences and business often use Excel spreadsheets for data entry and to do primitive analyses. 2

3 Colleagues from the social sciences may be more likely to use SPSS or some other statistical package to do data set up and manipulation. File types: text only (*.txt) and comma-separated files (*.csv) Importing: via IMPORT WIZARD (SAS/ACCESS) or Example data set for illustrations: SMSA was part of the Data and Story Library (DASL) and include properties of 60 Standard Metropolitan Statistical Areas. The documentation and a copy of this data file can be downloaded from (accessed: 10 Sept. 2008). The U.S. Department of Labor Statistics was the referenced data source, and this data file was * characterized as having free use authorization. s: Each of the 60 SMSA has 17 variables recorded for it including: 1) city name 2) mean January temperature (deg. F) 3) mean July temperature (deg. F) 4) relative humidity 5) annual rainfall (in.) 6) age-adjusted mortality 7) median education 8) population density 9) percent of nonwhites 10) percent white collar workers 11) population 12) population per household 13) median income 14) hazardous chemical pollution potential 15) nitrous oxide pollution potential 16) sulfur dioxide pollution potential and 17) nitrous oxide. The first line of the data file includes variable names although for the illustration, the line with the variable names has been removed Reading data directly as part of a program anyone for datalines? Display 2.4: Reading free format data into SAS using datalines (code that doesn t work correctly) Data SMSA_subset input city $ JanTemp JulyTemp RelHum Rain Mortality Education PopDensity pct_nonwhite pct_wc pop pop_per_house income HCPot NOxPot S02Pot NOx datalines Akron, OH Albany-Schenectady-Troy, NY Allentown, Bethlehem, PA-NJ Atlanta, GA

4 Baltimore, MD ods rtf bodytitle proc print data=smsa_subset var city JanTemp JulyTemp RelHum NOxPot S02Pot NOx ods rtf close Did this work? Display 2.5: PROC PRINT of the misread subset of SMSA observations Obs City JanTemp JulyTemp RelHum NOxPot S02Pot NOx 1 Akron, Albany-S Allentow Atlanta, Baltimor SAS Log (slightly edited) error message associated with processing the first observation is NOTE: Invalid data for JanTemp in line RULE: Akron, OH city=akron, JanTemp=. JulyTemp=27 RelHum=71 Rain=59 Mortality=36 Education= PopDensity=11.4 pct_nonwhite=3243 pct_wc=8.8 pop=42.6 pop_per_house= income=3.34 HCPot=29560 NOxPot=21 S02Pot=15 NOx=59 _ERROR_=1 _N_=1 Display 2.6: Reading the SMSA data with a length declaration and code to allow embedded blanks in the value of a character value. data SMSA_subset length city $ 27 input city & JanTemp JulyTemp RelHum Rain Mortality Education PopDensity pct_nonwhite pct_wc pop pop_per_house income HCPot NOxPot S02Pot NOx datalines Akron, OH Albany-Schenectady-Troy, NY Allentown, Bethlehem, PA-NJ Atlanta, GA Baltimore, MD

5 ods rtf bodytitle proc print data=smsa_subset var city JanTemp JulyTemp RelHum NOxPot S02Pot NOx ods rtf close Display 2.7: PROC PRINT output from the correctly read subset of the SMSA observations Obs City JanTemp JulyTemp RelHum NOxPot S02Pot NOx 1 Akron, OH Albany-Schenectady- Troy, NY 3 Allentown, Bethlehem, PA-NJ Atlanta, GA Baltimore, MD Reading data sets that were saved as text infile can be your friend /* Notes: 1) Two spaces separate each entry in the input data file. A global replace of tabs with two spaces was performed before saving the text file. 2) The first line contains the names of the variables, and the data begins on the 2nd line of the file. 3) Fort Worth had missing values for POP and INCOME and "." were entered in the data file. 4) Increased LENGTH to 39 to accomodate GREENSBORO-WINSTON-SALEM... */ options nodate nocenter data SMSA_from_txt infile "C:\Users\baileraj\Desktop\SMSA-DASL-2space-sep.txt" firstobs=2 length city $ 39 input city & JanTemp JulyTemp RelHum Rain Mortality Education PopDensity pct_nonwhite pct_wc pop pop_per_house income HCPot NOxPot S02Pot NOx ods rtf bodytitle proc print data=smsa_from_txt var city JanTemp JulyTemp RelHum NOxPot S02Pot NOx ods rtf close Display 2.9: Output from PROC PRINT of the SMSA subset read using infile Obs City JanTemp JulyTemp RelHum NOxPot S02Pot NOx 1 Akron, OH Albany-Schenectady-Troy, NY

