Fixed spacing issues; standardized (mostly) the way that printing is …

…done

R/adk.R

@@ -59,6 +59,21 @@
 #' Guideline for Characterization of Structural Materials,” SAE International,
 #' CMH-17-1G, Mar. 2012.
 #'
+#' @examples
+#' library(dplyr)
+#'
+#' carbon.fabric %>%
+#'   filter(test == "WT") %>%
+#'   filter(condition == "RTD") %>%
+#'   ad_ksample(strength, batch)
+#' ##
+#' ## Call:
+#' ## ad_ksample(data = ., x = strength, groups = batch)
+#' ##
+#' ## N = 18          k = 3
+#' ## ADK = 0.912     p-value = 0.95989
+#' ## Conclusion: Samples come from the same distribution ( alpha = 0.025 )
+#'
 #' @importFrom rlang enquo eval_tidy
 #' @importFrom kSamples ad.test
 #' @export
@@ -177,8 +192,14 @@ glance.adk <- function(x, ...) {  # nolint
 print.adk <- function(x, ...) {
   cat("\nCall:\n",
       paste(deparse(x$call), sep = "\n", collapse = "\n"), "\n\n", sep = "")
-  cat("N = ", x$n, "\tk = ", x$k, "\n")
-  cat("ADK = ", x$ad, "\tp-value = ", x$p, "\n")
+
+  justify <- c("left", "left")
+  width <- c(16L, 16L)
+
+  print_row_equal(list("N", x$n, "k", x$k),
+                  justify, width, ...)
+  print_row_equal(list("ADK", x$ad, "p-value", x$p),
+                  justify, width, ...)
   if (x$reject_same_dist) {
     cat("Conclusion: Samples do not come from the same distribution (alpha =",
         x$alpha, ")\n\n")

R/adtest.R

@@ -63,6 +63,21 @@
 #' Composites Guideline for Characterization of Structural
 #' Materials," SAE International, CMH-17-1G, Mar. 2012.
 #'
+#' @examples
+#' library(dplyr)
+#'
+#' carbon.fabric %>%
+#'   filter(test == "FC") %>%
+#'   filter(condition == "RTD") %>%
+#'   anderson_darling_normal(strength)
+#' ## Call:
+#' ## anderson_darling_normal(data = ., x = strength)
+#' ##
+#' ## Distribution:  Normal ( n = 18 )
+#' ## Test statistic:  A = 0.9224776
+#' ## Significance:  0.01212193  (assuming unknown parameters)
+#' ## Conclusion: Sample is not drawn from a Normal distribution (alpha = 0.05 )
+#'
 #' @importFrom rlang enquo eval_tidy
 #'
 #' @name anderson_darling
@@ -167,9 +182,9 @@ print.anderson_darling <- function(x, ...) {
   cat("\nCall:\n",
       paste(deparse(x$call), sep = "\n", collapse = "\n"), "\n\n", sep = "")
 
-  cat("Distribution: ", x$dist, "( n = ", x$n, ")", "\n")
+  cat("Distribution: ", x$dist, "( n =", x$n, ")", "\n")
 
-  cat("Test statistic: A = ", x$A, "\n")
+  cat("Test statistic:  A =", x$A, "\n")
   cat(
     "Significance: ",
     x$osl,
@@ -178,11 +193,11 @@ print.anderson_darling <- function(x, ...) {
   if (x$reject_distribution) {
     cat("Conclusion: Sample is not drawn from a",
         x$dist,
-        "distribution (alpha = ", x$alpha, ")")
+        "distribution (alpha =", x$alpha, ")")
   } else {
     cat("Conclusion: Sample is drawn from a",
         x$dist,
-        "distribution (alpha = ", x$alpha, ")")
+        "distribution (alpha =", x$alpha, ")")
   }
 }
 

R/basis.R

@@ -100,10 +100,14 @@ k_factor_normal <- function(n, p = 0.90, conf = 0.95) {
 #' value of \code{x} is expected to be a variable within \code{data}. If
 #' \code{data} is not specified, \code{x} must be a vector.
 #'
-#' When \code{modcv=TRUE} is set, which is only applicable to the pooling methods,
-#' the data is first modified according to the modified coefficient of variation (CV)
-#' rules. This modified data is then used when both calculating the basis values and
-#' also when performing the diagnostic tests. The modified CV approach is a way of
+#' When \code{modcv=TRUE} is set, which is only applicable to the
+#' pooling methods,
+#' the data is first modified according to the modified coefficient
+#' of variation (CV)
+#' rules. This modified data is then used when both calculating the
+#' basis values and
+#' also when performing the diagnostic tests. The modified CV approach
+#' is a way of
 #' adding extra variance to datasets with unexpectedly low variance.
 #'
 #' \code{basis_normal} calculate the basis value by subtracting \eqn{k} times
@@ -422,9 +426,9 @@ print.basis <- function(x, ...) {
 
   cat("Distribution: ", x$distribution, "\t")
 
-  cat("( n = ", x$n)
+  cat("( n =", x$n)
   if (!is.null(x$r) & !all(is.na(x$r))) {
-    cat(", r = ", x$r)
+    cat(", r =", x$r)
   }
   cat(" )\n")
 
