Clean a few lints

easystats/easystats#334

R/cor_lower.R

@@ -40,7 +40,7 @@ cor_lower.easycorrelation <- function(x, diag = FALSE, ...) {
   rownames(tri) <- rownames(m)
   colnames(tri) <- colnames(m)
 
-  tokeep <- c()
+  tokeep <- NULL
 
   for (param1 in rownames(m)) {
     for (param2 in colnames(m)) {

R/cor_smooth.R

@@ -105,7 +105,7 @@ cor_smooth.matrix <- function(x,
     x <- suppressWarnings(psych::cor.smooth(x, eig.tol = tol, ...))
   } else {
     out <- try(suppressMessages(mbend::bend(x, method = method, ...)), silent = TRUE)
-    if (inherits(out, as.character("try-error"))) {
+    if (inherits(out, "try-error")) {
       return(x)
     }
     x <- out$bent
@@ -133,7 +133,7 @@ is.positive_definite.matrix <- function(x, tol = 10^-12, ...) {
   eigens <- try(eigen(x), silent = TRUE)
 
   # validation checks
-  if (inherits(eigens, as.character("try-error"))) {
+  if (inherits(eigens, "try-error")) {
     stop(insight::format_message(
       "There is something seriously wrong with the correlation matrix, as some of the eigen values are NA."
     ), call. = FALSE)

R/cor_sort.R

@@ -31,7 +31,10 @@ cor_sort.easycorrelation <- function(x, distance = "correlation", ...) {
   reordered <- x[order(x$Parameter1, x$Parameter2), ]
 
   # Restore class and attributes
-  attributes(reordered) <- utils::modifyList(attributes(x)[!names(attributes(x)) %in% c("names", "row.names")], attributes(reordered))
+  attributes(reordered) <- utils::modifyList(
+    attributes(x)[!names(attributes(x)) %in% c("names", "row.names")],
+    attributes(reordered)
+  )
 
   # make sure Parameter columns are character
   reordered$Parameter1 <- as.character(reordered$Parameter1)

R/cor_test.R

@@ -162,7 +162,7 @@ cor_test <- function(data,
   if (!isFALSE(winsorize) && !is.null(winsorize)) {
     # set default (if not specified)
     if (isTRUE(winsorize)) {
-      winsorize <- .2
+      winsorize <- 0.2
     }
 
     # winsorization would otherwise fail in case of NAs present

R/cor_test_biserial.R

@@ -17,7 +17,11 @@
   if (.vartype(data[[binary]])$is_factor || .vartype(data[[binary]])$is_character) {
     data[[binary]] <- as.numeric(as.factor(data[[binary]]))
   }
-  data[[binary]] <- as.vector((data[[binary]] - min(data[[binary]], na.rm = TRUE)) / diff(range(data[[binary]], na.rm = TRUE), na.rm = TRUE))
+
+  data[[binary]] <- as.vector(
+    (data[[binary]] - min(data[[binary]], na.rm = TRUE)) /
+      (diff(range(data[[binary]], na.rm = TRUE), na.rm = TRUE))
+  )
 
   # Get biserial or point-biserial correlation
   if (method == "biserial") {

R/cor_test_biweight.R

@@ -8,8 +8,8 @@
   u <- (var_x - stats::median(var_x)) / (9 * stats::mad(var_x, constant = 1))
   v <- (var_y - stats::median(var_y)) / (9 * stats::mad(var_y, constant = 1))
 
-  I_x <- ifelse((1 - abs(u)) > 0, 1, 0)
-  I_y <- ifelse((1 - abs(v)) > 0, 1, 0)
+  I_x <- as.numeric((1 - abs(u)) > 0)
+  I_y <- as.numeric((1 - abs(v)) > 0)
 
   w_x <- I_x * (1 - u^2)^2
   w_y <- I_y * (1 - v^2)^2
@@ -31,7 +31,7 @@
     Parameter2 = y,
     r = r,
     t = p$statistic,
-    df_error = length(var_x) - 2,
+    df_error = length(var_x) - 2L,
     p = p$p,
     CI_low = ci_vals$CI_low,
     CI_high = ci_vals$CI_high,

R/cor_test_distance.R

@@ -14,7 +14,8 @@
       t = NA,
       df_error = NA,
       p = NA,
-      Method = "Distance"
+      Method = "Distance",
+      stringsAsFactors = FALSE
     )
   } else {
     rez <- .cor_test_distance_corrected(var_x, var_y, ci = ci)
@@ -27,7 +28,8 @@
       t = rez$t,
       df_error = rez$df,
       p = rez$p,
-      Method = "Distance (Bias Corrected)"
+      Method = "Distance (Bias Corrected)",
+      stringsAsFactors = FALSE
     )
   }
 

R/correlation.R

@@ -522,9 +522,12 @@ correlation <- function(data,
     data <- cbind(data, data2)
   }
 
