R：自动生成直方图

Question

我正在使用 R 编程语言。我为此示例创建了以下数据集：

var_1 <- rnorm(1000,10,10)
var_2 <- rnorm(1000, 5, 5)
var_3 <- rnorm(1000, 6,18)

favorite_food <- c("pizza","ice cream", "sushi", "carrots", "onions", "broccoli", "spinach", "artichoke", "lima beans", "asparagus", "eggplant", "lettuce", "cucumbers")
favorite_food <-  sample(favorite_food, 1000, replace=TRUE, prob=c(0.5, 0.45, 0.04, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001))


response <- c("a","b")
response <- sample(response, 1000, replace=TRUE, prob=c(0.3, 0.7))


data = data.frame( var_1, var_2, var_3, favorite_food, response)

data$favorite_food = as.factor(data$favorite_food)
data$response = as.factor(data$response)

从这里开始，我想为这个数据集中的两个分类变量制作直方图，并将它们放在同一页面上：

#make histograms and put them on the same page (note: I don't know why the "par(mfrow = c(1,2))" statement is not working)
par(mfrow = c(1,2))

histogram(data$response, main = "response"))

histogram(data$favorite_food, main = "favorite food"))

我的问题：是否可以为给定数据集中的所有分类变量自动生成直方图（无需手动为每个变量编写“histogram()”语句）并将它们打印在同一页面上？使用“ggplot2”库来解决这个问题会更好吗？

我可以为数据集中的每个单独的分类变量手动编写“histogram()”语句，但我正在寻找一种更快的方法来做到这一点。是否可以使用“for 循环”来做到这一点？

谢谢

Answer 1

这里尝试使用cowplot & ggplot2

library(ggplot2)
library(dplyr)
library(foreach)
library(cowplot)

list_variables <- c("response", "favorite_food")
all_plot <- foreach(current_var = c(list_variables)) %do% {
  # need to do this to avoid ggplot reference to same summary data afterward.
  data_summary_name <- paste0(current_var, "_summary")
  eval(substitute(
    {
      graph_data <- data %>%
        group_by(!!sym(current_var)) %>%
        summarize(count = n(), .groups = "drop") %>%
        mutate(share = count / sum(count))
      plot <- ggplot(graph_data) +
        geom_bar(mapping = aes(x = !!sym(current_var), y = share), width = 1,
          fill = "#00FFFF", color = "#000000", stat = "identity") +
        scale_y_continuous(labels = scales::percent) +
        ggtitle(current_var) + ylab("Perecent of Total") +
        theme_bw()
    }, list(graph_data = as.name(data_summary_name))
  )) 
  return(plot)
}

plot_grid(plotlist = all_plot, ncol = 2)

注意：关于我为什么使用eval 和substitue 的参考，您可以参考ggplot2 generate same plot for different variables in a for loop 上的这个问题

使用 facet_wrap 作为与 QuishSwash 类似的方法，而是以份额计算数据

list_variables <- c("response", "favorite_food")
# Calculate share for choosen variables defined in list_variables 
# You can adjust by having some variables selection based on some condition
summary_df <- bind_rows(foreach(current_var = c(list_variables)) %do% {
  data %>%
    group_by(variable = !!sym(current_var)) %>%
    summarize(count = n(), .groups = "drop") %>%
    mutate(share = count / sum(count),
      variable_name = current_var)
})

ggplot(summary_df) +
  geom_bar(
    aes(x = variable, y = share),
    fill = "#00FFFF", color = "#000000", stat = "identity") +
  facet_wrap(~variable_name, scales = "free") +
  scale_y_continuous(labels = scales::percent) +
  theme_bw()

^{由reprex package (v2.0.0) 于 2021 年 4 月 29 日创建}

Answer 2

这是在 for 循环中使用 barplot 的基本 R 替代方案：

cols <- names(data)[sapply(data, is.factor)]


#This would need some manual adjustment if number of columns increase
par(mfrow = c(1,length(cols))) 

for(i in cols) {
  barplot(table(data[[i]]), main = i)
}

Answer 3

ggplot2/tidyverse 的解决方案是将每一列加长为数据，然后使用 faceting 将它们全部绘制在同一页面中：

（编辑以仅绘制因子变量）

factor_vars <- sapply(data, is.factor)

varnames <- names(data)

deselect_not_factors <- varnames[!factor_vars]

library(tidyr)
library(ggplot2)

data_long <- data %>%
  pivot_longer(
    cols = -deselect_not_factors,
    names_to = "category",
    values_to = "value"
  )

ggplot(data_long) +
  geom_bar(
    aes(x = value)
  ) +
  facet_wrap(~category, scales = "free")

Answer 4

作为替代方案，您可以利用出色的DataExplorer package。

请注意，直方图适用于连续变量，因此您希望为分类变量创建条形图。这可以按如下方式完成：

if(require(DataExplorer)==FALSE) install.packages("DataExplorer"); library(DataExplorer)
DataExplorer::plot_histogram(data) # plots histograms for continuous variables
DataExplorer::plot_bar(data) # bar plots for categorical variables

详情请咨询package manual。