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我正在尝试使用 R 中的 cowplot 包并排放置一些图。

这是我得到的（相当糟糕的）结果： 在此处输入图像描述

正如你所看到的，这张图充满了麻烦：情节太小太小，文字字体太大，留有太多的空白。

我能够正确地生成和保存三个图，但是当我运行这个命令时，我得到了那个灾难图像：

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（准备运行）：

#!/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")

生成的图像保存为“general.pdf”。谁能告诉我我做错了什么？

谢谢！

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1 回答 1

4

用这个替换save_plot()行：

save_plot("general.pdf", general_plot, ncol = 2, nrow = 2)

我得到了这个结果，这似乎很好：

在此处输入图像描述

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，但这对于这些类型的图是不可避免的。我建议翻转轴，使条形水平运行。

于 2018-03-23T19:15:48.910 回答