diff --git a/NEWS.md b/NEWS.md
index 75a66ff..c40fa1c 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,138 +1,142 @@
-# corrr (development version)
-
-# corrr 0.4.4
-
-- Make `colpair_map()` more robust to input column names, with the exception of ".data" and ".env" (@jameslairdsmith, #131).
-
-- `correlate()` now removes non-numeric columns from data frame inputs (@thisisdaryn, #139).
-
-- `autoplot.cor_df()` method have been added for quick generation of correlation chart.
-
-- `network_plot()` now allows the user the option to map the color range to the range of correlations that are in the input `rdf` (@thisisdaryn, #158). 
-
-# corrr 0.4.3
-
-- Fix EOL issues and class attribute (@krlmlr, #93 and #90)
-
-- Handle correlation of exactly zero or 1 in `network_plot()` (@s-scherrer, #89)
-
-- Add `.order` argument to `rplot()` with options "default" and "alphabet" plus improved documentation (@mattwarkentin, #99 and @thisisdaryn, #114)
-
-- Make `network_plot()` more robust, for example to highly correlated data (@thisisdaryn, #107)
-
-- New `colpair_map()` allows for column comparisons using the values returned by an arbitrary function (@jameslairdsmith, #94).
-
-- `correlate()` now works with single-column data.frames and numeric vectors (@antoine-sachet, #122). Note the `diagonal` argument is ignored in these 2 cases.
-  
-- `network_plot()` now works with `cor_df` objects with only 1 or 2 columns (@antoine-sachet, #122)
-
-- The first column of a `cor_df` object is now named "term". Previously it was named "rowname" (@thisisdaryn, #117).
-
-# corrr 0.4.2
-
-- Updates to work with tibble 3.0.0  and dplyr 1.0.0
-
-# corrr 0.4.1
-
-- Updates maintainer
-
-# corrr 0.4.0
-
-- Adds `remove.dups` argument to `stretch()`.  It removes duplicates with out removing all NAs (#57)
-
-- Adds `dice()` function, wraps `focus(x,..., mirror = TRUE)` (#64)
-
-- Adds `retract()` function, opposite of `stretch()` (#65)
-
-- Improves `correlate()` for database backed tables
-
-- Fixes compatibility issues with `dplyr` 
-
-# corrr 0.3.2
-
-- Improves support for `tbl_sql()` objects
-
-- Switches correlation calculation for `tbl_spark()` tables to `sparklyr::ml_corr()`
-
-- Adds package level doc (@jsta, #66)
-
-- Fixes typo on error message (@jsta)
-
-- Removes Database vignette. Plan to re-add later on (#76)
-
-- Minor updates to Using corrr vignette
-
-# corrr 0.3.1
-
-- Fixes test and CRAN issues by removing `Ops.cor_df()`. 
-
-- Designates Edgar Ruiz as the new package maintainer
-
-# corrr 0.3.0
-
-## Small breaking changes
-
-The `diagonal` argument of `as_matrix` and `as_matrix.cor_df` is now an optional argument rather than set to `1` by default [#52](https://github.com/tidymodels/corrr/issues/52)
-
-## New Functions
-
-- `as_cordf` will coerce lists or matrices into correlation data frames if possible.
-- `focus_if` enables conditional variable selection.
-
-## New Functionality
-
-- Can use arithmetic operators (e.g., `+` or `-`) with correlation data frames.
-- Plotting functions (`rplot` and `network_plot`) will attempt to coerce objects to a correlation data frame (via `as_cordf`) if needed, making it possible to directly use these functions with other square-matrix-like objects.
-- `repel` option added to `network_plot` (default = `TRUE`).
-- `curved` option added to `network_plot` (default = `TRUE`).
-- `correlate()` now prints a message about the `method` and `use` parameters. Can be silenced with `quiet = TRUE`.
-- `correlate()` now supports data frame with a SQL back-end (`tbl_sql`)
-
-## Fixes
-
-- When `legend = TRUE` (now the default setting), `rplot` and `network_plot` generate a single, unlabeled legend referring to the size of the correlations.
-
-## Other
-
-- `correlate()` is now an S3 method so that it can adapt to `x`'s object type.
-
-- During the development of this version, ggplot v2.2.0 was released. Many changes in the plotting functions have been made to handle new features in the updated version of ggplot2.
-
-- Improvements to the package folder structure
-
-# corrr 0.2.1
-
-## New Functionality
-
-- Can keep leading zeros when using `fashion()` with new argument `leading_zeros = TRUE`.
-- New optional arguments added to plotting functions, `network_plot()` and `rplot()`:
-  - `legend` to display a legend mapping correlations to size and colour.
-  - `colours` (or `colors`) to change colours in plot.
-
-## Fixes
-
-- `network_plot()` no longer plots wrong colours if only positive correlations are included.
-- Colour scheme for `network_plot()` changed to match `rplot()`.
-- Other bug fixes.
-
-# corrr 0.2.0
-
-## New Functions
-
-- `network_plot()` the correlations.
-- `focus_()` for standard evaluation version of `focus()`.
-
-## New Functionality
-
-- `fashion()` will now attempt to work on any object (not just `cor_df`), making it useful for printing any data frame, matrix, vector, etc.
-- `print_cor` argument added to `rplot()` to overlay the correlations as text.
-
-## Other
-
-- `na_omit` argument in `stretch()` changed to `na.rm` to match `gather_()`.
-- Bug fixes.
-- Improvements.
-
-# corrr 0.1.0
-
-- First corrr release!
+# corrr (development version)
+
+- `rearrange(absolute = TRUE)` works again (@jmbarbone, #167)
+
+- Extra `cor_df` classes are no longer appended to `cor_df` objects (@jmbarbone, #168)
+
+# corrr 0.4.4
+
+- Make `colpair_map()` more robust to input column names, with the exception of ".data" and ".env" (@jameslairdsmith, #131).
+
+- `correlate()` now removes non-numeric columns from data frame inputs (@thisisdaryn, #139).
+
+- `autoplot.cor_df()` method have been added for quick generation of correlation chart.
