[英]Using Apply or Vectorize to apply custom function to a dataframe
I am attempting to apply a custom function that calls components of that dataframe to do a calculation. 我试图应用一个自定义函数,该函数调用该数据框的组件进行计算。 I have made a trivial example below because my actual problem is very hard to make a reproducible example. 我在下面做了一个琐碎的例子,因为我的实际问题很难做出可复制的例子。 In the below example I want to have the first two columns be added together to create a third column which is the sum of them. 在下面的示例中,我希望将前两列加在一起以创建第三列,即它们的总和。 Below is an example I found online that gets close to what I want: 下面是我在网上找到的一个接近我想要的示例:
celebrities=data.frame(name=c("Andrew","matt","Dany","Philip","John","bing","Monica"),
age=c(28,23,49,29,38,23,29),
income=c(25.2,10.5,11,21.9,44,11.5,45))
f=function(x,output){
name=x[1]
income=x[3]
cat(name,income,"\n")
}
apply(celebrities,1,f)
But when I try to take it and apply mathematical function it doesn't work: 但是,当我尝试使用它并应用数学函数时,它不起作用:
f2=function(x,output){
age=x[2]
income=x[3]
sum(age,income)
}
apply(celebrities,1,f2)
In essence what I need is for apply to take a dataset, go through every row of that dataset using the values in that row as inputs into the function and add a third column to the dataset with the results of the function. 本质上,我需要申请以获取数据集,使用该行中的值作为函数的输入遍历该数据集的每一行,并向该数据集添加第三列以及函数的结果。 Please let me know how I can clarify this question if needed. 请让我知道如何在需要时澄清这个问题。 I have referred to the questions below, but they don't seem to work for me. 我已经提到了以下问题,但它们似乎对我没有用。
Apply a function to every row of a matrix or a data frame 将函数应用于矩阵或数据框的每一行
How to assign new values from lapply to new column in dataframes in list 如何将新值从lapply分配给列表中数据框的新列
Call apply-like function on each row of dataframe with multiple arguments from each row 在数据框的每一行上调用类似应用的函数,每一行具有多个参数
For the particular task requested it could be 对于请求的特定任务,可能是
celebrities$newcol <- with(celebrities, age + income)
The +
function is inherently vectorized. +
函数本质上是矢量化的。 Using apply
with sum
is inefficient. 使用apply
与sum
是低效的。 Using apply
could have been greatly simplified by omitting the first column because that would avoid the coercion to a character matrix caused by the first column. 通过省略第一列可以大大简化了apply
使用,因为这样可以避免强制转换为由第一列引起的字符矩阵。
celebrities$newcol <- apply(celebrities[-1], function(x) sum(x) )
That way you would avoid coercing the vectors to "character" and then needing to coerce back the formerly-numeric columns to numeric
. 这样,您就可以避免将向量强制转换为“字符”,然后需要将之前的数字列强制转换回numeric
。 Using sum
inside apply does get around the fact that sum is not vectorized, but it's an example of inefficient R coding. 使用sum
内适用不回避的事实,和没有矢量得到的,但它的效率低下[R编码的一个例子。
You get automatic vectorization if the "inner" algorithm can be constructed completely from vectorized functions: the Math and Ops groups being the usual components. 如果“内部”算法可以完全由矢量化函数构造而成,则可以实现自动矢量化:Math和Ops组是通常的组件。 See ?Ops
. 请参阅?Ops
Otherwise, you may need to use mapply
or Vectorize
. 否则,您可能需要使用mapply
或Vectorize
。
Taking hints from @r2evans and @user2738526 I have made the modification to your function. 来自@ r2evans和@ user2738526的提示我已经对您的函数进行了修改。 Explicitly convert numbers to numeric. 将数字显式转换为数字。 The below code snippet works for your case: 以下代码段适用于您的情况:
f2=function(x,output){
age=as.numeric(x[2])
income=as.numeric(x[3])
sum(age,income)
}
apply(celebrities,1,f2)
[1] 53.2 33.5 60.0 50.9 82.0 34.5 74.0
Give this a try: 试试看:
library(dplyr)
celebrities=data.frame(name=c("Andrew","matt","Dany","Philip","John","bing","Monica"),
age=c(28,23,49,29,38,23,29),
income=c(25.2,10.5,11,21.9,44,11.5,45))
celebrities %>%
rowwise %>%
mutate(age_plus_income = sum(age, income))
(Obviously, for summing two columns, you'd be better off using mutate(celebrities, age_plus_income = age + income)
, but I assume your real example uses a more complicated function.) (很明显,对于两列的求和,最好使用mutate(celebrities, age_plus_income = age + income)
,但我认为您的实际示例使用的是更复杂的函数。)
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