使用ggplot的多x轴呈现z得分，iq得分和原始数据 [英] Multi x-axis using ggplot to present z-scores, iq scores and raw data
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问题描述
我只是进行情境化，所以我从事心理测验/心理测试.我具有由点"，百分位数"，"z_real"，"z_normal"，"iq"形成的数据集.我想要一个单一的ggplot，在其中我可以显示Z_score(来自原始数据)，z_score(具有基本正态分布)，然后具有两个补充的x轴，其原始值分别为原始分数"和原始分数".和"iq分数".这在统计数据中很常见，因为您可以在下面进行检查
Just contextualizing, I work with psychometrics/psychological testing. I have a dataset formed of "points","percentile","z_real","z_normal","iq". I would like to have a single ggplot in which I could present the Z_score (from my raw data), the z_score (with an underlying normal distribution), and then have two supplementary x-axes with "raw score" and "iq scores". That's pretty common in statistics, as you can check it below
这是当前情节
这是我所拥有的最佳解决方案
This is the best solution I've got
那是理想的情节
我正在与tidyverse一起工作，我想留在其中.以前的一些帖子对我有帮助，例如[[]] [5]和[[]] [6].
I'm working with tidyverse and I would like to remain within it. Some previous posts helped me, such as this [one][5] and this [one][6].
谢谢.(部分)数据和代码在这里:
Thank you. The (part of the) data and the codes are here:
ask_ds <- structure(list(points = c(17, 17, 2, 16, 11, 17, 20, 16, 19,
15, 9, 14, 14, 16, 13, 13, 22, 21, 25, 17, 17, 17, 20, 6, 11,
5, 10, 23, 21, 19, 11, 15, 13, 17, 17, 17, 9, 18, 12, 22, 21,
23, 8, 12, 6, 7, 22, 12, 21, 16, 12, 5, 19, 19, 21, 13, 12, 18,
22, 13, 21, 24, 23, 4, 12, 19, 2, 22, 14, 16, 24, 19, 7, 12,
18, 14, 9, 18, 7, 22, 3, 18, 14, 22, 15, 11, 15, 19, 25, 9, 19,
16, 13, 19, 14, 15, 20, 3, 23, 9, 7, 22, 9, 22, 17, 12, 14, 11,
7, 9, 19, 16, 19, 10, 24, 14, 12, 18, 19, 24, 22, 11, 11, 15,
17, 15, 11, 11, 14, 24, 14, 21, 16, 10, 19, 21, 15, 20, 15, 20,
22, 10, 22, 16, 14, 16, 8, 16, 9, 15), percentile = c(63, 61,
1, 56, 25, 59, 80, 55, 74, 49, 17, 45, 44, 57, 36, 32, 90, 85,
99, 63, 63, 60, 82, 7, 24, 5, 20, 93, 83, 75, 24, 50, 35, 64,
62, 61, 16, 65, 28, 90, 85, 93, 12, 28, 7, 8, 90, 26, 87, 55,
30, 4, 74, 73, 87, 33, 30, 67, 91, 35, 86, 95, 93, 3, 29, 75,
1, 89, 44, 55, 96, 75, 8, 27, 66, 41, 13, 68, 9, 91, 2, 65, 44,
89, 51, 22, 46, 74, 98, 13, 76, 58, 37, 73, 43, 50, 79, 2, 93,
17, 8, 91, 14, 92, 60, 26, 43, 25, 10, 14, 73, 57, 76, 18, 96,
40, 31, 70, 73, 96, 91, 24, 23, 47, 59, 51, 24, 22, 40, 96, 45,
85, 57, 17, 73, 86, 50, 79, 48, 81, 91, 21, 89, 53, 43, 58, 11,
53, 14, 47), z_real = structure(c(0.36, 0.36, -2.38, 0.18, -0.73,
0.36, 0.91, 0.18, 0.73, 0, -1.1, -0.19, -0.19, 0.18, -0.37, -0.37,
1.28, 1.09, 1.83, 0.36, 0.36, 0.36, 0.91, -1.65, -0.73, -1.83,
-0.92, 1.46, 1.09, 0.73, -0.73, 0, -0.37, 0.36, 0.36, 0.36, -1.1,
0.55, -0.55, 1.28, 1.09, 1.46, -1.28, -0.55, -1.65, -1.47, 1.28,
