绘图技巧 | 这种精美的”排序图“怎么做？(附练习数据）

DataCharm

发布于 2021-05-27 15:52:45

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发布于 2021-05-27 15:52:45

文章被收录于专栏：数据学术商业新闻数据学术商业新闻

今天小编给大家介绍一种”凹凸图(bump charts)“的绘制方法，其绘图函数主要来自R包-ggbump，本期的主要内容如下：

R-ggbump包基本绘图简介
R-ggbump包实例演示

R-ggbump包基本绘图函数简介

R-ggbump包主要包含：geom_bump()和geom_sigmoid(),两个函数主要绘制随时间变化的平滑曲线排名图，内置参数也几乎相同，如下：

( mapping = NULL,
  data = NULL,
  geom = "line",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  smooth = 8,
  direction = "x",
  inherit.aes = TRUE,
  ...)

其官网(https://github.com/davidsjoberg/ggbump)提供的例子如下(部分)：

Example Of geom_bump()

Example Of geom_sigmoid()

从以上也可以看出两个绘图函数所绘制的图形属于同一类别，下面我们通过实例数据进行两个绘图函数的理解。

R-ggbump包实例演示

geom_bump()绘图函数

「样例一：」我们直接构造数据并对结果继续美化操作，代码如下：

library(tidyverse)
library(ggtext)
library(hrbrthemes)
library(wesanderson)
library(LaCroixColoR)
library(RColorBrewer)
library(ggbump)
#数据构建和处理
test_01 <- tibble(country = c("India", "India", "India", "Sweden", "Sweden", "Sweden", "Germany", "Germany", "Germany", "Finland", "Finland", "Finland"),
             year = c(2011, 2012, 2013, 2011, 2012, 2013, 2011, 2012, 2013, 2011, 2012, 2013),
             value = c(492, 246, 246, 369, 123, 492, 246, 369, 123, 123, 492, 369))
test_01_plot <- test_01 %>% group_by(year) %>% mutate(rank=rank(value, ties.method = "random")) %>% ungroup
#可视化绘制
charts01_cus <- ggplot(data = test_01_plot,aes(x = year,y = rank,color=country))+
           ggbump::geom_bump(size=2,smooth = 8) +
           #添加圆点
           geom_point(size=8)+
           # 添加文本信息
           geom_text(data = test_01_plot %>% filter(year==min(year)),
                    aes(x=year-.1,label=country),size=6,fontface="bold",hjust = 1) +
           geom_text(data = test_01_plot %>% filter(year == max(year)),
                    aes(x = year + .1, label = country), size = 6,fontface="bold",hjust = 0) +
           #修改刻度
           scale_x_continuous(limits = c(2010.6, 2013.4),
                              breaks = seq(2011, 2013, 1)) +
           scale_color_manual(values = lacroix_palette("Pamplemousse", type = "discrete"))+
           labs(
               title = "Example of <span style='color:#D20F26'>ggbump::geom_bump function</span>",
               subtitle = "processed charts with <span style='color:#1A73E8'>geom_bump()</span>",
               caption = "Visualization by <span style='color:#DD6449'>DataCharm</span>")+
           hrbrthemes::theme_ft_rc(base_family = "Roboto Condensed") +
            theme(
                plot.title = element_markdown(hjust = 0.5,vjust = .5,color = "black",
                                              size = 20, margin = margin(t = 1, b = 12)),
                plot.subtitle = element_markdown(hjust = 0,vjust = .5,size=12),
                plot.caption = element_markdown(face = 'bold',size = 10),
                legend.position = "none",
                panel.grid.major = element_blank(),
                panel.grid.minor = element_blank())+
              scale_y_reverse()

可以看到仅使用geom_bump()即可绘制，到这里使用了更多的绘图函数和主题、样式等设置语句对其进行美化操作，可视化结果如下：

Exercise Of geom_bump()

「样例二：」

第二个小例子，我们通过构建虚拟数据进行可视化结果绘制，如下：

#读入数据
library(readxl)