+
+- `network_plot()` now allows the user the option to map the color range to the range of correlations that are in the input `rdf` (@thisisdaryn, #158). 
+
+# corrr 0.4.3
+
+- Fix EOL issues and class attribute (@krlmlr, #93 and #90)
+
+- Handle correlation of exactly zero or 1 in `network_plot()` (@s-scherrer, #89)
+
+- Add `.order` argument to `rplot()` with options "default" and "alphabet" plus improved documentation (@mattwarkentin, #99 and @thisisdaryn, #114)
+
+- Make `network_plot()` more robust, for example to highly correlated data (@thisisdaryn, #107)
+
+- New `colpair_map()` allows for column comparisons using the values returned by an arbitrary function (@jameslairdsmith, #94).
+
+- `correlate()` now works with single-column data.frames and numeric vectors (@antoine-sachet, #122). Note the `diagonal` argument is ignored in these 2 cases.
+  
+- `network_plot()` now works with `cor_df` objects with only 1 or 2 columns (@antoine-sachet, #122)
+
+- The first column of a `cor_df` object is now named "term". Previously it was named "rowname" (@thisisdaryn, #117).
+
+# corrr 0.4.2
+
+- Updates to work with tibble 3.0.0  and dplyr 1.0.0
+
+# corrr 0.4.1
+
+- Updates maintainer
+
+# corrr 0.4.0
+
+- Adds `remove.dups` argument to `stretch()`.  It removes duplicates with out removing all NAs (#57)
+
+- Adds `dice()` function, wraps `focus(x,..., mirror = TRUE)` (#64)
+
+- Adds `retract()` function, opposite of `stretch()` (#65)
+
+- Improves `correlate()` for database backed tables
+
+- Fixes compatibility issues with `dplyr` 
+
+# corrr 0.3.2
+
+- Improves support for `tbl_sql()` objects
+
+- Switches correlation calculation for `tbl_spark()` tables to `sparklyr::ml_corr()`
+
+- Adds package level doc (@jsta, #66)
+
+- Fixes typo on error message (@jsta)
+
+- Removes Database vignette. Plan to re-add later on (#76)
+
+- Minor updates to Using corrr vignette
+
+# corrr 0.3.1
+
+- Fixes test and CRAN issues by removing `Ops.cor_df()`. 
+
+- Designates Edgar Ruiz as the new package maintainer
+
+# corrr 0.3.0
+
+## Small breaking changes
+
+The `diagonal` argument of `as_matrix` and `as_matrix.cor_df` is now an optional argument rather than set to `1` by default [#52](https://github.com/tidymodels/corrr/issues/52)
+
+## New Functions
+
+- `as_cordf` will coerce lists or matrices into correlation data frames if possible.
+- `focus_if` enables conditional variable selection.
+
+## New Functionality
+
+- Can use arithmetic operators (e.g., `+` or `-`) with correlation data frames.
+- Plotting functions (`rplot` and `network_plot`) will attempt to coerce objects to a correlation data frame (via `as_cordf`) if needed, making it possible to directly use these functions with other square-matrix-like objects.
+- `repel` option added to `network_plot` (default = `TRUE`).
+- `curved` option added to `network_plot` (default = `TRUE`).
+- `correlate()` now prints a message about the `method` and `use` parameters. Can be silenced with `quiet = TRUE`.
+- `correlate()` now supports data frame with a SQL back-end (`tbl_sql`)
+
+## Fixes
+
+- When `legend = TRUE` (now the default setting), `rplot` and `network_plot` generate a single, unlabeled legend referring to the size of the correlations.
+
+## Other
+
+- `correlate()` is now an S3 method so that it can adapt to `x`'s object type.
+
+- During the development of this version, ggplot v2.2.0 was released. Many changes in the plotting functions have been made to handle new features in the updated version of ggplot2.
+
+- Improvements to the package folder structure
+
+# corrr 0.2.1
+
+## New Functionality
+
+- Can keep leading zeros when using `fashion()` with new argument `leading_zeros = TRUE`.
+- New optional arguments added to plotting functions, `network_plot()` and `rplot()`:
+  - `legend` to display a legend mapping correlations to size and colour.
+  - `colours` (or `colors`) to change colours in plot.
+
+## Fixes
+
+- `network_plot()` no longer plots wrong colours if only positive correlations are included.
+- Colour scheme for `network_plot()` changed to match `rplot()`.
+- Other bug fixes.
+
+# corrr 0.2.0
+
+## New Functions
+
+- `network_plot()` the correlations.
+- `focus_()` for standard evaluation version of `focus()`.
+
+## New Functionality
+
+- `fashion()` will now attempt to work on any object (not just `cor_df`), making it useful for printing any data frame, matrix, vector, etc.
+- `print_cor` argument added to `rplot()` to overlay the correlations as text.
+
+## Other
+
+- `na_omit` argument in `stretch()` changed to `na.rm` to match `gather_()`.
+- Bug fixes.
+- Improvements.
+
+# corrr 0.1.0
+
+- First corrr release!
