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【R语言数据分析开发指南】

news 2025/8/14 14:53:03

R语言数据分析开发指南

1. 环境配置与安装

1.1 安装R与RStudio

R语言下载：https://cran.r-project.org
RStudio IDE：https://posit.co/download/rstudio-desktop/

1.2 设置CRAN镜像（提高下载速度）

# 设置清华大学镜像
options(repos = c(CRAN = "https://mirrors.tuna.tsinghua.edu.cn/CRAN/"))# 或者设置中科大镜像
options(repos = c(CRAN = "https://mirrors.ustc.edu.cn/CRAN/"))

1.3 批量安装必需包

# 定义需要安装的包
required_packages <- c(# 数据处理"tidyverse", "data.table", "readr", "readxl", "janitor",# 可视化基础"ggplot2", "ggpubr", "patchwork", "RColorBrewer", "viridis",# 热图"pheatmap", "ComplexHeatmap", "circlize",# 韦恩图"VennDiagram", "ggVennDiagram", "eulerr",# 机器学习"glmnet", "caret", "e1071", "randomForest",# 模型评估"ROCR", "pROC", "MLmetrics",# 其他实用包"corrplot", "reshape2", "gridExtra"
)# 检查并安装缺失的包
new_packages <- required_packages[!(required_packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) {install.packages(new_packages, dependencies = TRUE)
}# 验证安装
cat("成功安装", length(required_packages) - length(new_packages), "个包\n")
if(length(new_packages) > 0) {cat("新安装的包:", paste(new_packages, collapse = ", "), "\n")
}

1.4 环境管理（推荐）

# 使用renv管理项目依赖
install.packages("renv")
renv::init()      # 初始化项目环境
renv::snapshot()  # 保存当前环境快照
renv::restore()   # 恢复环境

2. 数据读取与预处理

2.1 读取不同格式数据

library(tidyverse)
library(readxl)# CSV文件
data_csv <- read_csv("data/expression_data.csv")# Excel文件
data_excel <- read_excel("data/sample_info.xlsx", sheet = 1)# 制表符分隔文件
data_tsv <- read_tsv("data/gene_annotation.tsv")# RDS文件（R专用格式）
data_rds <- readRDS("data/processed_data.rds")

2.2 数据清洗与整理

library(janitor)# 清理列名
data_clean <- data_csv %>% clean_names() %>%                    # 标准化列名remove_empty(c("rows", "cols"))      # 移除空行空列# 处理基因表达矩阵
prepare_expression_matrix <- function(expr_data) {# 假设第一列是基因名gene_names <- expr_data[[1]]expr_matrix <- as.matrix(expr_data[, -1])rownames(expr_matrix) <- gene_names# 过滤低表达基因keep_genes <- rowSums(expr_matrix > 0) >= ncol(expr_matrix) * 0.1expr_matrix <- expr_matrix[keep_genes, ]# 对数转换（如果需要）expr_matrix <- log2(expr_matrix + 1)return(expr_matrix)
}# 标准化处理
standardize_data <- function(matrix_data, method = "zscore") {if (method == "zscore") {# Z-score标准化（按行）return(t(scale(t(matrix_data))))} else if (method == "minmax") {# Min-Max标准化return((matrix_data - min(matrix_data)) / (max(matrix_data) - min(matrix_data)))}
}# 示例使用
expr_matrix <- prepare_expression_matrix(data_csv)
expr_scaled <- standardize_data(expr_matrix, method = "zscore")

2.3 数据拆分

# 设置随机种子确保可重现
set.seed(123)# 按比例拆分训练集和测试集
split_data <- function(data_matrix, train_ratio = 0.7) {n_samples <- ncol(data_matrix)train_indices <- sample(1:n_samples, size = floor(n_samples * train_ratio))train_data <- data_matrix[, train_indices]test_data <- data_matrix[, -train_indices]return(list(train = train_data,test = test_data,train_indices = train_indices))
}# 使用示例
data_split <- split_data(expr_scaled, train_ratio = 0.7)
train_matrix <- data_split$train
test_matrix <- data_split$test

