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社区首页 >问答首页 >由多个组汇总和变异的tidyr .计算

问由多个组汇总和变异的tidyr .计算
EN

Stack Overflow用户
提问于 2022-06-16 20:42:23
回答 1查看 76关注 0票数 1

我有一些数据，见下面的子集。对于每种方法，我想要计算2uL和4 uL以及4 uL和8 uL之间的平均Cq的差异。

我有一个函数来计算每个方法的平均值，按卷分组。但我不知道如何添加另一列与之不同。我想我可能得总结一下总结表，但我有点糊涂了。任何帮助都可以。谢谢。

代码语言：javascript
复制
dat_summ<-
  dat %>%
  group_by(Volume,Method) %>%
  summarise(mean_Cq = mean(Cq,na.rm=T), sd_Cq=sd(Cq,na.rm=T),
            CV=(sd(Cq,na.rm=T)/mean(Cq,na.rm=T))*100)

我想要的但知道是否错了：

代码语言：javascript
复制
dat_summ<-
  dat %>%
  group_by(Volume,Method) %>%
  summarise(mean_Cq = mean(Cq,na.rm=T), sd_Cq=sd(Cq,na.rm=T),
            CV=(sd(Cq,na.rm=T)/mean(Cq,na.rm=T))*100)+
  **mutate(delta_doub=mean_Cq_for2uL-meanCq_for4uL)**

当前产出：

代码语言：javascript
复制
> dat_summ
# A tibble: 12 × 5
# Groups:   Volume [3]
   Volume Method mean_Cq sd_Cq    CV
   <chr>  <fct>    <dbl> <dbl> <dbl>
 1 2ul    2ew       20.0 0.295 1.47 
 2 2ul    3ew       21.9 1.79  8.18 
 3 2ul    Manual    22.2 0.248 1.12 
 4 2ul    WN2ew     20.5 0.604 2.94 
 5 4ul    2ew       19.3 0.278 1.44 
 6 4ul    3ew       21.2 1.33  6.29 
 7 4ul    Manual    22.2 0.139 0.627
 8 4ul    WN2ew     19.9 0.493 2.48 
 9 8ul    2ew       18.8 0.270 1.43 
10 8ul    3ew       20.8 1.21  5.81 
11 8ul    Manual    23.7 1.50  6.35 
12 8ul    WN2ew     19.5 0.463 2.38 



subset of dat:
    sample Method Volume    Cq
1   Sample 1    2ew    2ul 20.11
2   Sample 2    2ew    2ul 20.12
3   Sample 3    2ew    2ul 19.76
17  Sample 1  WN2ew    2ul 19.89
18  Sample 2  WN2ew    2ul 20.62
19  Sample 3  WN2ew    2ul 21.07
20  Sample 4  WN2ew    2ul 20.08
52  Sample 1    2ew    4ul 19.30
53  Sample 2    2ew    4ul 19.33
54  Sample 3    2ew    4ul 19.16
68  Sample 1  WN2ew    4ul 19.49
69  Sample 2  WN2ew    4ul 19.46
70  Sample 3  WN2ew    4ul 20.42
103 Sample 1    2ew    8ul 18.91
104 Sample 2    2ew    8ul 18.60
105 Sample 3    2ew    8ul 18.42
119 Sample 1  WN2ew    8ul 18.66
120 Sample 2  WN2ew    8ul 19.13
121 Sample 3  WN2ew    8ul 19.52

