Conditional non-equi join - r

Question

library(tidyverse)
iris <- iris
means <- iris %>%
group_by(Species) %>%
summarise_all(funs(mean))
sd <- iris %>%
group_by(Species) %>%
summarise_all(funs(sd))
bottom <- means[ ,2:5] - sd[ ,2:5]
bottom$Species <- c("setosa", "versicolor", "virginica")
print(bottom)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 4.653510 3.048936 1.288336 0.1406144 setosa
2 5.419829 2.456202 3.790089 1.1282473 versicolor
3 5.952120 2.651503 5.000105 1.7513499 virginica
top <- means[ ,2:5] + sd[ ,2:5]
top$Species <- c("setosa", "versicolor", "virginica")
print(top)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.358490 3.807064 1.635664 0.3513856 setosa
2 6.452171 3.083798 4.729911 1.5237527 versicolor
3 7.223880 3.296497 6.103895 2.3006501 virginica
How do I get the rows of Iris where the values for Sepal.Length, Sepal.Width, Petal.Length, and Petal.Width all fall between the values in the top and bottom data frames?
For example, I only want setosa rows where Sepal.Length > 4.65 & Sepal.Length < 5.35 and Sepal.Width is between 3.04 and 3.80, etc. Ideally the end result contains only the 4 numeric columns and the species column.
Thanks.

Answer 1

It would be much easier if you can filter from the beginning without the summarize step:
iris %>%
group_by(Species) %>%
filter_if(is.numeric, all_vars(. < mean(.) + sd(.) & . > mean(.) - sd(.)))
# A tibble: 54 x 5
# Groups: Species [3]
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# <dbl> <dbl> <dbl> <dbl> <fctr>
# 1 5.1 3.5 1.4 0.2 setosa
# 2 4.7 3.2 1.3 0.2 setosa
# 3 5.0 3.6 1.4 0.2 setosa
# 4 5.0 3.4 1.5 0.2 setosa
# 5 4.8 3.4 1.6 0.2 setosa
# 6 5.1 3.5 1.4 0.3 setosa
# 7 5.1 3.8 1.5 0.3 setosa
# 8 5.2 3.5 1.5 0.2 setosa
# 9 5.2 3.4 1.4 0.2 setosa
#10 4.7 3.2 1.6 0.2 setosa
# ... with 44 more rows
Not sure if you can avoid the summarize step, post as an option here.
Or use between:
iris %>%
group_by(Species) %>%
filter_if(is.numeric, all_vars(between(., mean(.) - sd(.), mean(.) + sd(.))))

Answer 2

Here is a solution using non-equi joins which is building on the (now deleted) approach of #Frank:
library(data.table)
# add a row number column and to reshape from wide to long
DT <- melt(data.table(iris)[, rn := .I], id = c("rn", "Species"))
# compute lower and upper bound for each variable and Species
mDT <- DT[, .(lb = lb <- mean(value) - (s <- sd(value)),
ub = lb + 2 * s), by = .(Species, variable)]
# find row numbers of items which fulfill conditions
selected_rn <-
# non-equi join
DT[DT[mDT, on = .(Species, variable, value > lb, value < ub), which = TRUE]][
# all uniqueN(mDT$variable) variables must have been selected
# for an item to pass (thanks to #Frank for tip to avoid hardcoded value)
, .N, by = rn][N == uniqueN(mDT$variable), rn]
head(iris[sort(selected_rn),])
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
8 5.0 3.4 1.5 0.2 setosa
12 4.8 3.4 1.6 0.2 setosa
18 5.1 3.5 1.4 0.3 setosa