diff --git a/R/cor_df.R b/R/cor_df.R
index ad45fdb..8e54efa 100644
--- a/R/cor_df.R
+++ b/R/cor_df.R
@@ -1,421 +1,423 @@
-# Utility --------------------------------------------------------------
-
-#' @export
-as_matrix.cor_df <- function(x, diagonal) {
-
-  # Separate term names
-  row_name <- x$term
-  x <- x[colnames(x) != "term"]
-  # Convert to matrix and set rownames
-  class(x) <- "data.frame"
-  x <- as.matrix(x)
-  # Reset diagonal
-  if (!missing(diagonal)) diag(x) <- diagonal
-  rownames(x) <- row_name
-  x
-}
-
-# Internal --------------------------------------------------------------------
-
-#' @export
-shave.cor_df <- function(x, upper = TRUE) {
-
-  # Separate term names
-  row_name <- x$term
-  x <- x[colnames(x) != "term"]
-
-  # Remove upper matrix
-  if (upper) {
-    x[upper.tri(x)] <- NA
-  } else {
-    x[lower.tri(x)] <- NA
-  }
-
-  # Reappend terms and class
-  new_cordf(x, row_name)
-}
-
-#' @export
-rearrange.cor_df <- function(x, method = "PCA", absolute = TRUE) {
-
-  # Convert to original matrix
-  m <- as_matrix(x, diagonal = 1)
-
-  if (absolute) abs(m)
-
-  if (method %in% c("BEA", "BEA_TSP", "PCA", "PCA_angle")) {
-    ord <- seriation::seriate(m, method = method)
-  } else {
-    ord <- seriation::seriate(dist(m), method = method)
-  }
-
-  ord <- seriation::get_order(ord)
-
-  # Arrange and return matrix
-  # "c(1, 1 + ..." to handle term column
-  x <- x[ord, c(1, 1 + ord)]
-  new_cordf(x)
-}
-
-
-# Reshape -----------------------------------------------------------------
-
-#' @export
-focus_.cor_df <- function(x, ..., .dots = NULL, mirror = FALSE) {
-  vars <- enquos(...)
-  row_name <- x$term
-  if (length(vars) > 0) {
-    x <- dplyr::select(x, !!!vars)
-  } else {
-    x <- dplyr::select(x, .dots)
-  }
-  # Get selected column names and
-  # append back term names if necessary
-  vars <- colnames(x)
-  if ("term" %in% vars) {
-    vars <- vars[vars != "term"]
-  } else {
-    x <- first_col(x, row_name)
-  }
-
-  # Exclude these or others from the rows
-  vars <- x$term %in% vars
-  if (mirror) {
-    x <- new_cordf(x[vars, ])
-  } else {
-    x <- x[!vars, ]
-  }
-  x
-}
-
-#' @export
-focus_if.cor_df <- function(x, .predicate, ..., mirror = FALSE) {
-
-  # Identify which variables to keep
-  to_keep <- map_lgl(
-    x[colnames(x) != "term"],
-    .predicate, ...
-  )
-
-  to_keep <- names(to_keep)[!is.na(to_keep) & to_keep]
-
-  if (!length(to_keep)) {
-    rlang::abort("No variables were TRUE given the function.")
-  }
-  # Create the network plot
-  focus_(x, .dots = to_keep, mirror = mirror)
-}
-
-# Output --------------------------------------------------------------------
-
-#' @export
-rplot.cor_df <- function(rdf,
-                         legend = TRUE,
-                         shape = 16,
-                         colours = c("indianred2", "white", "skyblue1"),
-                         print_cor = FALSE,
-                         colors,
-                         .order = c("default", "alphabet")) {
-  .order <- match.arg(.order)
-
-  if (!missing(colors)) {
-    colours <- colors
-  }
-
-  # Store order for factoring the variables
-  row_order <- rdf$term
-
-  # Convert data to relevant format for plotting
-  pd <- stretch(rdf, na.rm = TRUE)
-  pd$size <- abs(pd$r)
-  pd$label <- as.character(fashion(pd$r))
-
-  if (.order == "default") {
-    pd$x <- factor(pd$x, levels = row_order)
-    pd$y <- factor(pd$y, levels = rev(row_order))
-  }
-
-  plot_ <- list(
-    # Geoms
-    geom_point(shape = shape),
-    if (print_cor) geom_text(color = "black", size = 3, show.legend = FALSE),
-    scale_colour_gradientn(limits = c(-1, 1), colors = colours),
-    # Theme, labels, and legends
-    theme_classic(),
-    labs(x = "", y = ""),
-    guides(size = "none", alpha = "none"),
-    if (legend) labs(colour = NULL),
-    if (!legend) theme(legend.position = "none")
-  )
-
-  ggplot(pd, aes_string(
-    x = "x", y = "y", color = "r",
-    size = "size", alpha = "size",
-    label = "label"
-  )) +
-    plot_
-
-  #   # plot
-  #   ggplot(aes_string(x = "x", y = "y", color = "r",
-  #                                       size = "size", alpha = "size",
-  #                                       label = "label")) +
-  #   geom_point(shape = shape) +
-  #   scale_colour_gradientn(limits = c(-1, 1), colors = colours) +
-  #   labs(x = "", y ="") +
-  #   theme_classic()
-  #
-  # if (print_cor) {
-  #   p <- p + geom_text(color = "black", size = 3, show.legend = FALSE)
-  # }
-  #
-  # if (!legend) {
-  #   p <- p + theme(legend.position = "none")
-  # }
-  #
-  # p
-}
-
-#' @export
-network_plot.cor_df <- function(rdf,
-                                min_cor = .30,
-                                legend = c("full", "range", "none"),
-                                colours = c("indianred2", "white", "skyblue1"),
-                                repel = TRUE,
-                                curved = TRUE,
-                                colors) {
-  legend <- rlang::arg_match(legend)
-
-  if (min_cor < 0 || min_cor > 1) {
-    rlang::abort("min_cor must be a value ranging from zero to one.")
-  }
-
-  if (!missing(colors)) {
-    colours <- colors
-  }
-
-  rdf <- as_matrix(rdf, diagonal = 1)
-  distance <- 1 - abs(rdf)
-
-  points <- if (ncol(rdf) == 1) {
-    # 1 var: a single central point
-    matrix(c(0, 0), ncol = 2, dimnames = list(colnames(rdf)))
-  } else if (ncol(rdf) == 2) {
-    # 2 vars: 2 opposing points
-    matrix(c(0, -0.1, 0, 0.1), ncol = 2, dimnames = list(colnames(rdf)))
-  } else {
-    # More than 2 vars: multidimensional scaling to obtain x and y coordinates for points.
-    suppressWarnings(stats::cmdscale(distance, k = 2))
-  }
-
-  if (ncol(points) < 2) {
-    cont_flag <- FALSE
-    shift_matrix <- matrix(1,
-      nrow = nrow(rdf),
-      ncol = ncol(rdf)
-    )
-    diag(shift_matrix) <- 0
-
-    for (shift in 10^(-6:-1)) {
-      shifted_distance <- distance + shift * shift_matrix
-      points <- suppressWarnings(stats::cmdscale(shifted_distance))
-
-      if (ncol(points) > 1) {
-        cont_flag <- TRUE
-        break
-      }
-    }
-
-    if (!cont_flag) rlang::abort("Can't generate network plot.\nAttempts to generate 2-d coordinates failed.")
