HW3#
Solution to HW3#
quietly {
cls
if 0 { //background, purpose.
1. HW3 solution
2. One approach among many
}
if 1 { //methods, log, settings, data
clear
capture log close
log using "hw3.lastname.firstname.log", replace
set more off
//by default, stata times-out at 30 seconds
//increase if internet is slow, and dataset remote
set timeout1 1000
//ambiguity of filepaths is a hallmark of collaboration
//lets set up the paths for local and remote data access
local local_path "/users/d/downloads/hw1.txt" //change to whatever suits you
global path "https://github.com/jhustata/basic/raw/main/hw1.txt" //URL or filepath
//check if the local file exists
capture confirm file "`local_path'"
if _rc == 0 {
* if the local file exists, use the local path
global path "`local_path'"
}
else {
* if the local file does not exist, fall back to the URL
global path "${path}"
}
* now proceed to import the data using the defined path
import delimited "${path}", clear
}
if 2 { //results, Q1
gen htn = dx == "4=Hypertensive"
replace htn = 0 if missing(htn)
// Label values for hypertension
label define htn_lab 0 "No" 1 "Yes"
label values htn htn_lab
noi tab htn
// create space between Q1 & Q2
noi di ""
noi di ""
noi di ""
}
if 3 { //results, Q2
//capture program drop q2
//program define q2
//.xlsx output
putexcel set question2, replace
//labels
label variable init_age "Age, median (IQR)"
label variable prev "Previous transplant, %"
//label macros
local agelab: variable label init_age
local prevlab: variable label prev
local start_row = 1 //tracking .xlsx output
forvalues i = 0/1 {
//+5 points: for innovative use of loops to avoid repetition
//0=Males, 1=Females
//row 1
count if female==`i'
local female`i'_n=r(N)
//row2
sum init_age if female==`i', detail
local female`i'_age_p50: di %2.0f r(p50)
local female`i'_age_p25: di %2.0f r(p25)
local female`i'_age_p75: di %2.0f r(p75)
//row3
sum prev if female==`i'
local female`i'_prev: di %2.1f r(mean)*100
// Output for males and females, assuming they start from row 1
putexcel A`start_row' = "Question 2" ///
B`start_row' = "Males (N=`female0_n')" ///
C`start_row' = "Females (N=`female1_n')"
local age_label = "Age, median (IQR)"
local age_row = `start_row' + 1 // This will be row 2
putexcel A`age_row' = "`age_label'" ///
B`age_row' = "`female0_age_p50' (`female0_age_p25' - `female0_age_p75')" ///
C`age_row' = "`female1_age_p50' (`female1_age_p25' - `female1_age_p75')"
local prev_label = "Previous transplant, %"
local prev_row = `age_row' + 1 // This will be row 3
putexcel A`prev_row' = "`prev_label'" ///
B`prev_row' = "`female0_prev'" ///
C`prev_row' = "`female1_prev'"
}
//align output for .log file using "_col()"
local row1: di "Question 2" ///
_col(30) "Males (N=`female0_n')" ///
_col(60) "Females (N=`female1_n')"
local row2: di "`agelab'" ///
_col(30) "`female0_age_p50' (`female0_age_p25' - `female0_age_p75')" ///
_col(60) "`female1_age_p50' (`female1_age_p25' - `female1_age_p75')"
local row3: di "`prevlab'" ///
_col(30) "`female0_prev'" ///
_col(60) "`female1_prev'"
local excel_row=1
forvalues i=1/3 {
//.log file
noi di "`row`i''"
}
}
log close
// Restore initial settings
set more on
set timeout1 30
}
Annotated Explanation of the HW3 Solution Script#
The script is structured to manage data import and analysis with attention to potential variations in file locations and to perform detailed statistical analysis. Here’s a breakdown with annotations to help you understand each part of the code:
Handling File Path Ambiguity#
Link to Concept: The script settings handle ambiguity of file paths, a practical challenge when collaborating in different environments.
Advice: Use different file paths for collaborators and ensure TA compatibility to avoid losing points.
Code Explanation#
quietly { //first line of code
cls
if 0 { //background, purpose.
1. HW3 solution
2. One approach among many
} //last line of code
Purpose: This section, though commented out (
if 0), serves as a placeholder for background information or script metadata, which can include the script’s purpose or versioning info.
if 1 { //methods, log, settings, data
cls
clear
capture log close
log using "hw3.lastname.firstname.log", replace
set more off
set timeout1 1000 // Handling slow network responses
local local_path "/users/d/downloads/hw1.txt"
global path "https://github.com/jhustata/basic/raw/main/hw1.txt"
capture confirm file "`local_path'"
if _rc == 0 {
global path "`local_path'"
} else {
global path "${path}"
}
import delimited "${path}", clear
}
Dynamic Path Handling: This block sets up paths for local and remote data access and uses the
capture confirm fileto check if a local file exists, switching to a remote URL if not. This ensures flexibility depending on the user’s environment (source: stackoverflow).Logging: The
logcommand starts recording all commands and results, useful for debugging and reviewing the analysis process.
