R，为什么 cowplot plot_grid() 并排生成小图片? [英] R, why is cowplot plot_grid() generating tiny pictures side by side?
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问题描述
我正在尝试使用 R 中的 cowplot 包并排放置一些图.
I'm trying to put some plots side by side using the cowplot package in R.
这是我得到的(相当糟糕的)结果:
Here's the (quite bad) result I am getting:
正如你所看到的，这张图片充满了问题:情节太小太小，文字字体太大，还有太多的空白空间.
As you can see, this image is full of troubles: the plots are tiny and too small, the text font sizes are too big, and there's too much blank space left.
我能够正确生成并保存三个图中的每一个，但是当我运行此命令时，我得到了灾难图像:
I am able to generate and save correctly each of the three plots, but I get that disaster image when I run this command:
general_plot <- plot_grid(plot_cf, plot_pos_neg, plot_scores, ncol = 2, align="h", labels=c("A", "B", "C"), label_size=5)
我做错了什么?
这是我的完整代码(可以运行):
Here's my complete code (ready to run):
#!/usr/bin/env Rscript
if (!require("RColorBrewer")) {
install.packages("RColorBrewer")
library(RColorBrewer)
}
library(grid)
library(gridExtra)
library(gtable)
library(ggplot2)
library(cowplot)
SAVE_FILES = FALSE
digits = 2
generalTextSize = 5
# Confusion matrix barplot
barplot_creator <- function(inputText, saveFileName, upperYlim) {
confusionMatrixDataFrame <- read.table(header=TRUE, text=inputText)
confusionMatrixDataFrame$category = gsub("_", " ", confusionMatrixDataFrame$category)
confusionMatrixDataFrame$category = factor(confusionMatrixDataFrame$category, levels=unique(confusionMatrixDataFrame$category))
p <- ggplot(confusionMatrixDataFrame, aes(x=category, y=amount)) + geom_bar(aes(fill=category), stat="identity", position=position_dodge()) + ylim(0,upperYlim)
p + scale_colour_brewer(palette="RdBu") + xlab("") + ylab("")
p + theme(legend.text=element_text(size=generalTextSize), text = element_text(size=generalTextSize), axis.text.x = element_text(size=generalTextSize), axis.text.y = element_text(size=generalTextSize))
p
if (SAVE_FILES==TRUE) {
ggsave(saveFileName)
}
return(p)
}
# Three elements barplot
accuracy_f1score_mcc_barplot <- function(accuracy, f1_score, normMCC, saveFileName) {
thisText <- paste("\n category amount\n accuracy=",accuracy, " ", accuracy,"\n F1_score=", f1_score, " ", f1_score,"\n\n normMCC=", normMCC," ", normMCC,"\n", sep="")
upperYlim = 1.0
return(barplot_creator(thisText, saveFileName, upperYlim))
}
# Confusion matrix barplot
confusion_matrix_barplot <- function(tp, fn, tn, fp, saveFileName) {
thisText <- paste("\n category amount\n TP=",tp," ", tp,"\n FN=", fn," ", fn,"\n\n TN=",tn," ", tn,"\n FP=", fp," ", fp,"\n", sep="")
# confusionMatrixDataFrame <- read.table(header=TRUE, text='
# category amount
# TP 47
# FN 3
#
# TN 5
# FP 45
# ')
upperYlim = 100
return(barplot_creator(thisText, saveFileName, upperYlim))
}
# Positives negatives barplot
positives_negatives_barplot <- function(pos, neg, saveFileName) {
thisText <- paste("\n category amount\n positives=",pos, " ", pos,"\n negatives=", neg, " ", neg,"\n", sep="")
upperYlim = 100
return(barplot_creator(thisText, saveFileName, upperYlim))
}
# accuracy
accuracy <- function (tp, fn, tn, fp) {
accuracy_result <- (tp+tn)/(tn + tp + fp + fn)
accuracy_result <- round(accuracy_result, digits)
cat("accuracy = ", round(accuracy_result, digits), "\t\t in the [0; 1] interval \n")
return(accuracy_result)
}
# F1 score
f1_score <- function (tp, fn, tn, fp) {
f1_score_result <- (2*tp)/(2*tp + fp + fn)
f1_score_result <- round(f1_score_result, digits)
cat("F1 score = ", round(f1_score_result, digits), "\t\t in the [0; 1] interval\n")
return(f1_score_result)
}
# Matthews correlation coefficient
mcc <- function (tp, fn, tn, fp) {
TP <- tp
FN <- fn
TN <- tn
FP <- fp
#TP;TN;FP;FN # for debugging
sum1 <- TP+FP; sum2 <-TP+FN ; sum3 <-TN+FP ; sum4 <- TN+FN;
denom <- as.double(sum1)*sum2*sum3*sum4 # as.double to avoid overflow error on large products
if (any(sum1==0, sum2==0, sum3==0, sum4==0)) {
denom <- 1
}
mcc <- ((TP*TN)-(FP*FN)) / sqrt(denom)
mcc <- round(mcc, digits)
cat("MCC = ", round(mcc, digits), "\t\t\t in the [-1; +1] interval\n")
return(mcc)
}
# Confusion matrix pie
confusion_matrix_pie <- function(tp, fn, tn, fp, saveFileName) {
thisDataFrame <- data.frame(
category = c(paste("TP=", tp, ""), paste("FN=", fn, ""), paste("TN=", tn, ""), paste("FP=", fp, "")),
amount = c(tp, fn, tn, fp)
)