-
-    rlang::warn("Plot coordinates derived from correlation matrix have dimension < 2.\nPairwise distances have been adjusted to facilitate plotting.")
-  }
-
-
-
-  points <- data.frame(points)
-  colnames(points) <- c("x", "y")
-  points$id <- rownames(points)
-
-  # Create a proximity matrix of the paths to be plotted.
-  proximity <- abs(rdf)
-  proximity[upper.tri(proximity)] <- NA
-  diag(proximity) <- NA
-  proximity[proximity < min_cor] <- NA
-
-  # Produce a data frame of data needed for plotting the paths.
-  n_paths <- sum(!is.na(proximity))
-  paths <- data.frame(matrix(nrow = n_paths, ncol = 6))
-  colnames(paths) <- c("x", "y", "xend", "yend", "proximity", "sign")
-  path <- 1
-  for (row in 1:nrow(proximity)) {
-    for (col in 1:ncol(proximity)) {
-      path_proximity <- proximity[row, col]
-      if (!is.na(path_proximity)) {
-        path_sign <- sign(rdf[row, col])
-        x <- points$x[row]
-        y <- points$y[row]
-        xend <- points$x[col]
-        yend <- points$y[col]
-        paths[path, ] <- c(x, y, xend, yend, path_proximity, path_sign)
-        path <- path + 1
-      }
-    }
-  }
-
-  if(legend %in% c("full", "none")){
-    legend_range = c(-1, 1)
-  }
-  else if(legend == "range"){
-    legend_range = c(min(rdf[row(rdf)!=col(rdf)]),
-                     max(rdf[row(rdf)!=col(rdf)]))
-  }
-  plot_ <- list(
-    # For plotting paths
-    if (curved) {
-      geom_curve(
-        data = paths,
-        aes(
-          x = x, y = y, xend = xend, yend = yend,
-          alpha = proximity, size = proximity,
-          colour = proximity * sign
-        )
-      )
-    },
-    if (!curved) {
-      geom_segment(
-        data = paths,
-        aes(
-          x = x, y = y, xend = xend, yend = yend,
-          alpha = proximity, size = proximity,
-          colour = proximity * sign
-        )
-      )
-    },
-    scale_alpha(limits = c(0, 1)),
-    scale_size(limits = c(0, 1)),
-    scale_colour_gradientn(limits = legend_range, colors = colours),
-    # Plot the points
-    geom_point(
-      data = points,
-      aes(x, y),
-      size = 3, shape = 19, colour = "white"
-    ),
-    # Plot variable labels
-    if (repel) {
-      ggrepel::geom_text_repel(
-        data = points,
-        aes(x, y, label = id),
-        fontface = "bold", size = 5,
-        segment.size = 0.0,
-        segment.color = "white"
-      )
-    },
-    if (!repel) {
-      geom_text(
-        data = points,
-        aes(x, y, label = id),
-        fontface = "bold", size = 5
-      )
-    },
-    # expand the axes to add space for curves
-    expand_limits(
-      x = c(
-        min(points$x) - .1,
-        max(points$x) + .1
-      ),
-      y = c(
-        min(points$y) - .1,
-        max(points$y) + .1
-      )
-    ),
-    # Theme and legends
-    theme_void(),
-    guides(size = "none", alpha = "none"),
-    if (legend != "none") labs(colour = NULL),
-    if (legend == "none") theme(legend.position = "none")
-  )
-
-  ggplot() + plot_
-}
-
-#' Create a correlation matrix from a cor_df object
-#'
-#' This method provides a good first visualization of the correlation matrix.
-#'
-#' @param object A `cor_df` object.
-#' @param ... this argument is ignored.
-#' @inheritParams rearrange
-#' @param triangular Which part of the correlation matrix should be shown?
-#' Must be one of `"upper"`, `"lower"`, or `"full"`, and defaults to `"upper"`.
-#' @param barheight A single, non-negative number. Is passed to
-#' [ggplot2::guide_colourbar()] to determine the height of the guide colorbar.
-#' Defaults to 20, is likely to need manual adjustments.
-#' @param low A single color. Is passed to [ggplot2::scale_fill_gradient2()].
-#' The color of negative correlation. Defaults to `"#B2182B"`.
-#' @param mid A single color. Is passed to [ggplot2::scale_fill_gradient2()].
-#' The color of no correlation. Defaults to `"#F1F1F1"`.
-#' @param high A single color. Is passed to [ggplot2::scale_fill_gradient2()].
-#' The color of the positive correlation. Defaults to `"#2166AC"`.
