ggplot2(Barplot + LinePlot) - 双Y轴

时间:2017-06-19 22:34:33

标签: r plot ggplot2 geom-bar yaxis

我很难用ggplot2重新创建一个excel示例。我尝试了很多例子但由于某些原因我无法达到我想要的结果。有人可以看看我的例子吗?

df <- structure(list(OccuranceCT = c(4825, 9063, 10635, 8733, 5594, 
2850, 1182, 376, 135, 30, 11), TimesReshop = structure(1:11, .Label = c("1x", 
"2x", "3x", "4x", "5x", "6x", "7x", "8x", "9x", "10x", "11x"), class = "factor"), 
    AverageRepair_HrsPerCar = c(7.48951898445596, 6.50803925852367, 
    5.92154446638458, 5.5703551356922, 5.38877037897748, 5.03508435087719, 
    4.92951776649746, 4.83878377659575, 4.67829259259259, 4.14746333333333, 
    3.54090909090909)), .Names = c("OccuranceCT", "TimesReshop", 
"AverageRepair_HrsPerCar"), row.names = c(NA, 11L), class = "data.frame")

到目前为止我的情节:

Plot <- ggplot(df, aes(x=TimesReshop, y=OccuranceCT)) +
  geom_bar(stat = "identity", color="red", fill="#C00000") +
  labs(x = "Car Count", y = "Average Repair Per Hour") + 
  geom_text(aes(label=OccuranceCT), fontface="bold", vjust=1.4, color="black", size=4) +
  theme_minimal()

Plot

这是我到目前为止所做的:

1

我想要实现的目标是:

2

我将非常感谢您学习如何添加辅助轴并将条形图与线图结合起来。

2 个答案:

答案 0 :(得分:4)

这个回答是对你的评论的回答,而不是原来的问题。

从宽到长重塑意味着我们有一列用于因变量(OccuranceCT,AverageRepair_HrsPerCar)和另一列用于它们的值。然后我们可以将它们作为条形图,在它们自己的方面,如下:

library(tidyr)
library(ggplot2)

df %>% 
  gather(variable, value, -TimesReshop) %>% 
  ggplot(aes(TimesReshop, value)) + 
    geom_col() + 
    facet_grid(variable ~ ., scales = "free")

这允许对变量进行快速视觉比较,而不会产生可能误导性的解释,这些解释可能是由于在同一图中将不同的变量放在不同的值中而产生的。

enter image description here

答案 1 :(得分:3)

ggplot2支持双轴(好或坏),其中第二轴是主轴的线性变换。

我们可以为这个案例解决这个问题:

library(ggplot2)
ggplot(df, aes(x = TimesReshop)) +
  geom_col(aes( y = OccuranceCT, fill="redfill")) +
  geom_text(aes(y = OccuranceCT, label = OccuranceCT), fontface = "bold", vjust = 1.4, color = "black", size = 4) +
  geom_line(aes(y = AverageRepair_HrsPerCar * 1500, group = 1, color = 'blackline')) +
  geom_text(aes(y = AverageRepair_HrsPerCar * 1500, label = round(AverageRepair_HrsPerCar, 2)), vjust = 1.4, color = "black", size = 3) +
  scale_y_continuous(sec.axis = sec_axis(trans = ~ . / 1500)) +
  scale_fill_manual('', labels = 'Occurance', values = "#C00000") +
  scale_color_manual('', labels = 'Time Reshop', values = 'black') +
  theme_minimal()