CCPR Computing ServicesMore Efficient Programming

July 13, 2006

Outline

Thinking through a programming task Ways of efficiently documenting and organizing your

project Naming variables, programs, files Commenting code Including file header Implementing directory structure

Programming constructs Raw data -> finished product: are your results

replicable?

Before you start coding…

Think Clearly define the problem in writing Write down the solution/algorithm in English

Modularity Create test (if reasonable)

Translate one section to code Test the section thoroughly Translate/Test next section, etc.

Documentation - File Header

Each do-file/program/file you create should include: Your name Project name Project location Date Software Version Purpose of program Inputs, Outputs Special Notes

Naming Files, Variables, and Functions Use language standard (if it exists) Be aware of language-specific rules

Max length, underscore, case, reserved words Differentiating log files:

Programs MergeHH.sas, MergeHH.do Log files MergeHHsas.log, MergeHHsta.log

Meaningful variable names: LogWt vs. var1 AgeLt30 vs. x

Procedure that cleans missing values of Age: fixMissingAge

Matrix multiplication X transpose times X matXX

Commenting Code

Good code is self-commenting Naming conventions, structure/formatting, header should

explain 95% Comments should explain

Purpose of code, not every detail Tricks used Reasons for unusual coding

Comments do not fix sloppy code translate syntax

If it takes longer to read the comment than to read the code, don’t add a comment!

Commenting Code - Stata example

SAMPLE 2*Convert names in dataset to

lowercase.program def lowerVarNames foreach v of varlist _all { local LowName = lower("`v'")

if `"`v'"' != `"`LowName'"' { rename `v' `=lower("`v'")' }

}end

SAMPLE 1program def function1foreach v of varlist _all {local x = lower("`v'")if `"`v'"' != `"`x'"' {rename `v' `=lower("`v'")'}}end

Compare formatting, comments, variable name and function names

Directory Structure

A project consists of many different types of files

Use folders to separate files in a logical way

Be consistent across projects if possible

ATTIC folder for older versions

HOME

PROJECT NAME

DATA

RESULTS

LOG

PROGRAMS

ATTIC

Stata example: using directory structure** Paths:

global parentpath "C:\Documents and Settings\piersol\Summer06\prog\progtips"global pgmsloc "$parentpath\pgms"global logsloc "$parentpath\logs"global cleandataloc "$parentpath\data\clean"global rawdataloc "$parentpath\data\raw"

capture log closelog using "$logsloc\test200607", text replace**********************************************************************INSERT FILE HEADER HERE...then it’s included in log file.*********************************************************************macro list

webuse union, clearsave "$rawdataloc\union.dta", replace

*keep idcode year age gradesave "$cleandataloc\unionLJP.dta", replace

log close

Programming Constructs

Tools to simplify and clarify your coding Available in virtually all languages Constructs

Loops - for, foreach, do, while If/elseif/else– if, then, else, case continue exit

Loop Example 1 Problem: Given 4 indicator variables (south, union, black,

not_smsa) and 2 discrete variables (age, grade), generate 8 new indicator variables:

south_age21 = south and age > 21, south_gr12 = south and grade > 12 Similarly for union, black, not_smsa

Solution without loop 8 lines of code similar to:

generate newvar = (south==1 & age>21 & age<.) generate newvar = (south==1 & grade>12 & grade<.)

Solution with loopforeach j in south union black not_smsa {

gen `j'_age21 = (age>21 & age<. & `j'==1)

gen `j'_gr12 = (grade>12 & grade<. & `j'==1)

}

Loop Example 1, cont.*CHECK GENERATED VARIABLES AGAINST ORIGINAL VARIABLESforeach j in south union black not_smsa { qui count if `j'==1 & age>21 & age<. local origCount = r(N) qui count if `j'_age21==1 if `origCount' ~= `r(N)' { display "Counts do not match for `j'_age21!" } else display "Counts match for `j'_age21."

qui count if `j'==1 & grade>12 & grade<. local origCount = r(N) qui count if `j'_gr12==1 if `origCount' ~= `r(N)' { display "Counts do not match for `j'_gr21!" } else display "Counts match for `j'_gr21."}

Loop Example 2

Given indicator variables white, black, other, and continuous variable educyrs, create interaction variables

Solution using loop:local allraces "white black other"

foreach race of varlist `allraces' {

generate `race'_educ=`race'*educyrs

}

Loop Example 3

Problem: Dataset contains variables over multiple years (1970-1990) Need to perform a number of commands separately for 1970, 1975,

