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r - 分组列后计算函数中的值

问题描述

我已经在有土壤和没有土壤的罐子中采样了气体 x 和 y 的浓度。没有土壤的空罐子中的气体浓度最高，现在我想知道土壤吸收了多少气体。

浓度是在不同条件下用罐子测量的：source要么o要么要么要么。要么 要么和被命名为, , , , , .nactivitylowhighsamplingsoilblankjarsjar1jar2jar3jar4blank1blank2

我现在想计算每个独特测量条件下气体x和气体的相对浓度，例如= 、= 。ysourceoactivitylow

计算应该是((blank1+blank2)/2/jar1)。我在名为x_pct和的两列中给出了预期值y_pct。

关于如何设置有效代码的任何想法？

数据如下所示：

> df
   source activty    jar sampling  x  y x_pct y_pct
1       o     low blank1    blank 34 46  1.00  0.99
2       o    high blank1    blank 31 43  1.02  1.01
3       n     low blank1    blank 32 44  0.98  1.01
4       n    high blank1    blank 35 47  1.01  1.01
5       o     low   jar1     soil 21 33  1.62  1.38
6       o    high   jar1     soil 22 34  1.43  1.28
7       n     low   jar1     soil 23 34  1.37  1.31
8       n    high   jar1     soil 23 35  1.54  1.36
9       o     low   jar2     soil 27 39  1.26  1.17
10      o    high   jar2     soil 28 46  1.13  0.95
11      n     low   jar2     soil 29 41  1.09  1.09
12      n    high   jar2     soil 27 39  1.31  1.22
13      o     low blank2    blank 34 45  1.00  1.01
14      o    high blank2    blank 32 44  0.98  0.99
15      n     low blank2    blank 31 45  1.02  0.99
16      n    high blank2    blank 36 48  0.99  0.99
17      o     low   jar3     soil 25 37  1.36  1.23
18      o    high   jar3     soil 25 37  1.26  1.18
19      n     low   jar3     soil 26 38  1.21  1.17
20      n    high   jar3     soil 25 37  1.42  1.28
21      o     low   jar4     soil 19 34  1.79  1.34
22      o    high   jar4     soil 18 30  1.75  1.45
23      n     low   jar4     soil 20 34  1.58  1.31
24      n    high   jar4     soil 20 33  1.78  1.44

输入

df <- structure(list(source = c("o", "o", "n", "n", "o", "o", "n", 
"n", "o", "o", "n", "n", "o", "o", "n", "n", "o", "o", "n", "n", 
"o", "o", "n", "n"), activty = c("low", "high", "low", "high", 
"low", "high", "low", "high", "low", "high", "low", "high", "low", 
"high", "low", "high", "low", "high", "low", "high", "low", "high", 
"low", "high"), jar = c("blank1", "blank1", "blank1", "blank1", 
"jar1", "jar1", "jar1", "jar1", "jar2", "jar2", "jar2", "jar2", 
"blank2", "blank2", "blank2", "blank2", "jar3", "jar3", "jar3", 
"jar3", "jar4", "jar4", "jar4", "jar4"), sampling = c("blank", 
"blank", "blank", "blank", "soil", "soil", "soil", "soil", "soil", 
"soil", "soil", "soil", "blank", "blank", "blank", "blank", "soil", 
"soil", "soil", "soil", "soil", "soil", "soil", "soil"), x = c(34L, 
31L, 32L, 35L, 21L, 22L, 23L, 23L, 27L, 28L, 29L, 27L, 34L, 32L, 
31L, 36L, 25L, 25L, 26L, 25L, 19L, 18L, 20L, 20L), y = c(46L, 
43L, 44L, 47L, 33L, 34L, 34L, 35L, 39L, 46L, 41L, 39L, 45L, 44L, 
45L, 48L, 37L, 37L, 38L, 37L, 34L, 30L, 34L, 33L), x_pct = c(1, 
1.02, 0.98, 1.01, 1.62, 1.43, 1.37, 1.54, 1.26, 1.13, 1.09, 1.31, 
1, 0.98, 1.02, 0.99, 1.36, 1.26, 1.21, 1.42, 1.79, 1.75, 1.58, 
1.78), y_pct = c(0.99, 1.01, 1.01, 1.01, 1.38, 1.28, 1.31, 1.36, 
1.17, 0.95, 1.09, 1.22, 1.01, 0.99, 0.99, 0.99, 1.23, 1.18, 1.17, 
1.28, 1.34, 1.45, 1.31, 1.44)), class = "data.frame", row.names = c(NA, 
-24L))

标签： rtidyverse

解决方案

或许是这样的：


calculate <- function(d, y) {
    ## average blank:
    m <- colMeans( d[ grepl("blank",d$jar), c("x","y") ] )
    ## the ratio
    d %>% mutate( x_pct = m["x"] / x, y_pct = m["y"] / y )
}
df %>% group_by( source,activity ) %>%
    group_modify( calculate ) %>% print.data.frame

它给了我：


> df %>% group_by( source,activity ) %>%
+     group_modify( calculate ) %>% print.data.frame
   source activity    jar sampling  x  y     x_pct     y_pct
1       n     high blank1    blank 35 47 1.0142857 1.0106383
2       n     high   jar1     soil 23 35 1.5434783 1.3571429
3       n     high   jar2     soil 27 39 1.3148148 1.2179487
4       n     high blank2    blank 36 48 0.9861111 0.9895833
5       n     high   jar3     soil 25 37 1.4200000 1.2837838
6       n     high   jar4     soil 20 33 1.7750000 1.4393939
7       n      low blank1    blank 32 44 0.9843750 1.0113636
8       n      low   jar1     soil 23 34 1.3695652 1.3088235
9       n      low   jar2     soil 29 41 1.0862069 1.0853659
10      n      low blank2    blank 31 45 1.0161290 0.9888889
11      n      low   jar3     soil 26 38 1.2115385 1.1710526
12      n      low   jar4     soil 20 34 1.5750000 1.3088235
13      o     high blank1    blank 31 43 1.0161290 1.0116279
14      o     high   jar1     soil 22 34 1.4318182 1.2794118
15      o     high   jar2     soil 28 46 1.1250000 0.9456522
16      o     high blank2    blank 32 44 0.9843750 0.9886364
17      o     high   jar3     soil 25 37 1.2600000 1.1756757
18      o     high   jar4     soil 18 30 1.7500000 1.4500000
19      o      low blank1    blank 34 46 1.0000000 0.9891304
20      o      low   jar1     soil 21 33 1.6190476 1.3787879
21      o      low   jar2     soil 27 39 1.2592593 1.1666667
22      o      low blank2    blank 34 45 1.0000000 1.0111111
23      o      low   jar3     soil 25 37 1.3600000 1.2297297
24      o      low   jar4     soil 19 34 1.7894737 1.3382353

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