Mixing
The logic behind `reorder()` in `R`?
I have difficulties understanding the logic behind reorder().
Suppose Var is defined as below:
Var <- factor(c(0.2, 0.1, -0.1))
order(Var)
Now if I want to reorder it to c(1, 2, 3) I would run the following code, which works perfectly fine.
Needed_Order <- c(1, 2, 3)
Var <- reorder(Var, Needed_Order)
order(Var)
But it does not work if I want to reorder Var to c(3, 1, 2)
Needed_Order <- c(3,1,2)
Var <- reorder(Var, Needed_Order)
order(Var)
I expect to get 3 1 2 as the output of order(var) but it returns 2 3 1.
r
asked Nov 22 '18 at 3:15
MiladMilad
109110
add a comment |
I have difficulties understanding the logic behind reorder().
Suppose Var is defined as below:
Var <- factor(c(0.2, 0.1, -0.1))
order(Var)
Now if I want to reorder it to c(1, 2, 3) I would run the following code, which works perfectly fine.
Needed_Order <- c(1, 2, 3)
Var <- reorder(Var, Needed_Order)
order(Var)
But it does not work if I want to reorder Var to c(3, 1, 2)
Needed_Order <- c(3,1,2)
Var <- reorder(Var, Needed_Order)
order(Var)
I expect to get 3 1 2 as the output of order(var) but it returns 2 3 1.
r
asked Nov 22 '18 at 3:15
MiladMilad
109110
1
Why do you expect to get that? If you want to reorder the levels, you should supply the actual levels. If you read the help file for reorder (?reorder) you will seebymedian <- with(InsectSprays, reorder(spray, count, median))which shows you that you don't supply an array of nubers, you have to supply a function that will be used for the ordering.
– Elin
Nov 22 '18 at 4:09
@Elin so what function should I use if I want a particular order, which cannot be defined by a function? Note that I do not want to supply the actual levels, as there are so many of them and I need to repeat this process many times
– Milad
Nov 22 '18 at 4:15
Well what is the order you want? If it's not the levels themselves then you will need to provide some other values that are ***actually found in the data ***
– Elin
Nov 22 '18 at 4:27
add a comment |
I have difficulties understanding the logic behind reorder().
Suppose Var is defined as below:
Var <- factor(c(0.2, 0.1, -0.1))
order(Var)
Now if I want to reorder it to c(1, 2, 3) I would run the following code, which works perfectly fine.
Needed_Order <- c(1, 2, 3)
Var <- reorder(Var, Needed_Order)
order(Var)
But it does not work if I want to reorder Var to c(3, 1, 2)
Needed_Order <- c(3,1,2)
Var <- reorder(Var, Needed_Order)
order(Var)
I expect to get 3 1 2 as the output of order(var) but it returns 2 3 1.
r
asked Nov 22 '18 at 3:15
MiladMilad
109110
I have difficulties understanding the logic behind reorder().
Suppose Var is defined as below:
Var <- factor(c(0.2, 0.1, -0.1))
order(Var)
Now if I want to reorder it to c(1, 2, 3) I would run the following code, which works perfectly fine.
Needed_Order <- c(1, 2, 3)
Var <- reorder(Var, Needed_Order)
order(Var)
But it does not work if I want to reorder Var to c(3, 1, 2)
Needed_Order <- c(3,1,2)
Var <- reorder(Var, Needed_Order)
order(Var)
I expect to get 3 1 2 as the output of order(var) but it returns 2 3 1.
r
r
asked Nov 22 '18 at 3:15
MiladMilad
109110
asked Nov 22 '18 at 3:15
MiladMilad
109110
asked Nov 22 '18 at 3:15
MiladMilad
109110
asked Nov 22 '18 at 3:15
MiladMilad
109110
asked Nov 22 '18 at 3:15
MiladMilad
109110
109110
1
Why do you expect to get that? If you want to reorder the levels, you should supply the actual levels. If you read the help file for reorder (?reorder) you will seebymedian <- with(InsectSprays, reorder(spray, count, median))which shows you that you don't supply an array of nubers, you have to supply a function that will be used for the ordering.