6 Obs City JanTemp JulyTemp RelHum NOxPot S02Pot NOx 59 York, PA Youngstown-Warren, OH Sometimes variables are in particular columns or in particular formats data d1 infile 'M:\public.www\classes\sta402\SAS-programs\ch2-dat.txt' firstobs=16 expandtabs missover pad animal conc brood1 brood2 brood3 total 2. firstobs=16 declares that SAS should ignore the first 15 lines of the data file remaining options - expandtabs missover pad expands any tab characters to a standard tab spacing of 8 columns, assign missing values to variables that were not defined on the input line (not as critical here as compared to reading free-formatted data with missing values at the end of a line), and pads the datalines to be of the same length Reading comma-separated values text files with, delimiters DASL-SMSA-commasep.csv =A copy of the SMSA data set was constructed to have comma-separated values first row "Akron, OH",27,71,59,36,921.87,11.4,3243,8.8,42.6,660328,3.34,29560,21,15,59,15 DSD (delimiter sensitive data) option in an infile statement is needed for reading such data. Display 2.10: Reading input data from files using commas as delimiters between variable values options nodate nocenter data SMSA_from_CSV infile "C:\Users\baileraj\Desktop\DASL-SMSA-commasep.csv" firstobs=2 dsd length city $ 39 input city & JanTemp JulyTemp RelHum Rain Mortality Education PopDensity pct_nonwhite pct_wc pop pop_per_house income HCPot NOxPot S02Pot NOx ods rtf bodytitle proc print data=smsa_from_csv (firstobs=17 obs=25) var city JanTemp JulyTemp RelHum pop pop_per_house income ods rtf close Display 2.11: Observations 17 to 25 from the SMSA data file 6

7 Obs City JanTemp JulyTemp RelHum pop pop_per _house income 17 Dayton-Springfield, OH Denver, CO Detroit, MI Flint, MI Fort Worth, TX Grand Rapids, MI Greensboro-Winston-Salem-High Point, NC Hartford, CT Houston, TX Reading Excel spreadsheets directly Display 2.12: Importing an Excel spreadsheet using IMPORT or the Excel engine with a LIBNAME /* Assumes SAS/ACCESS engines are available */ /* IMPORT code produced by the IMPORT WIZARD */ PROC IMPORT OUT= WORK.DASL_FROM_EXCEL2 DATAFILE= "C:\Documents and Settings\baileraj\Desktop\DASL-SMSA.xls" DBMS=EXCEL REPLACE SHEET="Sheet1$" GETNAMES=YES MIXED=NO SCANTEXT=YES USEDATE=YES SCANTIME=YES RUN /* via EXCEL engine with LIBNAME */ options nodate title libname spreads excel "C:\Documents and Settings\baileraj\Desktop\DASL-SMSA.xls" header=yes ods rtf bodytitle /* list the contents of the spreadsheet that was read */ proc contents data=spreads._all_ varnum /* set contents of the 1st worksheet to SAS data set */ data DASL_from_Libname_Excel_engine set spreads.'sheet1$'n proc print data=dasl_from_libname_excel_engine 7

8 ods rtf close Display 2.13: Contents of the SAS library produced when referencing an Excel spreadsheet using a LIBNAME with an Excel engine. Directory Libref Engine Physical Name Schema/Owner SPREADS EXCEL C:\Documents and Settings\baileraj\Desktop\DASL-SMSA.xls Admin # Name Member Type DBMS Member Type 1 Sheet1$ DATA TABLE 2 Sheet2$ DATA TABLE 3 Sheet3$ DATA TABLE Data Set Name SPREADS.'Sheet1$'n Observations. Member Type DATA s 17 Engine EXCEL Indexes 0 Created. Observation Length 0 Last Modified. Deleted Observations 0 Protection Compressed NO Data Set Type Sorted NO Label Data Representation Encoding Default Default 8

