#234 Added code outputs

posts/zzz_DO_NOT_EDIT_how__i__reb.../how__i__rebuilt_a__lost__ec_g__data__script_in__r.qmd

@@ -77,6 +77,19 @@ egdtc <- vs %>%
   rename(EGDTC = VSDTC)
 ```
 
+The output:
+
+```
+USUBJID     VISIT               EGDTC     
+  <chr>       <chr>               <chr>     
+1 01-701-1015 SCREENING 1         2013-12-26
+2 01-701-1015 SCREENING 2         2013-12-31
+3 01-701-1015 BASELINE            2014-01-02
+4 01-701-1015 AMBUL ECG PLACEMENT 2014-01-14
+5 01-701-1015 WEEK 2              2014-01-16
+6 01-701-1015 WEEK 4              2014-01-30
+```
+
 ### 3. Generating a Grid of Patient Data
 
 Here, I create a grid of all possible combinations of subject IDs, test codes (e.g., `QT`, `HR`, `RR`, `ECGINT`), time points (e.g., after lying down, after standing), and visits. These combinations represent different test results collected across multiple visits.
@@ -114,6 +127,18 @@ eg <- expand.grid(
 )
 ```
 
+The output:
+
+```
+      USUBJID EGTESTCD                          EGTPT       VISIT
+1 01-701-1015       QT AFTER LYING DOWN FOR 5 MINUTES SCREENING 1
+2 01-701-1015       HR AFTER LYING DOWN FOR 5 MINUTES SCREENING 1
+3 01-701-1015       RR AFTER LYING DOWN FOR 5 MINUTES SCREENING 1
+4 01-701-1015   ECGINT AFTER LYING DOWN FOR 5 MINUTES SCREENING 1
+5 01-701-1015       QT    AFTER STANDING FOR 1 MINUTE SCREENING 1
+6 01-701-1015       HR    AFTER STANDING FOR 1 MINUTE SCREENING 1
+```
+
 ### 4. Generating Random Test Results
 
 For each combination in the grid, I generate random test results using a normal distribution to simulate realistic values for each test code. To determine the means and standard deviations, I used the original EG dataset as a reference. By analyzing the range and distribution of values in the original dataset, I could extract realistic means and standard deviations for each ECG test (e.g., QT, HR, RR, ECGINT). This approach allowed me to ensure that the synthetic data aligned closely with the patterns and variability observed in the original clinical data.