df<-read_excel("rank_data.xlsx")
#定义颜色
cols <- c(
  "#882B1A", "#676564", "#E8751A", "#779AC4", "#646E3F",
  "#9D49B9", "#C09F2F", "#65955B", "#284D95","#B34525")
#可视化绘制
charts02_cus <- ggplot(data = df,aes(x = race_num,y = rank,color=team_name,group=team_name)) +
  geom_bump(smooth = 7, size = 2.5) +
  geom_point(data = df %>% filter(race_num == 1),size = 5) +
  geom_point(data = df %>% filter(race_num == 7),size = 5, shape = 21, fill = "black",
             stroke = 2) +
  geom_text(data = df %>% filter(race_num == 7),aes(x = 7.12,label = team_name),
            family = "Cinzel",fontface = 'bold',size = 4, hjust = 0) +
  #添加序号
  geom_point(data = tibble(x = .8, y = 1:10), aes(x = x, y = y), 
            inherit.aes = F,shape=21,color = "grey95",size = 5,stroke = 1.) +
  geom_text(data = tibble(x = .8, y = 1:10), aes(x = x, y = y, label = y), 
             inherit.aes = F,size=2.5,fontface = 'bold',
             color = "grey95")+
  coord_cartesian(clip = "off") +
  scale_x_continuous(
      expand = c(.01, .01),
      limits = c(.8, 8.1),
      breaks = 1:7,
      labels = c("Begain",glue::glue("Race {1:5}"), "Finish"),
      sec.axis = dup_axis()
    ) +
  scale_y_reverse(
      expand = c(.05, .05),
      breaks = 1:16
    ) +
  scale_color_manual(
      values = cols,
      guide = F
    ) +
  labs(x="",y="",
        title = "Example of <span style='color:#D20F26'>ggbump::geom_bump() function</span>",
        subtitle = "processed charts with <span style='color:#1A73E8'>geom_bump()</span>",
        caption = "Visualization by <span style='color:#DD6449'>DataCharm</span>")+
  theme(
          plot.title = element_markdown(hjust = 0.5,vjust = .5,color = "gray50",face = 'bold',
                                              size = 20, margin = margin(t = 1, b = 12)),
          plot.subtitle = element_markdown(hjust = 0,vjust = .5,size=12,color = "gray50"),
          plot.caption = element_markdown(face = 'bold',size = 10),
          plot.background = element_rect(fill = "black", color = "black"),
          panel.background = element_rect(fill = "black", color = "black"),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          axis.text.x.top = element_text(size = 9,color = "grey95",family = "Cinzel",face = 'bold'),
          axis.text.y.left = element_blank(),
          axis.ticks = element_blank()
    )

(这里涉及到很多关于主题设置的语句，小编在绘制之后可是又对一些常用的语句熟悉了一遍哦，希望小伙伴们多练习)

Exercise2 Of geom_bump

geom_sigmoid()绘图函数

对于该函数，我们还是通过构建数据进行绘制：

data_test <- tibble(
    x = c(0.5,0.5,1,1,1,1,1,1),
    xend = c(1, 1, 3, 3, 3 ,3, 3, 3),
    y = c(4, 4, 6, 6, 6, 2, 2, 2),
    yend = c(6,2,7,6,5,3,2,1),
    group = c("Python","R","Numpy","Pandas","Matplolib","Dplyr","Data.table","Ggplot2")
)
#可视化绘制
charts04_cus <- ggplot(data_test) +
  geom_sigmoid(data = data_test %>% filter(xend < 3),
               aes(x = x, y = y, xend = xend, yend = yend, group = factor(group)),
               direction = "x", color = "#cb7575", size = 2, smooth = 6) + 
  geom_sigmoid(data = data_test %>% filter(group %in% c("Numpy","Pandas","Matplolib")),
              aes(x = x, xend = xend, y = y, yend = yend, group=group),
              direction = "x",color = "#cb7575", size = 2, smooth = 12) + 
  geom_sigmoid(data = data_test %>% filter(y==2),
              aes(x = x, xend = xend, y = y, yend = yend, group=group),
              direction = "x",color = "#cb7575", size = 2, smooth = 11) + 
  geom_label(data = tibble(x = 0.1, y = 4, label = "DataScience"), 
             aes(x, y, label = label), inherit.aes = F, size = 10, color = "white", 
             fill = "#004E66",family = "Cinzel",nudge_x = -.15) + 
  geom_label(data = data_test %>% filter(xend < 3),
             aes(x = xend, y = yend, label = group),inherit.aes = F, size = 8, 
             color = "white", fill = "#004E66", family = "Cinzel",
             hjust=0.5,nudge_y = .45,nudge_x = .3)+
  geom_label(data = data_test %>% filter(xend == 3),
             aes(x = xend, y = yend, label = group),inherit.aes = F, size = 7, 
             color = "white", fill = "#004E66", family = "Cinzel",hjust=0) +
  labs(x="",y="",
        title = "Example of <span style='color:#D20F26'>ggbump::geom_sigmoid() function</span>",
        subtitle = "processed charts with <span style='color:#1A73E8'>geom_sigmoid()</span>",
        caption = "Visualization by <span style='color:#DD6449'>DataCharm</span>")+
  scale_x_continuous(limits = c(-.5,4)) + 
  theme(plot.title = element_markdown(hjust = 0.5,vjust = .5,color = "gray50",face = 'bold',
                                              size = 20, margin = margin(t = 1, b = 12)),
        plot.subtitle = element_markdown(hjust = 0,vjust = .5,size=12,color = "gray50"),
        plot.caption = element_markdown(face = 'bold',size = 10),
        panel.grid = element_blank(),
        axis.line = element_blank(),
        axis.ticks = element_blank(),
        axis.text.y = element_blank(),
        axis.title.y = element_blank(),
        axis.text.x = element_blank(),
        panel.background = element_rect(fill = "#353848"),
        plot.background = element_rect(fill = "#353848",colour = "#353848"))

(还是一样，使用了很多常用的主题设置语句，希望小伙伴们掌握、熟悉)