9 s in Creation Order # Type Len Format Informat Label 1 City Char 39 $39. $39. city 2 JanTemp Num 8 JanTemp 3 JulyTemp Num 8 JulyTemp 4 RelHum Num 8 RelHum 5 Rain Num 8 Rain 6 Mortality Num 8 Mortality 7 Education Num 8 Education 8 PopDensity Num 8 PopDensity 9 _NonWhite Num 8 %NonWhite 10 _WC Num 8 %WC 11 Pop Num 8 pop 12 Pop_house Num 8 pop/house 13 income Num 8 income 14 HCPot Num 8 HCPot 15 NOxPot Num 8 NOxPot 16 S02Pot Num 8 S02Pot 17 NOx Num 8 NOx Reading SPSS data files the little (SPSS) engine that could The PROC IMPORT procedure described above can be applied to read an SPSS data set using a DBMS=SPSS option or by specifying a LIBNAME with an SPSS engine. 2.3 Writing out a file or making a simple report Display 2.14: Echoing the values of variables in a data set using a PUT statement libname myfiles "\\muserver2\users\b\baileraj\public.www\classes\sta402\data" data _null_ set myfiles.nitrofen put 18*'----' put _all_ /* useful to check values during input */ 9

10 Display 2.15: SAS LOG with the echo of the nitrofen variable values animal=1 conc=0 brood1=3 brood2=14 brood3=10 total=27 _ERROR_=0 _N_= animal=2 conc=0 brood1=5 brood2=12 brood3=15 total=32 _ERROR_=0 _N_= animal=3 conc=0 brood1=6 brood2=11 brood3=17 total=34 _ERROR_=0 _N_=3... animal=48 conc=310 brood1=4 brood2=0 brood3=0 total=4 _ERROR_=0 _N_= animal=49 conc=310 brood1=6 brood2=0 brood3=0 total=6 _ERROR_=0 _N_= animal=50 conc=310 brood1=5 brood2=0 brood3=0 total=5 _ERROR_=0 _N_=50 Display 2.16: Producing a formatted report with an annotated print out of the nitrofen data libname myfiles "\\muserver2\users\b\baileraj\public.www\classes\sta402\data" proc sort data=myfiles.nitrofen by conc data _NULL_ set myfiles.nitrofen by conc file 'C:\Users\baileraj\Desktop\nitrofen-put.txt' /* Write out header text information for the first observation in each conc*/ if FIRST.conc then do put 6*'+-----' '+' put 'Conc = ' conc 'Brood 'Brood 'Brood 'TOTAL' '-----' end /* Write out the data for all records */ brood1 brood2 brood3 total 3. data _NULL_ /* add last line to the output file */ file 'C:\Users\baileraj\Desktop\nitrofen-put.txt' MOD put 6*'+-----' '+' // 'Data from C. dubia Nitrofen exposure data [ & Oris 1993]' Display 2.17: Contents of the nitrofen-put.txt file constructed by PUT Conc = 0 Brood 1 Brood 2 Brood 3 TOTAL

11 lines deleted Conc = 310 Brood 1 Brood 2 Brood 3 TOTAL Data from C. dubia Nitrofen exposure data [ & Oris 1993] Display 2.18: Creating external data files containing the contents of a SAS data set libname myfiles "\\muserver2\users\b\baileraj\public.www\classes\sta402\data" /* create a TEXT file with the nitrofen data */ data _NULL_ set myfiles.nitrofen file 'C:\Users\baileraj\Desktop\nitrofen-TEXT.txt' total /* create a CSV file with the nitrofen data */ data _NULL_ set myfiles.nitrofen file 'C:\Users\baileraj\Desktop\nitrofen-CSV.csv' dsd put conc animal brood1 brood2 brood3 total /* create a CSV file with the nitrofen data and a row with variable names */ data _NULL_ file 'C:\Users\baileraj\Desktop\nitrofen-CSV2.csv' dsd put "conc,animal,brood1,brood2,brood3,total" data _NULL_ set myfiles.nitrofen file 'C:\Users\baileraj\Desktop\nitrofen-CSV2.csv' dsd MOD put conc animal brood1 brood2 brood3 total Display 2.19: First 4 lines from the 3 data files constructed using PUT statements nitrofen-text.txt

