使用facet_wrap和scales =“free”设置各个轴限制在ggplot2中 [英] Setting individual axis limits with facet_wrap and scales = "free" in ggplot2

问题描述

我正在创建一个分面图，用预测值与残差图并排查看预测值与实际值。我将使用 shiny 来帮助探索使用不同训练参数进行建模的结果。我用85％的数据训练模型，测试剩余的15％，并重复5次，每次收集实际/预测值。计算残差后，我的 data.frame 如下所示：

  （结果）
行动预计残差
 2 52.81000 52.86750 -0.05750133 
 3 44.46000 42.76825 1.69175252 
 4 54.58667 49.00482 5.58184181 
 5 36.23333 35.52386 0.70947731 
 6 53.22667 48.79429 4.43237981 
 7 41.72333 41.57504 0.14829173 
  

我想要的：

• pred 与 act 和 pred 与渣油


• x / y范围/ pred 与 act 相同，理想情况下从 min（min（results $ act ），min（results $ pred））至 max（max（results $ act），max（results $ pred））


• pred 与 resid 的x / y范围/ / em>受到我对实际预测情节的影响。仅在预测值和 y 上绘制 x 仅限于残差范围。

为了同时查看这两幅图，我将这些数据融合在一起：

  library（reshape2）
 plot < -  melt（results，id.vars =pred）
  

现在绘图：

  library（ggplot2）
p < -  ggplot（plot ，aes（x = pred，y = value））+ geom_point（size = 2.5）+ theme_bw（）
p < -  p + facet_wrap（〜variable，scales =free）
 
 print（p）
  

这与我想要的非常接近：

我想要的是实际与预测的x和y范围是相同的，但我不确定如何指定，并且我不需要那样做因为范围完全不同。

我尝试为 scale_x_continous 和 scale_y_continuous 添加类似内容：

（pre） min_xy < - min（min（plot $ pred），min（plot $ value））
max_xy < - max（max（plot $ pred）， max（plot $ value））

p < - ggplot（plot，aes（x = pred，y = value））+ geom_point（size = 2.5）+ theme_bw（）
p< - 限制= c（min_xy，max_xy））
p <-p + scale_y_continuous（限制= c（min_xy） ，max_xy））

print（p）


但是， min（）剩余价值。

最后一个想法是存储最小值的值，在熔化前执行和 pred 变量，然后将它们添加到熔化的数据框中，以指定在哪个方面他们出现：

头部（结果）
act pred resid
2 52.81000 52.86750 -0.05750133
3 44.46000 42.76825 1.69175252
4 54.58667 49.00482 5.58184181
5 36.23333 35.52386 0.70947731

min_xy < - min（min（results $ act），min（results $ pred））
max_xy < - max（max（results $ act） ，max（results $ pred））

plot < - melt（results，id.vars =pred）

plot < - rbind（plot，data。帧（pred = c（min_xy，max_xy），
variable = c（act，act），value = c（max_xy，min_xy）））

p < - ggplot （plot，aes（x = pred，y = value））+ geom_point（size = 2.5）+ theme_bw（）
p <-p + facet_wrap（〜variable，scales =free）

print（p）


这就是我想要的，除了显示点，也是：

有什么建议可以做这样的事情？

---

我看到这个想法添加 geom_blank（），但我不知道如何指定 aes（） bit，并使它正常工作，或者 geom_point（）等价于直方图使用 aes（y = max（.. count ..））。

