lintr

R/common_language.R

@@ -369,8 +369,8 @@ wmw_odds <- function(x, y = NULL, data = NULL,
     y <- data[data$g == "y", "r"]
 
     .foo <- function(p) {
-      diff <- stats::quantile(x, probs = c(p, 1 - p)) - stats::quantile(y, probs = c(1 - p, p))
-      min(abs(diff))
+      difference <- stats::quantile(x, probs = c(p, 1 - p)) - stats::quantile(y, probs = c(1 - p, p))
+      min(abs(difference))
     }
 
     stats::optim(

R/convert_between_odds_to_probs.R

@@ -78,10 +78,10 @@ probs_to_odds.data.frame <- function(probs, log = FALSE, select = NULL, exclude
 #' @keywords internal
 .odds_to_probs_df <- function(odds = NULL, probs = NULL, log = FALSE, select = NULL, exclude = NULL, ...) {
   # If vector
-  if (!is.null(odds)) {
-    df <- odds
+  if (is.null(odds)) {
+    mydata <- probs
   } else {
-    df <- probs
+    mydata <- odds
   }
 
   # check for formula notation, convert to character vector
@@ -93,55 +93,55 @@ probs_to_odds.data.frame <- function(probs, log = FALSE, select = NULL, exclude
   }
 
   # Variable order
-  var_order <- names(df)
+  var_order <- names(mydata)
 
   # Keep subset
-  if (!is.null(select) && select %in% names(df)) {
+  if (!is.null(select) && select %in% names(mydata)) {
     select <- as.vector(select)
-    to_keep <- as.data.frame(df[!names(df) %in% select])
-    df <- df[names(df) %in% select]
+    to_keep <- as.data.frame(mydata[!names(mydata) %in% select])
+    mydata <- mydata[names(mydata) %in% select]
   } else {
     to_keep <- NULL
   }
 
   # Remove exceptions
-  if (!is.null(exclude) && exclude %in% names(df)) {
+  if (!is.null(exclude) && exclude %in% names(mydata)) {
     exclude <- as.vector(exclude)
     if (is.null(to_keep)) {
-      to_keep <- as.data.frame(df[exclude])
+      to_keep <- as.data.frame(mydata[exclude])
     } else {
-      to_keep <- cbind(to_keep, as.data.frame(df[exclude]))
+      to_keep <- cbind(to_keep, as.data.frame(mydata[exclude]))
     }
 
-    df <- df[!names(df) %in% exclude]
+    mydata <- mydata[!names(mydata) %in% exclude]
   }
 
   # Remove non-numerics
-  is_num <- vapply(df, is.numeric, logical(1))
-  dfother <- df[!is_num]
-  dfnum <- df[is_num]
+  is_num <- vapply(mydata, is.numeric, logical(1))
+  dfother <- mydata[!is_num]
+  dfnum <- mydata[is_num]
 
   # Tranform
-  if (!is.null(odds)) {
-    dfnum <- data.frame(lapply(dfnum, odds_to_probs.numeric, log = log))
-  } else {
+  if (is.null(odds)) {
     dfnum <- data.frame(lapply(dfnum, probs_to_odds.numeric, log = log))
+  } else {
+    dfnum <- data.frame(lapply(dfnum, odds_to_probs.numeric, log = log))
   }
 
   # Add non-numerics
   if (is.null(ncol(dfother))) {
-    df <- dfnum
+    mydata <- dfnum
   } else {
-    df <- cbind(dfother, dfnum)
+    mydata <- cbind(dfother, dfnum)
   }
 
   # Add exceptions
   if (!is.null(select) || !is.null(exclude) && exists("to_keep")) {
-    df <- cbind(df, to_keep)
+    mydata <- cbind(mydata, to_keep)
   }
 
   # Reorder
-  df <- df[var_order]
+  mydata <- mydata[var_order]
 
-  return(df)
+  mydata
 }

data-raw/df data.R

@@ -16,7 +16,7 @@ screening_test <- data.frame(
   Test1 = rep(screening_test[[1]], times = screening_test$Freq),
   Test2 = rep(screening_test[[2]], times = screening_test$Freq)
 )
-i <- sample(1600, size = 800)
+i <- sample.int(1600, size = 800)
 screening_test$Diagnosis[i] <- screening_test$Test1[i]
 screening_test$Diagnosis[-i] <- screening_test$Test2[-i]
 screening_test$Diagnosis <- factor(screening_test$Diagnosis, labels = c("Neg", "Pos"))