-0.55, 1.09, 0.18, -0.55, -1.83, 0.73, 0.73, 1.09, -0.37, -0.55,
0.55, 1.28, -0.37, 1.09, 1.64, 1.46, -2.01, -0.55, 0.73, -2.38,
1.28, -0.19, 0.18, 1.64, 0.73, -1.47, -0.55, 0.55, -0.19, -1.1,
0.55, -1.47, 1.28, -2.2, 0.55, -0.19, 1.28, 0, -0.73, 0, 0.73,
1.83, -1.1, 0.73, 0.18, -0.37, 0.73, -0.19, 0, 0.91, -2.2, 1.46,
-1.1, -1.47, 1.28, -1.1, 1.28, 0.36, -0.55, -0.19, -0.73, -1.47,
-1.1, 0.73, 0.18, 0.73, -0.92, 1.64, -0.19, -0.55, 0.55, 0.73,
1.64, 1.28, -0.73, -0.73, 0, 0.36, 0, -0.73, -0.73, -0.19, 1.64,
-0.19, 1.09, 0.18, -0.92, 0.73, 1.09, 0, 0.91, 0, 0.91, 1.28,
-0.92, 1.28, 0.18, -0.19, 0.18, -1.28, 0.18, -1.1, 0), .Dim = c(150L,
1L), "`\`\`\`scaled:center\`\`\``" = 15.0143288084465, "`\`\`\`scaled:scale\`\`\``" = 5.47051980922509),
z_normal = c(0.33, 0.28, -2.33, 0.15, -0.67, 0.23, 0.84,
0.13, 0.64, -0.03, -0.95, -0.13, -0.15, 0.18, -0.36, -0.47,
1.28, 1.04, 2.33, 0.33, 0.33, 0.25, 0.92, -1.48, -0.71, -1.64,
-0.84, 1.48, 0.95, 0.67, -0.71, 0, -0.39, 0.36, 0.31, 0.28,
-0.99, 0.39, -0.58, 1.28, 1.04, 1.48, -1.17, -0.58, -1.48,
-1.41, 1.28, -0.64, 1.13, 0.13, -0.52, -1.75, 0.64, 0.61,
1.13, -0.44, -0.52, 0.44, 1.34, -0.39, 1.08, 1.64, 1.48,
-1.88, -0.55, 0.67, -2.33, 1.23, -0.15, 0.13, 1.75, 0.67,
-1.41, -0.61, 0.41, -0.23, -1.13, 0.47, -1.34, 1.34, -2.05,
0.39, -0.15, 1.23, 0.03, -0.77, -0.1, 0.64, 2.05, -1.13,
0.71, 0.2, -0.33, 0.61, -0.18, 0, 0.81, -2.05, 1.48, -0.95,
-1.41, 1.34, -1.08, 1.41, 0.25, -0.64, -0.18, -0.67, -1.28,
-1.08, 0.61, 0.18, 0.71, -0.92, 1.75, -0.25, -0.5, 0.52,
0.61, 1.75, 1.34, -0.71, -0.74, -0.08, 0.23, 0.03, -0.71,
-0.77, -0.25, 1.75, -0.13, 1.04, 0.18, -0.95, 0.61, 1.08,
0, 0.81, -0.05, 0.88, 1.34, -0.81, 1.23, 0.08, -0.18, 0.2,
-1.23, 0.08, -1.08, -0.08), iq = c(104.98, 104.19, 65.1,
102.26, 89.88, 103.41, 112.62, 101.88, 109.65, 99.62, 85.69,
98.12, 97.74, 102.65, 94.62, 92.98, 119.22, 115.55, 134.9,
104.98, 104.98, 103.8, 113.73, 77.86, 89.41, 75.33, 87.38,
122.14, 114.31, 110.12, 89.41, 100, 94.22, 105.38, 104.58,
104.19, 85.08, 105.78, 91.26, 119.22, 115.55, 122.14, 82.38,
91.26, 77.86, 78.92, 119.22, 90.35, 116.9, 101.88, 92.13,
73.74, 109.65, 109.19, 116.9, 93.4, 92.13, 106.6, 120.11,
94.22, 116.2, 124.67, 122.14, 71.79, 91.7, 110.12, 65.1,
118.4, 97.74, 101.88, 126.26, 110.12, 78.92, 90.81, 106.19,
96.59, 83.1, 107.02, 79.89, 120.11, 69.19, 105.78, 97.74,
118.4, 100.38, 88.42, 98.49, 109.65, 130.81, 83.1, 110.59,
103.03, 95.02, 109.19, 97.35, 100, 112.1, 69.19, 122.14,
85.69, 78.92, 120.11, 83.8, 121.08, 103.8, 90.35, 97.35,
89.88, 80.78, 83.8, 109.19, 102.65, 110.59, 86.27, 126.26,
96.2, 92.56, 107.87, 109.19, 126.26, 120.11, 89.41, 88.92,
