R 中数据集的分组和平均值

发布于 2025-01-14 20:49:50 字数 484 浏览 3 评论 0原文

我想计算不同三次重复的平均值,但似乎找不到一个好方法来做到这一点。我尝试过对数据集进行分组和总结,但仍然没有成功。我使用以下代码来修剪我的数据集:

Data_mini <- 
  Trimmed_Data %>% 
  group_by(Component.Name, Sample.Name) %>%
  summarise(Area.Ratio)
        summarise(DF_ABCR, mymean = mean(Area.Ratio))

我有一个带有组件名称、样本名称和面积比的虚拟数据集。我需要计算每个组件名称的 A1_ABCR1、B1_ABCR1 和 C1_ABCR1(样本名称)的平均值。请参阅图像以获取数据集的直观概述。谁能分享他们对如何最好地解决这个问题的看法?预先感谢您:) 虚拟数据集视觉概述

I want to calculate the mean of different triplicates, but can't seem to figure out a good way to do that. I've tried grouping and summarising my dataset, but still no luck. I've used the following code to trim my dataset :

Data_mini <- 
  Trimmed_Data %>% 
  group_by(Component.Name, Sample.Name) %>%
  summarise(Area.Ratio)
        summarise(DF_ABCR, mymean = mean(Area.Ratio))

I have a dummy dataset with component name, sample name and area ratio. I need to calculate the mean for A1_ABCR1, B1_ABCR1 and C1_ABCR1 (sample name) for each component name. See image for a visual overview of the dataset. Can anyone share their view on how to best tackle this problem? Thank you in advance :) Dummy dataset visual overview

如果你对这篇内容有疑问,欢迎到本站社区发帖提问 参与讨论,获取更多帮助,或者扫码二维码加入 Web 技术交流群。

扫码二维码加入Web技术交流群

发布评论

需要 登录 才能够评论, 你可以免费 注册 一个本站的账号。

评论(1

横笛休吹塞上声 2025-01-21 20:49:50

我创建了一个示例数据:

Trimmed_Data <- data.frame(Sample.Name = c("A1_ABCR1", "B1_ABCR1", "C1_ABCR1", "A1_ABCR1", "B1_ABCR1", "C1_ABCR1"),
                             Component.Name = c("BAD_acetylcarnitine", "BAD_acetylcarnitine", "BAD_acetylcarnitine", "BAD_alanine", "BAD_alanine", "BAD_alanine"),
                             Area.Ratio = runif(6, 0, 100))

示例数据:

  Sample.Name      Component.Name Area.Ratio
1    A1_ABCR1 BAD_acetylcarnitine   70.51099
2    B1_ABCR1 BAD_acetylcarnitine   48.85098
3    C1_ABCR1 BAD_acetylcarnitine   36.42945
4    A1_ABCR1         BAD_alanine   22.29663
5    B1_ABCR1         BAD_alanine   63.06249
6    C1_ABCR1         BAD_alanine   78.02675

您可以使用此代码来计算单位面积比率的平均值:

Data_mini <- 
    Trimmed_Data %>% 
    group_by(Component.Name, Sample.Name) %>%
    summarise(mean.Area.Ratio = mean(Area.Ratio))
  
  Data_mini

输出:

# A tibble: 6 × 3
# Groups:   Component.Name [2]
  Component.Name      Sample.Name mean.Area.Ratio
  <chr>               <chr>                 <dbl>
1 BAD_acetylcarnitine A1_ABCR1               70.5
2 BAD_acetylcarnitine B1_ABCR1               48.9
3 BAD_acetylcarnitine C1_ABCR1               36.4
4 BAD_alanine         A1_ABCR1               22.3
5 BAD_alanine         B1_ABCR1               63.1
6 BAD_alanine         C1_ABCR1               78.0

I created a sample data:

Trimmed_Data <- data.frame(Sample.Name = c("A1_ABCR1", "B1_ABCR1", "C1_ABCR1", "A1_ABCR1", "B1_ABCR1", "C1_ABCR1"),
                             Component.Name = c("BAD_acetylcarnitine", "BAD_acetylcarnitine", "BAD_acetylcarnitine", "BAD_alanine", "BAD_alanine", "BAD_alanine"),
                             Area.Ratio = runif(6, 0, 100))

Sample data:

  Sample.Name      Component.Name Area.Ratio
1    A1_ABCR1 BAD_acetylcarnitine   70.51099
2    B1_ABCR1 BAD_acetylcarnitine   48.85098
3    C1_ABCR1 BAD_acetylcarnitine   36.42945
4    A1_ABCR1         BAD_alanine   22.29663
5    B1_ABCR1         BAD_alanine   63.06249
6    C1_ABCR1         BAD_alanine   78.02675

You can use this code to calculate the mean per area ratio:

Data_mini <- 
    Trimmed_Data %>% 
    group_by(Component.Name, Sample.Name) %>%
    summarise(mean.Area.Ratio = mean(Area.Ratio))
  
  Data_mini

Output:

# A tibble: 6 × 3
# Groups:   Component.Name [2]
  Component.Name      Sample.Name mean.Area.Ratio
  <chr>               <chr>                 <dbl>
1 BAD_acetylcarnitine A1_ABCR1               70.5
2 BAD_acetylcarnitine B1_ABCR1               48.9
3 BAD_acetylcarnitine C1_ABCR1               36.4
4 BAD_alanine         A1_ABCR1               22.3
5 BAD_alanine         B1_ABCR1               63.1
6 BAD_alanine         C1_ABCR1               78.0
~没有更多了~
我们使用 Cookies 和其他技术来定制您的体验包括您的登录状态等。通过阅读我们的 隐私政策 了解更多相关信息。 单击 接受 或继续使用网站,即表示您同意使用 Cookies 和您的相关数据。
原文