12 0,1,3,14,10,27 0,2,5,12,15,32 0,3,6,11,17,34 0,4,6,12,15,33 nitrofen-csv.csv nitrofen-csv2.csv conc,animal,brood1,brood2,brood3,total 0,1,3,14,10,27 0,2,5,12,15,32 0,3,6,11,17, Concatenating data sets/adding observations SET Display 2.20: Adding observations / "rows" concatenating data sets Observation ID CONCATENATED WITH Observation ID PRODUCES Observation ID

13 Display 2.21: Combining observations for 5 separate concentration-specific data sets into a single analysis data set data conc_0 input conc = 0 * define a variable containing the value of the nitrofen conc. datalines data conc_80 input conc = 80 * define a variable containing the value of the nitrofen conc. datalines data conc_160 input conc = 160 * define a variable containing the value of the nitrofen conc. datalines data conc_235 input conc = 235 * define a variable containing the value of the nitrofen conc. datalines data conc_310 input conc = 310 * define a variable containing the value of the nitrofen conc. datalines data set_all5 set conc_0 conc_80 conc_160 conc_235 conc_310 ods rtf bodytitle proc print data=set_all5 var conc total ods rtf close Display 2.22: Combined data set formed from concatenating the 5 concentration-specific data sets Obs conc total 13

14 Obs conc total Merging data sets/adding variables MERGE Display 2.23: Adding variables / "columns" merging data sets Observation ID MERGED WITH Observation ID

15 PRODUCES Observation ID Display 2.24: Merging using DATA step programming data SMSA_subset_weather length city $ 27 input city & JanTemp JulyTemp RelHum Rain datalines Akron, OH Albany-Schenectady-Troy, NY Baltimore, MD Allentown, Bethlehem, PA-NJ Atlanta, GA data SMSA_subset_demog length city $ 27 input city & Mortality Education PopDensity pct_nonwhite pct_wc pop pop_per_house income datalines Akron, OH Albany-Schenectady-Troy, NY Baltimore, MD Allentown, Bethlehem, PA-NJ Atlanta, GA data SMSA_subset_pollution length city $ 27 input city & HCPot NOxPot S02Pot NOx datalines Akron, OH Albany-Schenectady-Troy, NY Baltimore, MD Allentown, Bethlehem, PA-NJ Atlanta, GA proc sort data=smsa_subset_weather by city proc sort data=smsa_subset_demog by city proc sort data=smsa_subset_pollution by city 15

16 data all_subset merge SMSA_subset_weather SMSA_subset_demog SMSA_subset_pollution by city ods rtf bodytitle proc print data=all_subset var city JanTemp mortality NOx ods rtf close Display 2.25: Resulting common SMSA data file after the merging Obs city JanTemp Mortality NOx 1 Akron, OH Albany-Schenectady-Troy, NY Allentown, Bethlehem, PA-NJ Atlanta, GA Baltimore, MD Database processing with PROC SQL The big idea is that PROC SQL looks at data sets as tables that can be manipulated, processed and displayed as a result of command queries. The basic syntax, excerpted from the SAS help documents, includes: PROC SQL CREATE TABLE name SELECT variables FROM list-of-tables/dataset GROUP BY /* optional */ HAVING /* optional */ Display 2.26: Requesting that a simple SQL table be constructed from a SAS data set containing the highest nitrofen concentration data data conc_310 input conc = 310 * define a variable containing the value of the nitrofen conc. datalines ods rtf bodytitle proc sql select conc,total from conc_310 ods rtf close 16

17 Display 2.27: Table with the total number of young produced in the highest nitrofen concentration condition conc total The real power of PROC SQL begins to surface as you manipulate table relations. For example, we concatentate the 5 nitrofen concentration data sets using a union of tables operation in Display In the PROC SQL command, we construct a SAS data set all_sql as a result of the line create table all_sql as and select all variables from each data set as part of the select * from conc_0 command (the * is a wildcard representing the selection of all variables. The union option stacks the table into a resultant table while order by conc,id sorts the values by id within conc. Display 2.28: Containing data sets using union of tables in PROC SQL data conc_0 input conc = 0 * define a variable containing the value of the nitrofen conc. id=_n_ * define an animal ID corresponding the observation number datalines data conc_80 input conc = 80 * define a variable containing the value of the nitrofen conc. id=_n_ * define an animal ID corresponding the observation number datalines data conc_160 input 17

