Question

我有一个数据集，并希望在建立预测模型之前进行一些探索性数据分析。所有变量都是分类的。我知道我可以使用'dataExplorer'进行一些快速的EDA：

library(tidyverse)
library(dataExplorer)

dat <- data.frame(circuit = sample(c("China", "Murica", "Brazil"), 100, replace = T),
                  driver = sample(c("Kimi", "Seb", "Max", "Lando", "Lance"), 100, replace = T),
                  opinion = sample(c("Garbage", "Not.Garbage"), 100, replace = T, prob = c(0.8, 0.2)))

dat %>%
     select(-opinion) %>%
     plot_bar

但是，我希望填写“电路”和“驱动程序”的条形，以表示每个变量的“意见”比例（请参见下文）。这样一来，我可以看到哪些预测变量与我的结果变量最紧密相关。

dat %>%
     ggplot(aes(x = circuit, fill = opinion)) +
     geom_histogram(stat = "count")

但是，我不想单独构建每个图，然后使用grid.arrange来组织它们。

感谢您的帮助:)

Answer 1

除非您调整plot_bar函数，否则我认为没有一种简便的方法，因为它目前旨在可视化单变量分布。运行以下功能，它应适用于您的示例：

library(tidyverse)
library(data.table) ## Note: You will need to load data.table
library(DataExplorer)

## Rewrite plot_bar
plot_bar2 <- function(data, group, with = NULL, maxcat = 50, order_bar = TRUE, binary_as_factor = TRUE, title = NULL, ggtheme = theme_gray(), theme_config = list(), nrow = 3L, ncol = 3L, parallel = FALSE) {
  frequency <- measure <- variable <- value <- NULL
  if (!is.data.table(data)) data <- data.table(data)
  split_data <- split_columns(data, binary_as_factor = binary_as_factor)
  if (split_data$num_discrete == 0) stop("No discrete features found!")
  discrete <- split_data$discrete
  ind <- DataExplorer:::.ignoreCat(discrete, maxcat = maxcat)
  if (length(ind)) {
    message(length(ind), " columns ignored with more than ", maxcat, " categories.\n", paste0(names(ind), ": ", ind, " categories\n"))
    drop_columns(discrete, names(ind))
    if (length(discrete) == 0) stop("Note: All discrete features ignored! Nothing to plot!")
  }
  feature_names <- names(discrete)
  if (is.null(with)) {
    dt <- discrete[, list(frequency = .N), by = feature_names]
  } else {
    if (is.factor(data[[with]])) {
      measure_var <- suppressWarnings(as.numeric(levels(data[[with]]))[data[[with]]])
    } else if (is.character(data[[with]])) {
      measure_var <- as.numeric(data[[with]])
    } else {
      measure_var <- data[[with]]
    }
    if (all(is.na(measure_var))) stop("Failed to convert `", with, "` to continuous!")
    if (with %in% names(discrete)) drop_columns(discrete, with)
    tmp_dt <- data.table(discrete, "measure" = measure_var)
    dt <- tmp_dt[, list(frequency = sum(measure, na.rm = TRUE)), by = feature_names]
  }
  dt2 <- suppressWarnings(melt.data.table(dt, id.vars = c(group, "frequency"), measure.vars = setdiff(feature_names, group))) # This line is updated
  layout <- DataExplorer:::.getPageLayout(nrow, ncol, ncol(discrete))
  plot_list <- DataExplorer:::.lapply(
    parallel = parallel,
    X = layout,
    FUN = function(x) {
      if (order_bar) {
        base_plot <- ggplot(dt2[variable %in% feature_names[x]], aes(x = reorder(value, frequency), y = frequency))
      } else {
        base_plot <- ggplot(dt2[variable %in% feature_names[x]], aes(x = value, y = frequency))
      }
      base_plot +
        geom_bar(stat = "identity", aes_string(fill = group)) + # This line is updated
        coord_flip() +
        xlab("") + ylab(ifelse(is.null(with), "Frequency", toTitleCase(with)))
    }
  )
  class(plot_list) <- c("multiple", class(plot_list))
  plotDataExplorer(
    plot_obj = plot_list,
    page_layout = layout,
    title = title,
    ggtheme = ggtheme,
    theme_config = theme_config,
    facet_wrap_args = list(
      "facet" = ~ variable,
      "nrow" = nrow,
      "ncol" = ncol,
      "scales" = "free"
    )
  )
}

## Create data and plot
dat <- data.frame(
  circuit = sample(c("China", "Murica", "Brazil"), 100, replace = T),
  driver = sample(c("Kimi", "Seb", "Max", "Lando", "Lance"), 100, replace = T),
  opinion = sample(c("Garbage", "Not.Garbage"), 100, replace = T, prob = c(0.8, 0.2))
)
plot_bar2(dat, group = "opinion")

情节看起来像这样：

如何使用DataExplorer分割描述性条形图

1 个答案: