我在 R 中使用 cowplot 和 ggplot2 的 plot_grids 缺少网格中的四个图之一？
Posted
技术标签:
【中文标题】我在 R 中使用 cowplot 和 ggplot2 的 plot_grids 缺少网格中的四个图之一？【英文标题】：My plot_grids using cowplot in R with ggplot2 are missing one of the four plots in the grid? 【发布时间】：2022-01-03 07:33:21 【问题描述】：对不起，我发布了多少代码，但我错过了使用 plot_grid 和包cowplot的四个ggplots之一，网格中应该有四个，并且由于某种原因其中一个丢失了，确实有人知道为什么吗？
这是缺少四个图表之一的 plot_grids：
这是错误：
> plot_grid("ANN Models with 08 Learning Rate", plot3_08, plot10_08, plot20_08, plot50_08, labels = c("N3", "N10", "N20", "N50"), ncol = 2, nrow = 2)
Warning message:
In as_grob.default(plot) :
Cannot convert object of class character into a grob.
这是可重现的代码：
library(Rcpp)
library(RSNNS)
library(ggplot2)
library(plotROC)
library(tidyr)
library(cowplot)
setwd("**set working directory**")
data <- read.csv("WDBC.csv", header=T)
data <- data[,1:4]
data <- scale(data) # normalizes the data
numHneurons3 = 3
numHneurons10 = 10
numHneurons20 = 20
numHneurons50 = 50
DecTargets = decodeClassLabels(data[,4])
train.test3 <- splitForTrainingAndTest(data, DecTargets,ratio = 0.50) # split
train.test10 <- splitForTrainingAndTest(data, DecTargets,ratio = 0.50) # split
train.test20 <- splitForTrainingAndTest(data, DecTargets,ratio = 0.50) # split
train.test50 <- splitForTrainingAndTest(data, DecTargets,ratio = 0.50) # split
model3_02 <- mlp(train.test3$inputsTrain, train.test3$targetsTrain, # build model3
size = numHneurons3, learnFuncParams = c(0.02),maxit = 10000,
inputsTest = train.test3$inputsTest,
targetsTest = train.test3$targetsTest)
model3_08 <- mlp(train.test3$inputsTrain, train.test3$targetsTrain, # build model3
size = numHneurons3, learnFuncParams = c(0.08),maxit = 10000,
inputsTest = train.test3$inputsTest,
targetsTest = train.test3$targetsTest)
model10_02 <- mlp(train.test10$inputsTrain, train.test10$targetsTrain, # build model10
size = numHneurons10, learnFuncParams = c(0.02),maxit = 10000,
inputsTest = train.test10$inputsTest,
targetsTest = train.test10$targetsTest)
model10_08 <- mlp(train.test10$inputsTrain, train.test10$targetsTrain, # build model10
size = numHneurons10, learnFuncParams = c(0.08),maxit = 10000,
inputsTest = train.test10$inputsTest,
targetsTest = train.test10$targetsTest)
model20_02 <- mlp(train.test20$inputsTrain, train.test20$targetsTrain, # build model20
size = numHneurons20, learnFuncParams = c(0.02),maxit = 10000,
inputsTest = train.test20$inputsTest,
targetsTest = train.test20$targetsTest)
model20_08 <- mlp(train.test20$inputsTrain, train.test20$targetsTrain, # build model20
size = numHneurons20, learnFuncParams = c(0.08),maxit = 10000,
inputsTest = train.test20$inputsTest,
targetsTest = train.test20$targetsTest)
model50_02 <- mlp(train.test50$inputsTrain, train.test50$targetsTrain, # build model50
size = numHneurons50, learnFuncParams = c(0.02),maxit = 10000,
inputsTest = train.test50$inputsTest,
targetsTest = train.test50$targetsTest)
model50_08 <- mlp(train.test50$inputsTrain, train.test50$targetsTrain, # build model50
size = numHneurons50, learnFuncParams = c(0.08),maxit = 10000,
inputsTest = train.test50$inputsTest,
targetsTest = train.test50$targetsTest)
trainFitTar3_02 <- cbind(fitted.values(model3_02), train.test3$targetsTrain)
predictions = predict(model3_02, train.test3$inputsTest)