3. 可视化：热图（Heatmap）

3.1 基础热图（pheatmap）

library(pheatmap)
library(RColorBrewer)# 选择高变异基因
select_top_variable_genes <- function(expr_matrix, n_genes = 100) {gene_vars <- apply(expr_matrix, 1, var, na.rm = TRUE)top_genes <- names(sort(gene_vars, decreasing = TRUE)[1:n_genes])return(expr_matrix[top_genes, ])
}# 创建样本注释
create_sample_annotation <- function(sample_names, groups) {annotation_df <- data.frame(Group = groups,row.names = sample_names)return(annotation_df)
}# 绘制热图
plot_basic_heatmap <- function(expr_data, annotation = NULL, title = "Gene Expression Heatmap") {# 选择颜色colors <- colorRampPalette(rev(brewer.pal(11, "RdBu")))(100)pheatmap(expr_data,color = colors,annotation_col = annotation,scale = "none",clustering_distance_rows = "euclidean",clustering_distance_cols = "euclidean",clustering_method = "complete",show_rownames = FALSE,show_colnames = TRUE,main = title,fontsize = 10,fontsize_row = 8,fontsize_col = 8)
}# 示例使用
top_var_genes <- select_top_variable_genes(expr_scaled, n_genes = 50)# 创建示例分组信息
sample_groups <- factor(rep(c("Control", "Treatment"), each = ncol(top_var_genes)/2))
sample_annotation <- create_sample_annotation(colnames(top_var_genes), sample_groups)# 绘制热图
plot_basic_heatmap(top_var_genes, annotation = sample_annotation)

3.2 高级热图（ComplexHeatmap）

library(ComplexHeatmap)
library(circlize)# 创建复杂热图
plot_complex_heatmap <- function(expr_data, annotation_data, title = "Complex Heatmap") {# 定义颜色映射col_fun <- colorRamp2(c(-2, 0, 2), c("#2166AC", "white", "#B2182B"))# 创建顶部注释top_annotation <- HeatmapAnnotation(Group = annotation_data$Group,col = list(Group = c("Control" = "#1B9E77", "Treatment" = "#D95F02")),annotation_name_side = "left")# 创建热图ht <- Heatmap(expr_data,name = "Z-score",col = col_fun,top_annotation = top_annotation,show_row_names = FALSE,show_column_names = TRUE,column_title = title,row_title = "Genes",clustering_distance_rows = "euclidean",clustering_distance_columns = "euclidean",row_dend_reorder = TRUE,column_dend_reorder = TRUE)return(ht)
}# 绘制复杂热图
complex_ht <- plot_complex_heatmap(top_var_genes, sample_annotation)
draw(complex_ht)

3.3 相关性热图

# 计算相关性矩阵
correlation_heatmap <- function(expr_data, method = "pearson") {cor_matrix <- cor(t(expr_data), method = method, use = "complete.obs")pheatmap(cor_matrix,color = colorRampPalette(c("#053061", "#2166AC", "#4393C3", "#92C5DE", "#D1E5F0", "#F7F7F7", "#FDBF6F", "#FF7F00", "#E31A1C", "#B10026"))(100),main = paste("Gene Correlation Heatmap (", method, ")", sep = ""),show_rownames = FALSE,show_colnames = FALSE)
}# 绘制相关性热图
correlation_heatmap(top_var_genes, method = "pearson")

4. 可视化：韦恩图（Venn Diagram）

4.1 使用VennDiagram包

library(VennDiagram)# 创建示例数据集
create_gene_sets <- function() {all_genes <- paste0("Gene_", 1:1000)set1 <- sample(all_genes, 200)  # 差异表达基因集1set2 <- sample(all_genes, 250)  # 差异表达基因集2set3 <- sample(all_genes, 180)  # 差异表达基因集3return(list("Condition_A" = set1,"Condition_B" = set2,"Condition_C" = set3))
}# 绘制韦恩图
plot_venn_diagram <- function(gene_sets, output_file = NULL) {venn_plot <- venn.diagram(x = gene_sets,filename = output_file,output = TRUE,# 颜色设置fill = c("#66C2A5", "#FC8D62", "#8DA0CB"),alpha = 0.6,# 文字设置cex = 1.2,fontfamily = "serif",fontface = "bold",cat.cex = 1.1,cat.fontface = "bold",cat.default.pos = "outer",cat.pos = c(-27, 27, 135),cat.dist = c(0.055, 0.055, 0.085),# 边框设置lwd = 2,lty = 'solid',col = c("#66C2A5", "#FC8D62", "#8DA0CB"),# 标题main = "Gene Set Overlap",main.pos = c(0.5, 1.05),main.fontface = "bold",main.fontsize = 14)if (is.null(output_file)) {grid::grid.newpage()grid::grid.draw(venn_plot)}return(venn_plot)
}# 使用示例
gene_sets <- create_gene_sets()
plot_venn_diagram(gene_sets)