-#' @return A ggplot object
-#'
-#' @rdname autoplot.cor_df
-#'
-#' @examples
-#' x <- correlate(mtcars)
-#'
-#' autoplot(x)
-#'
-#' autoplot(x, triangular = "lower")
-#'
-#' autoplot(x, triangular = "full")
-#' @export
-autoplot.cor_df <- function(object, ...,
-                            method = "PCA",
-                            triangular = c("upper", "lower", "full"),
-                            barheight = 20,
-                            low = "#B2182B",
-                            mid = "#F1F1F1",
-                            high = "#2166AC") {
-
-  triangular <- rlang::arg_match(triangular)
-
-  object <- rearrange(object, method = method)
-  if (triangular == "upper") {
-    object <- shave(object, upper = FALSE)
-  } else if (triangular == "lower") {
-    object <- shave(object, upper = TRUE)
-  }
-  object <- stretch(object)
-  object <- dplyr::mutate(object, x = factor(x, levels = unique(x)))
-  object <- dplyr::mutate(object, y = factor(y, levels = rev(unique(y))))
-  object <- dplyr::filter(object, !is.na(r))
-
-  res <- ggplot2::ggplot(object, ggplot2::aes(x, y, fill = r)) +
-    ggplot2::geom_tile(color = "white", size = 0.5) +
-    ggplot2::scale_fill_gradient2(
-      low = low, mid = mid, high = high, breaks = seq(-1, 1, by = 0.2),
-      limits = c(-1, 1)
-    ) +
-    ggplot2::theme_minimal() +
-    ggplot2::theme(
-      panel.grid = ggplot2::element_blank(),
-      axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1)
-    ) +
-    ggplot2::coord_fixed() +
-    ggplot2::labs(x = NULL, y = NULL, fill = NULL) +
-    ggplot2::guides(fill = ggplot2::guide_colourbar(barheight = barheight))
-
-  if (triangular == "upper") {
-    res <- res +
-      scale_x_discrete(position = "top") +
-      ggplot2::theme(
-        axis.text.x = ggplot2::element_text(angle = 315, vjust = 1, hjust = 1)
-      )
-  }
-
-  res
-}
-
-#' @importFrom ggplot2 autoplot
-#' @export
-ggplot2::autoplot
+# Utility --------------------------------------------------------------
+
+#' @export
+as_matrix.cor_df <- function(x, diagonal) {
+
+  # Separate term names
+  row_name <- x$term
+  x <- x[colnames(x) != "term"]
+  # Convert to matrix and set rownames
+  class(x) <- "data.frame"
+  x <- as.matrix(x)
+  # Reset diagonal
+  if (!missing(diagonal)) diag(x) <- diagonal
+  rownames(x) <- row_name
+  x
+}
+
+# Internal --------------------------------------------------------------------
+
+#' @export
+shave.cor_df <- function(x, upper = TRUE) {
+
+  # Separate term names
+  row_name <- x$term
+  x <- x[colnames(x) != "term"]
+
+  # Remove upper matrix
+  if (upper) {
+    x[upper.tri(x)] <- NA
+  } else {
+    x[lower.tri(x)] <- NA
+  }
+
+  # Reappend terms and class
+  new_cordf(x, row_name)
+}
+
+#' @export
+rearrange.cor_df <- function(x, method = "PCA", absolute = TRUE) {
+
+  # Convert to original matrix
+  m <- as_matrix(x, diagonal = 1)
+
+  if (absolute) {
+    m <- abs(m)
+  }
+
+  if (method %in% c("BEA", "BEA_TSP", "PCA", "PCA_angle")) {
+    ord <- seriation::seriate(m, method = method)
+  } else {
+    ord <- seriation::seriate(dist(m), method = method)
+  }
+
+  ord <- seriation::get_order(ord)
+
+  # Arrange and return matrix
+  # "c(1, 1 + ..." to handle term column
+  x <- x[ord, c(1, 1 + ord)]
+  new_cordf(x)
+}
+
+
+# Reshape -----------------------------------------------------------------
+
+#' @export
+focus_.cor_df <- function(x, ..., .dots = NULL, mirror = FALSE) {
+  vars <- enquos(...)
+  row_name <- x$term
+  if (length(vars) > 0) {
+    x <- dplyr::select(x, !!!vars)
+  } else {
+    x <- dplyr::select(x, .dots)
+  }
+  # Get selected column names and
+  # append back term names if necessary
+  vars <- colnames(x)
+  if ("term" %in% vars) {
+    vars <- vars[vars != "term"]
+  } else {
+    x <- first_col(x, row_name)
+  }
+
+  # Exclude these or others from the rows
+  vars <- x$term %in% vars
+  if (mirror) {
+    x <- new_cordf(x[vars, ])
+  } else {
+    x <- x[!vars, ]
+  }
+  x
+}
+
+#' @export
+focus_if.cor_df <- function(x, .predicate, ..., mirror = FALSE) {
+
+  # Identify which variables to keep
+  to_keep <- map_lgl(
+    x[colnames(x) != "term"],
+    .predicate, ...
+  )
+
+  to_keep <- names(to_keep)[!is.na(to_keep) & to_keep]
+
+  if (!length(to_keep)) {
+    rlang::abort("No variables were TRUE given the function.")
+  }
+  # Create the network plot
+  focus_(x, .dots = to_keep, mirror = mirror)
+}
+
+# Output --------------------------------------------------------------------
+
+#' @export
+rplot.cor_df <- function(rdf,
+                         legend = TRUE,
+                         shape = 16,
+                         colours = c("indianred2", "white", "skyblue1"),
+                         print_cor = FALSE,
+                         colors,
+                         .order = c("default", "alphabet")) {
+  .order <- match.arg(.order)
+
+  if (!missing(colors)) {
+    colours <- colors
+  }
+
+  # Store order for factoring the variables
+  row_order <- rdf$term
+
+  # Convert data to relevant format for plotting
+  pd <- stretch(rdf, na.rm = TRUE)
+  pd$size <- abs(pd$r)
+  pd$label <- as.character(fashion(pd$r))
+
+  if (.order == "default") {
+    pd$x <- factor(pd$x, levels = row_order)
+    pd$y <- factor(pd$y, levels = rev(row_order))
+  }
+
+  plot_ <- list(
+    # Geoms
+    geom_point(shape = shape),
+    if (print_cor) geom_text(color = "black", size = 3, show.legend = FALSE),
+    scale_colour_gradientn(limits = c(-1, 1), colors = colours),
+    # Theme, labels, and legends
+    theme_classic(),
+    labs(x = "", y = ""),
+    guides(size = "none", alpha = "none"),
+    if (legend) labs(colour = NULL),
+    if (!legend) theme(legend.position = "none")
+  )
+
+  ggplot(pd, aes_string(
+    x = "x", y = "y", color = "r",
+    size = "size", alpha = "size",
+    label = "label"
+  )) +
+    plot_
+
+  #   # plot
+  #   ggplot(aes_string(x = "x", y = "y", color = "r",
+  #                                       size = "size", alpha = "size",
+  #                                       label = "label")) +
+  #   geom_point(shape = shape) +
+  #   scale_colour_gradientn(limits = c(-1, 1), colors = colours) +
+  #   labs(x = "", y ="") +
+  #   theme_classic()
+  #
+  # if (print_cor) {
+  #   p <- p + geom_text(color = "black", size = 3, show.legend = FALSE)
+  # }
+  #
+  # if (!legend) {
+  #   p <- p + theme(legend.position = "none")
+  # }
+  #
+  # p
+}
+
+#' @export
+network_plot.cor_df <- function(rdf,
+                                min_cor = .30,
+                                legend = c("full", "range", "none"),
+                                colours = c("indianred2", "white", "skyblue1"),
+                                repel = TRUE,
+                                curved = TRUE,
+                                colors) {
+  legend <- rlang::arg_match(legend)
+
+  if (min_cor < 0 || min_cor > 1) {
+    rlang::abort("min_cor must be a value ranging from zero to one.")