> dput(dat)
structure(list(sample = c("Sample 1", "Sample 2", "Sample 3", 
"Sample 4", "Sample 5", "Sample 6", "Sample 7", "Sample 8", "Sample 9", 
"Sample 10", "Sample 11", "Sample 12", "Sample 13", "Sample 14", 
"Sample 15", "Sample 16", "Sample 1", "Sample 2", "Sample 3", 
"Sample 4", "Sample 5", "Sample 6", "Sample 7", "Sample 8", "Sample 9", 
"Sample 10", "Sample 11", "Sample 12", "Sample 13", "Sample 14", 
"Sample 15", "Sample 16", "Sample 1", "Sample 2", "Sample 3", 
"Sample 4", "Sample 5", "Sample 6", "Sample 7", "Sample 8", "Sample 10", 
"Sample 11", "Sample 12", "Sample 13", "Sample 14", "Sample 15", 
"Sample 16", "Sample 1", "Sample 2", "Sample 3", "Sample 4", 
"Sample 1", "Sample 2", "Sample 3", "Sample 4", "Sample 5", "Sample 6", 
"Sample 7", "Sample 8", "Sample 9", "Sample 10", "Sample 11", 
"Sample 12", "Sample 13", "Sample 14", "Sample 15", "Sample 16", 
"Sample 1", "Sample 2", "Sample 3", "Sample 4", "Sample 5", "Sample 6", 
"Sample 7", "Sample 8", "Sample 9", "Sample 10", "Sample 11", 
"Sample 12", "Sample 13", "Sample 14", "Sample 15", "Sample 16", 
"Sample 1", "Sample 2", "Sample 3", "Sample 4", "Sample 5", "Sample 6", 
"Sample 7", "Sample 8", "Sample 10", "Sample 11", "Sample 12", 
"Sample 13", "Sample 14", "Sample 15", "Sample 16", "Sample 1", 
"Sample 2", "Sample 3", "Sample 4", "Sample 1", "Sample 2", "Sample 3", 
"Sample 4", "Sample 5", "Sample 6", "Sample 7", "Sample 8", "Sample 9", 
"Sample 10", "Sample 11", "Sample 12", "Sample 13", "Sample 14", 
"Sample 15", "Sample 16", "Sample 1", "Sample 2", "Sample 3", 
"Sample 4", "Sample 5", "Sample 6", "Sample 7", "Sample 8", "Sample 9", 
"Sample 10", "Sample 11", "Sample 12", "Sample 13", "Sample 14", 
"Sample 15", "Sample 16", "Sample 1", "Sample 2", "Sample 3", 
"Sample 4", "Sample 5", "Sample 6", "Sample 7", "Sample 8", "Sample 10", 
"Sample 11", "Sample 12", "Sample 13", "Sample 14", "Sample 15", 
"Sample 16", "Sample 1", "Sample 2", "Sample 3", "Sample 4"), 
    Method = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 
    3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L), .Label = c("2ew", "3ew", 
    "Manual", "WN2ew"), class = "factor"), Volume = c("2ul", 
    "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", 
    "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", 
    "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", 
    "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", 
    "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", 
    "2ul", "2ul", "2ul", "2ul", "2ul", "4ul", "4ul", "4ul", "4ul", 
    "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", 
    "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", 
    "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", 
    "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", 
    "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", 
    "4ul", "4ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", 
    "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", 
    "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", 
    "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", 
    "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", 
    "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul"), 
    Cq = c(20.11, 20.12, 19.76, 20.07, 20.19, 19.87, 20.33, 19.81, 
    20.15, 19.79, 19.67, 20.23, 19.9, 20.9, 19.93, 19.96, 19.89, 
    20.62, 21.07, 20.08, 21.32, 21.15, 21.07, 20.85, 21.16, 21.03, 
    20.79, 19.39, 20.25, 19.6, 20.14, 20.32, 26.35, 21.36, 21.67, 
    21.13, 21.28, 21.27, 21.36, 21.08, 20.56, 26.18, 21.31, 21.35, 
    21.06, 21.15, 21.28, 22.2, 22.18, 21.96, 22.56, 19.3, 19.33, 
    19.16, 19.27, 19.42, 19.16, 19.53, 19.1, 19.38, 19.08, 19.2, 
    19.44, 19.18, 20.11, 19.43, 18.81, 19.49, 19.46, 20.42, 19.21, 
    20.69, 20.39, 20.19, 20.13, 20.29, 20.49, 20.09, 19.19, 19.63, 
    19.27, 19.82, 19.76, 25.57, 20.45, 20.83, 20.68, 20.72, 21.25, 
    21.14, 21.06, 20.47, 22.51, 20.49, 20.9, 20.47, 20.24, 20.71, 
    22.09, 22.07, 22.13, 22.37, 18.91, 18.6, 18.42, 18.64, 19.14, 
    18.77, 18.77, 18.71, 19.39, 18.7, 18.67, 19.18, 18.79, 19.22, 
    18.73, NA, 18.66, 19.13, 19.52, 19.02, 20.25, 19.66, 19.78, 
    19.71, 19.89, 20.25, 19.47, 19.06, 19.49, 18.84, 19.27, 19.22, 
    24.97, 20.05, 20.33, 20.05, 20.59, 20.39, 20.08, 20.73, 20.3, 
    20.76, 21.12, 20.81, 20.22, 20.32, 20.69, 22.15, 25.2, 24.69, 
    22.63)), row.names = c(NA, -153L), class = "data.frame")
tidyr
r
EN