4.2 使用ggVennDiagram包（推荐）

library(ggVennDiagram)# 现代化韦恩图
plot_modern_venn <- function(gene_sets, colors = NULL) {if (is.null(colors)) {colors <- c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3")}p <- ggVennDiagram(gene_sets, label_alpha = 0.8,category.names = names(gene_sets)) +scale_fill_gradient(low = "white", high = colors[1]) +theme_void() +labs(title = "Gene Set Overlap Analysis",subtitle = paste("Total unique genes:", length(unique(unlist(gene_sets)))))return(p)
}# 四个集合的韦恩图
create_four_sets <- function() {all_genes <- paste0("Gene_", 1:1500)list("Set_A" = sample(all_genes, 300),"Set_B" = sample(all_genes, 280),"Set_C" = sample(all_genes, 250),"Set_D" = sample(all_genes, 320))
}# 绘制四集合韦恩图
four_sets <- create_four_sets()
modern_venn <- plot_modern_venn(four_sets)
print(modern_venn)

4.3 交集分析

# 分析集合交集
analyze_intersections <- function(gene_sets) {library(dplyr)# 计算两两交集set_names <- names(gene_sets)n_sets <- length(gene_sets)intersection_results <- data.frame()for (i in 1:(n_sets-1)) {for (j in (i+1):n_sets) {intersection <- intersect(gene_sets[[i]], gene_sets[[j]])result <- data.frame(Set1 = set_names[i],Set2 = set_names[j],Intersection_Size = length(intersection),Jaccard_Index = length(intersection) / length(union(gene_sets[[i]], gene_sets[[j]])),stringsAsFactors = FALSE)intersection_results <- rbind(intersection_results, result)}}return(intersection_results)
}# 使用示例
intersection_analysis <- analyze_intersections(gene_sets)
print(intersection_analysis)

5. 机器学习：LASSO回归

5.1 LASSO回归用于特征选择

library(glmnet)# 准备LASSO回归数据
prepare_lasso_data <- function(expr_matrix, response_var) {# 转置矩阵：样本为行，基因为列x <- t(expr_matrix)y <- response_varreturn(list(x = x, y = y))
}# 执行LASSO回归
perform_lasso <- function(x, y, alpha = 1, nfolds = 10) {set.seed(123)# 交叉验证选择最优lambdacv_fit <- cv.glmnet(x, y, alpha = alpha, nfolds = nfolds, type.measure = "mse", family = "gaussian")# 提取系数coefficients <- coef(cv_fit, s = "lambda.min")selected_features <- rownames(coefficients)[which(coefficients != 0)]selected_features <- selected_features[selected_features != "(Intercept)"]return(list(model = cv_fit,selected_features = selected_features,lambda_min = cv_fit$lambda.min,lambda_1se = cv_fit$lambda.1se))
}# 可视化LASSO路径
plot_lasso_path <- function(lasso_result) {par(mfrow = c(1, 2))# 系数路径图plot(lasso_result$model$glmnet.fit, xvar = "lambda", label = TRUE)title("LASSO Coefficient Path")# 交叉验证误差图plot(lasso_result$model)title("Cross-Validation Error")par(mfrow = c(1, 1))
}# 示例：创建连续型响应变量
set.seed(123)
continuous_response <- rnorm(ncol(train_matrix))# 准备数据
lasso_data <- prepare_lasso_data(train_matrix, continuous_response)# 执行LASSO回归
lasso_result <- perform_lasso(lasso_data$x, lasso_data$y)# 显示结果
cat("Selected features:", length(lasso_result$selected_features), "\n")
cat("Feature names:\n")
print(head(lasso_result$selected_features, 10))# 绘制结果
plot_lasso_path(lasso_result)