-  if (ncol(data) <= 2 && any(sapply(data, is.factor)) && !include_factors) {
+  if (ncol(data) <= 2L && any(sapply(data, is.factor)) && !include_factors) {
     if (isTRUE(verbose)) {
-      warning(insight::format_message("It seems like there is not enough continuous variables in your data. Maybe you want to include the factors? We're setting `include_factors=TRUE` for you."), call. = FALSE)
+      warning(
+        insight::format_message("It seems like there is not enough continuous variables in your data. Maybe you want to include the factors? We're setting `include_factors=TRUE` for you."),
+        call. = FALSE
+      )
     }
     include_factors <- TRUE
   }

R/utils_remove_redundant.R

@@ -49,8 +49,8 @@
 
 #' @keywords internal
 .get_rows_non_NA <- function(m) {
-  rows <- c()
-  cols <- c()
+  rows <- NULL
+  cols <- NULL
 
   for (col in colnames(m)) {
     for (row in seq_len(nrow(m))) {

tests/testthat/test-cor_test.R

@@ -123,12 +123,12 @@ test_that("cor_test percentage", {
 test_that("cor_test shepherd", {
   set.seed(333)
   out <- cor_test(iris, "Petal.Length", "Petal.Width", method = "shepherd")
-  expect_equal(out$r, as.numeric(0.94762), tolerance = 0.01)
+  expect_equal(out$r, 0.94762, tolerance = 0.01)
 
   if (requiet("BayesFactor")) {
     set.seed(333)
     out2 <- cor_test(iris, "Petal.Length", "Petal.Width", method = "shepherd", bayesian = TRUE)
-    expect_equal(out2$rho, as.numeric(0.9429992), tolerance = 0.01)
+    expect_equal(out2$rho, 0.9429992, tolerance = 0.01)
   }
 })
 
@@ -137,38 +137,37 @@ test_that("cor_test blomqvist", {
   if (requiet("wdm")) {
     set.seed(333)
     out <- cor_test(iris, "Petal.Length", "Petal.Width", method = "blomqvist")
-    expect_equal(out$r, as.numeric(0.9066667), tolerance = 0.01)
+    expect_equal(out$r, 0.9066667, tolerance = 0.01)
   }
 })
 
 test_that("cor_test hoeffding and somers", {
   if (requiet("Hmisc")) {
     set.seed(333)
     out <- cor_test(iris, "Petal.Length", "Petal.Width", method = "hoeffding")
-    expect_equal(out$r, as.numeric(0.5629277), tolerance = 0.01)
+    expect_equal(out$r, 0.5629277, tolerance = 0.01)
 
     set.seed(333)
     df <- data.frame(x = 1:6, y = c(0, 0, 1, 0, 1, 1))
     out2 <- cor_test(df, "y", "x", method = "somers")
-    expect_equal(out2$Dxy, as.numeric(0.7777778), tolerance = 0.01)
+    expect_equal(out2$Dxy, 0.7777778, tolerance = 0.01)
   }
 })
 
 test_that("cor_test gamma", {
   set.seed(333)
   out <- cor_test(iris, "Petal.Length", "Petal.Width", method = "gamma")
-  expect_equal(out$r, as.numeric(0.8453925), tolerance = 0.01)
+  expect_equal(out$r, 0.8453925, tolerance = 0.01)
 })
 
 test_that("cor_test gaussian", {
+  requiet("BayesFactor")
   set.seed(333)
   out <- cor_test(iris, "Petal.Length", "Petal.Width", method = "gaussian")
-  expect_equal(out$r, as.numeric(0.87137), tolerance = 0.01)
+  expect_equal(out$r, 0.87137, tolerance = 0.01)
 
-  if (requiet("BayesFactor")) {
-    out <- cor_test(iris, "Petal.Length", "Petal.Width", method = "gaussian", bayesian = TRUE)
-    expect_equal(out$r, as.numeric(0.8620878), tolerance = 0.01)
-  }
+  out <- cor_test(iris, "Petal.Length", "Petal.Width", method = "gaussian", bayesian = TRUE)
+  expect_equal(out$r, 0.8620878, tolerance = 0.01)
 })
 
