如何使用dplyr在R中的数据框中配对行？ | 如何使用dplyr在R中的数据框中配对行

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问题描述

我有一个数据框，其中包含来自控件和实验组的观察数据，每个主题都有复制：
这是我的数据框的一个例子：

I have a dataframe containing observations from the control and the experimental group with replicates for each subject:Here is an example of my dataframe:

subject  group    replicate value
  A     control      1       10
  A     control      2       15
  A     experim      1       40
  A     experim      2       45
  B     control      1       5
  B     experim      1       30
  C     control      1       50
  C     experim      1       NA

我想将每个控制观察值与其相应的实验值进行配对，以计算配对值之间的比值。
所需的输出：

I'd like to pair each control observation with its corresponding experimental one in order to calculate the ratio between the paired values.The desired output:

subject  replicate  control   experim  ratio
  A         1         10        40       4
  A         2         15        45       3
  B         1          5        30       6
  C         1         50        NA       NA

Please, note that the number of replicates for subjects can vary (A has two replicates, B only one, C has one with a missing value). Ideally, I'd like to see this implemented with dplyr and pipes.

推荐答案

我们可以使用 dcast 从 data.table 转换为宽格式，然后通过将experim与control分开来创建ratio列。 p>

We can use dcast from data.table to convert to 'wide' format, then create the 'ratio' column by dividing 'experim' with 'control'

library(data.table)
dcast(setDT(df1), subject+replicate~group, value.var="value")[,
            ratio:= experim/control][]
#     subject replicate control experim ratio
#1:       A         1      10      40     4
#2:       A         2      15      45     3
#3:       B         1       5      30     6
#4:       C         1      50      NA    NA

或使用 spread 从 tidyr 转换为'wide'格式，然后用 mutate 创建'ratio'。

Or using spread from tidyr to convert to 'wide' format and then create the 'ratio' with mutate.

library(dplyr)
library(tidyr)
spread(df1, group, value) %>%
        mutate(ratio = experim/control)
#    subject replicate control experim ratio
#1       A         1      10      40     4
#2       A         2      15      45     3
#3       B         1       5      30     6
#4       C         1      50      NA    NA

或使用 reshape 从 base R

transform(reshape(df1, idvar = c("subject", "replicate"),
   timevar="group", direction="wide"), ratio = value.experim/value.control)

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