5.2 LASSO用于分类（Logistic LASSO）

# 分类LASSO
perform_lasso_classification <- function(x, y, alpha = 1, nfolds = 10) {set.seed(123)# 确保y是因子y <- as.factor(y)# 交叉验证cv_fit <- cv.glmnet(x, y, alpha = alpha, nfolds = nfolds,type.measure = "class", family = "binomial")# 提取系数coefficients <- coef(cv_fit, s = "lambda.min")selected_features <- rownames(coefficients)[which(coefficients != 0)]selected_features <- selected_features[selected_features != "(Intercept)"]return(list(model = cv_fit,selected_features = selected_features,lambda_min = cv_fit$lambda.min))
}# 示例：创建二分类响应变量
binary_response <- factor(sample(c("Control", "Treatment"), ncol(train_matrix), replace = TRUE))# 执行分类LASSO
lasso_class_data <- prepare_lasso_data(train_matrix, binary_response)
lasso_class_result <- perform_lasso_classification(lasso_class_data$x, lasso_class_data$y)cat("Classification LASSO selected features:", length(lasso_class_result$selected_features), "\n")

5.3 模型预测与评估

# 预测函数
predict_lasso <- function(lasso_model, new_x, type = "response") {predictions <- predict(lasso_model$model, newx = new_x, s = "lambda.min", type = type)return(predictions)
}# 回归评估
evaluate_regression <- function(actual, predicted) {mse <- mean((actual - predicted)^2)rmse <- sqrt(mse)mae <- mean(abs(actual - predicted))r_squared <- cor(actual, predicted)^2return(list(MSE = mse,RMSE = rmse,MAE = mae,R_squared = r_squared))
}# 示例预测
test_data <- prepare_lasso_data(test_matrix, rnorm(ncol(test_matrix)))
predictions <- predict_lasso(lasso_result, test_data$x)
evaluation <- evaluate_regression(test_data$y, predictions[,1])
print(evaluation)

6. 机器学习：支持向量机（SVM）

6.1 使用caret包的SVM

library(caret)
library(e1071)# 准备SVM数据
prepare_svm_data <- function(expr_matrix, labels, feature_selection = TRUE, top_n = 100) {# 转置矩阵data_df <- as.data.frame(t(expr_matrix))# 特征选择（可选）if (feature_selection && ncol(data_df) > top_n) {# 基于方差的特征选择feature_vars <- apply(expr_matrix, 1, var)top_features <- names(sort(feature_vars, decreasing = TRUE)[1:top_n])data_df <- data_df[, top_features]}# 添加标签data_df$Class <- as.factor(labels)return(data_df)
}# 训练SVM模型
train_svm_model <- function(train_data, method = "svmRadial") {# 设置训练控制参数ctrl <- trainControl(method = "repeatedcv",number = 5,              # 5折交叉验证repeats = 3,             # 重复3次classProbs = TRUE,       # 计算类别概率summaryFunction = twoClassSummary,savePredictions = "final")# 训练模型set.seed(123)svm_model <- train(Class ~ .,data = train_data,method = method,metric = "ROC",          # 使用ROC作为评估指标trControl = ctrl,preProcess = c("center", "scale"),  # 标准化tuneLength = 5          # 参数调优)return(svm_model)
}# 创建示例数据
train_labels <- factor(rep(c("Control", "Treatment"), length.out = ncol(train_matrix)))
test_labels <- factor(rep(c("Control", "Treatment"), length.out = ncol(test_matrix)))# 准备训练和测试数据
train_svm_data <- prepare_svm_data(train_matrix, train_labels, top_n = 50)
test_svm_data <- prepare_svm_data(test_matrix, test_labels, top_n = 50)# 训练模型
svm_model <- train_svm_model(train_svm_data)# 查看模型结果
print(svm_model)
plot(svm_model)

6.2 模型预测与评估

library(pROC)# SVM预测函数
predict_svm <- function(model, test_data) {# 预测类别predictions <- predict(model, newdata = test_data)# 预测概率probabilities <- predict(model, newdata = test_data, type = "prob")return(list(classes = predictions,probabilities = probabilities))
}# 评估分类性能
evaluate_classification <- function(actual, predicted, probabilities = NULL) {# 混淆矩阵conf_matrix <- confusionMatrix(predicted, actual)results <- list(confusion_matrix = conf_matrix,accuracy = conf_matrix$overall['Accuracy'],sensitivity = conf_matrix$byClass['Sensitivity'],specificity = conf_matrix$byClass['Specificity'])# 如果有概率预测，计算ROCif (!is.null(probabilities)) {roc_obj <- roc(response = actual, predictor = probabilities[, 1],levels = rev(levels(actual)))results$auc <- auc(roc_obj)results$roc <- roc_obj}return(results)
}# 执行预测
svm_predictions <- predict_svm(svm_model, test_svm_data)# 评估模型
svm_evaluation <- evaluate_classification(actual = test_svm_data$Class,predicted = svm_predictions$classes,probabilities = svm_predictions$probabilities
)# 打印结果
print(svm_evaluation$confusion_matrix)
cat("AUC:", round(svm_evaluation$auc, 3), "\n")# 绘制ROC曲线
plot(svm_evaluation$roc, col = "#D95F02", lwd = 2, main = "SVM ROC Curve")
legend("bottomright", legend = paste("AUC =", round(svm_evaluation$auc, 3)),col = "#D95F02", lwd = 2)

