R/ggplot2.R

In arulesViz: Visualizing Association Rules and Frequent Itemsets

#######################################################################
# arulesViz - Visualizing Association Rules and Frequent Itemsets
# Copyright (C) 2021 Michael Hahsler
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.

scatterplot_ggplot2 <- function(
    x,
    measure = c("support", "confidence"),
    shading = "lift",
    control = NULL,
    ...) {
  control <- c(control, list(...))
  control <- .get_parameters(
    control,
    list(
      main = paste("Scatter plot for", length(x), class(x)),
      colors = default_colors(2),
      jitter = NA,
      engine = "ggplot2"
    )
  )

  colors <- rev(control$colors)
  jitter <- control$jitter

  q <- quality(x)
  q[["order"]] <- as.factor(size(x))
  qnames <- names(q)
  measure <- qnames[pmatch(measure, qnames, duplicates.ok = TRUE)]
  if (!is.null(shading)) {
    shading <- qnames[pmatch(shading, qnames)]
    ### order to reduce overplotting
    o <- order(q[[shading]], decreasing = FALSE)
    q <- q[o, , drop = FALSE]
  }

  # for(i in 1:ncol(q)) {
  #   infin <- is.infinite(q[[i]])
  #   if(any(infin)) {
  #     replinfin <- signif(2 * max(q[[i]][!infin], na.rm = TRUE), 3)
  #     warning("plot: ", colnames(q)[i], " contains infinite values! Replaced by twice the max (", replinfin, ")!", call. = FALSE)
  #     q[[i]][infin] <- replinfin
  #   }
  # }

  ### add x/y-jitter
  jitter <- jitter[1]
  if (is.na(jitter) && any(duplicated(q[, measure]))) {
    message("To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.")
    jitter <- .jitter_default
  }

  if (!is.na(jitter) && jitter > 0) {
    for (m in measure) {
      if (is.numeric(q[[m]])) {
        q[[m]] <- jitter(q[[m]], factor = jitter, amount = 0)
      }
    }
  }

  if (!is.null(shading)) {
    p <-
      ggplot(q, aes(.data[[measure[1]]], y = .data[[measure[2]]], color = .data[[shading]])) +
      geom_point()
    
    if (shading != "order") {
      p <- p + scale_color_gradient(low = colors[1], high = colors[2])
    } else {
      p <- p + scale_color_discrete()
    }
    
  } else {
  
    p <-
      ggplot(q, aes(.data[[measure[1]]], y = .data[[measure[2]]])) +
      geom_point()
  }
  
  p + ggtitle(control$main) + theme_linedraw()
}

matrix_ggplot2 <- function(
    x,
    measure = c("lift"),
    shading = NA,
    control = NULL,
    ...) {
  control <- c(control, list(...))
  control <- .get_parameters(
    control,
    list(
      main = paste("Matrix for", length(x), "rules"),
      colors = default_colors(2),
      reorder = "measure",
      max = 1000,
      engine = "ggplot2"
    )
  )

  colors <- rev(control$colors)

  m <- rules2matrix(x, measure, control$reorder)

  # reverse rows so highest value is in the top-left hand corner
  m <- m[nrow(m):1, ]

  ## print labels
  writeLines("Itemsets in Antecedent (LHS)")
  print(colnames(m))
  writeLines("Itemsets in Consequent (RHS)")
  print(rownames(m))

  dimnames(m) <- list(seq_len(nrow(m)), seq_len(ncol(m)))

  # NOTE: nullify variables used for non-standard evaluation for tidyverse/ggplot2 below
  RHS <- LHS <- NULL

  d <-
    m %>%
    as_tibble() %>%
    dplyr::mutate(RHS = seq_len(nrow(m))) %>%
    pivot_longer(
      cols = -c(RHS),
      names_to = "LHS",
      values_to = measure
    )
  d$LHS <- as.integer(d$LHS)

  ggplot(d, aes(x = LHS, y = RHS, fill = .data[[measure]])) +
    geom_raster() +
    scale_fill_gradient(
      low = colors[1],
      high = colors[2],
      na.value = 0
    ) +
    ggtitle(control$main) +
    scale_x_continuous(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    theme_bw()
}

grouped_matrix_ggplot2 <- function(
    x,
    measure = c("support"),
    shading = "lift",
    control = NULL,
    ...) {
  control <- c(control, list(...))
  control <- .get_parameters(
    control,
    list(
      k = 20,
      aggr.fun = mean,
      rhs_max = 10,
      lhs_label_items = 2,
      col = default_colors(2),
      # max.shading = NA,
      groups = NULL,
      engine = "ggplot2"
    )
  )

  # get the clustering
  if (is.null(control$groups)) {
    gm <-
      rules2groupedMatrix(
        x,
        shading,
        measure,
        k = control$k,
        aggr.fun = control$aggr.fun,
        lhs_label_items = control$lhs_label_items
      )
  } else {
    gm <- control$groups
  }
  control$groupes <- NULL ### for interactive plot


  m <- gm$m
  m2 <- gm$m2

  not_shown_rhs <- 0
  if (nrow(m) > control$rhs_max) {
    not_shown_rhs <- nrow(m) - control$rhs_max
    m <- m[seq_len(control$rhs_max), , drop = FALSE]
    m2 <- m2[seq_len(control$rhs_max), , drop = FALSE]
  }

  # convert to data.frame
  df <- data.frame(
    LHS = rep(ordered(colnames(m), levels = colnames(m)), times = nrow(m)),
    RHS = rep(ordered(rownames(m), levels = rev(rownames(
      m
    ))), each = ncol(m)),
    measure = as.vector(t(m)),
    support = as.vector(t(m2))
  )

  # NULLify for CRAN
  LHS <- RHS <- NULL

  p <-
    ggplot(df, aes(
      x = LHS,
      y = RHS,
      size = support,
      color = measure
    )) +
    geom_point(na.rm = TRUE) +
    scale_color_gradient(low = control$col[2], high = control$col[1]) +
    labs(color = shading) +
    xlab("Items in LHS Groups") +
    ylab(paste(
      "RHS",
      if (not_shown_rhs > 0) {
        paste("(+", not_shown_rhs, " not shown)", sep = "")
      } else {
        ""
      }
    )) +
    theme_linedraw() +
    scale_x_discrete(position = "top") +
    scale_y_discrete(position = "right") +
    theme(
      axis.text.x = element_text(
        angle = 90,
        hjust = 0,
        vjust = .5
      ),
      legend.position = "bottom"
    ) +
    scale_size(range = c(2, 8))

  if (control$engine == "htmlwidget") {
    p <- plotly::ggplotly(p)
  }
  p
}