18 conc = 160* define a variable containing the value of the nitrofen conc. id=_n_ * define an animal ID corresponding the observation number datalines data conc_235 input conc = 235* define a variable containing the value of the nitrofen conc. id=_n_ * define an animal ID corresponding the observation number datalines data conc_310 input conc = 310* define a variable containing the value of the nitrofen conc. id=_n_ * define an animal ID corresponding the observation number datalines proc sql create table all_sql as select * from conc_0 union select * from conc_80 union select * from conc_160 union select * from conc_235 union select * from conc_310 order by conc,id ods rtf bodytitle proc print data=all_sql ods rtf close Display 2.29: Result of combining 5 nitrofen concentration-specific data sets / tables into a single data file using PROC SQL Obs total conc Id Display 2.30: An inner join/merge of the 3 SMSA subset data sets using PROC SQL 18

19 data SMSA_subset_weather length city $ 27 input city & JanTemp JulyTemp RelHum Rain datalines Akron, OH Albany-Schenectady-Troy, NY Baltimore, MD Allentown, Bethlehem, PA-NJ Atlanta, GA data SMSA_subset_demog length city $ 27 input city & Mortality Education PopDensity pct_nonwhite pct_wc pop pop_per_house income datalines Akron, OH Albany-Schenectady-Troy, NY Baltimore, MD Allentown, Bethlehem, PA-NJ Atlanta, GA data SMSA_subset_pollution length city $ 27 input city & HCPot NOxPot S02Pot NOx datalines Akron, OH Albany-Schenectady-Troy, NY Baltimore, MD Allentown, Bethlehem, PA-NJ Atlanta, GA proc sql create table SMSA_subset_sql as select * from SMSA_subset_weather w, SMSA_subset_demog d, SMSA_subset_pollution p where w.city=d.city and d.city=p.city ods rtf bodytitle proc print data=smsa_subset_sql var city JulyTemp education S02Pot ods rtf close Display 2.31: Table / data set produced by the inner join of the 3 SMSA subset data sets. Obs City JulyTemp Education S02Pot 1 Akron, OH Albany-Schenectady-Troy, NY Baltimore, MD

20 Obs City JulyTemp Education S02Pot 4 Allentown, Bethlehem, PA-NJ Atlanta, GA Display 2.32: Illustrating Left/Right/Full outer joins with a subset of the SMSA observations data SMSA_subset_weather2 length city $ 27 input city & JanTemp JulyTemp RelHum Rain datalines Akron, OH Baltimore, MD Allentown, Bethlehem, PA-NJ Atlanta, GA data SMSA_subset_demog2 length city $ 27 input city & Mortality Education PopDensity pct_nonwhite pct_wc pop pop_per_house income datalines Akron, OH Albany-Schenectady-Troy, NY Baltimore, MD Allentown, Bethlehem, PA-NJ ods rtf bodytitle /* inner join / conventional join */ proc sql title "inner join" select w.city,jantemp,julytemp,education,income from SMSA_subset_weather2 as w,smsa_subset_demog2 as d where w.city=d.city /* LEFT outer join - with duplicate columns */ proc sql title "LEFT outer join with duplicate columns" select * from SMSA_subset_weather2 as w left join SMSA_subset_demog2 as d on w.city=d.city /* LEFT outer join - eliminating duplicate columns using COALESCE */ proc sql title "LEFT outer join eliminating duplicate columns" select coalesce(w.city, d.city),jantemp,julytemp,education,income from SMSA_subset_weather2 as w left join SMSA_subset_demog2 as d on w.city=d.city 20

