I am trying to remove outliers from a list of two dataset:
#creation of dataset
repr = list(mtcars, airquality)
#detectig boxplot
g_stats = lapply(repr, function(x) boxplot(x, main = "Boxplot")$out)
This is the code that I have applied with lapply:
new = lapply(repr, function(x)
x[ !(x %in% g_stats), ])
unfortunately, I can see that in the new list of datasets, there is no difference at all (there should be a difference in row numbers at least, but I am not to make work the lapply function conditionally to the list with outliers).
I have also tried to build properly the outlier box
#for getting the ID corresponding to the values
id_out1 = lapply(repr, function(x) as.data.frame(Boxplot(x, id = TRUE)))
#for getting the real values
out1 = lapply(repr,
function(x) as.data.frame(boxplot(x, main = "Boxplot", plot = TRUE)$out))
outliers1 = NULL
seq = c(1, 2)
names = c('ID', 'value')
for (i in seq_along(seq)) {
outliers1[[seq[i]]] = if(nrow(id_out1[[i]]) == nrow(out1[[i]]))
{cbind(id_out1[[i]], out1[[i]])} else {next}
colnames(outliers1[[seq[i]]]) = names
}
But to me, it is pretty hard to exclude values in a list that conditionally to ID list and values in outliers1 list.
Can anyone suggest something?
We can use Map: it's similar to lapply, but instead of accepting just one list, it accepts an arbitrary number of lists.
repr = list(mtcars, airquality)
g_stats = lapply(repr, function(x) boxplot(x, main = "Boxplot", plot = FALSE)$out)
sapply(repr, nrow)
# [1] 32 153
repr2 <- Map(function(x, out) x[rowSums(apply(x, 1, `%in%`, out)) == 0,], repr, g_stats)
sapply(repr2, nrow)
# [1] 18 75
The data is indeed different:
lapply(repr, head)
# [[1]]
# mpg cyl disp hp drat wt qsec vs am gear carb
# Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
# Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
# Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
# Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
# Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
# Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
# [[2]]
# Ozone Solar.R Wind Temp Month Day
# 1 41 190 7.4 67 5 1
# 2 36 118 8.0 72 5 2
# 3 12 149 12.6 74 5 3
# 4 18 313 11.5 62 5 4
# 5 NA NA 14.3 56 5 5
# 6 28 NA 14.9 66 5 6
lapply(repr2, head)
# [[1]]
# mpg cyl disp hp drat wt qsec vs am gear carb
# Mazda RX4 21.0 6 160.0 110 3.90 2.62 16.46 0 1 4 4
# Datsun 710 22.8 4 108.0 93 3.85 2.32 18.61 1 1 4 1
# Hornet Sportabout 18.7 8 360.0 175 3.15 3.44 17.02 0 0 3 2
# Merc 240D 24.4 4 146.7 62 3.69 3.19 20.00 1 0 4 2
# Merc 230 22.8 4 140.8 95 3.92 3.15 22.90 1 0 4 2
# Merc 280 19.2 6 167.6 123 3.92 3.44 18.30 1 0 4 4
# [[2]]
# Ozone Solar.R Wind Temp Month Day
# 4 18 313 11.5 62 5 4
# 5 NA NA 14.3 56 5 5
# 6 28 NA 14.9 66 5 6
# 10 NA 194 8.6 69 5 10
# 11 7 NA 6.9 74 5 11
# 12 16 256 9.7 69 5 12
I think your case is understated, so I'll state an assumption that seems more reasonable:
You want to remove a row if a value in it is an outlier relative to its own column. That is, in your code, a number is considered as an outlier among all columns, but I think you should only consider it an outlier compared with its own column.
For this, a simple function:
anyoutlier <- function(dat) rowSums(sapply(dat, function(z) z %in% boxplot.stats(z)$out)) > 0
anyoutlier(mtcars)
# [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
# [19] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
Which we'll apply to each column of each frame:
repr <- list(mtcars, airquality)
repr2 <- lapply(repr, function(dat) dat[!anyoutlier(dat),])
sapply(repr2, nrow)
# [1] 28 148
sapply(repr, nrow)
# [1] 32 153
lapply(repr2, head)
# [[1]]
# mpg cyl disp hp drat wt qsec vs am gear carb
# Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
# Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
# Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
# Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
# Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
# Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
# [[2]]
# Ozone Solar.R Wind Temp Month Day
# 1 41 190 7.4 67 5 1
# 2 36 118 8.0 72 5 2
# 3 12 149 12.6 74 5 3
# 4 18 313 11.5 62 5 4
# 5 NA NA 14.3 56 5 5
# 6 28 NA 14.9 66 5 6