回答 1

Stack Overflow用户

回答已采纳

发布于 2022-06-16 20:50:37

如果mean增量应该针对每个“方法”，那么首先创建按“方法”分组的列(或者如果它基于所有的Method，那么我们就不需要任何分组)，得到“Cq”的mean差异，其中“音量”分别是'2ul‘和'4ul’，在分组中使用这个值来计算其余的汇总列。

代码语言：javascript
复制
library(dplyr)
dat %>%
   group_by(Method) %>%
   mutate(delta_doub =mean(Cq[Volume == '2ul'], na.rm = TRUE) - 
                      mean(Cq[Volume=='4ul'], na.rm = TRUE) ) %>% 
   group_by(Volume, Method, delta_doub) %>% 
   summarise(mean_Cq = mean(Cq,na.rm=TRUE), sd_Cq=sd(Cq,na.rm=TRUE),
            CV=(sd(Cq,na.rm=TRUE)/mean(Cq,na.rm=TRUE))*100, .groups = "drop")

-output

代码语言：javascript
复制
# A tibble: 12 × 6
   Volume Method delta_doub mean_Cq sd_Cq    CV
   <chr>  <fct>       <dbl>   <dbl> <dbl> <dbl>
 1 2ul    2ew        0.743     20.0 0.295 1.47 
 2 2ul    3ew        0.727     21.9 1.79  8.18 
 3 2ul    Manual     0.0600    22.2 0.248 1.12 
 4 2ul    WN2ew      0.638     20.5 0.604 2.94 
 5 4ul    2ew        0.743     19.3 0.278 1.44 
 6 4ul    3ew        0.727     21.2 1.33  6.29 
 7 4ul    Manual     0.0600    22.2 0.139 0.627
 8 4ul    WN2ew      0.638     19.9 0.493 2.48 
 9 8ul    2ew        0.743     18.8 0.270 1.43 
10 8ul    3ew        0.727     20.8 1.21  5.81 
11 8ul    Manual     0.0600    23.7 1.50  6.35 
12 8ul    WN2ew      0.638     19.5 0.463 2.38

也可以是

代码语言：javascript
复制
dat %>%
  group_by(Volume,Method) %>% 
  summarise(mean_Cq = mean(Cq,na.rm=TRUE), sd_Cq=sd(Cq,na.rm=TRUE),
             CV=(sd(Cq,na.rm=TRUE)/mean(Cq,na.rm=TRUE))*100,
   .groups = 'drop') %>% 
  mutate(delta_doub_2_4 = mean(mean_Cq[Volume == '2ul']) - 
      mean(mean_Cq[Volume == '4ul']), 
      delta_doub_4_8 = mean(mean_Cq[Volume == '4ul']) - 
              mean(mean_Cq[Volume == '8ul']))

-output

代码语言：javascript
复制
# A tibble: 12 × 7
   Volume Method mean_Cq sd_Cq    CV delta_doub_2_4 delta_doub_4_8
   <chr>  <fct>    <dbl> <dbl> <dbl>          <dbl>          <dbl>
 1 2ul    2ew       20.0 0.295 1.47           0.542        -0.0443
 2 2ul    3ew       21.9 1.79  8.18           0.542        -0.0443
 3 2ul    Manual    22.2 0.248 1.12           0.542        -0.0443
 4 2ul    WN2ew     20.5 0.604 2.94           0.542        -0.0443
 5 4ul    2ew       19.3 0.278 1.44           0.542        -0.0443
 6 4ul    3ew       21.2 1.33  6.29           0.542        -0.0443
 7 4ul    Manual    22.2 0.139 0.627          0.542        -0.0443
 8 4ul    WN2ew     19.9 0.493 2.48           0.542        -0.0443
 9 8ul    2ew       18.8 0.270 1.43           0.542        -0.0443
10 8ul    3ew       20.8 1.21  5.81           0.542        -0.0443
11 8ul    Manual    23.7 1.50  6.35           0.542        -0.0443
12 8ul    WN2ew     19.5 0.463 2.38           0.542        -0.0443
票数 1
EN
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：

https://stackoverflow.com/questions/72651669

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