1980, 1985. Solution without loop

bysort year: command1 if year==70 | year==75 | year==80 | year==85bysort year: command2 if year==70 | year==75 | year==80 | year==85

Solution with loopforeach year in 70 75 80 85 { di as result "***Regression for year = `year':" regress ln_wage grade tenure ttl_exp if year==`year' di as result "***Summarize for year = `year':" summarize ln_wage if year==`year'}

Loop Example 4 – pulling from 2 lists From Stata FAQ websiteCode:local agrp "cat dog cow pig"local bgrp "meow woof moo oinkoink"local n : word count `agrp'

forvalues i = 1/`n' { local a : word `i' of `agrp' local b : word `i' of `bgrp' di "`a' says `b'" }Resulting output:cat says meowdog says woofcow says moopig says oinkoink

Constructs - If/then/else Execute section of code if condition is true:

if condition then

{execute this code if condition true}

end

Execute one of two sections of code: if condition then

{execute this code if condition true}

else

{execute this code if condition false}

end

If/Else Example

Problem: need to execute commands on an operating system, but only if the os is Unix…the commands will fail if os is anything else

Solution:if "`c(os)'"~="Unix" { di as err "Sorry; this section requires Unix OS."}else { ** continue with unix commands…}

Constructs - Elseif/case Elseif - Execute one of many sections of code:

if condition1 then{execute this code if condition1 true}

elseif condition2 then{execute this code if condition2 true}

else{execute this code if condition1, condition2 are all false}

end

Case- same idea, different name

case condition1 then{execute this code if condition1 true}

case condition2 then{execute this code if condition2 true}

etc.

Elseif Example

Problem: Continue example from if…else, but execute different section of code for Unix, Windows, and Mac

Solution:if "`c(os)'"=="Unix" {

di "This is a Unix environment"

}

else if "`c(os)'" == "Windows" {

di "This is a Windows environment"

}

else if "`c(os)'" =="MacOSX" {

di "This is a MacOS” environment."

}

else {

di as err "`c(os)' not recognized."

}

Stata- If command vs. if qualifier ifcmd was designed to be used with a single expression Example:

Given variable x with 5 observations: 1, 1, 2, 1, 3, Compare the following three pieces of Stata code:if x==2 { replace x=99}

if x==1 { replace x=99}

replace x=99 if x==2

Stata- If command vs. if qualifier

Constucts -- Continue Example from Stata online help Continue is used to exit current iteration of loop and

continue with next iteration The following two loops produce the same result:

forvalues x = 1/10 { if mod(`x',2)==1 { display "`x' is odd" continue } display "`x' is even"}

forvalues x = 1/10 { if mod(`x',2)==1 { display "`x' is odd" } else { display "`x' is even" }}

Constructs – Exit

Stop execution of program Examples:

Do-file contains a number of data checks followed by analysis commands. If data checks reveal something unacceptable, you can exit out of do-file before running analysis.

Program requires user input. If user enters “bad” information, need to quit program.

Debugging. If particular error occurs then break. Check denominator prior to dividing. If equals zero, exit.

Raw data to finished product

Raw data

Analysis data

Runs/results

Finished product

Raw Data -> Analysis Data

Always have two distinct data files- the raw data and analysis data

A program should completely re-create analysis data from raw data

NO interactive changes!! Final changes must go in a program!!

Raw Data -> Analysis Data

Document all of the following: Outliers? Errors? Missing data? Changes to the data?

Remember to check- Consistency across variables Duplicates Individual records, not just summary stats “Smell tests”

Analysis Data -> Results

All results should be produced by a program Program should use analysis data (not raw) Have a “translation” of raw variable names ->

analysis variable names -> publication variable names

Analysis Data -> Results

Document- How were variances estimated? Why? What algorithms were used and why? Were

results robust? What starting values were used? Was

convergence sensitive? Did you perform diagnostics? Include in

programs/documentation.

Log files

Your log file should tell a story to the reader. As you print results to the log file, include

words explaining the results Include not only what your code is doing, but

your reasoning and thought process Don’t output everything to the log-file- use quietly and noisily in a meaningful way.

Project Clean-up

Create a zip file that contains everything necessary for complete replication

Use a readme.txt file to describe zip contents Delete/archive unused or old files Include any referenced files in zip When you have a final zip archive containing

everything- Open it in it’s own directory and run the script Check that all the results match

Questions/Feedback