Stata: Getting Starting and Being Productive with VA Data

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Stata Getting Starting and Being
Productivewith VA Data

• Give me six hours to chop down a tree and I will
  spend the first four sharpening the axe.
• --Abraham Lincoln
• Todd Wagner
• June 2007

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Outline

• Getting data into Stata
• Editing in Stata
• How does Stata handle data
• Stata notation and help
• Using Stata and Basic Stata commands

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Transferring Data

• Stattransfer or DBMS copy work
• Stattransfer often seeks to optimize the Stata
  dataset by default
• If transferring data with SCRSSN, FORCE
  Stattransfer to transfer SCRSSN as double
  precision

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Stattransfer
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Editing in Stata

• Any ASCII text editor will work
• Stata has a built in text editor, but it is
  limited.
• I recommend using another text editor
• http//fmwww.bc.edu/repec/bocode/t/textEditors.htm
  l

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Handling Data

• SAS processes one record at a time
• Stata processes all the records at the same time
• Loops are commonly used in SAS
• Loops are very rarely used in Stata

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Loading Data into Memory

• Stata reads the data into memory
• set mem 100m (before you load the data)
• You must have enough memory for your dataset
• With large datasets
• drop unnecessary variables
• Use the compress command (but dont compress
  SCRSSN)

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Stata Abbreviations

• Stata commands can be abbreviated with the first
  three letters
• regression income education female
• could be written
• reg income education female
• Can also abbreviate variables if uniquely defined
• reg inc educ fem

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Stata Help

• Statas built in help is great
• Help ltcommandgt
• Stata manuals are great because they review
  theory

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Stata and the Web

• Stata is web aware
• Check for updates periodically
• update all
• You can search for user-written programs
• findit output
• findit outreg (click to install)

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Stata in Windows

• Page up scrolls through the previous commands
• There is a graphical user interface (menus) if
  you forget a command
• We have Stata on rocky and tasha no graphical
  capabilities, no menus, and loss of some shortcuts

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Using Stata

• Create batch files called .do files
• I work interactively
• Run Stata and create do file as I go
• I can then use the do file as needed
• Debugging code and exploratory data analysis is
  very fast in Stata

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Sysdir, ls and cd

• Stata recognizes some unix commands, such as ls
  and cd
• Sysdir provides a listing of Statas working
  directories
• sysdir
• STATA C\Program Files\Stata9\
• UPDATES C\ProgramFiles\Stata9\ado\updates\
• BASE C\Program Files\Stata9\ado\base\
• SITE C\Program Files\Stata9\ado\site\
• PLUS c\ado\stbplus\
• PERSONAL c\ado\personal\
• OLDPLACE c\ado\

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Delimiters

• SAS recognizes as a delimiter
• Stata recognizes the carriage return
• Always add a carriage return after your last
  command
• You can change delimiters to
• delimit

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Missing Data

• Stata and SAS both use . as missing
• Stata implicitly values a missing as a very large
  number
• SAS implicitly values a missing as a very small
  number

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Generating and Recoding Variables

• In SAS you type
• quality0
• If VA1 then quality1
• In Stata you type
• gen quality0
• recode quality 01 if VA1 or
• replace quality1 if VA1

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Boolean Logic

• Stata is picky about Boolean logic
• gen yx if ab (must use two )
• gen yx if agtb bgt10 (must use )
• gen yx if altb (lt or gt must be before )

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Creating Dummy Variables

• Goal create dummy variable for each DRG
• gen drgnum1drg1 or
• tab drg, gen(drgnum)
• This second command automatically creates dummy
  variables

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Drop

• Drop ltvarnamesgt (drops variables)
• Drop if X1 (drop cases where value is 1)

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egen Commands

• You want to generate total costs for a medical
  center
• In SAS this is done by proc summary
• In Stata, you can type
• collapse (sum) costs, by (stan3) or
• sort sta3n
• by sta3n egen sumcosttotal(cost)

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ICD-9 Codes

• Stata has capabilities to handle ICD-9 diagnosis
  and procedure codes
• You can
• check to see if codes are valid
• generate identifiers based on codes or ranges of
  codes

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Dates

• Same date functions as SAS

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Combining Data

• Merge
• this automatically creates a variable called
  _merge
• merge1 obs. from master data
  
• merge2 obs. from only one using dataset
  
• merge3 obs. from at least two datasets, master
  or using
• merge scrssn admitday disday using data_y
• Append (stacking data)

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Explicit Subscripting

• Identify the most recent encounter in an
  encounter database
• gsort id -date
• by id gen n_n
• by id gen N_N
• gen selectn1

Ascending sort by ID and reverse by date
Record counter from 1 to N per person
Total number of records per person
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Using Stata
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Stata Interface in Windows
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Set, Clear and More

• Set sets system parameters
• Need to set memory size to open a database
• set mem 100m
• Clear erases data from memory
• When output is gt1 page, you are asked to continue
  (set more off)

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Summarizing Data
. sum gender age educ Variable
Obs Mean Std. Dev. Min
Max ---------------------------------------------
------------------------ gender 4085
1.496206 .5000468 1 2
age 4085 64.5601 9.451724
50 94 educ 4085
4.398286 1.662883 1 9

• Sum lt gt, d provides more details on each
  variable
• Tabstat provides summary info, including totals

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Tabulating Data

• . tab gender
• gender Freq. Percent Cum.
• -----------------------------------------------
• 1 2,058 50.38 50.38
• 2 2,027 49.62 100.00
• -----------------------------------------------
• Total 4,085 100.00
• . table gender
• ----------------------
• gender Freq.
• ---------------------
• 1 2,058
• 2 2,027
• ----------------------

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Tabulating Data

• tab gender age
• too many values
• r(134)
• tab age gender
• gender
• age 1 2 Total
• -------------------------------------------
• 50 49 69 118
• 51 72 71 143
• 94 1 0 1
• -------------------------------------------
• Total 2,058 2,027 4,085

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Tabstat

• . tabstat age, by (gender)
• gender mean
• -------------------
• 1 64.77454
• 2 64.34238
• -------------------
• Total 64.5601
• --------------------

• . table gender, c(mean age)
• -----------------------
• gender mean(age)
• ----------------------
• 1 64.77454
• 2 64.34238
• -----------------------

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Graphing

• Diagnostic graphics
• Presenting
• results

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Basic Analytical Functions

• OLS (reg)
• Logistic, probit, count data (e.g., CLAD)
• Multinomials
• GLM/HLM
• Duration models
• Semi and non-parametric models

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Output

• Linear regression Number of obs 1306
• F( 21, 1284) 10.88
• Prob gt F 0.0000
• R-squared 0.1398
• Root MSE 90.367
• Robust
• wtp Coef. Std. Err. t Pgtt 95
  Conf.Interval
• ethn1 1.990048 8.742036 0.23 0.820 -15.16019 1
  9.14029
• Ethn2 -25.74654 11.69993 -2.20 0.028 -48.69961 -2.
  793467
• ethn3 -35.59552 11.98309 -2.97 0.003 -59.1041 -12
  .08694
• ethn4 -3.244168 11.16836 -0.29 0.771 -25.15441
  18.66607
• english -11.44402 9.699576 -1.18 0.238 -30.47277
  7.584741
• lifeus 37.34419 13.86037 2.69 0.007 10.15274 64
  .53564
• age1999 -.6272524 .3097408 -2.03 0.043 -1.234906
  -.0195987
• income .8068256 .1714309 4.71 0.000 .4705102 1.
  143141

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Outreg

• Outputs data to a delimited file
• Delimited file can be read into Excel
• Very flexible
• Creates publishable tables