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假设500人中有男女若干,现要比较饭前(before)和饭后(after)某生理指标的变化。由于数据誊写错误,测量指标中出现了一些异常值(outlier)。 问,如果按照性别以及饭前饭后分组,怎样去掉各组内的异常值?
思路:先定义识别和转换异常值的函数, 将一个向量中的异常值转换为NA。再用dplyr程序包将该函数应用于各组数据。
解答:
library(ggplot2) library(dplyr)
## ## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats': ## ## filter, lag
## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union
## 参考 https://stackoverflow.com/questions/49982794/remove-outliers-by-group-in-r ## 对于一个向量x, 先计算其上下四分位数 ## 若任何值超过上四分位数的1.5倍,或低于下四分位数的1.5倍,一般认为是异常值 ## 下面的函数将异常值转换为NA remove_outliers <- function(x, na.rm = TRUE, ...) { qnt <- quantile(x, probs=c(.25, .75), na.rm = na.rm, ...) H <- 1.5 * IQR(x, na.rm = na.rm) y <- x y[x < (qnt[1] - H)] <- NA y[x > (qnt[2] + H)] <- NA y } ## 生成一套随机数据 test_dat <- data.frame( ID = c(1:500,1:500), age = rep(sample(18:70, 500, replace = TRUE), 2) , gender = gl(2, 500, labels = c("male", "female")), meal = gl(2, 500, labels = c("before", "after"))[sample(1:1000)], value = c(c(rnorm(490), rnorm(10)*5), c(rnorm(490), rnorm(10)*5) + 3) ) head(test_dat)
## ID age gender meal value ## 1 1 25 male after -1.233542780 ## 2 2 25 male after -0.003499835 ## 3 3 54 male after -0.265865215 ## 4 4 24 male after 1.281983039 ## 5 5 21 male after -0.114771555 ## 6 6 41 male after 0.763784322
ggplot(test_dat, aes(x = gender, y = value, fill = meal)) + geom_boxplot() + ggtitle("Original")
test_dat2 <- test_dat %>% group_by_at(.vars = c("meal", "gender")) %>% mutate(value_new = case_when(TRUE ~ remove_outliers(value), TRUE ~ value)) ggplot(test_dat2, aes(x = gender, y = value_new, fill = meal)) + geom_boxplot() + ggtitle("Outlier Removed")
## Warning: Removed 18 rows containing non-finite values (stat_boxplot).
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