+  }
+
+  if (!missing(colors)) {
+    colours <- colors
+  }
+
+  rdf <- as_matrix(rdf, diagonal = 1)
+  distance <- 1 - abs(rdf)
+
+  points <- if (ncol(rdf) == 1) {
+    # 1 var: a single central point
+    matrix(c(0, 0), ncol = 2, dimnames = list(colnames(rdf)))
+  } else if (ncol(rdf) == 2) {
+    # 2 vars: 2 opposing points
+    matrix(c(0, -0.1, 0, 0.1), ncol = 2, dimnames = list(colnames(rdf)))
+  } else {
+    # More than 2 vars: multidimensional scaling to obtain x and y coordinates for points.
+    suppressWarnings(stats::cmdscale(distance, k = 2))
+  }
+
+  if (ncol(points) < 2) {
+    cont_flag <- FALSE
+    shift_matrix <- matrix(1,
+      nrow = nrow(rdf),
+      ncol = ncol(rdf)
+    )
+    diag(shift_matrix) <- 0
+
+    for (shift in 10^(-6:-1)) {
+      shifted_distance <- distance + shift * shift_matrix
+      points <- suppressWarnings(stats::cmdscale(shifted_distance))
+
+      if (ncol(points) > 1) {
+        cont_flag <- TRUE
+        break
+      }
+    }
+
+    if (!cont_flag) rlang::abort("Can't generate network plot.\nAttempts to generate 2-d coordinates failed.")
+
+    rlang::warn("Plot coordinates derived from correlation matrix have dimension < 2.\nPairwise distances have been adjusted to facilitate plotting.")
+  }
+
+
+
+  points <- data.frame(points)
+  colnames(points) <- c("x", "y")
+  points$id <- rownames(points)
+
+  # Create a proximity matrix of the paths to be plotted.
+  proximity <- abs(rdf)
+  proximity[upper.tri(proximity)] <- NA
+  diag(proximity) <- NA
+  proximity[proximity < min_cor] <- NA
+
+  # Produce a data frame of data needed for plotting the paths.
+  n_paths <- sum(!is.na(proximity))
+  paths <- data.frame(matrix(nrow = n_paths, ncol = 6))
+  colnames(paths) <- c("x", "y", "xend", "yend", "proximity", "sign")
+  path <- 1
+  for (row in 1:nrow(proximity)) {
+    for (col in 1:ncol(proximity)) {
+      path_proximity <- proximity[row, col]
+      if (!is.na(path_proximity)) {
+        path_sign <- sign(rdf[row, col])
+        x <- points$x[row]
+        y <- points$y[row]
+        xend <- points$x[col]
+        yend <- points$y[col]
+        paths[path, ] <- c(x, y, xend, yend, path_proximity, path_sign)
+        path <- path + 1
+      }
+    }
+  }
+
+  if(legend %in% c("full", "none")){
+    legend_range = c(-1, 1)
+  }
+  else if(legend == "range"){
+    legend_range = c(min(rdf[row(rdf)!=col(rdf)]),
+                     max(rdf[row(rdf)!=col(rdf)]))
+  }
+  plot_ <- list(
+    # For plotting paths
+    if (curved) {
+      geom_curve(
+        data = paths,
+        aes(
+          x = x, y = y, xend = xend, yend = yend,
+          alpha = proximity, size = proximity,
+          colour = proximity * sign
+        )
+      )
+    },
+    if (!curved) {
+      geom_segment(
+        data = paths,
+        aes(
+          x = x, y = y, xend = xend, yend = yend,
+          alpha = proximity, size = proximity,
+          colour = proximity * sign
+        )
+      )
+    },
+    scale_alpha(limits = c(0, 1)),
+    scale_size(limits = c(0, 1)),
+    scale_colour_gradientn(limits = legend_range, colors = colours),
+    # Plot the points
+    geom_point(
+      data = points,
+      aes(x, y),
+      size = 3, shape = 19, colour = "white"
+    ),
+    # Plot variable labels
+    if (repel) {
+      ggrepel::geom_text_repel(
+        data = points,
+        aes(x, y, label = id),
+        fontface = "bold", size = 5,
+        segment.size = 0.0,
+        segment.color = "white"
+      )
+    },
+    if (!repel) {
+      geom_text(
+        data = points,
+        aes(x, y, label = id),
+        fontface = "bold", size = 5
+      )
+    },
+    # expand the axes to add space for curves
+    expand_limits(
+      x = c(
+        min(points$x) - .1,
+        max(points$x) + .1
+      ),
+      y = c(
+        min(points$y) - .1,
+        max(points$y) + .1
+      )
+    ),
+    # Theme and legends
+    theme_void(),
+    guides(size = "none", alpha = "none"),
+    if (legend != "none") labs(colour = NULL),
+    if (legend == "none") theme(legend.position = "none")
+  )
+
+  ggplot() + plot_
+}
+
+#' Create a correlation matrix from a cor_df object
+#'
+#' This method provides a good first visualization of the correlation matrix.
+#'
+#' @param object A `cor_df` object.
+#' @param ... this argument is ignored.
+#' @inheritParams rearrange
+#' @param triangular Which part of the correlation matrix should be shown?
+#' Must be one of `"upper"`, `"lower"`, or `"full"`, and defaults to `"upper"`.
