调整用于移除异常值的代码 - 循环中的函数不是 运行
Adapting a code for removing outliers- Function not running in loop
本质上我是在尝试改编 and https://www.statology.org/remove-outliers-r/.
中的代码
第一个代码删除了高于第三个四分位数的异常值。我想改用四分位法。我还需要让它与我的数据集一起工作，即它需要嵌套到一个循环中，该循环通过名为 Group 的列对我的数据框的行进行子集化。我调整了上面的代码来为我工作，但我似乎无法让它与四分位数方法一起工作到 运行。请帮忙。
A-F组各有12行，这些是重复的，列是不同的时间点（分钟）：
# A tibble: 72 x 15
Group `0` `14` `27` `42` `60` `80` `95` `110` `125` `139` `169` `200` `229` `311`
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 A -0.921 -0.717 -0.310 0.608 2.35 4.22 6.04 7.63 10.5 12.1 15.2 20.1 23.6 34.8
2 A -0.926 -0.728 -0.367 0.392 1.92 3.66 5.80 8.17 13.8 12.4 15.5 19.6 23.7 35.0
3 A -0.928 -0.699 -0.212 0.864 2.73 4.37 7.13 9.60 14.7 15.2 18.9 25.0 29.7 44.4
4 A -0.919 -0.590 0.0813 1.47 4.07 6.47 9.43 12.5 26.3 18.7 24.6 30.8 36.9 55.4
5 A -0.925 -0.672 -0.199 0.701 2.37 4.06 5.94 7.97 15.2 12.4 16.1 20.1 24.8 37.9
6 A -0.911 -0.563 0.185 1.76 4.99 7.98 11.8 15.2 31.8 25.7 32.2 41.3 48.4 73.4
7 A -0.918 -0.675 -0.0975 1.21 3.09 5.43 7.83 10.8 18.3 15.0 20.8 26.7 31.9 48.8
8 A -0.923 -0.646 -0.0773 1.24 3.51 5.57 7.48 10.1 15.5 16.3 21.2 25.2 30.4 46.7
9 A -0.926 -0.634 -0.00355 1.32 3.80 6.37 8.36 11.8 13.9 16.7 23.3 27.6 32.1 49.8
10 A -0.920 -0.653 -0.0496 1.17 3.22 5.18 6.98 9.34 11.0 13.3 19.1 22.6 27.2 40.9
# … with 62 more rows
改编来自 的代码我制作了这个：
（if numeric是为了处理组列，我本来也把所有的负值都设为NA。我最近改变了这个，但我想保留那个功能，以防我们恢复。）
Outlies = function(x, probs){
if(class(x) == "numeric"){
x[x > quantile(x, probs)] = NA
return(x)
}else{
return(x)
}
}
Group<- c('A', 'B', 'C', 'D','E','F')
Cleandata = NULL
for (char in Group){
subset <- my_data[which(my_data$Group == char),]
df <- do.call(bind_cols, lapply(subset, Outlies, 3/4))
Cleandata <- bind_rows(Cleandata, df)
}
输出：
# A tibble: 72 x 15
Group `0` `14` `27` `42` `60` `80` `95` `110` `125` `139` `169` `200` `229` `311`
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 A -0.921 -0.717 -0.310 0.608 2.35 4.22 6.04 7.63 10.5 12.1 15.2 20.1 23.6 34.8
2 A -0.926 -0.728 -0.367 0.392 1.92 3.66 5.80 8.17 13.8 12.4 15.5 19.6 23.7 35.0
3 A -0.928 -0.699 -0.212 0.864 2.73 4.37 7.13 9.60 14.7 15.2 18.9 25.0 29.7 44.4
4 A -0.919 NA NA NA NA NA NA NA NA NA NA NA NA NA
5 A -0.925 -0.672 -0.199 0.701 2.37 4.06 5.94 7.97 15.2 12.4 16.1 20.1 24.8 37.9
6 A NA NA NA NA NA NA NA NA NA NA NA NA NA NA
7 A NA -0.675 -0.0975 1.21 3.09 5.43 7.83 10.8 NA 15.0 20.8 26.7 31.9 48.8
8 A -0.923 -0.646 -0.0773 1.24 3.51 5.57 7.48 10.1 15.5 16.3 21.2 25.2 30.4 46.7
9 A -0.926 NA NA NA NA NA NA NA 13.9 NA NA NA NA NA
10 A -0.920 -0.653 -0.0496 1.17 3.22 5.18 6.98 9.34 11.0 13.3 19.1 22.6 27.2 40.9
# … with 62 more rows
这 运行 完全符合我的要求，但我意识到我不喜欢这种去除异常值的方法，我想使用四分位数法，其中异常值“低于 Q1 − 1.5 IQR 或高于 Q3 + 1.5 IQR”我也尝试从这里改编一些代码https://www.statology.org/remove-outliers-r/。
新代码和输出：
Outliesplease = function(x){
Q1 <- quantile(x, probs=.25)