---

这里是数据（我的实际，预测和融化前残值）：

 >输出（结果）
结构（列表（act = c（52.81,44.46,54.5866666666667,36.2333333333333，
 53.2266666666667,41.7233333333333,35.2966666666667,30.6833333333333，
 39.25,35.8866666666667,25.1,29.0466666666667,23.2766666666667， 
 56.3866666666667,42.92,41.57,27.92,23.16,38.0166666666667，
 61.8966666666667,37.41,41.6333333333333,35.9466666666667，
 48.9933333333333,30.5666666666667,32.08,40.3633333333333，
 53.2266666666667,64.6066666666667,38.5366666666667， 41.7233333333333，
 25.78，33.4066666666667，27.8033333333333，39.3266666666667，
 48.9933333333333，25.2433333333333，32.67，55.17，42.92，54.5866666666667，
 23.16，64.6066666666667，40.7966666666667，39.0166666666667，
 41.6333333333333，35.8866666666667， 25.1,23.2766666666667,44.46，
 34.2166666666667,40.8033333333333,24.5766666666667,35.73，
 61.8966666666667,62.1833333333333,74.666666666667,39.4666666666667，
 36.6,27.1333333333333），pred = c（52 0.8675013282404，42.7682474758679，
 49.0048248585123，35.5238560262515，48.7942868566949，41.5750416040131，
 33.9548164913007，29.9787449128663，37.6443975781139，36.7196211666685，
 27.6043278172077，27.0615724310721，31.2073056885252，55.0886903524179，
 43.0895814712768，43.0895814712768，32.3549865881578 ，26.2428426737583，
 36.6926037128343，56.7987490221996，45.0370788180147，41.8231642271826，
 38.3297859332601，49.5343916620086，30.8535641206809，29.0117492750411，
 36.9767968381391，49.0826677983065，54.4678549541069，35.5059204731218，
 41.5333417555995，27.6069075391361，31.2404889715121，27.8920960978598 ，
 37.8505531149324，49.2616631533957，30.366837650159，31.1623492639066，
 55.0456078770405，42.772538591063，49.2419293590535，26.1963523976241，
 54.4080781796616，44.9796700541254，34.6996927469131，41.6227713664027，
 36.8449646519306，27.5318686661673，31.6641793552795，42.8198894266632，
 40.5769177148146，40.57691 77148146，29.3807781312816，36.8579132935989，
 55.5617033901752，55.8097119335638，55.1041728261666，43.6094641699075，
 37.0674887276681，27.3876960746536），渣油= C（-0.0575013282403773，
 1.69175252413213，5.58184180815435，0.709477307081826，4.43237980997177，
 0.148291729320228，1.34185017536599，0.704588420467079，1.60560242188613，
 -0.832954500001826，-2.50432781720766，1.98509423559461，-7.93063902185855，
 1.29797631424874，-0.169581471276786，-1.51958147127679，-4.43498658815778，
 -3.08284267375831，1.32406295383237，5.09791764446704， - 7.62707881801468，
 -0.189830893849219，-2.38311926659339，-0.541058328675241，-0.286897454014273，
 3.06825072495888，3.38653649519422，4.14399886836018，10.1388117125598，
 3.03074619354486，0.189991577733821，-1.82690753913609，2.16617769515461，
 -0.088762764526507， 1.47611355173427，-0.268329820062384，-5.12350431682565，
 1.5076507360934,0.124392122959534,0.147461408936991,5.3447373076 1318 
 -3.03635239762411 10.1985884870051 -4.18300338745873 4.31697391975358 
 0.0105619669306023 -0.958297985263961 -2.43186866616734 -8.38751268861282 $ b $ 1.64011057333683 -6.36025104814794 0.226415618518729 -4.80411146461488 
  - 1.1279132935989,6.33496327649151,6.37362139976954,19.5424938405001，
 -4.17279750324084，-0.467488727668119，-0.254362741320246）），.Names = c（act，
pred，resid），row.names = c （2L，3L，4L，5L，6L，7L，8L，9L，
 10L，11L，12L，13L，15L，16L，17L，18L，19L，20L，21L，22L，23L， 24L，25L，26L，28L，29L，30L，31L，32L，33L，34L，35L，36L，37L，
 38L，39L，41L，42L，43L，44L，45L，46L，47L，48L ，49L，50L，51L，
 52L，54L，55L，56L，57L，58L，59L，60L，61L，62L，63L，64L，65L 
），class =data.frame） 
  

解决方案

> geom_blank layer，

  range_act < -  range（range（results $ act），ran ge（results $ pred））
 
d<  -  reshape2 :: melt（results，id.vars =pred）
 
 dummy<  -  data.frame（pred = range_act，value = range_act，
 variable =act，stringsAsFactors = FALSE）
 
 ggplot（d，aes（x = pred，y = value））+ 
 facet_wrap （〜variable，scales =free）+ 
 geom_point（size = 2.5）+ 
 geom_blank（data = dummy）+ 
 theme_bw（）
  

