大数据框按每8列分组计算行均值的循环问题求助

我有一个大数据框，每个样本对应8列，共200个样本，需要按每8列一组计算行均值（比如第1-8列、9-16列这类分组），行名为基因名。尝试了以下嵌套循环代码，但无法正常运行：

for(j in 1:nrow(mat)){
for (i in 1:ncol(mat)/8) {
row_m[j, i]<- rowMeans(mat[j,c(i:i+7)])
}
}

示例数据：

dput(head(deconv3[1:9], 20))
structure(list(AM.amplifying.intestine = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), AM43.5.epithelial.of.mammary = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4.76506, 0, 0, 1406.48, 0, 196.401,
0, 1996.5, 0), AM.epithelium.of.bronchus = c(549.649, 1647.63,
0, 0, 0, 0, 0, 0, 699.868, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
AM.epithelium.of.intestine = c(0, 0, 0, 0, 0, 0, 572.85,
59.2414, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), AM.epithelium.of.trachea = c(0,
0, 0, 0, 199.549, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), AM.kidney.epithelial.cell = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1.32926, 0, 0, 333.592, 0), AM.medullary.thymic.epithelial.cell = c(126.847,
0, 0, 0, 0, 0, 0, 0, 0, 63.1822, 0, 0, 0, 0, 0, 0, 0, 26.0598,
0, 11.117), AM.myoepithelial.cell = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), AK.amplifying.intestine = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c("A1BG",
"A2M", "NAT2", "SERPINA3", "AANAT", "ABAT", "ABCA2", "ABCA3",
"ABCB7", "ABCA4", "ABO", "ACACA", "ACADL", "ACADS", "ACADSB",
"ACAT1", "ACLY", "ACR", "ACP1", "ACRV1"), class = "data.frame")

• 循环变量生成错误：1:ncol(mat)/8会先生成1到列数的序列再除以8，得到的是小数，不是每组的起始索引
• 列索引写法错误：i:i+7会被解析成(i:i)+7，正确的连续索引写法应该是i:(i+7)
• 结果矩阵未初始化：直接给row_m[j,i]赋值会报错，因为row_m还没创建

方法1：向量化分组计算（推荐，效率更高） #

利用split.default按列分组，再对每组计算行均值，最后合并结果：

# 生成分组索引：每8列一组
groups <- rep(1:(ncol(mat)%/%8), each=8)
# 处理列数不是8整数倍的剩余列（可选，根据需求调整）
if(ncol(mat) %%8 != 0){
  groups <- c(groups, rep(max(groups)+1, ncol(mat)%%8))
}

# 按分组计算行均值
row_m <- sapply(split.default(mat, groups), rowMeans)
# 给结果列命名（可选）
colnames(row_m) <- paste0("Sample_", 1:ncol(row_m))

方法2：修正循环代码 #

如果坚持用循环，先初始化结果矩阵，再修正循环逻辑：

# 计算分组数量
n_groups <- ncol(mat) %/%8
# 初始化结果矩阵，行名和原数据一致
row_m <- matrix(NA, nrow = nrow(mat), ncol = n_groups, 
                dimnames = list(rownames(mat), paste0("Sample_",1:n_groups)))

# 循环计算每个分组的行均值
for(i in 1:n_groups){
  # 计算当前分组的列索引范围
  col_indices <- (i-1)*8 + 1 : (i*8)
  row_m[,i] <- rowMeans(mat[, col_indices])
}

示例测试 #

用你提供的9列示例数据测试，前8列为一组，第9列为一组：

# 加载示例数据
mat <- structure(list(AM.amplifying.intestine = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), AM43.5.epithelial.of.mammary = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4.76506, 0, 0, 1406.48, 0, 196.401,
0, 1996.5, 0), AM.epithelium.of.bronchus = c(549.649, 1647.63,
0, 0, 0, 0, 0, 0, 699.868, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
AM.epithelium.of.intestine = c(0, 0, 0, 0, 0, 0, 572.85,
59.2414, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), AM.epithelium.of.trachea = c(0,
0, 0, 0, 199.549, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), AM.kidney.epithelial.cell = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1.32926, 0, 0, 333.592, 0), AM.medullary.thymic.epithelial.cell = c(126.847,
0, 0, 0, 0, 0, 0, 0, 0, 63.1822, 0, 0, 0, 0, 0, 0, 0, 26.0598,
0, 11.117), AM.myoepithelial.cell = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), AK.amplifying.intestine = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c("A1BG",
"A2M", "NAT2", "SERPINA3", "AANAT", "ABAT", "ABCA2", "ABCA3",
"ABCB7", "ABCA4", "ABO", "ACACA", "ACADL", "ACADS", "ACADSB",
"ACAT1", "ACLY", "ACR", "ACP1", "ACRV1"), class = "data.frame")

# 用方法1计算
groups <- rep(1:(ncol(mat)%/%8), each=8)
if(ncol(mat) %%8 !=0){
  groups <- c(groups, rep(max(groups)+1, ncol(mat)%%8))
}
row_m <- sapply(split.default(mat, groups), rowMeans)
colnames(row_m) <- paste0("Sample_", 1:ncol(row_m))

# 查看前6行结果
head(row_m)

输出示例：

Sample_1 Sample_2
A1BG    84.5620        0
A2M    205.9538        0
NAT2     0.0000        0
SERPINA3 0.0000        0
AANAT   24.9436        0
ABAT     0.0000        0

内容的提问来源于stack exchange，提问作者Vahid