@@ -438,13 +442,13 @@ print.basis <- function(x, ...) {
                           x$override)
 
   if (x$conf == 0.95 & x$p == 0.9) {
-    cat("B-Basis: ", " ( p = ", x$p, ", conf = ", x$conf, ")\n")
+    cat("B-Basis: ", " ( p =", x$p, ", conf =", x$conf, ")\n")
   }
   else if (x$conf == 0.95 & x$p == 0.99) {
-    cat("A-Basis: ", " ( p = ", x$p, ", conf = ", x$conf, ")\n")
+    cat("A-Basis: ", " ( p =", x$p, ", conf =", x$conf, ")\n")
   }
   else {
-    cat("Basis: ", " ( p = ", x$p, ", conf = ", x$conf, ")\n")
+    cat("Basis: ", " ( p =", x$p, ", conf =", x$conf, ")\n")
   }
 
   if (is.numeric(x$basis)) {

R/equiv.R

@@ -105,14 +105,14 @@
 #' ##
 #' ## Call:
 #' ## equiv_mean_extremum(mean_qual = 100, sd_qual = 5.5, n_sample = 6,
-#' ##                     alpha = 0.01, modcv = TRUE)
+#' ##     alpha = 0.01, modcv = TRUE)
 #' ##
 #' ## Modified CV used: CV* = 0.0675 ( CV = 0.055 )
 #' ##
 #' ## For alpha = 0.01 and n = 6
 #' ## ( k1 = 3.128346 and k2 = 1.044342 )
-#' ##               Min Individual      Sample Mean
-#' ## Thresholds:         78.88367         92.95069
+#' ##                   Min Individual   Sample Mean
+#' ##      Thresholds:    78.88367        92.95069
 #'
 #' @seealso
 #' \code{\link{k_equiv}}
@@ -308,12 +308,8 @@ print.equiv_mean_extremum <- function(x, ...) {
   cat("\nCall:\n",
       paste(deparse(x$call), sep = "\n", collapse = "\n"), "\n\n", sep = "")
 
-  printrow <- function(c1, c2, c3) {
-    cat(format(c1, justify = "right", width = 16L, ...),
-        format(c2, justify = "right", width = 16L, ...),
-        format(c3, justify = "right", width = 16L, ...),
-        "\n")
-  }
+  justify <- c("right", "centre", "centre")
+  col_width <- c(16L, 16L, 16L)
 
   if (x$modcv) {
     cat("Modified CV used: CV* =", format(x$cv_star, ...),
@@ -325,13 +321,19 @@ print.equiv_mean_extremum <- function(x, ...) {
        "and n =", format(x$n_sample, ...),
       "\n( k1 =", format(x$k1, ...),
       "and k2 =", format(x$k2, ...), ")\n")
-  printrow("", "Min Individual", "Sample Mean")
+
+  print_row(list("", "Min Individual", "Sample Mean"),
+            justify, col_width, ...)
+
   if (!is.null(x$min_sample)) {
-    printrow("Sample:", x$min_sample, x$mean_sample)
+    print_row(list("Sample:", x$min_sample, x$mean_sample),
+              justify, col_width, ...)
   }
-  printrow("Thresholds:", x$threshold_min_indiv, x$threshold_mean)
+  print_row(list("Thresholds:", x$threshold_min_indiv, x$threshold_mean),
+            justify, col_width, ...)
   if (!is.null(x$result_min_indiv)) {
-    printrow("Equivalency:", x$result_min_indiv, x$result_mean)
+    print_row(list("Equivalency:", x$result_min_indiv, x$result_mean),
+              justify, col_width, ...)
   }
 }
 
@@ -866,32 +868,30 @@ print.equiv_change_mean <- function(x, ...) {
   cat("\nCall:\n",
       paste(deparse(x$call), sep = "\n", collapse = "\n"), "\n\n", sep = "")
 
-  printrow <- function(c1, c2, c3) {
-    cat(format(c1, justify = "right", width = 16L, ...),
-        format(c2, justify = "centre", width = 16L, ...),
-        format(c3, justify = "centre", width = 16L, ...),
-        "\n")
-  }
-
-  printrow2 <- function(c1, c2) {
-    cat(format(c1, justify = "right", width = 16L, ...),
-        format(c2, justify = "centre", width = 32L, ...),
-        "\n")
-  }
-
   cat("For alpha =", format(x$alpha, ...), "\n")
 
