每当我想在 R 中做一些 “map”py 时，我通常会尝试在`apply`

系列中使用一个函数。

但是，我从来没有完全理解它们之间的区别 - 如何 { `sapply`

， `lapply`

等} 将函数应用于输入 / 分组输入，输出将是什么样的，甚至输入可以是什么 - 所以我经常只是经历它们直到我得到我想要的东西。

有人可以解释如何使用哪一个？

我当前（可能不正确 / 不完整）的理解是......

`sapply(vec, f)`

：输入是一个向量。输出是矢量 / 矩阵，其中元素`i`

是`f(vec[i])`

，如果`f`

具有多元素输出，则为您提供矩阵`lapply(vec, f)`

：和`sapply`

，但输出是一个列表？-
`apply(matrix, 1/2, f)`

：input 是一个矩阵。 output 是一个向量，其中元素`i`

是 f（矩阵的 row / col i） -
`tapply(vector, grouping, f)`

：output 是一个矩阵 / 数组，其中矩阵 / 数组中的元素是向量的分组`g`

处的`f`

的值，`g`

被推送到行 / 列名称 -
`by(dataframe, grouping, f)`

：让`g`

成为一个分组。将`f`

应用于组 / 数据帧的每一列。漂亮打印分组和每列的`f`

值。 -
`aggregate(matrix, grouping, f)`

类似于`by`

但是代替漂亮打印输出，合计枝一切成数据帧。

附带问题：我还没有学过 plyr 或 reshape - 会`plyr`

`reshape`

`plyr`

或`reshape`

所有这些？

R has many *apply functions which are ably described in the help files (e.g. `?apply`

). There are enough of them, though, that beginning useRs may have difficulty deciding which one is appropriate for their situation or even remembering them all. They may have a general sense that "I should be using an *apply function here", but it can be tough to keep them all straight at first.

Despite the fact (noted in other answers) that much of the functionality of the *apply family is covered by the extremely popular `plyr`

package, the base functions remain useful and worth knowing.

This answer is intended to act as a sort of **signpost** for new useRs to help direct them to the correct *apply function for their particular problem. Note, this is **not** intended to simply regurgitate or replace the R documentation! The hope is that this answer helps you to decide which *apply function suits your situation and then it is up to you to research it further. With one exception, performance differences will not be addressed.

**apply**-*When you want to apply a function to the rows or columns of a matrix (and higher-dimensional analogues); not generally advisable for data frames as it will coerce to a matrix first.*`# Two dimensional matrix M <- matrix(seq(1,16), 4, 4) # apply min to rows apply(M, 1, min) [1] 1 2 3 4 # apply max to columns apply(M, 2, max) [1] 4 8 12 16 # 3 dimensional array M <- array( seq(32), dim = c(4,4,2)) # Apply sum across each M[*, , ] - i.e Sum across 2nd and 3rd dimension apply(M, 1, sum) # Result is one-dimensional [1] 120 128 136 144 # Apply sum across each M[*, *, ] - i.e Sum across 3rd dimension apply(M, c(1,2), sum) # Result is two-dimensional [,1] [,2] [,3] [,4] [1,] 18 26 34 42 [2,] 20 28 36 44 [3,] 22 30 38 46 [4,] 24 32 40 48`

If you want row/column means or sums for a 2D matrix, be sure to investigate the highly optimized, lightning-quick

`colMeans`

,`rowMeans`

,`colSums`

,`rowSums`

.**lapply**-*When you want to apply a function to each element of a list in turn and get a list back.*This is the workhorse of many of the other *apply functions. Peel back their code and you will often find

`lapply`

underneath.`x <- list(a = 1, b = 1:3, c = 10:100) lapply(x, FUN = length) $a [1] 1 $b [1] 3 $c [1] 91 lapply(x, FUN = sum) $a [1] 1 $b [1] 6 $c [1] 5005`

**sapply**-*When you want to apply a function to each element of a list in turn, but you want a***vector**back, rather than a list.If you find yourself typing

`unlist(lapply(...))`

, stop and consider`sapply`

.`x <- list(a = 1, b = 1:3, c = 10:100) # Compare with above; a named vector, not a list sapply(x, FUN = length) a b c 1 3 91 sapply(x, FUN = sum) a b c 1 6 5005`

In more advanced uses of

`sapply`

it will attempt to coerce the result to a multi-dimensional array, if appropriate. For example, if our function returns vectors of the same length,`sapply`

will use them as columns of a matrix:`sapply(1:5,function(x) rnorm(3,x))`

If our function returns a 2 dimensional matrix,

`sapply`

will do essentially the same thing, treating each returned matrix as a single long vector:`sapply(1:5,function(x) matrix(x,2,2))`

Unless we specify

`simplify = "array"`

, in which case it will use the individual matrices to build a multi-dimensional array:`sapply(1:5,function(x) matrix(x,2,2), simplify = "array")`

Each of these behaviors is of course contingent on our function returning vectors or matrices of the same length or dimension.

