Let's break down why you're hitting that type mismatch error and walk through how to fix it, along with addressing the date format inconsistency between your two datasets.

First: Understand the Root of the Type Mismatch #

The date() function in Stata only works on string variables. If your DateCollected variable is already a numeric (date-formatted) variable, running generate dateCollected = date(DateCollected, "DMY") will throw a type mismatch error immediately.

Looking at your dataset samples:

• In Creatinine.dta, DateCollected is a string (e.g., "12/5/2007"), which should work with date()—but if some records have non-standard values (empty strings, garbage text, or mixed formats), that can also trigger errors.
• Your Report.dta uses numeric codes for categorical variables (e.g., sex=2 for Female) and string dates with month abbreviations (e.g., "15-Apr-07"), which creates mismatches when merged with Creatinine.dta's string sex and slash-separated dates.

Step 1: Diagnose the DateCollected Variable #

Before running the generate command, run these lines to check what you're working with:

describe DateCollected
codebook DateCollected

• If the output says type: numeric, you don't need to convert it—just adjust the display format with format %tdCCYY/NN/DD DateCollected.
• If it's type: string, look for problematic entries with:

  list id DateCollected if missing(date(DateCollected, "DMY"))
  

  This will show you any strings that date() can't parse (e.g., "N/A", "07/12/2007" if some dates are MDY instead of DMY).

Step 2: Standardize Dates Across Both Datasets #

To fix the format inconsistency between Creatinine.dta and Report.dta, clean each dataset separately first:

Clean Report.dta first:

use "Archive\Report.dta", clear
// Convert string dates to numeric dates
foreach var in dob tx1 censor_date DeathDate {
    gen `var'_num = date(`var', "DMY") // "DMY" recognizes month abbreviations like "Apr"
    format %tdCCYY/NN/DD `var'_num
    drop `var'
    rename `var'_num `var'
}
save "Archive\Report_cleaned.dta", replace

Clean Creatinine.dta next:

use "\\files\Creatinine.dta", clear
// Fix DateCollected first
gen dateCollected = date(DateCollected, "DMY")
// Check for failed conversions and fix if needed (e.g., try MDY for mixed formats)
list id DateCollected if missing(dateCollected)
// replace dateCollected = date(DateCollected, "MDY") if missing(dateCollected)
format %tdCCYY/NN/DD dateCollected
drop DateCollected
rename dateCollected DateCollected

// Standardize other dates to match Report.dta
foreach var in dob tx1 censor_date DeathDate {
    gen `var'_num = date(`var', "DMY")
    format %tdCCYY/NN/DD `var'_num
    drop `var'
    rename `var'_num `var'
}

// Fix sex variable to match Report.dta's numeric code
gen sex_num = cond(sex == "Male", 1, cond(sex == "Female", 2, .))
drop sex
rename sex_num sex
label define sex1 1 "Male" 2 "Female" // Recreate label if needed
label values sex sex1

Step 3: Merge the Cleaned Datasets #

Now that both datasets have consistent variable types, run your merge safely:

merge m:1 id using "Archive\Report_cleaned.dta"
// Keep only matched records (adjust based on your needs)
drop if _merge != 3
drop _merge

// Rest of your labeling and cleaning code here...
label define org 1 "Heart" 2 "Lung" 3 "Liver" 5 "Multiple" 6 "Small Bowel" 7 "Pancreas" 8 "Stomach"
label values organ1 organ2 organ3 org
// ... (continue with your existing labeling, ordering, and duplicate removal)

Key Takeaways #

• Always confirm variable types before running conversion functions like date().
• Merge only after standardizing variable types (dates, categoricals) across all datasets.
• Check for failed date conversions—mixed formats (DMY vs MDY) are a common hidden issue.

内容的提问来源于stack exchange，提问作者user8722680