I'm creating a facetted plot to view predicted vs. actual values side by side with a plot of predicted value vs. residuals. I'll be using shiny to help explore the results of modeling efforts using different training parameters. I train the model with 85% of the data, test on the remaining 15%, and repeat this 5 times, collecting actual/predicted values each time. After calculating the residuals, my data.frame looks like this:

head(results)
       act     pred       resid
2 52.81000 52.86750 -0.05750133
3 44.46000 42.76825  1.69175252
4 54.58667 49.00482  5.58184181
5 36.23333 35.52386  0.70947731
6 53.22667 48.79429  4.43237981
7 41.72333 41.57504  0.14829173

What I want:

• Side by side plot of pred vs. act and pred vs. resid
• The x/y range/limits for pred vs. act to be the same, ideally from min(min(results$act), min(results$pred)) to max(max(results$act), max(results$pred))
• The x/y range/limits for pred vs. resid not to be affected by what I do to the actual vs. predicted plot. Plotting for x over only the predicted values and y over only the residual range is fine.

In order to view both plots side by side, I melt the data:

library(reshape2)
plot <- melt(results, id.vars = "pred")

Now plot:

library(ggplot2)
p <- ggplot(plot, aes(x = pred, y = value)) + geom_point(size = 2.5) + theme_bw()
p <- p + facet_wrap(~variable, scales = "free")

print(p)

That's pretty close to what I want:

What I'd like is for the x and y ranges for actual vs. predicted to be the same, but I'm not sure how to specify that, and I don't need that done for the predicted vs. residual plot since the ranges are completely different.

I tried adding something like this for both scale_x_continous and scale_y_continuous:

min_xy <- min(min(plot$pred), min(plot$value))
max_xy <- max(max(plot$pred), max(plot$value))

p <- ggplot(plot, aes(x = pred, y = value)) + geom_point(size = 2.5) + theme_bw()
p <- p + facet_wrap(~variable, scales = "free")
p <- p + scale_x_continuous(limits = c(min_xy, max_xy))
p <- p + scale_y_continuous(limits = c(min_xy, max_xy))

print(p)

But that picks up the min() of the residual values.

One last idea I had is to store the value of the minimum act and pred variables before melting, and then add them to the melted data frame in order to dictate in which facet they appear:

head(results)
       act     pred       resid
2 52.81000 52.86750 -0.05750133
3 44.46000 42.76825  1.69175252
4 54.58667 49.00482  5.58184181
5 36.23333 35.52386  0.70947731

min_xy <- min(min(results$act), min(results$pred))
max_xy <- max(max(results$act), max(results$pred))

plot <- melt(results, id.vars = "pred")

plot <- rbind(plot, data.frame(pred = c(min_xy, max_xy),
  variable = c("act", "act"), value = c(max_xy, min_xy)))

p <- ggplot(plot, aes(x = pred, y = value)) + geom_point(size = 2.5) + theme_bw()
p <- p + facet_wrap(~variable, scales = "free")

print(p)

That does what I want, with the exception that the points show up, too:

Any suggestions for doing something like this?

---

I saw this idea to add geom_blank(), but I'm not sure how to specify the aes() bit and have it work properly, or what the geom_point() equivalent is to the histogram use of aes(y = max(..count..)).

---

Here's data to play with (my actual, predicted, and residual values prior to melting):

