问题

我是新手使用R，但有一些Matlab的经验。我正在尝试在一个图表上制作三种不同区域测量值的比较图，按动物模型的基因型进行分面。我想要按基因型（Control和Cre-）来绘制Glom.Area、Glom.Tuft和Mesangial.Area。

我的代码如下：

# 设置环境
library(tidyverse)
library(ggbeeswarm)
library(readxl)

# 工作目录
setwd("C:/Users/k_nic/OneDrive/Desktop")

# 读取CSV文件
t1 <- read.csv("sample.csv", header = TRUE)

# 绘制一个变量的图表
ggplot(t1, aes(x = Genotype, y = Glom.Area))

数据集：

   Genotype Sex  Glom.Area  Glom.Tuft  Mesangial.Area
1   Control   M   6617.669   3804.877        1917.310
2   Control   M   7986.696   5377.977        2761.949
3   Control   M   8617.410   4443.345        2335.244
4   Control   M   7785.355   4307.021        2381.512
5   Control   M   9159.720   5568.449        3163.232
6   Control   M   8484.327   5314.041        3238.861
7   Control   M  10315.521   6105.294        3429.906
8   Control   M   5517.161   3090.970        1878.288
9      Cre-   M   6075.166   3438.421        2147.758
10     Cre-   M   8947.698   5809.953        3744.117
11     Cre-   M   6677.471   4315.344        2113.502
12     Cre-   M   7616.932   4293.291        2763.791
13     Cre-   M   8191.464   4778.529        2159.007
14     Cre-   M   8549.339   6043.961        2922.295
15     Cre-   M  11057.196   6546.740        3771.891
16     Cre-   M   5911.768   3382.556        1754.674

我希望它看起来类似于以下图表：

非常感谢任何帮助！

# Setup
library(tidyverse)
library(ggbeeswarm)
library(readxl)

# work space
setwd("C:/Users/k_nic/OneDrive/Desktop")

# Read in csv file
t1 <- read.csv("sample.csv", header = TRUE)

# plot for one varible
ggplot(t1, aes(x = Genotype, y= Glom.Area))

   Genotype Sex Glom.Area Glom.Tuft Mesangial.Area
1   Control   M  6617.669  3804.877       1917.310
2   Control   M  7986.696  5377.977       2761.949
3   Control   M  8617.410  4443.345       2335.244
4   Control   M  7785.355  4307.021       2381.512
5   Control   M  9159.720  5568.449       3163.232
6   Control   M  8484.327  5314.041       3238.861
7   Control   M 10315.521  6105.294       3429.906
8   Control   M  5517.161  3090.970       1878.288
9      Cre-   M  6075.166  3438.421       2147.758
10     Cre-   M  8947.698  5809.953       3744.117
11     Cre-   M  6677.471  4315.344       2113.502
12     Cre-   M  7616.932  4293.291       2763.791
13     Cre-   M  8191.464  4778.529       2159.007
14     Cre-   M  8549.339  6043.961       2922.295
15     Cre-   M 11057.196  6546.740       3771.891
16     Cre-   M  5911.768  3382.556       1754.674

答案1

得分: 0

在这种方法中，我们将三个测量转化为长格式的数据。我们将测量名称映射到 x 轴，相应的数值映射到 y 轴。将基因型映射到填充美观度会产生一对列。

``` r
library(tidyverse)

df %>%
  pivot_longer(c(Glom.Area, Glom.Tuft, Mesangial.Area)) %>%
  ggplot(aes(name, value, fill = Genotype)) +
  geom_col(position = position_dodge())

^{创建于2023年06月15日，使用 reprex v2.0.2}

<details>
<summary>英文:</summary>

In this approach, we pivot the three measurements into long-form data. We map the measurement names to the x-axis and their respective values along the y. Mapping the Genotypes to the fill aesthetic yields the pairs of columns.

``` r
library(tidyverse)

df %&gt;%
  pivot_longer(c(Glom.Area, Glom.Tuft, Mesangial.Area)) %&gt;%
  ggplot(aes(name, value, fill = Genotype)) +
  geom_col(position = position_dodge())

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^{Created on 2023-06-15 with reprex v2.0.2}