+#' @param barheight A single, non-negative number. Is passed to
+#' [ggplot2::guide_colourbar()] to determine the height of the guide colorbar.
+#' Defaults to 20, is likely to need manual adjustments.
+#' @param low A single color. Is passed to [ggplot2::scale_fill_gradient2()].
+#' The color of negative correlation. Defaults to `"#B2182B"`.
+#' @param mid A single color. Is passed to [ggplot2::scale_fill_gradient2()].
+#' The color of no correlation. Defaults to `"#F1F1F1"`.
+#' @param high A single color. Is passed to [ggplot2::scale_fill_gradient2()].
+#' The color of the positive correlation. Defaults to `"#2166AC"`.
+#' @return A ggplot object
+#'
+#' @rdname autoplot.cor_df
+#'
+#' @examples
+#' x <- correlate(mtcars)
+#'
+#' autoplot(x)
+#'
+#' autoplot(x, triangular = "lower")
+#'
+#' autoplot(x, triangular = "full")
+#' @export
+autoplot.cor_df <- function(object, ...,
+                            method = "PCA",
+                            triangular = c("upper", "lower", "full"),
+                            barheight = 20,
+                            low = "#B2182B",
+                            mid = "#F1F1F1",
+                            high = "#2166AC") {
+
+  triangular <- rlang::arg_match(triangular)
+
+  object <- rearrange(object, method = method)
+  if (triangular == "upper") {
+    object <- shave(object, upper = FALSE)
+  } else if (triangular == "lower") {
+    object <- shave(object, upper = TRUE)
+  }
+  object <- stretch(object)
+  object <- dplyr::mutate(object, x = factor(x, levels = unique(x)))
+  object <- dplyr::mutate(object, y = factor(y, levels = rev(unique(y))))
+  object <- dplyr::filter(object, !is.na(r))
+
+  res <- ggplot2::ggplot(object, ggplot2::aes(x, y, fill = r)) +
+    ggplot2::geom_tile(color = "white", size = 0.5) +
+    ggplot2::scale_fill_gradient2(
+      low = low, mid = mid, high = high, breaks = seq(-1, 1, by = 0.2),
+      limits = c(-1, 1)
+    ) +
+    ggplot2::theme_minimal() +
+    ggplot2::theme(
+      panel.grid = ggplot2::element_blank(),
+      axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1)
+    ) +
+    ggplot2::coord_fixed() +
+    ggplot2::labs(x = NULL, y = NULL, fill = NULL) +
+    ggplot2::guides(fill = ggplot2::guide_colourbar(barheight = barheight))
+
+  if (triangular == "upper") {
+    res <- res +
+      scale_x_discrete(position = "top") +
+      ggplot2::theme(
+        axis.text.x = ggplot2::element_text(angle = 315, vjust = 1, hjust = 1)
+      )
+  }
+
+  res
+}
+
+#' @importFrom ggplot2 autoplot
+#' @export
+ggplot2::autoplot
diff --git a/R/utility.R b/R/utility.R
index 9d309bc..74332c4 100644
--- a/R/utility.R
+++ b/R/utility.R
@@ -1,97 +1,101 @@
-#' Coerce lists and matrices to correlation data frames
-#'
-#' A wrapper function to coerce objects in a valid format (such as correlation
-#' matrices created using the base function, \code{\link[stats]{cor}}) into a
-#' correlation data frame.
-#'
-#' @param x A list, data frame or matrix that can be coerced into a correlation
-#'   data frame.
-#' @param diagonal Value (typically numeric or NA) to set the diagonal to
-#' @return A correlation data frame
-#' @export
-#' @examples
-#' x <- cor(mtcars)
-#' as_cordf(x)
-#' as_cordf(x, diagonal = 1)
-as_cordf <- function(x, diagonal = NA) {
-  if (inherits(x, "cor_df")) {
-    rlang::warn("x is already a correlation data frame.")
-    return(x)
-  }
-  x <- as.data.frame(x)
-  row_name <- x$term
-  x <- x[colnames(x) != "term"]
-  rownames(x) <- row_name
-  if (ncol(x) != nrow(x)) {
-    rlang::abort(
-      "Input object x is not a square. ",
-      "The number of columns must be equal to the number of rows."
-    )
-  }
-  if (ncol(x) > 1) diag(x) <- diagonal
-  new_cordf(x, names(x))
-}
-
-new_cordf <- function(x, term = NULL) {
-  if (!is.null(term)) {
-    x <- first_col(x, term)
-  }
-  class(x) <- c("cor_df", class(x))
-  x
-}
-
-#' Add a first column to a data.frame
-#'
-#' Add a first column to a data.frame. This is most commonly used to append a
-#' term column to create a cor_df.
-#'
-#' @param df Data frame
-#' @param ... Values to go into the column
-#' @param var Label for the column, with the default "term"
-#' @export
-#' @examples
-#' first_col(mtcars, 1:nrow(mtcars))
-first_col <- function(df, ..., var = "term") {
-  stopifnot(is.data.frame(df))
-
-  if (tibble::has_name(df, var)) {
-    rlang::abort(paste("There is a column named ", var, " already!"))
-  }
-
-  new_col <- tibble::tibble(...)
-  names(new_col) <- var
-  new_df <- c(new_col, df)
-  dplyr::as_tibble(new_df)
-}
-
-#' Number of pairwise complete cases.
-#'
-#' Compute the number of complete cases in a pairwise fashion for \code{x} (and
-#' \code{y}).
-#'
-#' @inheritParams stats::cor
-#' @return Matrix of pairwise sample sizes (number of complete cases).
-#' @export
-#' @examples
-#' pair_n(mtcars)
-pair_n <- function(x, y = NULL) {
-  if (is.null(y)) y <- x
-  x <- t(!is.na(x)) %*% (!is.na(y))
-  class(x) <- c("n_mat", "matrix")
-  x
-}
-
-#' Convert a correlation data frame to matrix format
-#'
-#' Convert a correlation data frame to original matrix format.