Q3 <- quantile(x, probs=.75)
iqr = Q3-Q1
upper_limit = Q3 + (iqr*1.5)
lower_limit = Q1 - (iqr*1.5)
if(class(x) == "numeric"){
x[x> upper_limit | x < lower_limit] = NA
return(x)
}else{
return(x)
}
}
> Outliesplease(D$`42`)
[1] NA 2.428675 2.428384 2.714187 2.457054 2.464337 2.686667 2.166072 2.690987 2.632692
[11] NA 2.849850
但是这段代码在我在顶部使用的 for 循环中不起作用。我想是因为它不能只处理单列数组。
错误：
Error in (1 - h) * qs[i] : non-numeric argument to binary operator
5.
quantile.default(x, probs = 0.25)
4.
quantile(x, probs = 0.25)
3.
FUN(X[[i]], ...)
2.
lapply(subset, Outliesplease)
1.
do.call(bind_cols, lapply(subset, Outliesplease))
我试过制作新的嵌套循环，但到目前为止它们还没有奏效，而且变得很长。非常感谢任何帮助。
这个 returns 一个 tibble 但不产生和 NAs，x[[i]] 在我尝试完成这些行时给出错误下标越界。
Outliesplease = function(x){
for(i in 1:ncol(x)){
if(class(x[[i]]) == "numeric"){
Q1 <- quantile(x, probs=.25)
Q3 <- quantile(x, probs=.75)
iqr = Q3-Q1
upper_limit = Q3 + (iqr*1.5)
lower_limit = Q1 - (iqr*1.5)
x[x> upper_limit | x < lower_limit] = NA
return(x)
}else{
return(x)
}
}
}
数据（dput( my_data[1:24,1:10]）：
structure(list(Group = c("A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B"), `0` = c(-0.920718681650864, -0.926284584901359,
-0.928389338231378, -0.919206377406332, -0.925489455865574, -0.910748387172736,
-0.917616119334762, -0.923166431819849, -0.925629772754242, -0.920438047873528,
-0.92294036572144, -0.915745227485856, -1.69199983248014, -1.70459419320581,
-1.70676758766494, -1.69535549688934, -1.70584725776368, -1.70666847521404,
-1.70083499953221, -1.71102942305384, -1.70695165364519, -1.70062969516963,
-1.7134930754049, -1.70824719496773), `14` = c(-0.717087694662867,
-0.727938867386521, -0.699111542147963, -0.590022956591343, -0.671788725771233,
-0.563409520040657, -0.674930265000854, -0.646204279737445, -0.634129231929299,
-0.652767991974024, -0.700865503256312, -0.674283248236441, -1.38233713854987,
-1.4058905045613, -1.331003968442, -1.32096529305751, -1.34241605921761,
-1.30258701287546, -1.36194121204584, -1.38785911795742, -1.32276347609535,
-1.41574511396555, -1.43304023664842, -1.43429330120629), `27` = c(-0.310028400637171,
-0.367347849658025, -0.212126189260467, 0.0813387880058272, -0.198866243281347,
0.184635404213543, -0.0975496542803897, -0.0773362269295024,
-0.00354513425557315, -0.0496080506521766, -0.212157370791282,
-0.145584802501048, -0.532695653169101, -0.741999911097547, -0.470785768657541,
-0.64911738567826, -0.543272367572791, -0.522904758911869, -0.552964149379117,
-0.612636924284542, -0.477362587721147, -0.634356709954235, -0.777630837197797,
-0.788073041846688), `42` = c(0.607589483572235, 0.391672973443089,
0.864400571365382, 1.47484919551526, 0.700635171524487, 1.76405009844259,
1.2081067901575, 1.23822814892489, 1.32286261893977, 1.17345631403922,
0.885775510739131, 1.23909343640501, 0.891691855547406, 0.549640628553635,
