有条件地应用功能 [英] Apply function conditionally

问题描述

我有一个这样的数据框：

I have a dataframe like this:

experiment iter  results
    A       1     30.0
    A       2     23.0
    A       3     33.3
    B       1     313.0
    B       2     323.0
    B       3     350.0
 ....

有没有一种方法可以通过应用带条件的函数来计算结果。在上面的示例中，该条件是特定实验的所有迭代。

Is there a way to tally results by applying a function with conditions. In the above example, that condition is all iterations of a particular experiment.

A   sum of results (30 + 23, + 33.3)
B   sum of results (313 + 323 + 350)

我在想应用功能，但找不到使它正常工作的方法。

I am thinking of "apply" function, but can't find a way to get it work.

推荐答案

有很多替代方法可以做到这一点。请注意，如果您对不同于 sum 的另一个函数感兴趣，则只需更改参数 FUN = any.function ，例如，如果您想要平均值， var 长度等，只需将这些函数插入 FUN 参数，例如， FUN = mean ， FUN = var 等。让我们探讨一些替代方法：

There are a lot of alternatives to do this. Note that if you are interested in another function different from sum, then just change the argument FUN=any.function, e.g, if you want mean, var length, etc, then just plug those functions into FUN argument, e.g, FUN=mean, FUN=var and so on. Let's explore some alternatives:

汇总基本函数。

> aggregate(results ~ experiment, FUN=sum, data=DF)
  experiment results
1          A    86.3
2          B   986.0

---

或者也许 tapply 吗？

> with(DF, tapply(results, experiment, FUN=sum))
    A     B 
 86.3 986.0 

---

也来自plyr软件包的 ddply

> # library(plyr)
> ddply(DF[, -2], .(experiment), numcolwise(sum))
  experiment results
1          A    86.3
2          B   986.0

> ## Alternative syntax
> ddply(DF, .(experiment), summarize, sumResults = sum(results))
  experiment sumResults
1          A       86.3
2          B      986.0

---

dplyr 软件包

> require(dplyr)
> DF %>% group_by(experiment) %>% summarise(sumResults = sum(results))
Source: local data frame [2 x 2]

  experiment  sumResults
1          A        86.3
2          B       986.0

---

使用 sapply 和 split 等效于 tapply 。

> with(DF, sapply(split(results, experiment), sum))
    A     B 
 86.3 986.0 

---

如果您担心时间安排， data.table 是您的朋友：

> # library(data.table)
> DT <- data.table(DF)
> DT[, sum(results), by=experiment]
   experiment    V1
1:          A  86.3
2:          B 986.0

---

不是很流行，但是doBy包很好（相当于聚合 ，甚至在语法上也是如此！）

---

Not so popular, but doBy package is nice (equivalent to aggregate, even in syntax!)

> # library(doBy)
> summaryBy(results~experiment, FUN=sum, data=DF)
  experiment results.sum
1          A        86.3
2          B       986.0

---

在这种情况下， by 也有帮助

> (Aggregate.sums <- with(DF, by(results, experiment, sum)))
experiment: A
[1] 86.3
------------------------------------------------------------------------- 
experiment: B
[1] 986

如果希望结果为矩阵，则使用 cbind 或 rbind

If you want the result to be a matrix then use either cbind or rbind

> cbind(results=Aggregate.sums)
  results
A    86.3
B   986.0

---

sqldf 也可能是一个不错的选择

---

sqldf from sqldf package also could be a good option

> library(sqldf)
> sqldf("select experiment, sum(results) `sum.results`
      from DF group by experiment")
  experiment sum.results
1          A        86.3
2          B       986.0

---

xtabs 也有效（仅当 FUN = sum 时）

> xtabs(results ~ experiment, data=DF)
experiment
    A     B 
 86.3 986.0

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