-#'
-#' @param x A correlation data frame. See \code{\link{correlate}} or \code{\link{as_cordf}}.
-#' @inheritParams as_cordf
-#' @return Correlation matrix
-#' @export
-#' @examples
-#' x <- correlate(mtcars)
-#' as_matrix(x)
-as_matrix <- function(x, diagonal) {
-  UseMethod("as_matrix")
-}
+#' Coerce lists and matrices to correlation data frames
+#'
+#' A wrapper function to coerce objects in a valid format (such as correlation
+#' matrices created using the base function, \code{\link[stats]{cor}}) into a
+#' correlation data frame.
+#'
+#' @param x A list, data frame or matrix that can be coerced into a correlation
+#'   data frame.
+#' @param diagonal Value (typically numeric or NA) to set the diagonal to
+#' @return A correlation data frame
+#' @export
+#' @examples
+#' x <- cor(mtcars)
+#' as_cordf(x)
+#' as_cordf(x, diagonal = 1)
+as_cordf <- function(x, diagonal = NA) {
+  if (inherits(x, "cor_df")) {
+    rlang::warn("x is already a correlation data frame.")
+    return(x)
+  }
+  x <- as.data.frame(x)
+  row_name <- x$term
+  x <- x[colnames(x) != "term"]
+  rownames(x) <- row_name
+  if (ncol(x) != nrow(x)) {
+    rlang::abort(
+      "Input object x is not a square. ",
+      "The number of columns must be equal to the number of rows."
+    )
+  }
+  if (ncol(x) > 1) diag(x) <- diagonal
+  new_cordf(x, names(x))
+}
+
+new_cordf <- function(x, term = NULL) {
+  if (!is.null(term)) {
+    x <- first_col(x, term)
+  }
+
+  if (!inherits(x, "cor_df")) {
+    class(x) <- c("cor_df", class(x))
+  }
+
+  x
+}
+
+#' Add a first column to a data.frame
+#'
+#' Add a first column to a data.frame. This is most commonly used to append a
+#' term column to create a cor_df.
+#'
+#' @param df Data frame
+#' @param ... Values to go into the column
+#' @param var Label for the column, with the default "term"
+#' @export
+#' @examples
+#' first_col(mtcars, 1:nrow(mtcars))
+first_col <- function(df, ..., var = "term") {
+  stopifnot(is.data.frame(df))
+
+  if (tibble::has_name(df, var)) {
+    rlang::abort(paste("There is a column named ", var, " already!"))
+  }
+
+  new_col <- tibble::tibble(...)
+  names(new_col) <- var
+  new_df <- c(new_col, df)
+  dplyr::as_tibble(new_df)
+}
+
+#' Number of pairwise complete cases.
+#'
+#' Compute the number of complete cases in a pairwise fashion for \code{x} (and
+#' \code{y}).
+#'
+#' @inheritParams stats::cor
+#' @return Matrix of pairwise sample sizes (number of complete cases).
+#' @export
+#' @examples
+#' pair_n(mtcars)
+pair_n <- function(x, y = NULL) {
+  if (is.null(y)) y <- x
+  x <- t(!is.na(x)) %*% (!is.na(y))
+  class(x) <- c("n_mat", "matrix")
+  x
+}
+
+#' Convert a correlation data frame to matrix format
+#'
+#' Convert a correlation data frame to original matrix format.
+#'
+#' @param x A correlation data frame. See \code{\link{correlate}} or \code{\link{as_cordf}}.
+#' @inheritParams as_cordf
+#' @return Correlation matrix
+#' @export
+#' @examples
+#' x <- correlate(mtcars)
+#' as_matrix(x)
+as_matrix <- function(x, diagonal) {
+  UseMethod("as_matrix")
+}
diff --git a/tests/testthat/test-rearrange.R b/tests/testthat/test-rearrange.R
index 6841331..01d7bc0 100644
--- a/tests/testthat/test-rearrange.R
+++ b/tests/testthat/test-rearrange.R
@@ -1,10 +1,32 @@
-test_that("Rearrange return correct order", {
-  d <- datasets::iris[, 1:4]
-  d[1, 1] <- NA
-  d <- correlate(d)
-
-  expect_equal(
-    colnames(rearrange(d)),
-    c("term", "Petal.Length", "Petal.Width", "Sepal.Length", "Sepal.Width")
-  )
-})
+test_that("Rearrange return correct order", {
+  d <- datasets::iris[, 1:4]
+  d[1, 1] <- NA
+  d <- correlate(d)
+
+  expect_equal(
+    colnames(rearrange(d, absolute = FALSE)),
+    c("term", "Petal.Length", "Petal.Width", "Sepal.Length", "Sepal.Width")
+  )
+})
+
+test_that("rearrange(absolute) works again [#167]", {
+  df <- data.frame(
+    x = 1:10,
+    y = -c(1:10),
+    z = 1:10
+  )
+
+  x <- correlate(df, quiet = TRUE)
+  obj <- rearrange(x, absolute = FALSE)
+  exp <- as_cordf(tibble(
+    term = c("x", "z", "y"),
+    x = c(NA, 1, -1),
+    z = c(1, NA, -1),
+    y = c(-1, -1, NA)
+  ))
+  expect_identical(obj, exp)
+
+  # should not change the order
+  obj <- rearrange(x, absolute = TRUE)
+  expect_identical(obj, x)
+})
diff --git a/tests/testthat/test-utilities.R b/tests/testthat/test-utilities.R
index a358f68..cd4b967 100644
--- a/tests/testthat/test-utilities.R
+++ b/tests/testthat/test-utilities.R
@@ -1,6 +1,12 @@
-test_that("pair_n works", {
-  expect_s3_class(
-    pair_n(1),
-    "matrix"
-  )
-})
+test_that("pair_n works", {
+  expect_s3_class(
+    pair_n(1),
+    "matrix"
+  )
+})
+
+test_that("new_cordf() doesn't append again", {
+  x <- as_cordf(data.frame(a = 1))
+  obj <- new_cordf(x)
+  expect_identical(obj, x)
+})