如何计算每个受试者由 R 中的变量区分的 2 个均值

Question

这是我关于 stack noveflow 的第一个问题。我尝试过一些研究，但还没有达到目的。我正在尝试计算每个主题的两个不同均值，它们由 R 中的变量区分。 我创建了这个示例数据集：

airquality <- data.frame(subject = c("CityA", "CityA","CityA","CityA", "CityA", "CityA", "CityA", "CityA",
                                  "CityB","CityB","CityB", "CityB", "CityB", "CityB", "CityB", "CityB",
                                  "CityC", "CityC", "CityC", "CityC", "CityC", "CityC", "CityC", "CityC"),
                         code = c("AA_high", "BB_high", "AA_low", "BB_low", "AB_high", "AB_low", "BA_high", "BA_low",
                                  "AA_high", "BB_high", "AA_low", "BB_low", "AB_high", "AB_low", "BA_high", "BA_low",
                                  "AA_high", "BB_high", "AA_low", "BB_low", "AB_high", "AB_low", "BA_high", "BA_low"),
                         measure = c("2", "6", "5", "3", "5", "5", "4", "6",
                                   "7", "8", "6", "7", "4", "12", "9", "7",
                                   "8", "12", "11", "9", "15", "11", "10", "16"))
print(airquality)

目标是每个主题有 2 个平均值：第一个平均值是所有代码以“AA”或“BB”开头的度量的平均值。 每个科目的第二个平均值应该是所有以“AB”或“BA”开头的代码的测量平均值。

我从这个开始：

df1 <- filter(airquality, substr(code, 1, 2) == "AA" | substr(code, 1,2) == "BB") 
df1 <- df1 %>% 
  group_by(subject) %>% 
  mutate(mean_AABB = mean(measure))
View(df1)

df2 <- filter(airquality, substr(code, 1, 2) == "AB" | substr(code, 1,2) == "BA") 
df2 <- df2 %>% 
  group_by(subject) %>% 
  mutate(mean_ABBA = mean(measure))
View(df2)

但这似乎不起作用，我需要将列添加到数据框中，这样我就可以用这些方法进行更多计算。 因此，下一步是将这些类型的方法添加到数据框中。

我非常感谢您的帮助！ 非常感谢， 麦可

Answer 1

关于：

library(dplyr)

airquality |>
  group_by(first_two_equal = substr(code, 1, 1) == substr(code, 2, 2)) |>
  mutate(measure = as.numeric(measure), ## !
         mean = mean(measure)
         )

（请注意示例数据的

measure

模式为“字符”，因此您需要将其转换为“数字”）