1.24951612115659, 0.674047992821404, 0.89237856324296, 0.887670721824986,
0.951194723394137, 0.768459681768936, 0.920901710721185, 0.816104452810994,
0.6222900550668, 0.528862411167537), `60` = c(2.35269503717001,
1.91507004756281, 2.7283623250476, 4.06823270417236, 2.37029701131513,
4.99217264375453, 3.09496358284071, 3.51067575166759, 3.79890223175695,
3.22287801762694, 2.83296856554955, 2.80565354455553, 3.05406359423973,
2.59544904552122, 3.6861957266493, 2.78357863625995, 2.84691149238807,
2.9002410704356, 3.33699424426883, 2.78563875934661, 3.24748154218031,
3.03239336479549, 2.89073335460952, 2.42163412447745), `80` = c(4.22194985570809,
3.66147743007211, 4.37329721090187, 6.4729211791019, 4.06143513045467,
7.97675963801785, 5.43250263115993, 5.5739108734064, 6.37457463091108,
5.1765490354642, 4.88228113377942, 4.80671269384903, 5.47211005896236,
5.16329690032557, 6.82715424934945, 4.78752620164185, 5.19603940642802,
5.0833981059748, 5.50798876618987, 5.42161934468718, 5.42710592679083,
5.27515238063233, 5.28819274738708, 4.39058083576842), `95` = c(6.03690983371438,
5.7998522456926, 7.12957303653705, 9.43214199804714, 5.94493990857525,
11.8180436006606, 7.82973872022466, 7.47695867596535, 8.35890488892228,
6.98265345871901, 6.67122012432051, 6.08213864416168, 6.50883337488658,
7.03303204826091, 9.19775416793183, 6.64392364546973, 7.52055911480048,
7.31507776569264, 8.43660594228742, 7.0626383532383, 7.77525687524329,
7.60673031140136, 7.58134336504819, 5.40544277680737), `110` = c(7.63013794609452,
8.16696697598995, 9.59730277139201, 12.4602350231802, 7.96996206430016,
15.2471700889893, 10.8204762714413, 10.1465498459346, 11.785006768762,
9.33682005834538, 9.48028628162565, 9.04492195300255, 8.75188972807613,
9.4568624324399, 12.0358816763534, 8.95913386291792, 9.57352486661554,
9.18373683558959, 11.3559065479999, 9.10912639844067, 9.55695184893212,
9.52058465891086, 9.83057300803695, 7.86015376890397), `125` = c(10.5039681429026,
13.8202876481251, 14.6520393918524, 26.3212361902925, 15.1860698793572,
31.8006028973928, 18.2751539787646, 15.4837911355797, 13.9013596282443,
10.9885525179173, 12.881450732196, 13.1384800907048, 8.97031233148782,
10.4978971399785, 15.050280678709, 7.78937332003644, 9.42176246589807,
10.7493595868453, 12.8327174637843, 10.1658562705261, 12.13019425285,
11.1022919450563, 12.4059675680316, 10.0799753318171)), row.names = c(NA,
-24L), class = c("tbl_df", "tbl", "data.frame"))
我今天早些时候想问一个问题，但我没有正确地提出问题。我打算删除它，但 Stack 建议不要这样做。抱歉，如果你之前试图帮助我
您可以编写一个函数来删除离群值，并按组仅将数值传递给它。使用 dplyr 您可以按如下方式执行此操作 -
Outliesplease = function(x){
Q1 <- quantile(x, probs=.25)
Q3 <- quantile(x, probs=.75)
iqr = Q3-Q1
upper_limit = Q3 + (iqr*1.5)
lower_limit = Q1 - (iqr*1.5)
x[x> upper_limit | x < lower_limit] = NA
return(x)
}
library(dplyr)