 

tests/testthat/test-correlation.R

@@ -107,20 +107,20 @@ test_that("format checks", {
   skip_if_not_installed("psych")
 
   out <- correlation(iris, include_factors = TRUE)
-  expect_equal(c(nrow(summary(out, redundant = TRUE)), ncol(summary(out, redundant = TRUE))), c(7, 8))
-  expect_equal(c(nrow(summary(out)), ncol(summary(out))), c(6, 7))
+  expect_identical(c(nrow(summary(out, redundant = TRUE)), ncol(summary(out, redundant = TRUE))), c(7L, 8L))
+  expect_identical(c(nrow(summary(out)), ncol(summary(out))), c(6L, 7L))
 
   out <- correlation(iris, method = "auto", include_factors = TRUE)
-  expect_equal(c(nrow(summary(out, redundant = TRUE)), ncol(summary(out, redundant = TRUE))), c(7, 8))
-  expect_equal(c(nrow(summary(out)), ncol(summary(out))), c(6, 7))
+  expect_identical(c(nrow(summary(out, redundant = TRUE)), ncol(summary(out, redundant = TRUE))), c(7L, 8L))
+  expect_identical(c(nrow(summary(out)), ncol(summary(out))), c(6L, 7L))
 
   expect_true(all(c("Pearson correlation", "Point-biserial correlation", "Tetrachoric correlation") %in% out$Method))
 
   # X and Y
   out <- correlation(iris[1:2], iris[3:4])
-  expect_equal(c(nrow(out), ncol(out)), c(4, 11))
-  expect_equal(c(nrow(summary(out, redundant = TRUE)), ncol(summary(out, redundant = TRUE))), c(2, 3))
-  expect_equal(c(nrow(summary(out)), ncol(summary(out))), c(2, 3))
+  expect_identical(c(nrow(out), ncol(out)), c(4L, 11L))
+  expect_identical(c(nrow(summary(out, redundant = TRUE)), ncol(summary(out, redundant = TRUE))), c(2L, 3L))
+  expect_identical(c(nrow(summary(out)), ncol(summary(out))), c(2L, 3L))
 
   # Grouped
   skip_if_not_installed("poorman")
@@ -129,22 +129,22 @@ test_that("format checks", {
   out <- iris %>%
     group_by(Species) %>%
     correlation(include_factors = TRUE)
-  expect_equal(c(nrow(out), ncol(out)), c(18, 12))
-  expect_equal(c(nrow(summary(out, redundant = TRUE)), ncol(summary(out, redundant = TRUE))), c(12, 6))
-  expect_equal(c(nrow(summary(out)), ncol(summary(out))), c(9, 5))
+  expect_identical(c(nrow(out), ncol(out)), c(18L, 12L))
+  expect_identical(c(nrow(summary(out, redundant = TRUE)), ncol(summary(out, redundant = TRUE))), c(12L, 6L))
+  expect_identical(c(nrow(summary(out)), ncol(summary(out))), c(9L, 5L))
 
   # pipe and select
   out <- iris %>%
     correlation(
       select = "Petal.Width",
       select2 = c("Sepal.Length", "Sepal.Width")
     )
-  expect_equal(c(nrow(out), ncol(out)), c(2, 11))
-  expect_equal(c(nrow(summary(out, redundant = TRUE)), ncol(summary(out, redundant = TRUE))), c(1, 3))
-  expect_equal(c(nrow(summary(out)), ncol(summary(out))), c(1, 3))
+  expect_identical(c(nrow(out), ncol(out)), c(2L, 11L))
+  expect_identical(c(nrow(summary(out, redundant = TRUE)), ncol(summary(out, redundant = TRUE))), c(1L, 3L))
+  expect_identical(c(nrow(summary(out)), ncol(summary(out))), c(1L, 3L))
   expect_equal(out[["r"]], c(0.8179, -0.3661), tolerance = 1e-2)
-  expect_equal(out$Parameter1, c("Petal.Width", "Petal.Width"))
-  expect_equal(out$Parameter2, c("Sepal.Length", "Sepal.Width"))
+  expect_identical(out$Parameter1, c("Petal.Width", "Petal.Width"))
+  expect_identical(out$Parameter2, c("Sepal.Length", "Sepal.Width"))
 
   # Bayesian full partial
   skip_if_not_installed("BayesFactor")
@@ -159,9 +159,9 @@ test_that("format checks", {
     partial = TRUE,
     partial_bayesian = TRUE
   )
-  expect_equal(c(nrow(out), ncol(out)), c(6, 14))
-  expect_equal(c(nrow(summary(out, redundant = TRUE)), ncol(summary(out, redundant = TRUE))), c(4, 5))
-  expect_equal(c(nrow(summary(out)), ncol(summary(out))), c(3, 4))
+  expect_identical(c(nrow(out), ncol(out)), c(6L, 14L))
+  expect_identical(c(nrow(summary(out, redundant = TRUE)), ncol(summary(out, redundant = TRUE))), c(4L, 5L))
+  expect_identical(c(nrow(summary(out)), ncol(summary(out))), c(3L, 4L))
 })
 
 
@@ -185,7 +185,7 @@ test_that("correlation doesn't fail when BFs are NA", {
 
   set.seed(123)
   df_corr <- correlation(subset(df, vore == "carni"), bayesian = TRUE)
-  expect_equal(nrow(df_corr), 15L)
+  expect_identical(nrow(df_corr), 15L)
 })
 
 test_that("as.data.frame for correlation output", {