ggplot2 (Barplot + LinePlot) - 双 Y 轴

ggplot2 (Barplot + LinePlot) - Dual Y axis

我很难用 ggplot2 重新创建 excel 示例。我尝试了很多例子,但由于某种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

这是我目前得到的:

而我想要实现的是:

如果能学习如何添加次轴以及如何将条形图与线图结合起来,我将不胜感激。

ggplot2 支持双轴(无论好坏),其中第二个轴是主轴的线性变换。

我们可以解决这个问题:

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()

此回答是对您的评论的回复,而不是对原始问题的回复。

从宽变长意味着我们有一列用于因变量(OccuranceCT,AverageRepair_HrsPerCar),另一列用于它们的值。然后我们可以在各自的方面将每个绘制为条形图,如下所示:

library(tidyr)
library(ggplot2)

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

这样可以快速直观地比较变量,而不会因为将具有完全不同值的不同变量放在同一图中而产生潜在的误导性解释。