**vapply**-*When you want to use*`sapply`

but perhaps need to squeeze some more speed out of your code.For

`vapply`

, you basically give R an example of what sort of thing your function will return, which can save some time coercing returned values to fit in a single atomic vector.`x <- list(a = 1, b = 1:3, c = 10:100) #Note that since the advantage here is mainly speed, this # example is only for illustration. We're telling R that # everything returned by length() should be an integer of # length 1. vapply(x, FUN = length, FUN.VALUE = 0L) a b c 1 3 91`

**mapply**-*For when you have several data structures (e.g. vectors, lists) and you want to apply a function to the 1st elements of each, and then the 2nd elements of each, etc., coercing the result to a vector/array as in*`sapply`

.This is multivariate in the sense that your function must accept multiple arguments.

`#Sums the 1st elements, the 2nd elements, etc. mapply(sum, 1:5, 1:5, 1:5) [1] 3 6 9 12 15 #To do rep(1,4), rep(2,3), etc. mapply(rep, 1:4, 4:1) [[1]] [1] 1 1 1 1 [[2]] [1] 2 2 2 [[3]] [1] 3 3 [[4]] [1] 4`

**Map**-*A wrapper to*`mapply`

with`SIMPLIFY = FALSE`

, so it is guaranteed to return a list.`Map(sum, 1:5, 1:5, 1:5) [[1]] [1] 3 [[2]] [1] 6 [[3]] [1] 9 [[4]] [1] 12 [[5]] [1] 15`

**rapply**-*For when you want to apply a function to each element of a***nested list**structure, recursively.To give you some idea of how uncommon

`rapply`

is, I forgot about it when first posting this answer! Obviously, I'm sure many people use it, but YMMV.`rapply`

is best illustrated with a user-defined function to apply:`# Append ! to string, otherwise increment myFun <- function(x){ if(is.character(x)){ return(paste(x,"!",sep="")) } else{ return(x + 1) } } #A nested list structure l <- list(a = list(a1 = "Boo", b1 = 2, c1 = "Eeek"), b = 3, c = "Yikes", d = list(a2 = 1, b2 = list(a3 = "Hey", b3 = 5))) # Result is named vector, coerced to character rapply(l, myFun) # Result is a nested list like l, with values altered rapply(l, myFun, how="replace")`

**tapply**-*For when you want to apply a function to***subsets**of a vector and the subsets are defined by some other vector, usually a factor.The black sheep of the *apply family, of sorts. The help file's use of the phrase "ragged array" can be a bit confusing, but it is actually quite simple.

A vector:

`x <- 1:20`

A factor (of the same length!) defining groups:

`y <- factor(rep(letters[1:5], each = 4))`

Add up the values in

`x`

within each subgroup defined by`y`

:`tapply(x, y, sum) a b c d e 10 26 42 58 74`

More complex examples can be handled where the subgroups are defined by the unique combinations of a list of several factors.

`tapply`

is similar in spirit to the split-apply-combine functions that are common in R (`aggregate`

,`by`

,`ave`

,`ddply`

, etc.) Hence its black sheep status.

在旁注中，这里是各种`plyr`

函数如何对应 base `*apply`

函数（从 plyr 网页的介绍到 plyr 文档http://had.co.nz/plyr/ ）

```
Base function Input Output plyr function
---------------------------------------
aggregate d d ddply + colwise
apply a a/l aaply / alply
by d l dlply
lapply l l llply
mapply a a/l maply / mlply
replicate r a/l raply / rlply
sapply l a laply
```

`plyr`

的目标之一是为每个函数提供一致的命名约定，对函数名中的输入和输出数据类型进行编码。它还提供输出的一致性，因为`dlply()`

输出很容易传递给`ldply()`

以产生有用的输出等。

从概念上讲，学习`plyr`

并不比理解 base `*apply`

函数困难。

在我的日常使用中， `plyr`

和`reshape`

函数已经取代了几乎所有这些函数。但是，从介绍到 Plyr 文件：

相关函数

`tapply`

和`sweep`

在`plyr`

没有相应的函数，并且仍然有用。`merge`

对于将摘要与原始数据相结合非常有用。

来自http://www.slideshare.net/hadley/plyr-one-data-analytic-strategy 的幻灯片 21：

（希望很明显， `apply`

对应`aaply`

的`aaply`

和`aggregate`

对应于`ddply`

的`ddply`

等。如果你没有从这张图片中得到它，那么同一幻灯片的幻灯片 20 将会澄清。）

（左边是输入，顶部是输出）