– Elin
Nov 22 '18 at 4:09
@Elin so what function should I use if I want a particular order, which cannot be defined by a function? Note that I do not want to supply the actual levels, as there are so many of them and I need to repeat this process many times
– Milad
Nov 22 '18 at 4:15
Well what is the order you want? If it's not the levels themselves then you will need to provide some other values that are ***actually found in the data ***
– Elin
Nov 22 '18 at 4:27
add a comment |
1
Why do you expect to get that? If you want to reorder the levels, you should supply the actual levels. If you read the help file for reorder (?reorder) you will seebymedian <- with(InsectSprays, reorder(spray, count, median))which shows you that you don't supply an array of nubers, you have to supply a function that will be used for the ordering.
– Elin
Nov 22 '18 at 4:09
@Elin so what function should I use if I want a particular order, which cannot be defined by a function? Note that I do not want to supply the actual levels, as there are so many of them and I need to repeat this process many times
– Milad
Nov 22 '18 at 4:15
Well what is the order you want? If it's not the levels themselves then you will need to provide some other values that are ***actually found in the data ***
– Elin
Nov 22 '18 at 4:27
1
1
Why do you expect to get that? If you want to reorder the levels, you should supply the actual levels. If you read the help file for reorder (?reorder) you will see
bymedian <- with(InsectSprays, reorder(spray, count, median)) which shows you that you don't supply an array of nubers, you have to supply a function that will be used for the ordering.– Elin
Nov 22 '18 at 4:09
Why do you expect to get that? If you want to reorder the levels, you should supply the actual levels. If you read the help file for reorder (?reorder) you will see
bymedian <- with(InsectSprays, reorder(spray, count, median)) which shows you that you don't supply an array of nubers, you have to supply a function that will be used for the ordering.– Elin
Nov 22 '18 at 4:09
@Elin so what function should I use if I want a particular order, which cannot be defined by a function? Note that I do not want to supply the actual levels, as there are so many of them and I need to repeat this process many times
– Milad
Nov 22 '18 at 4:15
@Elin so what function should I use if I want a particular order, which cannot be defined by a function? Note that I do not want to supply the actual levels, as there are so many of them and I need to repeat this process many times
– Milad
Nov 22 '18 at 4:15
Well what is the order you want? If it's not the levels themselves then you will need to provide some other values that are ***actually found in the data ***
– Elin
Nov 22 '18 at 4:27
Well what is the order you want? If it's not the levels themselves then you will need to provide some other values that are ***actually found in the data ***
– Elin
Nov 22 '18 at 4:27
add a comment |
2 Answers
2
active
oldest
votes
I think @prosoitos already has a great answer. I just wanted to illustrate why the reorder function exists and how it's useful.
Ordering Groups in a Plot
Let's consider the classic iris dataset
> data(iris)
> head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
and suppose we want to plot the Sepal.Width values, comparing by Species
boxplot(Sepal.Width ~ Species, iris)
but the order here is by species name, whereas we think the plot would look nicer if we ordered by the mean sepal width of each species. And that's where reorder is a quick, powerful solution for this:
iris$Species <- reorder(iris$Species, iris$Sepal.Width, FUN=mean)
boxplot(Sepal.Width ~ Species, iris)
What happened here is that the iris$Sepal.Width values corresponding to each level in iris$Species had the function mean applied to them and the result was attached to the factor as the scores attribute:
> attr(iris$Species, 'scores')
setosa versicolor virginica
3.428 2.770 2.974
These scores were then used to rank (in ascending order) the levels in the factor, and assign them that order:
> levels(iris$Species)
[1] "versicolor" "virginica" "setosa"
Note that this doesn't change the order of any of the data in the data frame, but only the order of the codes used in the factor. The FUN argument makes the reorder function quite general, so that one could order by min or max or whatever function you'd like to compute on the grouped data.