6.3 多类分类SVM

# 多类分类示例
create_multiclass_data <- function(expr_matrix, n_classes = 3) {n_samples <- ncol(expr_matrix)class_labels <- factor(sample(paste0("Class_", 1:n_classes), n_samples, replace = TRUE))data_df <- as.data.frame(t(expr_matrix))# 特征选择feature_vars <- apply(expr_matrix, 1, var)top_features <- names(sort(feature_vars, decreasing = TRUE)[1:100])data_df <- data_df[, top_features]data_df$Class <- class_labelsreturn(data_df)
}# 多类分类评估
evaluate_multiclass <- function(actual, predicted) {conf_matrix <- confusionMatrix(predicted, actual)# 计算每个类别的F1分数precision <- conf_matrix$byClass[, "Precision"]recall <- conf_matrix$byClass[, "Recall"]f1_scores <- 2 * (precision * recall) / (precision + recall)return(list(confusion_matrix = conf_matrix,accuracy = conf_matrix$overall['Accuracy'],f1_scores = f1_scores,macro_f1 = mean(f1_scores, na.rm = TRUE)))
}# 创建多类数据
multiclass_data <- create_multiclass_data(train_matrix, n_classes = 3)# 训练多类SVM
multiclass_svm <- train_svm_model(multiclass_data)
print(multiclass_svm)

7. 结果保存与可视化

7.1 保存模型和结果

# 创建结果目录
dir.create("results", showWarnings = FALSE)
dir.create("results/models", showWarnings = FALSE)
dir.create("results/plots", showWarnings = FALSE)
dir.create("results/data", showWarnings = FALSE)# 保存模型
save_models <- function() {saveRDS(lasso_result, "results/models/lasso_model.rds")saveRDS(svm_model, "results/models/svm_model.rds")# 保存特征选择结果writeLines(lasso_result$selected_features, "results/data/lasso_selected_genes.txt")# 保存预测结果write.csv(svm_evaluation$confusion_matrix$table, "results/data/svm_confusion_matrix.csv")
}save_models()

7.2 生成综合报告

# 生成模型性能报告
generate_performance_report <- function() {report <- paste("=== 模型性能报告 ===",paste("生成时间:", Sys.time()),"","1. LASSO回归结果:",paste("  - 选择特征数量:", length(lasso_result$selected_features)),paste("  - 最优lambda:", round(lasso_result$lambda_min, 6)),"","2. SVM分类结果:",paste("  - 准确率:", round(svm_evaluation$accuracy, 3)),paste("  - AUC:", round(svm_evaluation$auc, 3)),paste("  - 敏感性:", round(svm_evaluation$sensitivity, 3)),paste("  - 特异性:", round(svm_evaluation$specificity, 3)),"","3. 数据信息:",paste("  - 训练样本数:", ncol(train_matrix)),paste("  - 测试样本数:", ncol(test_matrix)),paste("  - 总基因数:", nrow(expr_scaled)),"",sep = "\n")writeLines(report, "results/performance_report.txt")cat(report)
}generate_performance_report()

7.3 保存图形

# 批量保存图形
save_plots <- function() {# 保存热图png("results/plots/heatmap.png", width = 1200, height = 1000, res = 150)plot_basic_heatmap(top_var_genes, sample_annotation)dev.off()# 保存韦恩图png("results/plots/venn_diagram.png", width = 800, height = 600, res = 150)plot_venn_diagram(gene_sets, output_file = NULL)dev.off()# 保存LASSO路径图png("results/plots/lasso_path.png", width = 1000, height = 500, res = 150)plot_lasso_path(lasso_result)dev.off()# 保存ROC曲线png("results/plots/roc_curve.png", width = 600, height = 600, res = 150)plot(svm_evaluation$roc, col = "#D95F02", lwd = 2, main = "SVM ROC Curve")legend("bottomright", legend = paste("AUC =", round(svm_evaluation$auc, 3)),col = "#D95F02", lwd = 2)dev.off()cat("所有图形已保存到 results/plots/ 目录\n")
}save_plots()

