问题描述
问题：我需要将几个不同的大数据帧（例如50k行）分成较小的块，每个数据帧的行数相同。但是，我不想手动设置每个数据集的块的大小。相反，我想要的代码：
The problem: I need to divide several different, large dataframes (e.g. 50k rows) into smaller chunks which each have the same number of rows. However, I don't want to have to manually set the size of the chunks for each dataset. Instead, I want code that:
- 检查数据帧的长度，并确定大约几千行的块数
原始数据框可以分解为 - 最小化必须丢弃的剩余行数
这里提供的答案是相关的：
The answers provided here are relevant: Split a vector into chunks in R
但是，我不想手动设置块大小。我想要代码找到最小化剩余的最佳块大小。
However, I don't want to have to manually set a chunk size. I want the code to find the "optimal" chunk size that will minimize the remainder.
示例：（基于Harlan在上述链接的答案）
Example: (Based on Harlan's answer at above link)
df <- rnorm(20752)
max <- 20
x <- seq_along(df)
df <- split(df, ceiling(x/max))
str(df)
> List of 5
> $ 1: num [1:5000] -1.4 -0.496 -1.185 -2.071 -1.118 ...
> $ 2: num [1:5000] 0.522 1.607 -2.228 -2.044 0.997 ...
> $ 3: num [1:5000] 0.295 0.486 -1.085 0.515 0.96 ...
> $ 4: num [1:5000] 0.695 -0.58 -1.676 1.052 1.266 ...
> $ 5: num [1:752] -0.6468 0.1731 0.5788 -0.0584 0.8479 ...
如果我选择了一个大小为4100行的块，我将有5个块，剩余的252行。这更可取，因为我会丢弃更少的数据点。只要这块块至少有几千行，我不在乎他们的大小。
If I had chosen a chunk size of 4100 rows, I would have 5 chunks with a remainder of 252 rows. That's more desirable because I would discard fewer datapoints. As long as the chunks are a few thousand rows at least, I don't care exactly what size they are.
推荐答案
这里是一个强力的方法（但非常快）：
Here's a brute force approach (but very fast) :
# number of rows of your data.frame (from your example... )
nrows <- 20752
# acceptable range for sub-data.frame size
subSetSizes <- 4000:10000
remainders <- nrows %% subSetSizes
minIndexes <- which(remainders == min(remainders))
chunckSizesHavingMinRemainder <- subSetSizes[minIndexes]
# > chunckSizesHavingMinRemainder
# [1] 5188
# the remainder of 20752 / 5188 is indeed 0 (the only minimum)
# nrows %% 5188
# > [1] 0
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