条形图由线连接/如何连接两个以grid.arrange排列的图形R / ggplot2

This is a really interesting problem. I approximated it using the patchwork library, which lets you add ggplots together and gives you an easy way to control their layout—I much prefer it to doing anything grid.arrange-based, and for some things it works better than cowplot.

I expanded on the dataset just to get some more values in the two data frames.

library(tidyverse)
library(patchwork)

set.seed(1017)

state1 <- data_frame(
  state = rep(state.name[1:5], each = 3),
  value = floor(runif(15, 1, 100)),
  type = rep(c("state", "local", "fed"), times = 5)
)

state2 <- data_frame(
  state = rep(state.name[1:5], each = 3),
  value = floor(runif(15, 1, 100)),
  type = rep(c("state", "local", "fed"), times = 5)
)

Then I made a data frame that assigns ranks to each state based on other values in their original data frame (state1 or state2).

ranks <- bind_rows(
  state1 %>% mutate(position = 1),
  state2 %>% mutate(position = 2)
)  %>%
  group_by(position, state) %>%
  summarise(state_total = sum(value)) %>%
  mutate(rank = dense_rank(state_total)) %>%
  ungroup()

I made a quick theme to keep things very minimal and drop axis marks:

theme_min <- function(...) theme_minimal(...) +
  theme(panel.grid = element_blank(), legend.position = "none", axis.title = element_blank())

The bump chart (the middle one) is based on the ranks data frame, and has no labels. Using factors instead of numeric variables for position and rank gave me a little more control over spacing, and lets the ranks line up with discrete 1 through 5 values in a way that will match the state names in the bar charts.

p_ranks <- ggplot(ranks, aes(x = as.factor(position), y = as.factor(rank), group = state)) +
  geom_path() +
  scale_x_discrete(breaks = NULL, expand = expand_scale(add = 0.1)) +
  scale_y_discrete(breaks = NULL) +
  theme_min()
p_ranks

For the left bar chart, I sort the states by value and turn the values negative to point to the left, then give it the same minimal theme:

p_left <- state1 %>%
  mutate(state = as.factor(state) %>% fct_reorder(value, sum)) %>%
  arrange(state) %>%
  mutate(value = value * -1) %>%
  ggplot(aes(x = state, y = value, fill = type)) +
    geom_col(position = "stack") +
    coord_flip() +
    scale_y_continuous(breaks = NULL) +
    theme_min() +
    scale_fill_brewer()
p_left

The right bar chart is pretty much the same, except the values stay positive and I moved the x-axis to the top (becomes right when I flip the coordinates):

p_right <- state2 %>%
  mutate(state = as.factor(state) %>% fct_reorder(value, sum)) %>%
  arrange(state) %>%
  ggplot(aes(x = state, y = value, fill = type)) +
    geom_col(position = "stack") +
    coord_flip() +
    scale_x_discrete(position = "top") +
    scale_y_continuous(breaks = NULL) +
    theme_min() +
    scale_fill_brewer()

Then because I've loaded patchwork, I can add the plots together and specify the layout.

p_left + p_ranks + p_right +
  plot_layout(nrow = 1)

您可能需要更多地调整间距和边距，例如expand_scale通过凹凸图调用。我还没有尝试过沿y轴使用轴标记（即翻转后的底部），但是我有种感觉，如果不将虚拟轴添加到等级中，事情可能会被甩掉。仍然有很多问题要摆弄，但这是您提出的一个很酷的可视化项目！