在ggplot中組合條形圖和散點圖

Question

我有一個主題的數據，如果客戶談論積極，消極或中立，主題影響和平均差異。

data <- data.frame(topic = c("a", "b", "c", "d"), impact = runif(4, min = -2, max = 2), 
               difference = runif(4, min = -30, max = 30),n = round(runif(4, min = 1, max = 100)), 
               share_positive = c(0.04, 0.9, 0.3, 0.37), share_neutral = c(0.7, 0.06, 0.48, 0.4), 
               share_negative = c(0.26, 0.04, 0.22, 0.23))

我把它放在一個簡單的散點圖中：

ggplot(data = data, aes(x = difference, y = impact, size = n, label = topic)) +
  geom_point() +
  geom_text(size = 4, vjust = -1, colour = "black")

然后我添加了顏色以區分正面和不太正面的主題：

ggplot(data = data, aes(x = difference, y = impact, size = n, label = topic, colour = share_positive)) +
  geom_point() +
  scale_color_continuous(low = "grey", high = "green", guide = FALSE) +
  geom_text(size = 4, vjust = -1, colour = "black")

如果沒有着色，那么在本主題中使用條形圖表示負面，中性和正面反饋的份額會很好。 我的想法是這樣的（b和d的例子）：

不幸的是，我完全不知道，我怎么能把這個結合起來，如果它甚至可能的話。 Googeling也沒有幫助。 你能幫助我嗎？

Answer 1

這是一種方法，但它有點“hacky”。 我們使用difference列和share_*列手動定義調用geom_rect時xmin和xmax geom_rect 。 為了確保我們實際得到一個條形，我們在ymax添加一點軟糖因子（0.1）

library(tidyverse)
theme_set(theme_bw())

data %>%
    mutate(neg_left = difference - share_negative,
           neutral_right = difference + share_neutral,
           positive_right = neutral_right + share_positive) %>%
    ggplot(., aes(ymin = impact, ymax = impact + .1))+
    geom_text(aes(x = difference, y = impact, label = topic), vjust = 1)+
    geom_rect(aes(xmin = neg_left, xmax = difference), fill = 'red', colour = 'black')+
    geom_rect(aes(xmin = difference, xmax = neutral_right), fill = 'grey', colour = 'black')+
    geom_rect(aes(xmin = neutral_right, xmax = positive_right), fill = 'green', colour = 'black')

數據（使用set.seed進行再現）

set.seed(456)
data <- data.frame(topic = c("a", "b", "c", "d"), impact = runif(4, min = -2, max = 2), 
                   difference = runif(4, min = -30, max = 30),n = round(runif(4, min = 1, max = 100)), 
                   share_positive = c(0.04, 0.9, 0.3, 0.37), share_neutral = c(0.7, 0.06, 0.48, 0.4), 
                   share_negative = c(0.26, 0.04, 0.22, 0.23))