本文参考自echarts4r: 从入门到应用-by袁凡

Echarts4r绘图

使用的数据为烏魯木齊地窩堡國際機場天气平均统计（中国） -by Weather Spark（这个里面的图画的太好看了，真是自愧不如）

折线图

使用echarts4r时，要在一个图形容器中显示多个变量，则要展示的变量一个接一个用管道符引入。 我们根据数据手动输入每个月的温度、降雨量、降雪量。利用一堆管道绘制出来各个月份的直方图，再自定义各个轴的显示。由于温度与降雨量、降雪量的数值不同，则需要另外起一个轴来专门当作温度轴y_index=1：

library(echarts4r)

data <- data.frame(
  month = paste0(c(1:12), "月"),# 定义一年十二个月
  rainfall = c(0,0.4,4.5,18.5,24.5,23.5,25.2,18.6,15.0,12.2,5.1,0.3), 
  snowfall = c(29.4,37.1,39.3,14.9,1.4,0.0,0.0,0.0,0.1,8.6,34.9,46.3), 
  Temperature = c(-11,-8,1,13,19,24,26,25,19,10,0,-8)  #温度
)

data |>
  e_charts(month) |> # 横轴 使用管道来简单链接
  e_bar(snowfall,y_index = 0,name="降雪量") |> # 纵轴
  e_bar(rainfall,y_index = 0,name="降雨量") |> # 纵轴
  e_line(Temperature,y_index = 1,name="温度") |> # 纵轴
  e_x_axis(
    axisLabel = list(interval = 0, rotate = 30),
    name = "月份", # 坐标轴标题
    nameLocation = "center", # 横坐标轴标题的位置
    nameGap = 30
  ) |>
  e_y_axis(
    y_index = 0,
    min = 0, # 最小值
    max = 50, # 最大值
    interval = 5, # 显示间隔
    name = "降雪/雨量", # 坐标轴名称
    formatter = "{value} 毫米"
  ) |>
  e_y_axis(
    index = 1,
    min = 0, # 最小值
    max = 30, # 最大值
    interval = 4, # 显示间隔
    name = "温度", # 坐标轴名称
    formatter = "{value} ℃"
  ) |>
    e_tooltip(trigger = "item")|>
  e_mark_point("降雪量", data = list(type = "min")) |> # 标记最小值
  e_mark_point("降雪量", data = list(type = "max")) |> # 标记最大值
  e_mark_point("降雨量", data = list(type = "min")) |> # 标记最小值
  e_mark_point("降雨量", data = list(type = "max")) |># 标记最大值
  e_mark_line(c("降雪量", "降雨量"), data = list(type = "max"), title = "平均值") |>
   e_datazoom(
    x_index = 0, # 指定主X轴
    start = 0, # 指定缩放组件的起点
    end = 100
  ) |> # 指定缩放组件的终点
  e_datazoom(y_index = 0) # 指定主Y轴

由于下雪和下雨本质上都是降水量，这里再把两者合并起来：

library(echarts4r)

data <- data.frame(
  month = paste0(c(1:12), "月"),# 定义一年十二个月
  rainfall = c(0,0.4,4.5,18.5,24.5,23.5,25.2,18.6,15.0,12.2,5.1,0.3), 
  snowfall = c(29.4,37.1,39.3,14.9,1.4,0.0,0.0,0.0,0.1,8.6,34.9,46.3), 
  Temperature = c(-11,-8,1,13,19,24,26,25,19,10,0,-8)  #温度
)
data |>
  e_charts(month) |> # 横轴 使用管道来简单链接
  e_bar(snowfall,y_index = 0,stack = "group1",name="降雪量") |> # 纵轴
  e_bar(rainfall,y_index = 0,stack = "group1",name="降雨量") |> # 纵轴
  e_line(Temperature,y_index = 1,stack = "group2",name="温度") |> # 纵轴
  e_x_axis(
    axisLabel = list(interval = 0, rotate = 30),
    name = "月份", # 坐标轴标题
    nameLocation = "center", # 横坐标轴标题的位置
    nameGap = 30
  ) |>
  e_y_axis(
    y_index = 0,
    min = 0, # 最小值
    max = 50, # 最大值
    interval = 5, # 显示间隔
    name = "降雪/雨量", # 坐标轴名称
    formatter = "{value} 毫米"
  ) |>
  e_y_axis(
    index = 1,
    min = 0, # 最小值
    max = 30, # 最大值
    interval = 4, # 显示间隔
    name = "温度", # 坐标轴名称
    formatter = "{value} ℃"
  )

扇形图

data |>
  e_chart(month) |>
  e_pie(rainfall) |>
  e_legend(
    type = "scroll", # 图例类型为滚动展示
    selector = c("all", "inverse"), # 增加全选、反选的选择器按钮
    selectorPosition = "start", # 选择器按钮的位置，start表示放在图例前面，end表示放在图例后面
    orient = "horizontal", # 水平布局
    left = 20
  )

三维图

data |>
  e_charts(rainfall) |>
  e_bar_3d(snowfall,month) |>
  e_visual_map(dimension = 2)

随机热力图测试

library(echarts4r)

df <- data.frame(
  x = seq(50),
  y = rnorm(50, 10, 3),
  z = rnorm(50, 11, 2),
  w = rnorm(50, 9, 2)
)

df |> 
  e_charts(x) |> 
  e_scatter(y, z) |> 
  e_visual_map(z, scale = e_scale) |> # scale color
  e_legend(FALSE) # hide legend
df |> 
  head(10) |> 
  e_charts(x) |> 
  e_effect_scatter(y, z) |> 
  e_visual_map(z) |> # scale color
  e_legend(FALSE) # hide legend