> dput(results)
structure(list(act = c(52.81, 44.46, 54.5866666666667, 36.2333333333333, 
53.2266666666667, 41.7233333333333, 35.2966666666667, 30.6833333333333, 
39.25, 35.8866666666667, 25.1, 29.0466666666667, 23.2766666666667, 
56.3866666666667, 42.92, 41.57, 27.92, 23.16, 38.0166666666667, 
61.8966666666667, 37.41, 41.6333333333333, 35.9466666666667, 
48.9933333333333, 30.5666666666667, 32.08, 40.3633333333333, 
53.2266666666667, 64.6066666666667, 38.5366666666667, 41.7233333333333, 
25.78, 33.4066666666667, 27.8033333333333, 39.3266666666667, 
48.9933333333333, 25.2433333333333, 32.67, 55.17, 42.92, 54.5866666666667, 
23.16, 64.6066666666667, 40.7966666666667, 39.0166666666667, 
41.6333333333333, 35.8866666666667, 25.1, 23.2766666666667, 44.46, 
34.2166666666667, 40.8033333333333, 24.5766666666667, 35.73, 
61.8966666666667, 62.1833333333333, 74.6466666666667, 39.4366666666667, 
36.6, 27.1333333333333), pred = c(52.8675013282404, 42.7682474758679, 
49.0048248585123, 35.5238560262515, 48.7942868566949, 41.5750416040131, 
33.9548164913007, 29.9787449128663, 37.6443975781139, 36.7196211666685, 
27.6043278172077, 27.0615724310721, 31.2073056885252, 55.0886903524179, 
43.0895814712768, 43.0895814712768, 32.3549865881578, 26.2428426737583, 
36.6926037128343, 56.7987490221996, 45.0370788180147, 41.8231642271826, 
38.3297859332601, 49.5343916620086, 30.8535641206809, 29.0117492750411, 
36.9767968381391, 49.0826677983065, 54.4678549541069, 35.5059204731218, 
41.5333417555995, 27.6069075391361, 31.2404889715121, 27.8920960978598, 
37.8505531149324, 49.2616631533957, 30.366837650159, 31.1623492639066, 
55.0456078770405, 42.772538591063, 49.2419293590535, 26.1963523976241, 
54.4080781796616, 44.9796700541254, 34.6996927469131, 41.6227713664027, 
36.8449646519306, 27.5318686661673, 31.6641793552795, 42.8198894266632, 
40.5769177148146, 40.5769177148146, 29.3807781312816, 36.8579132935989, 
55.5617033901752, 55.8097119335638, 55.1041728261666, 43.6094641699075, 
37.0674887276681, 27.3876960746536), resid = c(-0.0575013282403773, 
1.69175252413213, 5.58184180815435, 0.709477307081826, 4.43237980997177, 
0.148291729320228, 1.34185017536599, 0.704588420467079, 1.60560242188613, 
-0.832954500001826, -2.50432781720766, 1.98509423559461, -7.93063902185855, 
1.29797631424874, -0.169581471276786, -1.51958147127679, -4.43498658815778, 
-3.08284267375831, 1.32406295383237, 5.09791764446704, -7.62707881801468, 
-0.189830893849219, -2.38311926659339, -0.541058328675241, -0.286897454014273, 
3.06825072495888, 3.38653649519422, 4.14399886836018, 10.1388117125598, 
3.03074619354486, 0.189991577733821, -1.82690753913609, 2.16617769515461, 
-0.088762764526507, 1.47611355173427, -0.268329820062384, -5.12350431682565, 
1.5076507360934, 0.124392122959534, 0.147461408936991, 5.34473730761318, 
-3.03635239762411, 10.1985884870051, -4.18300338745873, 4.31697391975358, 
0.0105619669306023, -0.958297985263961, -2.43186866616734, -8.38751268861282, 
1.64011057333683, -6.36025104814794, 0.226415618518729, -4.80411146461488, 
-1.1279132935989, 6.33496327649151, 6.37362139976954, 19.5424938405001, 
-4.17279750324084, -0.467488727668119, -0.254362741320246)), .Names = c("act", 
"pred", "resid"), row.names = c(2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
10L, 11L, 12L, 13L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 
24L, 25L, 26L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 
38L, 39L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 
52L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L
), class = "data.frame")

解决方案

Here's some code with a dummy geom_blank layer,

range_act <- range(range(results$act), range(results$pred))

d <- reshape2::melt(results, id.vars = "pred")

dummy <- data.frame(pred = range_act, value = range_act,
                    variable = "act", stringsAsFactors=FALSE)

ggplot(d, aes(x = pred, y = value)) +
  facet_wrap(~variable, scales = "free") +
  geom_point(size = 2.5) + 
  geom_blank(data=dummy) + 
  theme_bw()

这篇关于使用facet_wrap和scales =“free”设置各个轴限制在ggplot2中的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！