thisDataFrame$category = factor(thisDataFrame$category, levels=unique(thisDataFrame$category)) # we set as levels to respect this order in the plot legend
head(thisDataFrame)
# Barplot
thisBarPlot<- ggplot(thisDataFrame, aes(x="", y=amount, fill=category))+
geom_bar(width = 1.0, stat = "identity")
thisPie <- thisBarPlot + coord_polar("y") + xlab("") + ylab("") + scale_fill_brewer(palette="RdBu")
thisPie + theme(legend.text=element_text(size=generalTextSize))
thisPie
if (SAVE_FILES==TRUE) {
ggsave(saveFileName)
}
return(thisPie)
}
# positive_negative pie
positive_negative_pie <- function(pos, neg, saveFileName) {
thisDataFrame <- data.frame(
category = c(paste("positives=", pos, ""), paste("negatives=", neg, "")),
amount = c(pos, neg)
)
thisDataFrame$category = factor(thisDataFrame$category, levels=unique(thisDataFrame$category)) # we set as levels to respect this order in the plot legend
head(thisDataFrame)
# Barplot
thisBarPlot<- ggplot(thisDataFrame, aes(x="", y=amount, fill=category))+
geom_bar(width = 1.0, stat = "identity")
thisPie <- thisBarPlot + coord_polar("y") + xlab("") + ylab("") + scale_fill_brewer(palette="RdBu")
thisPie + theme(legend.text=element_text(size=generalTextSize))
thisPie
if (SAVE_FILES==TRUE) {
ggsave(saveFileName)
}
return(thisPie)
}
#
# Function that generates all the plot and their files
#
generate_all_the_plots <- function(tp, fn, tn, fp, addTitle) {
randomValue = sample(1:10000000, 1)
test_title = paste(addTitle, "_test",randomValue,"__TP",tp,"_FN",fn,"_TN",tn,"_FP",fp, sep="")
positives = tp + fn
negatives = tn + fp
confusion_matrix_pie_file_name = paste("../plots/", test_title, "_confusion_matrix_pie.pdf", sep="")
plot_cf <- confusion_matrix_pie(tp, fn, tn, fp, confusion_matrix_pie_file_name)
positive_negative_pie_file_name = paste("../plots/", test_title, "_positive_negative_pie.pdf", sep="")
plot_pos_neg<-positive_negative_pie(positives, negatives, positive_negative_pie_file_name)
current_accuracy = accuracy(tp, fn, tn, fp)
current_f1_score = f1_score(tp, fn, tn, fp)
current_MCC = mcc(tp, fn, tn, fp)
current_normMCC = (current_MCC+1)/2 # normalized MCC
current_normMCC = round(current_normMCC, digits)
cat("normMCC = ", round(current_normMCC, digits), "\t\t in the [0; 1] interval\n")
confusion_matrix_barplot_file_name = paste("../plots/", test_title, "_confusion_matrix_barplot.pdf", sep="")
# confusion_matrix_barplot(tp, fn, tn, fp, confusion_matrix_barplot_file_name)
positives_negatives_barplot_file_name = paste("../plots/", test_title, "_positives_negatives_barplot.pdf", sep="")
# positives_negatives_barplot(positives, negatives, positives_negatives_barplot_file_name)
accuracy_f1score_mcc_barplot_file_name = paste("../plots/", test_title, "_accuracy_f1score_mcc.pdf", sep="")
plot_scores <- accuracy_f1score_mcc_barplot(current_accuracy, current_f1_score, current_normMCC, accuracy_f1score_mcc_barplot_file_name)
## Side by side
# right_col_plot = plot_grid(plot_cf, plot_pos_neg, labels=c("b", "c"), align="v", ncol=1)
# plot_grid(plot_scores, right_col_plot, ncol = 2, rel_widths=c(1.3, 1))
theme_set(theme_cowplot(font_size=12))
general_plot <- plot_grid(plot_cf, plot_pos_neg, plot_scores, ncol = 2, align="h", labels=c("A", "B", "C"), label_size=5)
save_plot("general.pdf", general_plot)
#(plot_cf, plot_pos_neg, plot_scores)
}
exampleA = c(90, 1, 0, 9)
exampleB = c(47, 3, 5, 45)
exampleC = c(1, 9, 89, 1)
# tp = exampleC[1]
# fn = exampleC[2]
# tn = exampleC[3]
# fp = exampleC[4]
selected_example <- exampleC
addTitle = "ExampleC"
#library(gsubfn) # need 0.7-0 or later
generate_all_the_plots(selected_example[1], selected_example[2], selected_example[3], selected_example[4], addTitle)
#if (file.exists("../plots/Rplots.pdf")) file.remove("../plots/Rplots.pdf")
生成的图像保存在general.pdf"中.谁能告诉我我做错了什么?
The image generated is saved into "general.pdf". Can anyone tell me what I'm doing wrong?
谢谢！
推荐答案
将 save_plot() 行替换为:
save_plot("general.pdf", general_plot, ncol = 2, nrow = 2)
我得到了这个结果，看起来基本没问题:
I get this result, which seems mostly fine:
标签(A、B、C)太小了，但那是因为您将它们的大小设置为 5.它应该至少为 12.并且 c 部分中的轴刻度标签重叠，但这对于那些类型是不可避免的的情节.我建议翻转轴，使条形水平运行.
The labels (A, B, C) are way too small, but that's because you set their size to 5. It should be at least 12. And the axis tick labels in part c overlap, but that's unavoidable for those kinds of plots. I would recommend flipping the axes so the bars run horizontally.
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