Overall, I think the key thing is that the second argument in the reorder function, which was thought to be a desired order, is instead for inputting the weights or values associated to each entry in the factor.
answered Nov 22 '18 at 7:42
mervmerv
25.3k674109
Thanks for your answer. This is in fact what I was looking for :)
– Milad
Nov 22 '18 at 21:49
1
And the forcats function which does that isfct_reorder()
– prosoitos
Nov 23 '18 at 0:59
add a comment |
The function lvls_reorder() from the tidyverse package forcats does what you want:
Var <- factor(c(0.2, 0.1, -0.1))
Needed_Order <- c(3, 1, 2)
Var <- forcats::lvls_reorder(Var, Needed_Order)
order(Var)
Result
[1] 3 1 2
Explanations
Let's use an example with distinct elements for the values, the levels and the order positions of the levels to make it easier to visualize what is going on:
f <- factor(c(a = "A", b = "B", c = "C"))
f
# a b c
# A B C
# Levels: A B C
order(f)
# [1] 1 2 3
Now, let's use stats::reorder():
reorder(f, c(3, 1, 2))
# a b c
# A B C
# attr(,"scores")
# A B C
# 3 1 2
# Levels: B C A
reorder() assigns the values 3 1 2 as "scores" attributes for the levels A B C and reorders those levels according to these scores: reordering the scores to 1 2 3 reorders the levels to B C A.
Since order() returns a permutation which rearranges the factor into (by default) an ascending order, we get:
order(reorder(f, c(3, 1, 2)))
# [1] 2 3 1
In comparison, forcats::lvls_reorder() simply reorders the levels by indexing them with the values 3 1 2 (what you were trying to do):
lvls_reorder(f, c(3, 1, 2))
# a b c
# A B C
# Levels: C A B
Which gives the order:
order(lvls_reorder(f, c(3, 1, 2)))
# [1] 3 1 2
answered Nov 22 '18 at 4:27
prosoitosprosoitos
935419
One thing I'd suggest adding to this is howreorderactually computes thescoresattribute, because that is then what is used to determine the new order for thelevelsattribute. It is sort of obscured in both OP and here because you both used vectors where the number of levels equals the number of entries, so you never see what theFUN=meanargument is doing.
– merv
Nov 22 '18 at 6:05
Right. I've been editing my answer a million times already and I still have a very obscure explanation. I am still working on it :P Thank you for your suggestion!
– prosoitos
Nov 22 '18 at 6:06
Note: feel free to post a new answer with a great explanation if you want. I am still battling it and I am sure that you would do this much better (I never use factors and while I know what works, it isn't yet very clear to me why things work the way they do. Which is why it is taking me so much time to explain it...)
– prosoitos
Nov 22 '18 at 6:09
1
Oh, it's definitely already a good, insightful answer (+1)! Thatreorderfunction is just weird though. Definitely an artifact of statisticians designing the base language.
– merv
Nov 22 '18 at 6:11
1
@prosoitos: Thanks for your insightful answer. This was helpful for me
– Milad
Nov 22 '18 at 21:48
|
show 2 more comments
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2 Answers
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active
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2 Answers
2
active
oldest
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active
oldest
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oldest
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I think @prosoitos already has a great answer. I just wanted to illustrate why the reorder function exists and how it's useful.
Ordering Groups in a Plot
Let's consider the classic iris dataset
> data(iris)
> head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
and suppose we want to plot the Sepal.Width values, comparing by Species
boxplot(Sepal.Width ~ Species, iris)
but the order here is by species name, whereas we think the plot would look nicer if we ordered by the mean sepal width of each species. And that's where reorder is a quick, powerful solution for this:
iris$Species <- reorder(iris$Species, iris$Sepal.Width, FUN=mean)
boxplot(Sepal.Width ~ Species, iris)
What happened here is that the iris$Sepal.Width values corresponding to each level in iris$Species had the function mean applied to them and the result was attached to the factor as the scores attribute:
> attr(iris$Species, 'scores')
setosa versicolor virginica
3.428 2.770 2.974
These scores were then used to rank (in ascending order) the levels in the factor, and assign them that order:
> levels(iris$Species)
[1] "versicolor" "virginica" "setosa"
Note that this doesn't change the order of any of the data in the data frame, but only the order of the codes used in the factor. The FUN argument makes the reorder function quite general, so that one could order by min or max or whatever function you'd like to compute on the grouped data.