8. 项目结构建议

project_name/
├── data/                          # 原始数据
│   ├── expression_data.csv
│   ├── sample_info.xlsx
│   └── gene_annotation.tsv
├── scripts/                       # R脚本
│   ├── 01_data_preprocessing.R
│   ├── 02_exploratory_analysis.R
│   ├── 03_visualization.R
│   ├── 04_machine_learning.R
│   └── 05_generate_report.R
├── results/                       # 结果输出
│   ├── models/                    # 保存的模型
│   ├── plots/                     # 图形文件
│   └── data/                      # 处理后的数据
├── functions/                     # 自定义函数
│   ├── data_utils.R
│   ├── plot_utils.R
│   └── ml_utils.R
├── renv/                          # 环境管理（可选）
├── README.md                      # 项目说明
├── main.R                         # 主执行脚本
└── .Rprofile                      # R配置文件

主执行脚本示例（main.R）

# 主执行脚本
cat("开始数据分析流程...\n")# 加载必要的包和函数
source("functions/data_utils.R")
source("functions/plot_utils.R")
source("functions/ml_utils.R")# 执行分析步骤
source("scripts/01_data_preprocessing.R")
source("scripts/02_exploratory_analysis.R")
source("scripts/03_visualization.R")
source("scripts/04_machine_learning.R")
source("scripts/05_generate_report.R")cat("分析流程完成！\n")
cat("结果已保存到 results/ 目录\n")

9. 常见问题解决

9.1 安装问题

# 问题1: 包安装失败
# 解决方案: 更换镜像或使用二进制包
install.packages("package_name", type = "binary")# 问题2: 依赖包冲突
# 解决方案: 使用renv管理环境
renv::restore()# 问题3: 内存不足
# 解决方案: 增加内存限制
memory.limit(size = 8000)  # Windows系统

9.2 数据处理问题

# 问题1: 缺失值处理
handle_missing_values <- function(data_matrix, method = "remove") {if (method == "remove") {# 移除含有缺失值的行return(data_matrix[complete.cases(data_matrix), ])} else if (method == "impute") {# 用均值填充缺失值for (i in 1:ncol(data_matrix)) {data_matrix[is.na(data_matrix[, i]), i] <- mean(data_matrix[, i], na.rm = TRUE)}return(data_matrix)}
}# 问题2: 数据类型转换
ensure_numeric_matrix <- function(data_df) {# 确保所有列都是数值型numeric_cols <- sapply(data_df, is.numeric)if (!all(numeric_cols)) {warning("发现非数值列，将尝试转换")data_df[, !numeric_cols] <- lapply(data_df[, !numeric_cols], as.numeric)}return(as.matrix(data_df))
}

9.3 性能优化

# 问题1: 大数据集处理慢
# 解决方案: 使用data.table或并行计算
library(data.table)
library(parallel)# 并行计算示例
parallel_correlation <- function(expr_matrix, n_cores = 4) {cl <- makeCluster(n_cores)clusterEvalQ(cl, library(stats))result <- parApply(cl, expr_matrix, 1, function(x) {cor(x, method = "pearson")})stopCluster(cl)return(result)
}# 问题2: 内存使用优化
# 解决方案: 分批处理大数据
process_in_batches <- function(data_matrix, batch_size = 1000, fun) {n_rows <- nrow(data_matrix)n_batches <- ceiling(n_rows / batch_size)results <- list()for (i in 1:n_batches) {start_idx <- (i - 1) * batch_size + 1end_idx <- min(i * batch_size, n_rows)batch_data <- data_matrix[start_idx:end_idx, ]results[[i]] <- fun(batch_data)# 垃圾回收gc()}return(do.call(rbind, results))
}

9.4 调试技巧

# 调试函数
debug_info <- function(obj, name = "object") {cat("=== Debug Info for", name, "===\n")cat("Class:", class(obj), "\n")cat("Dimensions:", dim(obj), "\n")cat("Length:", length(obj), "\n")if (is.numeric(obj)) {cat("Range:", range(obj, na.rm = TRUE), "\n")cat("NA count:", sum(is.na(obj)), "\n")}cat("First few elements:\n")print(head(obj))cat("========================\n")
}# 使用示例
debug_info(expr_matrix, "expression_matrix")