fix note

R/CompareCohorts.R

@@ -64,7 +64,7 @@ computeStandardizedDifference <- function(covariateData1, covariateData2, cohort
     covariates1 <- covariateData1$covariates
     if (!is.null(cohortId1)) {
       covariates1 <- covariates1 %>%
-        filter(cohortDefinitionId == cohortId1)
+        filter(.data$cohortDefinitionId == cohortId1)
     }
     covariates1 <- covariates1 %>%
       select(
@@ -76,7 +76,7 @@ computeStandardizedDifference <- function(covariateData1, covariateData2, cohort
     covariates2 <- covariateData2$covariates
     if (!is.null(cohortId2)) {
       covariates2 <- covariates2 %>%
-        filter(cohortDefinitionId == cohortId2)
+        filter(.data$cohortDefinitionId == cohortId2)
     }
     covariates2 <- covariates2 %>%
       select(
@@ -108,7 +108,7 @@ computeStandardizedDifference <- function(covariateData1, covariateData2, cohort
     covariates1 <- covariateData1$covariatesContinuous
     if (!is.null(cohortId1)) {
       covariates1 <- covariates1 %>%
-        filter(cohortDefinitionId == cohortId1)
+        filter(.data$cohortDefinitionId == cohortId1)
     }
     covariates1 <- covariates1 %>%
       select(
@@ -121,7 +121,7 @@ computeStandardizedDifference <- function(covariateData1, covariateData2, cohort
     covariates2 <- covariateData2$covariatesContinuous
     if (!is.null(cohortId2)) {
       covariates2 <- covariates2 %>%
-        filter(cohortDefinitionId == cohortId2)
+        filter(.data$cohortDefinitionId == cohortId2)
     }
     covariates2 <- covariates2 %>%
       select(

R/HelperFunctions.R

@@ -48,7 +48,7 @@ filterByRowId <- function(covariateData, rowIds) {
     stop("Cannot filter aggregated data by rowId")
   }
   covariates <- covariateData$covariates %>%
-    filter(rowId %in% rowIds)
+    filter(.data$rowId %in% rowIds)
 
   result <- Andromeda::andromeda(
     covariates = covariates,

R/Normalization.R

@@ -71,14 +71,14 @@ tidyCovariateData <- function(covariateData,
   } else {
     newCovariates <- covariateData$covariates
     covariateData$maxValuePerCovariateId <- covariateData$covariates %>%
-      group_by(covariateId) %>%
-      summarise(maxValue = max(covariateValue, na.rm = TRUE))
+      group_by(.data$covariateId) %>%
+      summarise(maxValue = max(.data$covariateValue, na.rm = TRUE))
     on.exit(covariateData$maxValuePerCovariateId <- NULL)
 
     if (removeRedundancy || minFraction != 0) {
       covariateData$valueCounts <- covariateData$covariates %>%
-        group_by(covariateId) %>%
-        summarise(n = count(), nDistinct = n_distinct(covariateValue))
+        group_by(.data$covariateId) %>%
+        summarise(n = count(), nDistinct = n_distinct(.data$covariateValue))
       on.exit(covariateData$valueCounts <- NULL, add = TRUE)
     }
 
@@ -87,39 +87,39 @@ tidyCovariateData <- function(covariateData,
     if (removeRedundancy) {
       covariateData$binaryCovariateIds <- covariateData$maxValuePerCovariateId %>%
         inner_join(covariateData$valueCounts, by = "covariateId") %>%
-        filter(maxValue == 1 & nDistinct == 1) %>%
-        select(covariateId = covariateId)
+        filter(.data$maxValue == 1 & .data$nDistinct == 1) %>%
+        select(covariateId = .data$covariateId)
       on.exit(covariateData$binaryCovariateIds <- NULL, add = TRUE)
 
       if (covariateData$binaryCovariateIds %>% count() %>% pull() != 0) {
         if (isTemporalCovariateData(covariateData)) {
           # Temporal
           covariateData$temporalValueCounts <- covariateData$covariates %>%
             inner_join(covariateData$binaryCovariateIds, by = "covariateId") %>%
-            group_by(covariateId, timeId) %>%
+            group_by(.data$covariateId, .data$timeId) %>%
             count()
           on.exit(covariateData$temporalValueCounts <- NULL, add = TRUE)
 
           # First, find all single covariates that, for every timeId, appear in every row with the same value
           covariateData$deleteCovariateTimeIds <- covariateData$temporalValueCounts %>%
             filter(n == populationSize) %>%
-            select(covariateId, timeId)
+            select(.data$covariateId, .data$timeId)
           on.exit(covariateData$deleteCovariateTimeIds <- NULL, add = TRUE)
 
