有条件地应用函数 [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
 ....

有没有办法通过应用带有条件的函数来统计结果.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)

我正在考虑应用"功能，但找不到让它工作的方法.

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

推荐答案

有很多替代方案可以做到这一点.请注意，如果您对不同于 sum 的另一个函数感兴趣，那么只需更改参数 FUN=any.function，例如，如果您想要 mean>、var length 等，然后将这些函数插入到 FUN 参数中，例如，FUN=mean，FUN=var 等等.让我们探索一些替代方案:

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:

aggregate 函数在 base 中.

aggregate function in base.

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

<小时>

或者也许 tapply ?

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

<小时>

还有来自plyr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

<小时>

还有 dplyr 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

<小时>

使用sapply和split，相当于tapply.

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

<小时>

如果您担心时间，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

<小时>

不太流行，但是 doBy 包很好(相当于 aggregate，即使在语法上！)

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

<小时>

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

如果你希望结果是一个矩阵，那么使用 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

<小时>

sqldf 来自 sqldf 包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

<小时>

xtabs 也有效(仅当 FUN=sum 时)

---

xtabs also works (only when FUN=sum)

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

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