如何使用唯一的行来减少数据框中的列的值 [英] How do I substract a value from a column in a dataframe, with unique rows

问题描述

很难在标题中解释问题。但是在这里我有一个数据框，你可以看到我有3个流名。我有一个与每个流名称相关联的3个唯一值。我想要从值列中找到的相应流的值中减去这些唯一值，然后将其附加到名为错误

Hard to explain the question in a title. But here I have a data frame, and you can see I have 3 stream names. I have a 3 unique values associated with each stream name. I would like these unique values to be subtracted from the value for the corresponding stream found in the value column and then appended to the data frame in a new column titled error

    stream  n  rates   means     column     value
1    Brooks 3   3.0  0.9629152      1    0.42707006
2   Siouxon 3   3.0  0.5831929      1    0.90503736
3  Speelyai 3   3.0  0.6199235      1    0.08554021
4    Brooks 4   7.5  0.9722707      1    1.43338843
5   Siouxon 4   7.5  0.5865031      1    0.50574543
6  Speelyai 4   7.5  0.6118634      1    0.32252396
7    Brooks 5  10.0  0.9637475      1    0.88984211
8   Siouxon 5  10.0  0.5804420      1    0.47501800
9  Speelyai 5  10.0  0.5959238      1    0.15079491
10   Brooks 6  13.0  0.9486575      1    1.32422105
11  Siouxon 6  13.0  0.5846854      1    0.39479684
12 Speelyai 6  13.0  0.5597146      1    0.37005941

以下是要从值中扣除的唯一值列

> true.lwd.sp <- 0.583984402 (speelyai)
> true.lwd.sx <- 0.585852702 (souixon)
> true.lwd.br <- 0.944062036 (brooks)

感谢任何帮助。有一天我可能会知道如何做所有这些简单的任务！

Thanks for any help. One day I may know how to do all these simple tasks!

推荐答案

我们可以创建一个新的数据集并匹配'stream'列与新数据集中的相应列，获取数字索引以从df2获取相应的值，并从df1或原始数据集中减去。

We could create a new dataset and match the 'stream' column with the corresponding column in the new dataset, get the numeric index to get corresponding 'value' from 'df2', and subtract from the 'df1' or original dataset.

 df1$error <- df1$value-df2$value[match(df1$stream, df2$stream)]
 df1
 #    stream n rates     means column      value       error
 #1    Brooks 3   3.0 0.9629152      1 0.42707006 -0.51699198
 #2   Siouxon 3   3.0 0.5831929      1 0.90503736  0.31918466
 #3  Speelyai 3   3.0 0.6199235      1 0.08554021 -0.49844419
 #4    Brooks 4   7.5 0.9722707      1 1.43338843  0.48932639
 #5   Siouxon 4   7.5 0.5865031      1 0.50574543 -0.08010727
 #6  Speelyai 4   7.5 0.6118634      1 0.32252396 -0.26146044
 #7    Brooks 5  10.0 0.9637475      1 0.88984211 -0.05421993
 #8   Siouxon 5  10.0 0.5804420      1 0.47501800 -0.11083470
 #9  Speelyai 5  10.0 0.5959238      1 0.15079491 -0.43318949
 #10   Brooks 6  13.0 0.9486575      1 1.32422105  0.38015901
 #11  Siouxon 6  13.0 0.5846854      1 0.39479684 -0.19105586
 #12 Speelyai 6  13.0 0.5597146      1 0.37005941 -0.21392499

数据

data

 df1 <- structure(list(stream = c("Brooks", "Siouxon", "Speelyai", 
 "Brooks", 
 "Siouxon", "Speelyai", "Brooks", "Siouxon", "Speelyai", "Brooks", 
 "Siouxon", "Speelyai"), n = c(3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 
 5L, 6L, 6L, 6L), rates = c(3, 3, 3, 7.5, 7.5, 7.5, 10, 10, 10, 
 13, 13, 13), means = c(0.9629152, 0.5831929, 0.6199235, 0.9722707, 
 0.5865031, 0.6118634, 0.9637475, 0.580442, 0.5959238, 0.9486575, 
 0.5846854, 0.5597146), column = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
 1L, 1L, 1L, 1L, 1L), value = c(0.42707006, 0.90503736, 0.08554021, 
 1.43338843, 0.50574543, 0.32252396, 0.88984211, 0.475018, 0.15079491, 
 1.32422105, 0.39479684, 0.37005941)), .Names = c("stream", "n", 
 "rates", "means", "column", "value"), class = "data.frame", 
 row.names = c("1", 
 "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"))

 df2 <- data.frame(stream=c('Brooks', 'Siouxon', 'Speelyai'), 
      value=c(0.944062036, 0.585852702, 0.583984402), stringsAsFactors=FALSE)

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