[英]Remove outlier from a single cell in R
I am a newbie in R and I am stuck with a problem removing some outliers.我是 R 的新手,我遇到了删除一些异常值的问题。 I have a dataframe which is something like this:我有一个 dataframe 这是这样的:
Item1 Item2 Item3
4.05 3.9 3.6
12 3.7 4
4.01 3.8 4
My desired result should be something like the table below, namely a table where the outliers of every column are removed我想要的结果应该类似于下表,即每列的异常值都被删除的表
Item1 Item2 Item3
4.05 3.9 3.6
NA 3.7 4
4.01 3.8 4
So far I have written a code which can detect the outliers, but I am stuck with removing them, as the entire column changes instead of the single value.到目前为止,我已经编写了一个可以检测异常值的代码,但是我坚持要删除它们,因为整个列都发生了变化,而不是单个值。
find_outlier <- function(log_reaction_time) {
media <- mean(log_reaction_time)
devst <- sd(log_reaction_time)
result <-which(log_reaction_time < media - 2 * devst | log_reaction_time > media + 2 * devst)
log_reaction_time2 <- ifelse (log_reaction_time %in% result, NA, log_reaction_time)
}
apply(log_reaction_time, 2, find_outlier)
I guess the problem comes from the fact that I apply the function over the columns (2), as I want to find the outliers of the column, but then I want to remove only the relevant values...我想问题出在我在列 (2) 上应用 function 的事实,因为我想找到列的异常值,但我只想删除相关值......
We will use same dataset to show this:我们将使用相同的数据集来展示这一点:
#Data
df1 <- structure(list(Item1 = c(4.05, 12, 4.01), Item2 = c(3.9, 3.7,
3.8), Item3 = c(3.6, 4, 4)), class = "data.frame", row.names = c(NA,
-3L))
df1
Item1 Item2 Item3
1 4.05 3.9 3.6
2 12.00 3.7 4.0
3 4.01 3.8 4.0
Now the function:现在 function:
#Function
find_outlier <- function(log_reaction_time) {
media <- mean(log_reaction_time)
devst <- sd(log_reaction_time)
result <-which(log_reaction_time < media - 2 * devst | log_reaction_time > media + 2 * devst)
log_reaction_time[result] <- NA
return(log_reaction_time)
}
apply(df1, 2, find_outlier)
Item1 Item2 Item3
[1,] 4.05 3.9 3.6
[2,] 12.00 3.7 4.0
[3,] 4.01 3.8 4.0
To highlight, second value for Item1
is not set to NA
because mean(df1$Item1)=6.69
and sd(df1$Item1)=4.60
.要突出显示, Item1
的第二个值未设置为NA
因为mean(df1$Item1)=6.69
和sd(df1$Item1)=4.60
。 So when the condition checks in the intervals you will have mean(df1$Item1)-2*sd(df1$Item1)=-2.51
and mean(df1$Item1)+2*sd(df1$Item1)=15.89
where 12
is not in those limits.因此,当条件检查间隔时,您将有mean(df1$Item1)-2*sd(df1$Item1)=-2.51
和mean(df1$Item1)+2*sd(df1$Item1)=15.89
其中12
是不在这些范围内。 You will have to define other criteria to assign it NA
.您将必须定义其他标准来分配它NA
。
Not quite sure which you want but here's a tidyverse solution for either...不太确定你想要哪个,但这里有一个 tidyverse 解决方案...
library(dplyr)
df %>%
mutate_all(function(x) ifelse(x < mean(x) - 2 * sd(x) | x > mean(x) + 2 * sd(x) ,
NA_real_,
x))
#> # A tibble: 3 x 3
#> Item1 Item2 Item3
#> <dbl> <dbl> <dbl>
#> 1 4.05 3.9 3.6
#> 2 12 3.7 4
#> 3 4.01 3.8 4
media <- mean(as.matrix(df))
devst <- sd(as.matrix(df))
df %>%
mutate_all(function(x) ifelse(x < media - 2 * devst | x > media + 2 * devst ,
NA_real_,
x))
#> # A tibble: 3 x 3
#> Item1 Item2 Item3
#> <dbl> <dbl> <dbl>
#> 1 4.05 3.9 3.6
#> 2 NA 3.7 4
#> 3 4.01 3.8 4
Your data您的数据
library(readr)
df <- read_table("Item1 Item2 Item3
4.05 3.9 3.6
12 3.7 4
4.01 3.8 4")
Using dplyr
, if df
is the first data.frame in your post, the following should work:使用dplyr
,如果df
是您帖子中的第一个 data.frame ,则以下内容应该有效:
library(dplyr)
df %>%
mutate(across(everything(), find_outlier)) -> new_df
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