   if (x$modcv) {
     cat("Modified CV used\n")
   }
 
-  printrow("", "Qualification", "Sample")
-  printrow("Number", format(x$n_qual, ...), format(x$n_sample, ...))
-  printrow("Mean", format(x$mean_qual, ...), format(x$mean_sample, ...))
-  printrow("SD", format(x$sd_qual, ...), format(x$sd_sample, ...))
-  printrow2("Result", x$result)
-  printrow2("Passing Range", paste0(format(x$threshold[1], ...),
-                                    " to ",
-                                    format(x$threshold[2], ...))
-  )
+  justify3 <- c("right", "centre", "centre")
+  width3 <- c(16L, 16L, 16L)
+
+  justify2 <- c("right", "centre")
+  width2 <- c(16L, 32L)
+
+  print_row(list("", "Qualification", "Sample"),
+            justify3, width3, ...)
+  print_row(list("Number", x$n_qual, x$n_sample),
+            justify3, width3, ...)
+  print_row(list("Mean", x$mean_qual, x$mean_sample),
+            justify3, width3, ...)
+  print_row(list("SD", x$sd_qual, x$sd_sample),
+            justify3, width3, ...)
+  print_row(list("Result", x$result),
+            justify2, width2, ...)
+  print_row(list("Passing Range", paste0(format(x$threshold[1], ...),
+                                      " to ",
+                                      format(x$threshold[2], ...))),
+            justify2, width2, ...)
 }

R/levene.R

@@ -46,6 +46,20 @@
 #' Guideline for Characterization of Structural Materials,” SAE International,
 #' CMH-17-1G, Mar. 2012.
 #'
+#' @examples
+#' library(dplyr)
+#'
+#' carbon.fabric.2 %>%
+#'   filter(test == "FC") %>%
+#'   levene_test(strength, condition)
+#' ##
+#' ## Call:
+#' ## levene_test(data = ., x = strength, groups = condition)
+#' ##
+#' ## n = 91          k = 5
+#' ## F = 3.883818    p-value = 0.00600518
+#' ## Conclusion: Samples have unequal variance ( alpha = 0.05 )
+#'
 #' @importFrom rlang enquo eval_tidy
 #' @importFrom stats var.test median pf
 #'
@@ -193,18 +207,24 @@ glance.levene <- function(x, ...) {  # nolint
 print.levene <- function(x, ...) {
   cat("\nCall:\n",
       paste(deparse(x$call), sep = "\n", collapse = "\n"), "\n\n", sep = "")
-  cat("n = ", x$n, "\tk = ", x$k, "\n")
+
+  justify <- c("left", "left")
+  width <- c(16L, 16L)
+
+  print_row_equal(list("n", x$n, "k", x$k),
+            justify, width, ...)
 
   if (x$modcv == TRUE) {
     cat("Modified CV Approach Used", "\n")
   }
 
-  cat("F = ", x$f, "\tp-value = ", x$p, "\n")
+  print_row_equal(list("F", x$f, "p-value", x$p),
+            justify, width, ...)
   if (x$reject_equal_variance) {
-    cat("Conclusion: Samples have unequal variance ( alpha=",
+    cat("Conclusion: Samples have unequal variance ( alpha =",
         x$alpha, ")\n\n")
   } else {
-    cat("Conclusion: Samples have equal variances ( alpha=",
+    cat("Conclusion: Samples have equal variances ( alpha =",
         x$alpha, ")\n\n")
   }
 }

R/mnr.R

@@ -273,20 +273,20 @@ augment.mnr <- function(x, data = x$data, ...) {  # nolint
 print.mnr <- function(x, ...) {
   cat("\nCall:\n",
       paste(deparse(x$call), sep = "\n", collapse = "\n"), "\n\n", sep = "")
-  cat("MNR = ", x$mnr, " ( critical value = ", x$crit, ")\n\n")
+  cat("MNR =", x$mnr, " ( critical value =", x$crit, ")\n\n")
   if (nrow(x$outliers) == 0) {
     cat("No outliers detected\n\n")
   } else {
     cat("Outliers:\n")
 
-    col_width <- max(nchar(as.character(x$outliers[["index"]])), 5) + 2
-    cat(format("Index", width = col_width, justify = "right"))
-    cat("  ")
-    cat("Value\n")
+    justify <- c("right", "left", "left")
+    width <- c(8L, 2L, 16L)
+
+    print_row(list("Index", " ", "Value"), justify, width, ...)
+
     for (j in seq(along.with = x$outliers$index)) {
-      cat(format(x$outliers[["index"]][j], width = col_width))
-      cat("  ")
-      cat(x$outliers[["value"]][j], "\n")
+      print_row(list(x$outliers[["index"]][j], " ", x$outliers[["value"]][j]),
+                justify, width, ...)
     }
 
   }

R/util.R

@@ -0,0 +1,29 @@
+
+print_row <- function(content, justify, width, ...) {
+  # content must be a list, justify and width must be vectors
+  for (i in seq_along(content)) {
+    cat(format(
+      format(content[[i]], ...),
+      justify = justify[i],
+      width = width[i],
+      ...))
+  }
+  cat("\n")
+}
+
+print_row_equal <- function(content, justify, column_width, ...) {
+  # content must be a list with the content:
+  # (var_name1, var1, var_name2, var2, etc)
+  # the length of content will be twice the length of the other vectors
+  for (i in seq_along(justify)) {
+    cat(format(
+      paste0(content[[2 * i - 1]],
+             " = ",
+             format(content[[2 * i]], ...)
+             ),
+      justify = justify[i],
+      width = column_width[i],
+      ...))
+  }
+  cat("\n")
+}