R/plot_na.R

In inspectdf: Inspection, Comparison and Visualisation of Data Frames

#' @importFrom ggplot2 scale_color_discrete
#' @importFrom ggplot2 annotate
#' 
plot_na_single <- function(
    df_plot, 
    text_labels = TRUE, 
    col_palette = 0, 
    label_angle = NULL, 
    label_color = NULL, 
    label_size = NULL
){
  df_names <- attr(df_plot, "df_names")
  # define a plot wide nudge interval
  nudge <- max(df_plot$pcnt) / 50
  # convert col_name to factor
  df_plot <- df_plot %>% 
    mutate(col_name = factor(col_name, levels = as.character(col_name)))
  
  # construct bar plot of missingness
  plt <- df_plot %>% 
    ggplot(aes(x = col_name, y = pcnt, fill = col_name, label = cnt)) +
    geom_bar(stat = "identity") + 
    labs(x = '', y = "% of column that is NA", 
         title = paste0("Prevalence of NAs in df::", df_names$df1), 
         subtitle = paste0("df::", df_names$df1,  " has ", nrow(df_plot), 
                           " columns, of which ", sum(df_plot$cnt > 0), 
                           " have missing values")) +
    theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
    scale_fill_manual(values = user_colours(nrow(df_plot), col_palette)) +
    guides(fill = 'none')
  
  # add text annotation to plot if requested
  if(text_labels){
    x = df_plot$col_name
    y = df_plot$pcnt
    # whether ys are zero or not
    big_bar <- 0.15 * max(y, na.rm = T)
    # label_df
    label_df <- tibble(col_name = x, pcnt = y)
    label_df$fill <- NA
    # labels white 
    label_white <- label_df %>% filter(pcnt > big_bar) 
    max_lab <- ifelse(all(is.na(label_white$pcnt)), NA, max(label_white$pcnt, na.rm = T))
    # labels grey
    label_grey <- label_df %>% 
      filter(pcnt <= big_bar, pcnt > 0) %>%
      mutate(ymax = pcnt + 0.5 * max_lab)
    # labels zero
    label_zero <- label_df %>% filter(pcnt == 0)

    # add white labels at the top of the bigger bars
    if(nrow(label_white) > 0){
      plt <- plt + 
        annotate(
          'text',
          x = label_white$col_name,
          y = label_white$pcnt - nudge,
          label = round(label_white$pcnt, 1),
          color = ifelse(is.null(label_color), "white", label_color),
          angle = ifelse(is.null(label_angle), 90, label_angle), 
          size  = ifelse(is.null(label_size), 3.5, label_size), 
          hjust = 1    
        )
    }
    # add grey labels to relatively short bars, if any
    if(nrow(label_grey) > 0){
      plt <- plt + 
        annotate(
          'text',
          x = label_grey$col_name,
          y = label_grey$pcnt + nudge,
          label = round(label_grey$pcnt, 1),
          color = ifelse(is.null(label_color), "gray50", label_color),
          angle = ifelse(is.null(label_angle), 90, label_angle), 
          size  = ifelse(is.null(label_size), 3.5, label_size), 
          hjust = 0
        )
    }
    # add 0 labels, if any
    if(nrow(label_zero) > 0){
      plt <- plt + 
        annotate(
          'text',
          x = label_zero$col_name,
          y = nudge,
          label = 0,
          color = ifelse(is.null(label_color), "gray50", label_color),
          angle = ifelse(is.null(label_angle), 90, label_angle), 
          size  = ifelse(is.null(label_size), 3.5, label_size), 
          hjust = 0
        )
    }
  }
  plt
}

plot_na_pair <- function(
    df_plot, 
    text_labels = TRUE, 
    col_palette = 0, 
    label_angle = NULL, 
    label_color = NULL, 
    label_size = NULL, 
    alpha = 0.05
){
  df_names <- attr(df_plot, "df_names")
  leg_text <- as.character(unlist(df_names))
  na_tab  <- df_plot
  df_plot <- df_plot %>% 
    select(-starts_with("cnt")) %>% 
    gather(key = "data_frame", value = "pcnt", -col_name, -p_value) %>%
    mutate(data_frame = gsub("pcnt_", "", data_frame))
  df_plot <- df_plot[seq(dim(df_plot)[1],1),]
  p_val_tab <- df_plot %>% 
    mutate(is_sig = as.integer(p_value < alpha) + 2, index = 1:nrow(df_plot)) %>%
    replace_na(list(is_sig = 1)) %>%
    select(is_sig, index) 
  plt <- ggplot(df_plot, aes(x = factor(col_name, levels = rev(as.character(na_tab$col_name))), 
                                 y = pcnt, color = as.factor(data_frame))) +
    geom_blank() + theme_bw() + 
    theme(panel.border = element_blank(), panel.grid.major = element_blank()) +
    geom_rect(
      fill = c(NA, "gray50", user_colours(9, col_palette)[9])[p_val_tab$is_sig], alpha = 0.2,
      xmin = p_val_tab$index - 0.4, xmax = p_val_tab$index + 0.4,
      ymin = -100, ymax = 200, linetype = "blank") +
    geom_hline(yintercept = 0, linetype = "dashed", color = "lightsteelblue4") + 
    geom_point(size = 1.25 * dot_size(nrow(df_plot)), color = "black", na.rm = TRUE) + 
    geom_point(size = dot_size(nrow(df_plot)), na.rm = TRUE) +
    scale_colour_manual(values = get_best_pair(col_palette), 
                        name = "Data frame", labels = leg_text) + 
    coord_flip() + 
    labs(x = "", 
         title =  paste0("% NA in df::", df_names$df1, " and df::", df_names$df2),
         subtitle = bquote("Color/gray stripes mean different/equal missingness")) + 
    labs(y = "% of column that is NA", x = "") +
    guides(color = guide_legend(override.aes = list(fill = NA)))
  
  plt
}

plot_na_grouped <- function(
    df_plot, 
    text_labels = TRUE, 
    col_palette = 0, 
    label_angle = NULL, 
    label_color = NULL, 
    label_size = NULL,
    plot_type = 1
){
  df_names <- attr(df_plot, "df_names")
  # group variable name
  group_name <- colnames(df_plot)[1]
  if(plot_type == 1){
    # get ordering of variable pairs by median correlation
    col_ord <- df_plot %>% 
      ungroup %>%
      group_by(col_name) %>%
      summarize(md_pcnt = median(pcnt, na.rm = T)) %>%
      arrange(md_pcnt) %>%
      .$col_name
    # create pair columns and arrange by col_ord
    out <- df_plot %>% 
      ungroup %>%
      mutate(col_name = factor(col_name, levels = col_ord)) %>%
      arrange(col_name) 
    # jitter points if number of column pairs <= 10
    jitter_width <- ifelse(length(unique(out$col_name)) > 10, 0, 0.25) 
    plt <- out %>%
      ggplot(aes_string(x = 'col_name', y = 'pcnt', col = 'col_name', group = group_name)) + 
      geom_jitter(alpha = 0.5, width = jitter_width, height = 0, size = 1.8) + 
      theme(legend.position='none') + 
      coord_flip() + 
      ylab("Missingness by group") +
      xlab("")
  } else {
    plt <- plot_grouped(
      df = df_plot, 
      value = "pcnt", 
      series = "col_name", 
      group = group_name, 
      plot_type = plot_type, 
      col_palette = col_palette, 
      text_labels = text_labels, 
      ylab = "% missing")
  } 
  plt
}