r语言 - 如何使用函数"summarySEwithin()"分析重复测量数据？

Question

我有一个关于用重复测量的数据集绘制误差条的问题。我有这样的数据集：

    Food_type Quantity Temperature Time Diameter
    Food1      High       High      6    0.00100
    Food1      Medium     High      6    0.00090
    Food1      Low        High      6    0.00085
    Food2      High       High      6    0.00100
    Food2      Medium     High      6    0.00090
    Food2      Low        High      6    0.00085
    ...

我用不同的食物类型、不同的食物浓度喂养一只动物幼虫，并在不同的温度下培养幼虫，每6天测量一次它们的大小。

我想绘制这个数据集的误差条，因为数据是重复测量的，所以在绘制数据之前，我需要使用函数"SummarySEwithin（）"来汇总数据。我还使用了"SummarySE（）"one_answers"normDatawithin（）"的函数，遵循的方法（在此处输入链接描述）

这是我的代码：

    summarySEwithin(data, measurevar = "Diameter", groupvars= c("Quantity","Temperature", "Time"),idvar="quality")

我还试图在数据集中添加一列"主题"，并编写代码：

    summarySEwithin(Data, measurevar = "Diameter", groupvars= c("Quality","Quantity","Temperature", "Time"),idvar="Subject")

它们都有"未使用的参数"错误。

有人能帮我处理案子吗？任何答复都将不胜感激。非常感谢。

Answer 1

一些测试数据：

data <- data.frame(expand.grid(Quality = c('Food1','Food2'),
                           Quantity = c('High', 'Medium', 'Low'), 
                           Temperature = c('High','Low'), 
                           Time = 1:6,
                           Sample = 1:3))
data$Diameter <- runif(NROW(data))

dplyr解决方案：

library(dplyr)
# I'm using standard dev as my measurement of error, but you can use
# any other definition you want
plotdata <-
 data %>% 
  group_by(Quality, Quantity, Temperature, Time) %>%
  summarise(DiameterMean = mean(Diameter),
            DiameterErr = sd(Diameter)) %>%
  arrange(Time)

data.table解决方案：

library(data.table)
data <- setDT(data)
plotdata <- data[,.(DiameterMean = mean(Diameter),
                    DiameterErr = sd(Diameter)), 
                 by = .(Quality, Quantity, Temperature, Time)][order(Time)]

现在使用plotly或ggplot2:进行绘图

library(plotly)
plot_ly(data = plotdata,
        y = DiameterMean, 
        x = Time, 
        error_y = list(array = DiameterErr), 
        color = paste0(Quantity,'-', Quality,'-',Temperature))

ggplot2:

library(ggplot2)
ggplot(data = plotdata) +
  geom_line(aes(x = Time, 
                y = DiameterMean, 
                color = paste0(Quantity,'-', Quality,'-',Temperature))) +
  geom_errorbar(aes(x = Time,
                    ymin = DiameterMean - DiameterErr,
                    ymax = DiameterMean + DiameterErr,
                    color = paste0(Quantity,'-', Quality,'-',Temperature)))