如何在r中编写函数以对记录进行标定? [英] How do I write a function in r to do cacluations on a record?
问题描述
在C#中，我习惯了数据集和当前记录的概念. 对于我来说，很容易在当前记录中编写带有条件的calc-price函数.
In C# I am used to the concept of a data set and a current record. It would be easy for me to write a complicated calc-price function with conditions on the current record.
我在理解如何在r中执行此操作时遇到了麻烦.
I am having trouble understanding how to do this in r.
我尝试了以下
train <- read.csv("Train.csv" )
df <- as.data.frame.matrix(train)
v = c( df$Fuel.Type ,df$No.Gears)
names(v ) <- c( "FuelType" ,"NoGears")
df$FEType = FEType( v)
我的函数定义为
FEType <- function(v ){
ret="Low"
if (v["FuelType"]=='G') {
ret ="High"
}
return(ret)
}
这不符合我的预期 当我检查v时，我发现它包含的是总计，而不是我期望的当前行.
This is not working how I expected and when I examine v I see that it contains aggregate totals rather than the current row I expected.
我要去哪里错了?
在问题中此处请参阅最后一段中的一些提示.
In the question here I see some hints in the last paragraph.
要重现问题，表明我想做什么，
To reproduce the problem, indicating what I want to do, I have
IsPretty <-function(PetalWidth){
if (PetalWidth >0.3) return("Y")
return("N")
}
df <- iris
df$Pretty = IsPretty(df$Petal.Width)
这给出了错误
条件的长度为> 1，并且只会使用第一个元素
the condition has length > 1 and only the first element will be used
这促使我研究向量.但是我不确定这是正确的方向.
Which led me to look into vectors. But I am not confident that is the right direction.
[更新]
我习惯于思考表格和当前记录. 因此我在想
I am used to thinking of tables and current records. Thus I was thinking that
df$Pretty = IsPretty(df$Petal.Width)
将具有通过计算的isPretty属性向我的数据框中添加一列的效果
would have the effect of adding a column to my data frame with the calculated isPretty property
为什么在计算中不包括条件?
Why can I not include if conditions in my calculation?
推荐答案
向量化是您在R中需要习惯的最基本的(也是最不寻常的)事情之一.许多(大多数?)R运算是向量化的.但是有些事情不是-if(){}else{}是非矢量化的事情之一.它用于控制流(是否运行代码块)，而不用于矢量操作. ifelse()是用于向量的单独函数，其中第一个自变量是"test"，而第二个和第三个自变量是"if yes".和如果不是"，则为否".结果.测试是向量，返回的值是测试中每个项目的适当是/否"结果. 结果将与测试的长度相同.
Vectorization is one of the most fundamental (and unusual) things you'll need to get used to in R. Many (most?) R operations are vectorized. But a few things aren't - and if(){}else{} is one of the non-vectorized things. It's used for control flow (whether or not to run a code block) not for vector operations. ifelse() is a separate function that is used for vectors, where the first argument is a "test", and the 2nd and 3rd arguments are the "if yes" and "if no" results. The test is a vector, and the returned value is the appropriate yes/no result for each item in test. The result will be the same length as the test.
所以我们将这样编写您的IsPretty函数:
So we would write your IsPretty function like this:
IsPretty <- function(PetalWidth){
return(ifelse(PetalWidth > 0.3, "Y", "N"))
}
df <- iris
df$Pretty = IsPretty(df$Petal.Width)
与测试条件长度为1的if(){...}else{...}块形成对比，并且可以在...中运行任意代码-可能返回比测试更大的结果，或者返回更小的结果，或者没有结果-可能修改其他对象...您可以在if(){}else()内部执行任何操作，但测试条件的长度必须为1.
Contrast to an if(){...}else{...} block where the test condition is of length one, and arbitrary code can be run in the ... - may return a bigger result than the test, or a smaller result, or no result - might modify other objects... You can do anything inside if(){}else(), but the test condition must have length 1.
您可以一次使用您的IsPretty函数-它对于任何一行都可以正常使用.因此，我们可以将其放入如下所示的循环中，一次检查一行，一次给if()一个测试，一次分配一个结果.但是R已针对矢量化进行了优化，这会明显变慢并且是个坏习惯.
You could use your IsPretty function one row at a time - it will work fine for any one row. So we could put it in a loop as below, checking one row at time, giving if() one test at a time, assigning results one at a time. But R is optimized for vectorization, and this will be noticeably slower and is a bad habit.
IsPrettyIf <-function(PetalWidth){
if (PetalWidth >0.3) return("Y")
return("N")
}
for(i in 1:nrow(df)) {
df$PrettyLoop[i] = IsPrettyIf(df$Petal.Width[i])
}
下面的基准显示矢量化版本的速度提高了50倍.这是一个简单的案例，并且数据如此之小，所以没什么大不了的，但是在较大的数据上，或者在操作更为复杂的情况下，矢量化代码和非矢量化代码之间的差异可能是几分钟到几天.
A benchmark below shows that the vectorized version is 50x faster. This is such a simple case and such small data that it doesn't much matter, but on larger data, or with more complex operations the difference between vectorized and non-vectorized code can be minutes vs days.
microbenchmark::microbenchmark(
loop = {
for(i in 1:nrow(df)) {
df$PrettyLoop[i] = IsPrettyIf(df$Petal.Width[i])
}
},
vectorized = {
df$Pretty = IsPretty(df$Petal.Width)
}
)
Unit: microseconds
expr min lq mean median uq max neval
loop 3898.9 4365.6 5880.623 5442.3 7041.10 11344.6 100
vectorized 47.7 59.6 112.288 67.4 83.85 1819.4 100
对于R学习者来说这是一个常见的障碍-您可以在Stack Overflow上找到许多问题，人们在需要ifelse()时正在使用if(){}else{}，反之亦然. 为什么ifelse无法返回向量?是来自问题另一面的常见问题解答.
This is a common bump for R learners - you can find many questions on Stack Overflow where people are using if(){}else{} when they need ifelse() or vice versa. Why can't ifelse return vectors? is a FAQ coming from the opposite side of the problem.
df <- iris
## The condition has length equal to the number of rows in the data frame
df$Petal.Width > 0.3
#> [1] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13] FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
## ... truncated
## R warns us that only the first value (which happens to be FALSE) is used
result = if(df$Petal.Width > 0.3) {"Y"} else {"N"}
#> Warning in if (df$Petal.Width > 0.3) {: the condition has length > 1 and only
#> the first element will be used
## So the result is a single "N"
result
#> [1] "N"
length(result)
#> [1] 1
## R "recycles" inputs that are of insufficient length
## so we get a full column of "N"
df$Pretty = result
head(df)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species Pretty
#> 1 5.1 3.5 1.4 0.2 setosa N
#> 2 4.9 3.0 1.4 0.2 setosa N
#> 3 4.7 3.2 1.3 0.2 setosa N
#> 4 4.6 3.1 1.5 0.2 setosa N
#> 5 5.0 3.6 1.4 0.2 setosa N
#> 6 5.4 3.9 1.7 0.4 setosa N
由 reprex软件包(v0.3.0)创建于2020-11-08 sup>
Created on 2020-11-08 by the reprex package (v0.3.0)
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