更新： 进行手动操作，剪切并粘贴成多张纸。找到相同的解决方法会很棒。
问题： 给定以下虚拟数据集：
structure(list(V1 = structure(c(8L, 6L, 2L, 4L, 1L, 1L, 1L, 1L,
9L, 5L, 2L, 1L, 1L, 1L, 1L, 10L, 7L, 3L), .Label = c("", "1",
"12", "5", "Age", "Class A", "Height", "Number of Boys", "More Boys",
"More Girls"), class = "factor"), V2 = structure(c(1L, 5L, 3L,
4L, 1L, 1L, 1L, 1L, 1L, 6L, 3L, 1L, 1L, 1L, 1L, 1L, 7L, 2L), .Label = c("",
"12", "2", "6", "Class B", "Time", "Weight"), class = "factor"),
V3 = structure(c(1L, 5L, 3L, 4L, 1L, 1L, 1L, 1L, 1L, 6L,
3L, 1L, 1L, 1L, 1L, 1L, 7L, 2L), .Label = c("", "13", "3",
"7", "Class C", "Next", "Time"), class = "factor"), V4 = structure(c(1L,
5L, 3L, 4L, 1L, 1L, 1L, 1L, 1L, 6L, 3L, 1L, 1L, 1L, 1L, 1L,
6L, 2L), .Label = c("", "14", "4", "8", "Class D", "Day"), class = "factor"),
V5 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA), V6 = c(NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), V7 = c(NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
), V8 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA), V9 = c(NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), V10 = structure(c(5L,
4L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L), .Label = c("", "1", "8", "Class E", "Number of Girls"
), class = "factor"), V11 = structure(c(1L, 4L, 3L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("",
"2", "8", "Class F"), class = "factor"), V12 = structure(c(1L,
4L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L), .Label = c("", "3", "9", "Class G"), class = "factor"),
V13 = structure(c(1L, 4L, 2L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("", "0", "4",
"Class Q"), class = "factor"), V14 = c(NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)), class = "data.frame", row.names = c(NA,
-18L))
外观看起来像（被截断）
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14
1 Number of Boys NA NA NA NA NA Number of Girls NA
2 Class A Class B Class C Class D NA NA NA NA NA Class E Class F Class G Class Q NA
3 1 2 3 4 NA NA NA NA NA 8 8 9 0 NA
4 5 6 7 8 NA NA NA NA NA 1 2 3 4 NA
5 NA NA NA NA NA NA
6 NA NA NA NA NA NA
7 NA NA NA NA NA NA
8 NA NA NA NA NA NA
9 More Boys NA NA NA NA NA NA
10 Age Time Next Day NA NA NA NA NA NA
11 1 2 3 4 NA NA NA NA NA NA
12 NA NA NA NA NA NA
我们希望可以看到，它们是同一工作表上的单独“文件”。我一直在寻找一种快速的方法来找出不同的数据集，但是还没有。可以解决吗？ 我的想法是使用基于序列的选择，例如每20行选择一次，但是如果您有数百万行，这显然会失败？
预期的Outtut（及其相应的行）
Three data sets:
A: Number of Boys
B: Number of Girls
C: More Boys
谢谢。
答案 0 :(得分：1)
在https://stackoverflow.com/a/42120347/6197649使用@alexis_laz的解决方案
library(Matrix)
x <- !is.na(df) & df != ""
m <- Matrix(x)
#> 18 x 14 sparse Matrix of class "lgCMatrix"
#> [[ suppressing 14 column names 'V1', 'V2', 'V3' ... ]]
#>
#> [1,] | . . . . . . . . | . . . .
#> [2,] | | | | . . . . . | | | | .
#> [3,] | | | | . . . . . | | | | .
#> [4,] | | | | . . . . . | | | | .
#> [5,] . . . . . . . . . . . . . .
#> [6,] . . . . . . . . . . . . . .
#> [7,] . . . . . . . . . . . . . .
#> [8,] . . . . . . . . . . . . . .
#> [9,] | . . . . . . . . . . . . .
#> [10,] | | | | . . . . . . . . . .
#> [11,] | | | | . . . . . . . . . .
#> [12,] . . . . . . . . . . . . . .
#> [13,] . . . . . . . . . . . . . .
#> [14,] . . . . . . . . . . . . . .
#> [15,] . . . . . . . . . . . . . .
#> [16,] | . . . . . . . . . . . . .
