分布图

作者

[编辑] 郑虎;

[审核] .

分布图是一种采用置信分布的可视化图形。

环境配置

  • 系统: Cross-platform (Linux/MacOS/Windows)

  • 编程语言: R

  • 依赖包: ggdist; tidyr; broom; modelr; ggplot2

# 安装包
if (!requireNamespace("ggdist", quietly = TRUE)) {
  install.packages("ggdist")
}
if (!requireNamespace("tidyr", quietly = TRUE)) {
  install.packages("tidyr")
}
if (!requireNamespace("broom", quietly = TRUE)) {
  install.packages("broom")
}
if (!requireNamespace("modelr", quietly = TRUE)) {
  install.packages("modelr")
}
if (!requireNamespace("ggplot2", quietly = TRUE)) {
  install.packages("ggplot2")
}

# 加载包
library(ggdist)
library(tidyr)
library(broom)
library(modelr)
library(ggplot2)

数据准备

载入数据为 5 种条件及其对应的值。

# 加载数据
data <- read.delim("files/Hiplot/066-ggdist-data.txt", header = T)

# 整理数据格式
data[, 1] <- factor(data[, 1], levels = rev(unique(data[, 1])))
data <- tibble(data)
data2 = lm(response ~ condition, data = data)
data3 <- data_grid(data, condition) %>%
  augment(data2, newdata = ., se_fit = TRUE)

# 查看数据
head(data)
# A tibble: 6 × 2
  condition response
  <fct>        <dbl>
1 A           -0.420
2 B            1.69 
3 C            1.37 
4 D            1.04 
5 E           -0.144
6 A           -0.301

可视化

# 分布图
p <- ggplot(data3, aes_(y = as.name(colnames(data[1])))) +
  stat_dist_halfeye(aes(dist = "student_t", arg1 = df.residual(data2),
                        arg2 = .fitted, arg3 = .se.fit),
                    scale = .5) +
  geom_point(aes_(x = as.name(colnames(data[2]))),
             data = data, pch = "|", size = 2,
             position = position_nudge(y = -.15)) +
  ggtitle("ggdist Plot") + 
  xlab("response") + ylab("condition") +
  theme_ggdist() +
  theme(text = element_text(family = "Arial"),
        plot.title = element_text(size = 12,hjust = 0.5),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 10),
        axis.text.x = element_text(angle = 0, hjust = 0.5,vjust = 1),
        legend.position = "right",
        legend.direction = "vertical",
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10))

p
图 1: 分布图

图示给出的是条件下均值的置信度分布，可以看出 5 种条件下对应值的大致分布情况。