如何为每一列重复计算? [英] How to repeat a computation for every column?

问题描述

我有一个类似于以下内容的数据集:

I have a dataset which looks like the following:

V1 V2 V3 ...
1  1  2 ...
1  1  2 ...
2  3  NA...
NA 2  1 ...
2  1  3 ...

它包含2535列，我想对所有列使用以下索引.我已经设法计算出第一列的索引.

it contains 2535 columns and I want to use the following index for all columns. I've managed to compute the index for the first column.

 ((max(table(df$V1)))-0.5*
             ((table(df$V1)["1"]+table(df$V1)["2"]+table(df$V1)["3"])
              - (max(table(df$V1)))))/(table(df$V1)["1"]+table(df$V1)["2"]+table(df$V1)["3"])

有没有一种方法可以计算每2535列的索引?我曾考虑过使用循环，但没有设法写一个循环...

Is there a way how to compute the index for every 2535 of the columns? I thought about using a loop but didn't manage to write one...

推荐答案

您应该查看tidyverse软件包dplyr，特别是mutate_at或mutate_if.

You should have a look at the tidyverse package dplyr, specifically mutate_at or mutate_if.

您的职能是什么?即您想对每列执行什么操作?

What is your function? i.e. what is the operation you want to do on every column?

如果要对数据中的所有数字列执行此操作:

If you want to do this on ALL numeric columns in the data:

library(tidyverse)
mutate_if(data, is.numeric, your_function)


data = data.frame(V1 = sample(1:5, 10, replace = T),
    V2 = sample(1:5, 10, replace = T),
    V3 = sample(1:5, 10, replace = T))

mutate_if(data, is.numeric, mean) %>% unique()

       V1  V2  V3
    1 2.9 2.4 2.7

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