trainFitTar3_08 <- cbind(fitted.values(model3_08), train.test3$targetsTrain)
predictions = predict(model3_08, train.test3$inputsTest)
trainFitTar10_02 <- cbind(fitted.values(model10_02), train.test10$targetsTrain)
predictions = predict(model10_02, train.test10$inputsTest)
trainFitTar10_08 <- cbind(fitted.values(model10_08), train.test10$targetsTrain)
predictions = predict(model10_08, train.test10$inputsTest)
trainFitTar20_02 <- cbind(fitted.values(model20_02), train.test20$targetsTrain)
predictions = predict(model20_02, train.test20$inputsTest)
trainFitTar20_08 <- cbind(fitted.values(model20_08), train.test20$targetsTrain)
predictions = predict(model20_08, train.test20$inputsTest)
trainFitTar50_02 <- cbind(fitted.values(model50_02), train.test50$targetsTrain)
predictions = predict(model50_02, train.test50$inputsTest)
trainFitTar50_08 <- cbind(fitted.values(model50_08), train.test50$targetsTrain)
predictions = predict(model50_08, train.test50$inputsTest)
#--------------------------------------
# GGPlots of the Iterative Error:
#--------------------------------------
test_error_m302 <- model3_02$IterativeTestError
train_error_m302 <- model3_02$IterativeFitError
test_error_m308 <- model3_08$IterativeTestError
train_error_m308 <- model3_08$IterativeFitError
test_error_m1002 <- model10_02$IterativeTestError
train_error_m1002 <- model10_02$IterativeFitError
test_error_m1008 <- model10_08$IterativeTestError
train_error_m1008 <- model10_08$IterativeFitError
test_error_m2002 <- model20_02$IterativeTestError
train_error_m2002 <- model20_02$IterativeFitError
test_error_m2008 <- model20_08$IterativeTestError
train_error_m2008 <- model20_08$IterativeFitError
test_error_m5002 <- model50_02$IterativeTestError
train_error_m5002 <- model50_02$IterativeFitError
test_error_m5008 <- model50_08$IterativeTestError
train_error_m5008 <- model50_08$IterativeFitError
error_df_m3_02 <- data.frame(iter = c(seq_along(test_error_m302),
seq_along(train_error_m302)),
Error = c(test_error_m302, train_error_m302),
type = c(rep("test", length(test_error_m302)),
rep("train", length(train_error_m302))
))
error_df_m3_08 <- data.frame(iter = c(seq_along(test_error_m308),
seq_along(train_error_m308)),
Error = c(test_error_m308, train_error_m308),
type = c(rep("test", length(test_error_m308)),
rep("train", length(train_error_m308))
))
error_df_m10_02 <- data.frame(iter = c(seq_along(test_error_m1002),
seq_along(train_error_m1002)),
Error = c(test_error_m1002, train_error_m1002),
type = c(rep("test", length(test_error_m1002)),
rep("train", length(train_error_m1002))
))
error_df_m10_08 <- data.frame(iter = c(seq_along(test_error_m1008),
seq_along(train_error_m1008)),
Error = c(test_error_m1008, train_error_m1008),
type = c(rep("test", length(test_error_m1008)),
rep("train", length(train_error_m1008))
))
error_df_m20_02 <- data.frame(iter = c(seq_along(test_error_m2002),
seq_along(train_error_m2002)),
Error = c(test_error_m2002, train_error_m2002),
type = c(rep("test", length(test_error_m2002)),
rep("train", length(train_error_m2002))
))
error_df_m20_08 <- data.frame(iter = c(seq_along(test_error_m2008),
seq_along(train_error_m2008)),
Error = c(test_error_m2008, train_error_m2008),
