My plot_grids using cowplot in R with ggplot2 are missing one of the four plots in the grid?
Question
Sorry for the mess of how much code I'm posting but I'm missing one of the four ggplots using plot_grid with the package cowplot, there should be four in the grid and for some reason one of them is missing, does anyone know why?
Here's the plot_grids that are missing one of the four graphs:
Here's the error:
> 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.
Here's the reproducible code:
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)
Here's the data and data frame:
> 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
I attempted adding title_theme but it gives the error 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)
1 answers
solution1
0 2021-11-25 00:52:00
Here is an example:
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))
Created on 2021-11-25 by the reprex package (v2.0.1)
Does this solve your problem?
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