my_data <- my_data %>%
group_by(Group) %>%
mutate(across(where(is.numeric), Outliesplease))
本质上我是在尝试改编
第一个代码删除了高于第三个四分位数的异常值。我想改用四分位法。我还需要让它与我的数据集一起工作，即它需要嵌套到一个循环中，该循环通过名为 Group 的列对我的数据框的行进行子集化。我调整了上面的代码来为我工作，但我似乎无法让它与四分位数方法一起工作到 运行。请帮忙。
A-F组各有12行，这些是重复的，列是不同的时间点（分钟）：
# A tibble: 72 x 15
Group `0` `14` `27` `42` `60` `80` `95` `110` `125` `139` `169` `200` `229` `311`
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 A -0.921 -0.717 -0.310 0.608 2.35 4.22 6.04 7.63 10.5 12.1 15.2 20.1 23.6 34.8
2 A -0.926 -0.728 -0.367 0.392 1.92 3.66 5.80 8.17 13.8 12.4 15.5 19.6 23.7 35.0
3 A -0.928 -0.699 -0.212 0.864 2.73 4.37 7.13 9.60 14.7 15.2 18.9 25.0 29.7 44.4
4 A -0.919 -0.590 0.0813 1.47 4.07 6.47 9.43 12.5 26.3 18.7 24.6 30.8 36.9 55.4
5 A -0.925 -0.672 -0.199 0.701 2.37 4.06 5.94 7.97 15.2 12.4 16.1 20.1 24.8 37.9
6 A -0.911 -0.563 0.185 1.76 4.99 7.98 11.8 15.2 31.8 25.7 32.2 41.3 48.4 73.4
7 A -0.918 -0.675 -0.0975 1.21 3.09 5.43 7.83 10.8 18.3 15.0 20.8 26.7 31.9 48.8
8 A -0.923 -0.646 -0.0773 1.24 3.51 5.57 7.48 10.1 15.5 16.3 21.2 25.2 30.4 46.7
9 A -0.926 -0.634 -0.00355 1.32 3.80 6.37 8.36 11.8 13.9 16.7 23.3 27.6 32.1 49.8
10 A -0.920 -0.653 -0.0496 1.17 3.22 5.18 6.98 9.34 11.0 13.3 19.1 22.6 27.2 40.9
# … with 62 more rows
改编来自
（if numeric是为了处理组列，我本来也把所有的负值都设为NA。我最近改变了这个，但我想保留那个功能，以防我们恢复。）
Outlies = function(x, probs){
if(class(x) == "numeric"){
x[x > quantile(x, probs)] = NA
return(x)
}else{
return(x)
}
}
Group<- c('A', 'B', 'C', 'D','E','F')
Cleandata = NULL
for (char in Group){
subset <- my_data[which(my_data$Group == char),]
df <- do.call(bind_cols, lapply(subset, Outlies, 3/4))
Cleandata <- bind_rows(Cleandata, df)
}
输出：
# A tibble: 72 x 15
Group `0` `14` `27` `42` `60` `80` `95` `110` `125` `139` `169` `200` `229` `311`
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 A -0.921 -0.717 -0.310 0.608 2.35 4.22 6.04 7.63 10.5 12.1 15.2 20.1 23.6 34.8
2 A -0.926 -0.728 -0.367 0.392 1.92 3.66 5.80 8.17 13.8 12.4 15.5 19.6 23.7 35.0
3 A -0.928 -0.699 -0.212 0.864 2.73 4.37 7.13 9.60 14.7 15.2 18.9 25.0 29.7 44.4
4 A -0.919 NA NA NA NA NA NA NA NA NA NA NA NA NA
5 A -0.925 -0.672 -0.199 0.701 2.37 4.06 5.94 7.97 15.2 12.4 16.1 20.1 24.8 37.9
6 A NA NA NA NA NA NA NA NA NA NA NA NA NA NA
7 A NA -0.675 -0.0975 1.21 3.09 5.43 7.83 10.8 NA 15.0 20.8 26.7 31.9 48.8
8 A -0.923 -0.646 -0.0773 1.24 3.51 5.57 7.48 10.1 15.5 16.3 21.2 25.2 30.4 46.7
9 A -0.926 NA NA NA NA NA NA NA 13.9 NA NA NA NA NA
10 A -0.920 -0.653 -0.0496 1.17 3.22 5.18 6.98 9.34 11.0 13.3 19.1 22.6 27.2 40.9
# … with 62 more rows
这 运行 完全符合我的要求，但我意识到我不喜欢这种去除异常值的方法，我想使用四分位数法，其中异常值“低于 Q1 − 1.5 IQR 或高于 Q3 + 1.5 IQR”我也尝试从这里改编一些代码https://www.statology.org/remove-outliers-r/。
新代码和输出：
Outliesplease = function(x){
Q1 <- quantile(x, probs=.25)
Q3 <- quantile(x, probs=.75)
iqr = Q3-Q1
upper_limit = Q3 + (iqr*1.5)
