[英]Two y axes on the same scale on the same plot in R
I am currently trying to reproduce a plot that looks like this: 我目前正在尝试重现如下所示的情节:
Ignoring the graduated scales on the right side, there are two y-axes on the graph. 忽略右侧的刻度尺,图形上有两个y轴。 X is the VIP score, and the y scale is determined by the weighted sum of absolute regression coefficients, however this scale is NOT visible, and masses are seen on the left y axis.
X是VIP得分,y比例由绝对回归系数的加权和确定,但是该比例不可见,在左侧y轴上可以看到质量。 The masses are categorical variables, in this case, that match each value in the continuous variable of weighted sum of absolute regression coefficients.
质量是分类变量,在这种情况下,与绝对回归系数的加权和的连续变量中的每个值匹配。
My question is how do I use ggplot2, or another R package, to reproduce this? 我的问题是如何使用ggplot2或另一个R包来重现此内容? Labelling the points directly using ggrepel is not an option as there are too many masses in my dataset.
不能直接使用ggrepel标记点,因为数据集中的质量太多。 Is there a way to create a scatterplot with two y axes BUT the second y axis is a categorical variable?
有没有一种方法可以创建带有两个y轴的散点图,但第二个y轴是分类变量?
Sample data: 样本数据:
Masses Overall VIP1
1 82.07010 38.26669006 1.484957089
2 84.08570 34.22745192 1.328724766
3 95.08570 38.65684978 1.500047945
4 96.08571 13.13685100 0.512968559
5 98.10140 36.07639404 1.400239372
6 99.04410 17.37079280 0.676731759
7 105.07530 9.38047849 0.367677099
8 110.10130 36.66816959 1.423128458
9 111.10160 13.64197654 0.532506138
10 113.06040 10.09391101 0.395271714
This seems terrible, but it's what you are asking for. 这看起来很糟糕,但这就是您要的。 Calling your data
dd
: 调用您的数据
dd
:
ggplot(dd, aes(x = VIP1, y = Overall)) +
geom_point() +
scale_y_continuous(breaks = dd$Overall, labels = dd$Masses)
We use scale_y_continuous
because the variable you want to define the y axis positions, Overall
, is continuous. 我们使用
scale_y_continuous
因为变量要定义y轴位置, Overall
,是连续的。
Using this data: 使用此数据:
dd = read.table(text = " Masses Overall VIP1
1 82.07010 38.26669006 1.484957089
2 84.08570 34.22745192 1.328724766
3 95.08570 38.65684978 1.500047945
4 96.08571 13.13685100 0.512968559
5 98.10140 36.07639404 1.400239372
6 99.04410 17.37079280 0.676731759
7 105.07530 9.38047849 0.367677099
8 110.10130 36.66816959 1.423128458
9 111.10160 13.64197654 0.532506138
10 113.06040 10.09391101 0.395271714", header = TRUE)
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