           # Next, find groups of covariates (analyses) that together cover everyone:
           analysisIds <- covariateData$temporalValueCounts %>%
             anti_join(covariateData$deleteCovariateTimeIds, by = c("covariateId", "timeId")) %>%
             inner_join(covariateData$covariateRef, by = "covariateId") %>%
-            group_by(analysisId) %>%
+            group_by(.data$analysisId) %>%
             summarise(n = sum(n, na.rm = TRUE)) %>%
             filter(n == populationSize) %>%
-            select(analysisId)
+            select(.data$analysisId)
 
           # For those, find most prevalent covariate, and mark it for deletion:
           valueCounts <- analysisIds %>%
             inner_join(covariateData$covariateRef, by = "analysisId") %>%
             inner_join(covariateData$temporalValueCounts, by = "covariateId") %>%
-            select(analysisId, covariateId, timeId, n) %>%
+            select(.data$analysisId, .data$covariateId, .data$timeId, .data$n) %>%
             collect()
           valueCounts <- valueCounts[order(valueCounts$analysisId, -valueCounts$n), ]
           Andromeda::appendToTable(
@@ -137,25 +137,25 @@ tidyCovariateData <- function(covariateData,
           # First, find all single covariates that appear in every row with the same value
           toDelete <- covariateData$valueCounts %>%
             inner_join(covariateData$binaryCovariateIds, by = "covariateId") %>%
-            filter(n == populationSize) %>%
-            select(covariateId) %>%
+            filter(.data$n == populationSize) %>%
+            select(.data$covariateId) %>%
             collect()
           deleteCovariateIds <- toDelete$covariateId
 
           # Next, find groups of covariates (analyses) that together cover everyone:
           analysisIds <- covariateData$valueCounts %>%
             inner_join(covariateData$binaryCovariateIds, by = "covariateId") %>%
-            filter(!covariateId %in% deleteCovariateIds) %>%
+            filter(!.data$covariateId %in% deleteCovariateIds) %>%
             inner_join(covariateData$covariateRef, by = "covariateId") %>%
-            group_by(analysisId) %>%
+            group_by(.data$analysisId) %>%
             summarise(n = sum(n, na.rm = TRUE)) %>%
-            filter(n == populationSize) %>%
-            select(analysisId)
+            filter(.data$n == populationSize) %>%
+            select(.data$analysisId)
           # For those, find most prevalent covariate, and mark it for deletion:
           valueCounts <- analysisIds %>%
             inner_join(covariateData$covariateRef, by = "analysisId") %>%
             inner_join(covariateData$valueCounts, by = "covariateId") %>%
-            select(analysisId, covariateId, n) %>%
+            select(.data$analysisId, .data$covariateId, n) %>%
             collect()
           valueCounts <- valueCounts[order(valueCounts$analysisId, -valueCounts$n), ]
           deleteCovariateIds <- c(deleteCovariateIds, valueCounts$covariateId[!duplicated(valueCounts$analysisId)])
@@ -168,9 +168,9 @@ tidyCovariateData <- function(covariateData,
     if (minFraction != 0) {
       minCount <- floor(minFraction * populationSize)
       toDelete <- covariateData$valueCounts %>%
-        filter(n < minCount) %>%
-        filter(!covariateId %in% ignoreCovariateIds) %>%
-        select(covariateId) %>%
+        filter(.data$n < minCount) %>%
+        filter(!.data$covariateId %in% ignoreCovariateIds) %>%
+        select(.data$covariateId) %>%
         collect()
 
       metaData$deletedInfrequentCovariateIds <- toDelete$covariateId
@@ -179,15 +179,15 @@ tidyCovariateData <- function(covariateData,
     }
     if (length(deleteCovariateIds) > 0) {
       newCovariates <- newCovariates %>%
-        filter(!covariateId %in% deleteCovariateIds)
+        filter(!.data$covariateId %in% deleteCovariateIds)
     }
 
     if (normalize) {
       ParallelLogger::logInfo("Normalizing covariates")
       newCovariates <- newCovariates %>%
         inner_join(covariateData$maxValuePerCovariateId, by = "covariateId") %>%
-        mutate(covariateValue = covariateValue / maxValue) %>%
-        select(-maxValue)
+        mutate(covariateValue = .data$covariateValue / .data$maxValue) %>%
+        select(-.data$maxValue)
       metaData$normFactors <- covariateData$maxValuePerCovariateId %>%
         collect()
     }