Style code (GHA)

Author: DougManuel

R/alcohol.R

@@ -34,25 +34,25 @@
 #'   - 5–9 points -> score of 4 ("High risk").
 #'
 #' @details
-#' This function implements Canada's Low-Risk Alcohol Drinking Guidelines (Step 1 only) to assess 
-#' alcohol consumption risk. The scoring system considers both the quantity of alcohol consumed 
+#' This function implements Canada's Low-Risk Alcohol Drinking Guidelines (Step 1 only) to assess
+#' alcohol consumption risk. The scoring system considers both the quantity of alcohol consumed
 #' and biological sex differences in alcohol metabolism.
-#' 
+#'
 #' **Risk Categories:**
 #' - Low risk (0 points): Safe consumption levels
 #' - Marginal risk (1-2 points): Slightly elevated risk
 #' - Medium risk (3-4 points): Moderate health concerns
 #' - High risk (5-9 points): Significant health risks
-#' 
+#'
 #' **Sex-Based Differences:**
-#' Women generally have lower tolerance thresholds due to physiological differences in 
+#' Women generally have lower tolerance thresholds due to physiological differences in
 #' alcohol metabolism, reflected in the sex-specific point allocations.
-#' 
+#'
 #' **Non-response Handling:**
 #' Invalid inputs or survey non-response values result in tagged NA ("b").
 #'
 #' @note
-#' This function implements only Step 1 of the guidelines. Step 2 (additional drinking pattern 
+#' This function implements only Step 1 of the guidelines. Step 2 (additional drinking pattern
 #' assessments) is not included due to data limitations in the survey.
 #'
 #' @examples

R/blood-pressure.R

@@ -8,12 +8,12 @@
 #'
 #' @return [numeric] The adjusted systolic blood pressure as a numeric.
 #'
-#' @details Blood pressure measurements in survey settings may require adjustment to account for 
+#' @details Blood pressure measurements in survey settings may require adjustment to account for
 #'          measurement conditions and equipment differences. This function applies a standardized adjustment
 #'          using the formula: SBP_adj = 11.4 + (0.93 * BPMDPBPS).
-#'          
-#'          Non-response handling: Values >= 996 indicate survey non-response and are converted to 
-#'          tagged NA ("b") to distinguish from missing measurements. Negative values are also 
+#'
+#'          Non-response handling: Values >= 996 indicate survey non-response and are converted to
+#'          tagged NA ("b") to distinguish from missing measurements. Negative values are also
 #'          treated as invalid and converted to tagged NA.
 #'
 #' @examples
@@ -51,12 +51,12 @@ adjust_SBP <- function(BPMDPBPS) {
 #'
 #' @return [numeric] The adjusted diastolic blood pressure as a numeric.
 #'
-#' @details Blood pressure measurements in survey settings may require adjustment to account for 
+#' @details Blood pressure measurements in survey settings may require adjustment to account for
 #'          measurement conditions and equipment differences. This function applies a standardized adjustment
 #'          using the formula: DBP_adj = 15.6 + (0.83 * BPMDPBPD).
-#'          
-#'          Non-response handling: Values >= 996 indicate survey non-response and are converted to 
-#'          tagged NA ("b") to distinguish from missing measurements. Negative values are also 
+#'
+#'          Non-response handling: Values >= 996 indicate survey non-response and are converted to
+#'          tagged NA ("b") to distinguish from missing measurements. Negative values are also
 #'          treated as invalid and converted to tagged NA.
 #'
 #' @examples
@@ -130,15 +130,15 @@ adjust_DBP <- function(BPMDPBPD) {
 #' # Returns: c(1, 2, 1)
 #'
 #' @details This function implements clinical guidelines for hypertension classification:
-#'          
+#'
 #'          **Blood Pressure Thresholds:**
 #'          - General population: >= 140/90 mmHg indicates hypertension
 #'          - Diabetes or CKD patients: >= 130/80 mmHg indicates hypertension (lower threshold)
-#'          
+#'
 #'          **Medication Logic:**
 #'          - Anyone taking hypertension medication is classified as hypertensive
 #'          - Medication status may be adjusted based on comorbidities (diabetes, CKD, cardiovascular disease)
-#'          
+#'
 #'          **Non-response Handling:**
 #'          - Values >= 996 indicate survey non-response codes
 #'          - Invalid blood pressure readings result in tagged NA ("b")

R/diabetes.R

@@ -1,7 +1,7 @@
 #' @title Diabetes derived variable
 #'
-#' @description This function evaluates diabetes status using a comprehensive approach that combines 
-#' laboratory measurements, self-reported diagnosis, and medication usage to create an inclusive 
+#' @description This function evaluates diabetes status using a comprehensive approach that combines
+#' laboratory measurements, self-reported diagnosis, and medication usage to create an inclusive
 #' diabetes classification.
 #'
 #' @param diab_m [integer] An integer indicating whether the respondent has diabetes based on HbA1c level. 1 for "Yes", 2 for "No".
@@ -39,20 +39,20 @@
 #' #   mutate(diabetes_status = determine_inclusive_diabetes(diab_m, CCC_51, diab_drug2))
 #'
 #' @details This function classifies diabetes status based that considers:
-#'          
+#'
 #'          **Data Sources:**
 #'          - Laboratory: HbA1c levels indicating diabetes (diab_m)
 #'          - Self-report: Participant-reported diabetes diagnosis (CCC_51)
 #'          - Medication: Current diabetes medication usage (diab_drug2)
-#'          
+#'
 #'          **Classification Logic:**
 #'          - ANY positive indicator results in diabetes classification
 #'          - ALL negative indicators required for "no diabetes" classification
 #'          - Sophisticated missing data handling preserves available information
-#'          
+#'
 #'          **Missing Data Strategy:**
-#'          The function maximizes data utility by making classifications based on available 
-#'          information when some parameters are missing, only returning NA when insufficient 
+#'          The function maximizes data utility by making classifications based on available
+#'          information when some parameters are missing, only returning NA when insufficient
 #'          data exists for classification.
 #'
 #' @seealso Related health condition functions: [determine_hypertension()], [calculate_GFR()]