98.87, 103.41, 100.38, 89.41, 88.42, 96.2, 126.26, 98.12,
115.55, 102.65, 85.69, 109.19, 116.2, 100, 112.1, 99.25,
113.17, 120.11, 87.9, 118.4, 101.13, 97.35, 103.03, 81.6,
101.13, 83.8, 98.87)), row.names = c(NA, -150L), class = c("tbl_df",
"tbl", "data.frame"))
ask_ds %>%
select(z_real, z_normal) %>%
pivot_longer(everything()) %>%
ggplot(., aes(value, fill = name)) +
geom_density(alpha=.2) +
scale_x_continuous(breaks=-3:3, labels=parse(text=paste(-3:3, '*sigma')) ,
"Standard deviation",
sec.axis = sec_axis(trans = ~.* sd(ask_ds$points) +
mean(ask_ds$points),
"Raw score"))
gridExtra::grid.arrange(ask_ds %>%
select(z_real, z_normal) %>%
pivot_longer(everything()) %>%
ggplot(., aes(value, fill = name)) +
geom_density(alpha=.2) ,
ggplot(ask_ds, aes(points)) +
geom_density()
)
推荐答案
我对 Patchwork 的事一无所知-太酷了，谢谢@teunbrand.我有一个不太优雅的解决方案，但以为我会以同样的方式发布它.在这里，您是在绘制区域内手动制作轴.
I hadn't known about the patchwork thing - that's really cool, thanks @teunbrand. I have a less elegant solution, but thought I would post it just the same. Here, you're making the axes by hand inside the plotting region.
## turn your data into a stand alone dataset
plot.dat <- ask_ds %>%
select(z_real, z_normal) %>%
pivot_longer(everything())
## identify the values for the x-axis
xv <- c(-2, -1, 0, 1, 2)
## identify the middle values for the y-tick marks
yv1 <- c(-.025,-.025,-.025,-.025,-.025)
yv2 <- c(-.065,-.065,-.065,-.065,-.065)
## identify the transformations for the tick mark labels on other axes
rv <- xv* sd(ask_ds$points) + mean(ask_ds$points)
iq <- xv*15 + 100
## make the plot
ggplot() +
geom_density(data=plot.dat, aes(value, fill = name), alpha=.2) +
## add an abline that will serve as the raw data axis
geom_abline(slope=0, intercept=-.025) +
## add segments for the tick marks
geom_segment(aes(x=xv, y=yv1 - .005, xend=xv, yend=yv1 + .005)) +
## add the text labels
geom_text(aes(x=xv, y = yv1 - .011, label=sprintf("%.2f", rv))) +
## repeat a bit lower for the IQ axis
geom_abline(slope=0, intercept=-.065) +
geom_segment(aes(x=xv, y=yv2 - .005, xend=xv, yend=yv2 + .005)) +
geom_text(aes(x=xv, y = yv2 - .011, label=sprintf("%.2f", iq))) +
## format the actual x-axis
scale_x_continuous(breaks=-3:3, labels=parse(text=paste(-3:3, '*sigma')) ,
"Standard deviation") +
## add a y-axis to identify the two new scales.
scale_y_continuous(sec.axis = sec_axis(~.,
breaks = c(-.025, -.065),
labels = c("Raw Data", "IQ"))) +
## put the legend on top - this keeps it from being pushed aside for the second
## y-axis labels.
theme(legend.position="top")
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