lower_limit = Q1 - (iqr*1.5)
if(class(x) == "numeric"){
x[x> upper_limit | x < lower_limit] = NA
return(x)
}else{
return(x)
}
}
> Outliesplease(D$`42`)
[1] NA 2.428675 2.428384 2.714187 2.457054 2.464337 2.686667 2.166072 2.690987 2.632692
[11] NA 2.849850
但是这段代码在我在顶部使用的 for 循环中不起作用。我想是因为它不能只处理单列数组。
错误：
Error in (1 - h) * qs[i] : non-numeric argument to binary operator
5.
quantile.default(x, probs = 0.25)
4.
quantile(x, probs = 0.25)
3.
FUN(X[[i]], ...)
2.
lapply(subset, Outliesplease)
1.
do.call(bind_cols, lapply(subset, Outliesplease))
我试过制作新的嵌套循环，但到目前为止它们还没有奏效，而且变得很长。非常感谢任何帮助。
这个 returns 一个 tibble 但不产生和 NAs，x[[i]] 在我尝试完成这些行时给出错误下标越界。
Outliesplease = function(x){
for(i in 1:ncol(x)){
if(class(x[[i]]) == "numeric"){
Q1 <- quantile(x, probs=.25)
Q3 <- quantile(x, probs=.75)
iqr = Q3-Q1
upper_limit = Q3 + (iqr*1.5)
lower_limit = Q1 - (iqr*1.5)
x[x> upper_limit | x < lower_limit] = NA
return(x)
}else{
return(x)
}
}
}
数据（dput( my_data[1:24,1:10]）：
structure(list(Group = c("A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B"), `0` = c(-0.920718681650864, -0.926284584901359,
-0.928389338231378, -0.919206377406332, -0.925489455865574, -0.910748387172736,
-0.917616119334762, -0.923166431819849, -0.925629772754242, -0.920438047873528,
-0.92294036572144, -0.915745227485856, -1.69199983248014, -1.70459419320581,
-1.70676758766494, -1.69535549688934, -1.70584725776368, -1.70666847521404,
-1.70083499953221, -1.71102942305384, -1.70695165364519, -1.70062969516963,
-1.7134930754049, -1.70824719496773), `14` = c(-0.717087694662867,
-0.727938867386521, -0.699111542147963, -0.590022956591343, -0.671788725771233,
-0.563409520040657, -0.674930265000854, -0.646204279737445, -0.634129231929299,
-0.652767991974024, -0.700865503256312, -0.674283248236441, -1.38233713854987,
-1.4058905045613, -1.331003968442, -1.32096529305751, -1.34241605921761,
-1.30258701287546, -1.36194121204584, -1.38785911795742, -1.32276347609535,
-1.41574511396555, -1.43304023664842, -1.43429330120629), `27` = c(-0.310028400637171,
-0.367347849658025, -0.212126189260467, 0.0813387880058272, -0.198866243281347,
0.184635404213543, -0.0975496542803897, -0.0773362269295024,
-0.00354513425557315, -0.0496080506521766, -0.212157370791282,
-0.145584802501048, -0.532695653169101, -0.741999911097547, -0.470785768657541,
-0.64911738567826, -0.543272367572791, -0.522904758911869, -0.552964149379117,
-0.612636924284542, -0.477362587721147, -0.634356709954235, -0.777630837197797,
-0.788073041846688), `42` = c(0.607589483572235, 0.391672973443089,
0.864400571365382, 1.47484919551526, 0.700635171524487, 1.76405009844259,
1.2081067901575, 1.23822814892489, 1.32286261893977, 1.17345631403922,
0.885775510739131, 1.23909343640501, 0.891691855547406, 0.549640628553635,
1.24951612115659, 0.674047992821404, 0.89237856324296, 0.887670721824986,