R/exercise.R

@@ -29,14 +29,14 @@
 #' )
 #' # Returns: c(35, 39.28571)
 #'
-#' @details This function processes physical activity data from accelerometer measurements 
+#' @details This function processes physical activity data from accelerometer measurements
 #'          to create a weekly activity summary.
-#'          
+#'
 #'          **Data Quality Requirements:**
 #'          - Requires complete 7-day data (missing days result in tagged NA)
 #'          - This conservative approach ensures reliable activity estimates
 #'          - Zero values are preserved (represent valid no-activity days)
-#'          
+#'
 #'          **Clinical Context:**
 #'          Accelerometer data provides objective measures of moderate-to-vigorous physical activity (MVPA),
 #'          crucial for assessing adherence to physical activity guidelines.

R/income.R

@@ -10,19 +10,19 @@
 #'         DHHDHSZ) are non-response values (THI_01 >= 996, DHHDHSZ >= 996), the adjusted household income will be
 #'         NA(b) (Not Available).
 #'
-#' @details This function applies equivalence scales to adjust household income for household size, 
+#' @details This function applies equivalence scales to adjust household income for household size,
 #'          allowing for meaningful income comparisons across different household compositions.
-#'          
+#'
 #'          **Equivalence Scale Logic:**
 #'          - First adult: Weight = 1.0 (full weight)
 #'          - Second adult: Weight = 0.4 (economies of scale)
 #'          - Additional members: Weight = 0.3 each (further economies)
-#'          
+#'
 #'          **Examples:**
 #'          - Single person: weight = 1.0
 #'          - Two adults: weight = 1.4 (1.0 + 0.4)
 #'          - Family of four: weight = 2.0 (1.0 + 0.4 + 0.3 + 0.3)
-#'          
+#'
 #'          **Non-response Handling:**
 #'          Income values >= 996 or household size <= 0 indicate survey non-response and result in tagged NA ("b").
 #'

R/kidney.R

@@ -11,26 +11,26 @@
 #' @return [numeric] The calculated GFR. If any of the input parameters (LAB_BCRE, PGDCGT, CLC_SEX, CLC_AGE)
 #'         are non-response values (LAB_BCRE >= 996, PGDCGT >= 96, CLC_SEX >= 6, CLC_AGE >= 996) or out of bounds, the GFR will be NA(b).
 #'
-#' @details This function implements the Modification of Diet in Renal Disease (MDRD) equation 
+#' @details This function implements the Modification of Diet in Renal Disease (MDRD) equation
 #'          to estimate glomerular filtration rate, a key indicator of kidney function.
-#'          
+#'
 #'          **Clinical Significance:**
 #'          GFR estimates are essential for:
 #'          - Chronic kidney disease (CKD) classification
 #'          - Medication dosing adjustments
 #'          - Cardiovascular risk assessment
-#'          
+#'
 #'          **Formula Application:**
 #'          Base: GFR = 175 × (creatinine^-1.154) × (age^-0.203)
 #'          Adjustments:
 #'          - Female: × 0.742
 #'          - Black ethnicity: × 1.210
-#'          
+#'
 #'          **Unit Conversion:**
 #'          Serum creatinine converted from µmol/L to mg/dL (÷ 88.4)
-#'          
+#'
 #'          **Non-response Handling:**
-#'          Values >= 996 (LAB_BCRE), >= 96 (PGDCGT), >= 6 (CLC_SEX), >= 996 (CLC_AGE) 
+#'          Values >= 996 (LAB_BCRE), >= 96 (PGDCGT), >= 6 (CLC_SEX), >= 996 (CLC_AGE)
 #'          indicate survey non-response and result in tagged NA ("b").
 #'
 #' @examples

R/smoking.R

@@ -64,20 +64,20 @@
 #'
 #' @export
 #'
-#' @details Pack-years is a standardized measure of lifetime cigarette exposure used in epidemiological 
+#' @details Pack-years is a standardized measure of lifetime cigarette exposure used in epidemiological
 #'          research and clinical practice. The calculation varies by smoking pattern:
-#'          
+#'
 #'          **Smoking Patterns:**
 #'          - Daily smokers: Consistent daily consumption over time period
 #'          - Occasional smokers: Variable consumption adjusted for frequency
 #'          - Former smokers: Historical consumption during smoking periods
-#'          
+#'
 #'          **Minimum Values:**
-#'          The function applies minimum pack-year values (0.0137 or 0.007) to prevent 
+#'          The function applies minimum pack-year values (0.0137 or 0.007) to prevent
 #'          underestimation of health risks for light smokers.
-#'          
+#'
 #'          **Clinical Significance:**
-#'          Pack-years data is crucial for lung cancer screening guidelines, COPD risk 
+#'          Pack-years data is crucial for lung cancer screening guidelines, COPD risk
 #'          assessment, and cardiovascular disease evaluation.
 #'
 #' @seealso https://big-life-lab.github.io/cchsflow/reference/pack_years_fun.html