#> [17,] | | | | . . . . . . . . . .
#> [18,] | | | | . . . . . . . . . .
sm = as.matrix(summary(m))
d = dist(sm, "manhattan")
gr = cutree(hclust(d, "single"), h = 1)
res <- sparseMatrix(i = sm[, "i"], j = sm[, "j"], x = gr)
#> 18 x 13 sparse Matrix of class "dgCMatrix"
#>
#> [1,] 1 . . . . . . . . 4 . . .
#> [2,] 1 1 1 1 . . . . . 4 4 4 4
#> [3,] 1 1 1 1 . . . . . 4 4 4 4
#> [4,] 1 1 1 1 . . . . . 4 4 4 4
#> [5,] . . . . . . . . . . . . .
#> [6,] . . . . . . . . . . . . .
#> [7,] . . . . . . . . . . . . .
#> [8,] . . . . . . . . . . . . .
#> [9,] 2 . . . . . . . . . . . .
#> [10,] 2 2 2 2 . . . . . . . . .
#> [11,] 2 2 2 2 . . . . . . . . .
#> [12,] . . . . . . . . . . . . .
#> [13,] . . . . . . . . . . . . .
#> [14,] . . . . . . . . . . . . .
#> [15,] . . . . . . . . . . . . .
#> [16,] 3 . . . . . . . . . . . .
#> [17,] 3 3 3 3 . . . . . . . . .
#> [18,] 3 3 3 3 . . . . . . . . .
res2 <- summary(res)
lapply(
split(res2[, c("i", "j")], res2$x),
function(area) {
df[min(area$i):max(area$i), min(area$j):max(area$j), drop = FALSE]
}
)
#> $`1`
#> V1 V2 V3 V4
#> 1 Number of Boys
#> 2 Class A Class B Class C Class D
#> 3 1 2 3 4
#> 4 5 6 7 8
#>
#> $`2`
#> V1 V2 V3 V4
#> 9 More Boys
#> 10 Age Time Next Day
#> 11 1 2 3 4
#>
#> $`3`
#> V1 V2 V3 V4
#> 16 More Girls
#> 17 Height Weight Time Day
#> 18 12 12 13 14
#>
#> $`4`
#> V10 V11 V12 V13
#> 1 Number of Girls
#> 2 Class E Class F Class G Class Q
#> 3 8 8 9 0
#> 4 1 2 3 4
由reprex package（v0.2.1）于2019-04-10创建
答案 1 :(得分：1)
以下代码创建一个列名称正确设置的data.frames列表。但是，这取决于您的工作表中“表列”至少由一个空列分隔的事实。
df <- apply(df, 2, function(x) gsub("^$|^ $", NA, x))
empty_cols <- sapply(1:ncol(df), function(i){length(which(is.na(df[, i])))==nrow(df)})
start_cols <- c(1, which(diff(empty_cols)==-1)+1)
if (is.na(df[1, 1])) start_cols <- start_cols[-1]
start_rows <- lapply(start_cols, function(i){
start_rows <- c(1, which(diff(is.na(df[, i]))==-1)+1)
if (is.na(df[1, i])) start_rows <- start_rows[-1]
start_rows})
end_rows <- lapply(start_cols, function(i){
end_rows <- c(1, which(diff(is.na(df[, i]))==1))
if (!is.na(df[nrow(df), i])) end_rows <- c(end_rows, nrow(df))
end_rows[-1]})
data.sets <- list()
for (i in 1:length(start_cols)) {
for (j in 1:length(start_rows[[i]])){
col <- start_cols[i]
row <- start_rows[[i]][j]
start_row <- row+1
end_row <- end_rows[[i]][j]
name <- df[row, col]
ncol <- which(diff(is.na(df[row+1, col:ncol(df)]))==1)[1]
end_col <- col+ncol-1
column_names <- df[start_row, col:end_col]
data <- df[(start_row+1):end_row, col:end_col]
data <- matrix(data, ncol = length(col:end_col))
data <- as.data.frame(data)
names(data) <- column_names
data.sets[[name]] <- data
}
}
> data.sets
$`Number of Boys`
Class A Class B Class C Class D
1 1 2 3 4
2 5 6 7 8
$`More Boys`
Age Time Next Day
1 1 2 3 4
$`More Girls`
Height Weight Time Day
1 12 12 13 14
$`Number of Girls`
Class E Class F Class G Class Q
1 8 8 9 0
2 1 2 3 4