如何简化基R中嵌套ifelse（）结构的处理？ [英] How to simplify handling with nested ifelse() structures in base R?

问题描述

我正面临嵌套的 ifelse（）结构：

df1$var <- ifelse(x < a, u, ifelse(x < b, v, ifelse(x < c, w, ...)))

其中 u，v，w，... s实际上是函数。

一个愚蠢的工作示例将是

A dumbed down working example would be

df1 <- data.frame(x = rbinom(100, 5, .5))
df1$y <- ifelse(x == 1, "s", ifelse(x == 2, "t", 
                                    ifelse(x == 3, "u", ifelse(x == 4, "v", "w"))))

我认为理想情况下可能存在 基础R 方法（为了速度而 ）简化此类代码;最终函数为

I presume there could be ideally a base R method (for sake of speed) to simplify such code; eventually a function as

rave.ifelse(x, 1=s, 2=t, ...)

我瞥了一眼 cut（x，5）但是从这个角度来看，这让我很困惑。

I took a glance at cut(x, 5) but it confused me from this point of view.

注意： x 的值可以无论是数字还是因素， == 也可以是任何逻辑运算符， s，t，... 是实际上功能。

Note: Values of x could be either numbers or factors, == could also be any logical operator and the s, t, ... are actually functions.

编辑：

edit:

注意： ifelse（）的数量已知并且很大。解决方案确实应该适合 df1 $ var< - ifelse（x< a，u，ifelse（x< b，v，ifelse（x< c，w，...）） ））情况，当 u，v，w，... s是函数时，例如 u = sample（0：9,1），v = runif（1），... 。它不应该明显慢于 ifelse（）。

Note: The number of ifelse()s is known and large. The solution really should fit to the df1$var <- ifelse(x < a, u, ifelse(x < b, v, ifelse(x < c, w, ...))) situation, when the u, v, w, ...s are functions, e.g. u=sample(0:9, 1), v=runif(1),.... It should not be significantly slower than ifelse().

推荐答案

In base R ，如果要替换多个元素，请创建一个键/值数据集并执行 merge

In base R, if there are multiple elements to be replaced, create a key/value dataset and do a merge

keyval <- data.frame(x = c(1, 2, 3, 4), y = c("s", "t", "u", "v"), stringsAsFactors = FALSE)
new <- merge(df1, keyval, by = 'x', all.x = TRUE)[['y']]
new[is.na(new)] <- "w"
df1$x <- new

数据

data

set.seed(24)
df1 <- data.frame(x = rbinom(100, 5, .5))

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