21 /* RIGHT outer join */ proc sql title "RIGHT outer join" select coalesce(w.city, d.city),jantemp,julytemp,education,income from SMSA_subset_weather2 as w right join SMSA_subset_demog2 as d on w.city=d.city /* FULL outer join */ proc sql title "FULL outer join" select coalesce(w.city, d.city),jantemp,julytemp,education,income from SMSA_subset_weather2 as w full join SMSA_subset_demog2 as d on w.city=d.city ods rtf close Display 2.33: Results from inner and left/right/full outer joins inner join City JanTemp JulyTemp Education Income Akron, OH Baltimore, MD Allentown, Bethlehem, PA-NJ LEFT outer join with duplicate columns (edited with particular columns deleted) City JanTemp JulyTemp Rain city Mortality Education income Akron, OH Akron, OH Allentown, Bethlehem, PA-NJ Atlanta, GA Baltimore, MD Allentown, Bethlehem, PA-NJ Baltimore, MD LEFT outer join eliminating duplicate columns

22 JanTemp JulyTemp Education income Akron, OH Allentown, Bethlehem, PA-NJ Atlanta, GA Baltimore, MD RIGHT outer join JanTemp JulyTemp Education income Akron, OH Albany-Schenectady-Troy, NY Allentown, Bethlehem, PA-NJ Baltimore, MD FULL outer join JanTemp JulyTemp Education income Akron, OH Albany-Schenectady-Troy, NY Allentown, Bethlehem, PA-NJ Atlanta, GA Baltimore, MD Display 2.34: Constructing the sample space based on tossing a coin and rolling a die. data coin_toss toss="heads" output toss="tails" output data die_roll face=1 output face=2 output face=3 output face=4 output face=5 output face=6 output proc sql create table roll_n_toss as select * from coin_toss,die_roll ods rtf bodytitle 22

23 proc print data=roll_n_toss ods rtf close Display 2.35: Data set constructed from the joining all rows from two tables Obs Toss face 1 Heads 1 2 Heads 2 3 Heads 3 4 Heads 4 5 Heads 5 6 Heads 6 7 Tails 1 8 Tails 2 9 Tails 3 10 Tails 4 11 Tails 5 12 Tails Next steps and a summary The chapter started with an emphasis on getting data into SAS from various sources and then moved into strategies for modifying data sets to add observations or variables. We introduced the SQL procedure for doing many of these manipulations from the perspective of manipulating tables using a structured query language. Consistent with the title of this book (the stat programming part), the goal of these manipulations was to produce a data set for analysis. Believe it or not: there are a few more topics associated with processing data in SAS that you might need to master. For example, we used the special variable FIRST.CONC in PUT illustrations. There are a number of other special internal variables such as those associated with sorting FIRST/ LAST and variables associated with variables being present in a data set such 23

24 as IN. There are a number of other procedures available for updating (PROC UPDATE), managing data sets efficiently (PROC COPY, PROC DATASETS), or reshaping datasets (PROC TRANSPOSE) and you are encouraged to explore these. Finally, the transporting of data sets between systems and software packages are facilitated by use of (PROC IMPORT, PROC EXPORT) while the movement of SAS catalogs between systems is facilitated by the use of (PROC CPORT, PROC CIMPORT). You are encouraged to dive into the help pages and other SAS resources to skim the overview and descriptions of these topics. Exercises 1. Generate a customized print out of the meat data from Ch. 5 (using PUT statements in a SAS data set). The report should look like Packaging Condition = Plastic Data Mean Std. Dev x.xx x.xx Packaging Condition = Vacuum Data Mean Std. Dev x.xx x.xx etc. raw data... logcount condition 7.66 Plastic 6.98 Plastic 7.80 Plastic 5.26 Vacuum 5.44 Vacuum 5.80 Vacuum 7.41 Mixed 7.33 Mixed 7.04 Mixed 3.51 Co Co2 24

25 3.66 Co2 2. Fifty animals were exposed to one of 5 concentration levels of nitrofen (10 animals per group but some observations may be missing in the data below). The data were recorded separately for three broods. Produce a combined data set containing observations that have data on all 3 broods. In addition, construct an additional variable corresponding to the total number of young produced in all three broods. Compare different joins for these data. Print out the results for the different combined data sets. Note that multiple observations are given on each line. Brood=1 data s: ID conc number of young Brood=2 data s: ID conc number of young Brood=3 data s: ID conc number of young

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