type = c(rep("test", length(test_error_m2008)),
rep("train", length(train_error_m2008))
))
error_df_m50_02 <- data.frame(iter = c(seq_along(test_error_m5002),
seq_along(train_error_m5002)),
Error = c(test_error_m5002, train_error_m5002),
type = c(rep("test", length(test_error_m5002)),
rep("train", length(train_error_m5002))
))
error_df_m50_08 <- data.frame(iter = c(seq_along(test_error_m5008),
seq_along(train_error_m5008)),
Error = c(test_error_m5008, train_error_m5008),
type = c(rep("test", length(test_error_m5008)),
rep("train", length(train_error_m5008))
))
plot3_02 <- ggplot(error_df_m3_02[c(5000:10000, 15000:20000),],
aes(iter, Error, color = type,
each = length(test_error_m302))) + geom_line() + ggtitle("Error Model 3 Neurons 02 Learning Rate")
plot3_08 <- ggplot(error_df_m3_08[c(5000:10000, 15000:20000),],
aes(iter, Error, color = type,
each = length(test_error_m308))) + geom_line() + ggtitle("Error Model 3 Neurons 08 Learning Rate")
plot10_02 <- ggplot(error_df_m10_02[c(5000:10000, 15000:20000),],
aes(iter, Error, color = type,
each = length(test_error_m1002))) + geom_line() + ggtitle("Error Model 10 Neurons 02 Learning Rate")
plot10_08 <- ggplot(error_df_m10_08[c(5000:10000, 15000:20000),],
aes(iter, Error, color = type,
each = length(test_error_m1008))) + geom_line() + ggtitle("Error Model 10 Neurons 08 Learning Rate")
plot20_02 <- ggplot(error_df_m20_08[c(5000:10000, 15000:20000),],
aes(iter, Error, color = type,
each = length(test_error_m2002))) + geom_line() + ggtitle("Error Model 20 Neurons 02 Learning Rate")
plot20_08 <- ggplot(error_df_m20_08[c(5000:10000, 15000:20000),],
aes(iter, Error, color = type,
each = length(test_error_m2008))) + geom_line() + ggtitle("Error Model 20 Neurons 08 Learning Rate")
plot50_02 <- ggplot(error_df_m50_02[c(5000:10000, 15000:20000),],
aes(iter, Error, color = type,
each = length(test_error_m2002))) + geom_line() + ggtitle("Error Model 50 Neurons 02 Learning Rate")
plot50_08 <- ggplot(error_df_m50_08[c(5000:10000, 15000:20000),],
aes(iter, Error, color = type,
each = length(test_error_m2008))) + geom_line() + ggtitle("Error Model 50 Neurons 08 Learning Rate")
plot_grid("ANN Models with 02 Learning Rate", plot3_02, plot10_02, plot20_02, plot50_02, labels = c("N3", "N10", "N20", "N50"), ncol = 2, nrow = 2)
plot_grid("ANN Models with 08 Learning Rate", plot3_08, plot10_08, plot20_08, plot50_08, labels = c("N3", "N10", "N20", "N50"), ncol = 2, nrow = 2)
这是数据和数据框：
> head(data, 10)
PatientID radius texture perimeter
[1,] -0.2361973 1.0960995 -2.0715123 1.26881726
[2,] -0.2361956 1.8282120 -0.3533215 1.68447255
[3,] 0.4313615 1.5784992 0.4557859 1.56512598
[4,] 0.4317407 -0.7682333 0.2535091 -0.59216612
[5,] 0.4318215 1.7487579 -1.1508038 1.77501133
[6,] -0.2361855 -0.4759559 -0.8346009 -0.38680772
[7,] -0.2361809 1.1698783 0.1605082 1.13712450
[8,] 0.4326197 -0.1184126 0.3581350 -0.07280278
[9,] -0.2361759 -0.3198854 0.5883121 -0.18391855
[10,] 0.4329621 -0.4731182 1.1044669 -0.32919213
> head(error_df)
iter Error type
1 1 6913.5938 test
2 2 2981.7415 test
3 3 1906.2921 test
4 4 1425.6680 test
5 5 1157.7373 test
6 6 988.3097 test
我尝试添加 title_theme 但它给出了错误 object 'title_theme1' not found:
title_theme1 <- ggdraw() +
draw_label("ANN Models with 02 Learning Rate",