if 2 { //results, Q1
gen htn = dx == "4=Hypertensive"
replace htn = 0 if missing(htn)
label define htn_lab 0 "No" 1 "Yes"
label values htn htn_lab
noi tab htn
noi di ""
noi di ""
noi di ""
}
Data Analysis for Q1: Analyzes hypertension data, creating and labeling a new variable
htn. Outputs are neatly tabulated and spaced for clarity. If you only wish to run Q2, you can changeif 2toif 0. Never highlight a section of your.dofile, as you may omit code that defines macros.
if 3 { //results, Q2
putexcel set question2, replace
label variable init_age "Age, median (IQR)"
label variable prev "Previous transplant, %"
local start_row = 1
forvalues i = 0/1 {
count if female==`i'
local female`i'_n=r(N)
sum init_age if female==`i', detail
local female`i'_age_p50: di %2.0f r(p50)
local female`i'_age_p25: di %2.0f r(p25)
local female`i'_age_p75: di %2.0f r(p75)
sum prev if female==`i'
local female`i'_prev: di %2.1f r(mean)*100
putexcel A`start_row' = "Question 2" B`start_row' = "Males (N=`female0_n')" C`start_row' = "Females (N=`female1_n')"
local age_row = `start_row' + 1
putexcel A`age_row' = "Age, median (IQR)" B`age_row' = "`female0_age_p50' (`female0_age_p25' - `female0_age_p75')" C`age_row' = "`female1_age_p50' (`female1_age_p25' - `female1_age_p75')"
local prev_row = `age_row' + 1
putexcel A`prev_row' = "Previous transplant, %" B`prev_row' = "`female0_prev'" C`prev_row' = "`female1_prev'"
}
}
Data Analysis for Q2: More complex, using loops to process gender-specific data. Outputs to Excel, showing innovative data handling and presentation. Labels and macros facilitate dynamic updating of results based on data.
log close
set more on
set timeout1 30
}
Cleanup: Properly closes the log file and restores Stata settings to their default state to avoid impacting
Version Control#
Managing your workflow in Stata effectively, especially when dealing with complex scripts, is crucial for both learning and practical application. Here’s some guidance on this topic:
Effective Management of Script Execution in Stata#
1. Understand Script Sections:
Familiarize Yourself: Before running any part of the script, understand what each section does. This will help you avoid running unnecessary parts that might alter your data or results unintentionally.
2. Use Conditional Blocks Wisely:
Leverage Conditional
if: Instead of manually highlighting and running sections of the code, use the conditionalifstatements to control which parts of the script execute. For example, changingif 1toif 0will prevent that block from running without needing to delete or comment out potentially large chunks of code.Modularity: Organize your script into distinct blocks that can be easily turned on or off. This approach helps in focusing on specific analyses or tasks without interference from other parts of the script.
3. Advantages of Conditional Execution:
Reduce Risk of Errors: By controlling script execution through conditions, you minimize the risk of accidentally running incorrect parts or using outdated data settings.
Save Time: Running only relevant sections saves time, particularly with large datasets or complex computations.
Keep Everything Intact: Using conditionals allows you to keep all your code in the script for reference without running it. This is especially useful for educational purposes and when scripts are handed over for grading or collaborative projects.
4. Script Control Examples:
Silencing Blocks: To skip a block, simply change its condition to zero (
if 0). This effectively “silences” the block without removing it from the script. For instance, if you are currently interested only in the results ofif 3, you can setif 1andif 2toif 0.Activating Specific Sections: To run a specific section, ensure that section’s
ifcondition evaluates to true (non-zero). You can manage this dynamically by setting a global or local macro at the beginning of your script and using it in yourifstatements.
5. Encourage Good Practices:
Commenting: Encourage adding comments on why certain blocks are turned off (e.g.,
if 0 // temporarily disabled for debugging).Version Control: Use of version control systems like Git. This allows you to experiment with different script versions without fear of losing the original settings or analyses. GitHub is the most widely used system for version control. Create an account and start saving your
.doand.adofiles there, as well as any other files such as manuscripts, presentations, capstones, theses. More on this in the Intermediate Class.
6. Example for Clarity:
// Define which sections to run
global run_section1 0 // Do not run section 1
global run_section2 0 // Do not run section 2
global run_section3 1 // Only run section 3
// Section 1: Data Preparation
if $run_section1 {
// Data cleaning and preparation steps here
}
// Section 2: Initial Analysis
if $run_section2 {
// Initial exploratory analysis here
}
// Section 3: Detailed Analysis
if $run_section3 {
// Detailed statistical analysis here
}
7. Continuous Learning:
Regular Revisions: Think of
.dofile script management as akin to DNA transcription in a cell. Just as a cell selectively activates certain genes in response to environmental changes, you’re encouraged to adaptively revise your scripts as new data or analysis needs arise. This dynamic process (e.g. changingif 0toif 2or the reverse), much like the activation and silencing of genes, ensures that your analytical tools remain responsive and tailored to specific tasks at hand.