0.951194723394137, 0.768459681768936, 0.920901710721185, 0.816104452810994,
0.6222900550668, 0.528862411167537), `60` = c(2.35269503717001,
1.91507004756281, 2.7283623250476, 4.06823270417236, 2.37029701131513,
4.99217264375453, 3.09496358284071, 3.51067575166759, 3.79890223175695,
3.22287801762694, 2.83296856554955, 2.80565354455553, 3.05406359423973,
2.59544904552122, 3.6861957266493, 2.78357863625995, 2.84691149238807,
2.9002410704356, 3.33699424426883, 2.78563875934661, 3.24748154218031,
3.03239336479549, 2.89073335460952, 2.42163412447745), `80` = c(4.22194985570809,
3.66147743007211, 4.37329721090187, 6.4729211791019, 4.06143513045467,
7.97675963801785, 5.43250263115993, 5.5739108734064, 6.37457463091108,
5.1765490354642, 4.88228113377942, 4.80671269384903, 5.47211005896236,
5.16329690032557, 6.82715424934945, 4.78752620164185, 5.19603940642802,
5.0833981059748, 5.50798876618987, 5.42161934468718, 5.42710592679083,
5.27515238063233, 5.28819274738708, 4.39058083576842), `95` = c(6.03690983371438,
5.7998522456926, 7.12957303653705, 9.43214199804714, 5.94493990857525,
11.8180436006606, 7.82973872022466, 7.47695867596535, 8.35890488892228,
6.98265345871901, 6.67122012432051, 6.08213864416168, 6.50883337488658,
7.03303204826091, 9.19775416793183, 6.64392364546973, 7.52055911480048,
7.31507776569264, 8.43660594228742, 7.0626383532383, 7.77525687524329,
7.60673031140136, 7.58134336504819, 5.40544277680737), `110` = c(7.63013794609452,
8.16696697598995, 9.59730277139201, 12.4602350231802, 7.96996206430016,
15.2471700889893, 10.8204762714413, 10.1465498459346, 11.785006768762,
9.33682005834538, 9.48028628162565, 9.04492195300255, 8.75188972807613,
9.4568624324399, 12.0358816763534, 8.95913386291792, 9.57352486661554,
9.18373683558959, 11.3559065479999, 9.10912639844067, 9.55695184893212,
9.52058465891086, 9.83057300803695, 7.86015376890397), `125` = c(10.5039681429026,
13.8202876481251, 14.6520393918524, 26.3212361902925, 15.1860698793572,
31.8006028973928, 18.2751539787646, 15.4837911355797, 13.9013596282443,
10.9885525179173, 12.881450732196, 13.1384800907048, 8.97031233148782,
10.4978971399785, 15.050280678709, 7.78937332003644, 9.42176246589807,
10.7493595868453, 12.8327174637843, 10.1658562705261, 12.13019425285,
11.1022919450563, 12.4059675680316, 10.0799753318171)), row.names = c(NA,
-24L), class = c("tbl_df", "tbl", "data.frame"))
我今天早些时候想问一个问题，但我没有正确地提出问题。我打算删除它，但 Stack 建议不要这样做。抱歉，如果你之前试图帮助我
您可以编写一个函数来删除离群值，并按组仅将数值传递给它。使用 dplyr 您可以按如下方式执行此操作 -
Outliesplease = function(x){
Q1 <- quantile(x, probs=.25)
Q3 <- quantile(x, probs=.75)
iqr = Q3-Q1
upper_limit = Q3 + (iqr*1.5)
lower_limit = Q1 - (iqr*1.5)
x[x> upper_limit | x < lower_limit] = NA
return(x)
}
library(dplyr)
my_data <- my_data %>%
group_by(Group) %>%
mutate(across(where(is.numeric), Outliesplease))