fontfamily = theme_georgia()$text$family,
fontface = theme_georgia()$plot.title$face, x = 0.05, hjust = 0)
plot_grid(title_theme, gridded, ncol = 1, rel_heights = c(0.2, 1))
title_theme2 <- ggdraw() +
draw_label("ANN Models with 08 Learning Rate",
fontfamily = theme_georgia()$text$family,
fontface = theme_georgia()$plot.title$face, x = 0.05, hjust = 0)
plot_grid(title_theme, gridded, ncol = 1, rel_heights = c(0.2, 1))
#plot_grid(plot3_02, plot10_02, plot20_02, plot50_02 + rremove("x.text"),
plot_grid(title_theme1, plot3_02, plot10_02, plot20_02, plot50_02, labels = c("N3", "N10", "N20", "N50"), ncol = 2, nrow = 2)
plot_grid(title_theme2, plot3_08, plot10_08, plot20_08, plot50_08, labels = c("N3", "N10", "N20", "N50"), ncol = 2, nrow = 2)
【问题讨论】：
我怀疑问题在于plot_grid() 期望将绘图列表作为第一个参数，例如从plot_grid("ANN Models with 02 Learning Rate", plot3_02, plot10_02, plot20_02, plot50_02, labels = c("N3", "N10", "N20", "N50"), ncol = 2, nrow = 2) 中删除“具有 02 学习率的 ANN 模型”，看看它是否能解决问题。 （请参阅***.com/questions/50973713/… 为您的情节添加标题/副标题）
谢谢！我会尝试重新运行它，但它总是需要很长时间才能运行
@jared_mamrot 我尝试使用 title_theme 但它不起作用，这就是链接堆栈帖子中的建议，我在最后更新了我的问题以显示附加组件加结果
【参考方案1】：
这是一个例子：
library(tidyverse)
library(palmerpenguins)
library(cowplot)
p1 <- penguins %>%
na.omit() %>%
ggplot(aes(x = bill_length_mm, y = body_mass_g)) +
geom_point() +
theme_bw(base_size = 14) +
labs(x = "Bill Length (mm)",
y = "Body Mass (g)")
p2 <- penguins %>%
na.omit() %>%
ggplot(aes(x = factor(species), y = body_mass_g)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(aes(color = species),
show.legend = FALSE,
width = 0.25,
alpha = 0.4,
size = 3) +
theme_bw(base_size = 14) +
labs(x = "Species",
y = "Body Mass (g)")
p3 <- penguins %>%
na.omit() %>%
ggplot(aes(x = bill_depth_mm, y = body_mass_g)) +
geom_hex(bins = 10, show.legend = FALSE) +
scale_x_continuous(expand = c(0:1)) +
scale_y_continuous(expand = c(0,2)) +
labs(x = "Species",
y = "Body Mass (g)") +
theme_bw(base_size = 14) +
theme(legend.position = "none")
p4 <- penguins %>%
na.omit() %>%
ggplot(aes(x = factor(sex), y = body_mass_g)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(aes(color = sex),
width = 0.25,
alpha = 0.4,
size = 3,
show.legend = FALSE) +
labs(x = "Sex",
y = "Body Mass (g)") +
theme_bw(base_size = 14)
plot_grid("title", p1, p2, p3, p4, labels = "AUTO", label_size = 16)
#> Warning in as_grob.default(plot): Cannot convert object of class character into
#> a grob.
plot_grid(p1, p2, p3, p4, labels = "AUTO", label_size = 16)
plots <- plot_grid(p1, p2, p3, p4, labels = "AUTO", label_size = 16)
title <- ggplot() +
labs(title = "ANN Models with 02 Learning Rate",
subtitle = "Subtitle here") +
theme_minimal()
plot_grid(title, plots, ncol = 1, rel_heights = c(0.1, 0.9))
title_theme1 <- ggdraw() +
draw_label("ANN Models with 02 Learning Rate",
fontfamily = theme_bw()$text$family,
fontface = theme_bw()$plot.title$face,
x = 0.05, hjust = 0)
plot_grid(title_theme1, plots, ncol = 1, rel_heights = c(0.1, 0.9))
由reprex package (v2.0.1) 于 2021 年 11 月 25 日创建
这能解决您的问题吗？
【讨论】：
以上是关于我在 R 中使用 cowplot 和 ggplot2 的 plot_grids 缺少网格中的四个图之一？的主要内容，如果未能解决你的问题，请参考以下文章
R语言使用cowplot包的plot_grid函数将两个ggplot2可视化结果并排组合起来并添加图像标签AB设置组合图像使用共享的图例（shared legend in cowplot）
将刻面ggplots（facet_wrap）与R中的cowplot对齐