Overall, I think the key thing is that the second argument in the reorder function, which was thought to be a desired order, is instead for inputting the weights or values associated to each entry in the factor.
answered Nov 22 '18 at 7:42
mervmerv
25.3k674109
Thanks for your answer. This is in fact what I was looking for :)
– Milad
Nov 22 '18 at 21:49
1
And the forcats function which does that isfct_reorder()
– prosoitos
Nov 23 '18 at 0:59
add a comment |
I think @prosoitos already has a great answer. I just wanted to illustrate why the reorder function exists and how it's useful.
Ordering Groups in a Plot
Let's consider the classic iris dataset
> data(iris)
> head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
and suppose we want to plot the Sepal.Width values, comparing by Species
boxplot(Sepal.Width ~ Species, iris)
but the order here is by species name, whereas we think the plot would look nicer if we ordered by the mean sepal width of each species. And that's where reorder is a quick, powerful solution for this:
iris$Species <- reorder(iris$Species, iris$Sepal.Width, FUN=mean)
boxplot(Sepal.Width ~ Species, iris)
What happened here is that the iris$Sepal.Width values corresponding to each level in iris$Species had the function mean applied to them and the result was attached to the factor as the scores attribute:
> attr(iris$Species, 'scores')
setosa versicolor virginica
3.428 2.770 2.974
These scores were then used to rank (in ascending order) the levels in the factor, and assign them that order:
> levels(iris$Species)
[1] "versicolor" "virginica" "setosa"
Note that this doesn't change the order of any of the data in the data frame, but only the order of the codes used in the factor. The FUN argument makes the reorder function quite general, so that one could order by min or max or whatever function you'd like to compute on the grouped data.
Overall, I think the key thing is that the second argument in the reorder function, which was thought to be a desired order, is instead for inputting the weights or values associated to each entry in the factor.
answered Nov 22 '18 at 7:42
mervmerv
25.3k674109
Thanks for your answer. This is in fact what I was looking for :)
– Milad
Nov 22 '18 at 21:49
1
And the forcats function which does that isfct_reorder()
– prosoitos
Nov 23 '18 at 0:59
add a comment |
I think @prosoitos already has a great answer. I just wanted to illustrate why the reorder function exists and how it's useful.
Ordering Groups in a Plot
Let's consider the classic iris dataset
> data(iris)
> head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
and suppose we want to plot the Sepal.Width values, comparing by Species
boxplot(Sepal.Width ~ Species, iris)
but the order here is by species name, whereas we think the plot would look nicer if we ordered by the mean sepal width of each species. And that's where reorder is a quick, powerful solution for this:
iris$Species <- reorder(iris$Species, iris$Sepal.Width, FUN=mean)
boxplot(Sepal.Width ~ Species, iris)
What happened here is that the iris$Sepal.Width values corresponding to each level in iris$Species had the function mean applied to them and the result was attached to the factor as the scores attribute:
> attr(iris$Species, 'scores')
setosa versicolor virginica
3.428 2.770 2.974
These scores were then used to rank (in ascending order) the levels in the factor, and assign them that order:
> levels(iris$Species)
[1] "versicolor" "virginica" "setosa"
Note that this doesn't change the order of any of the data in the data frame, but only the order of the codes used in the factor. The FUN argument makes the reorder function quite general, so that one could order by min or max or whatever function you'd like to compute on the grouped data.
Overall, I think the key thing is that the second argument in the reorder function, which was thought to be a desired order, is instead for inputting the weights or values associated to each entry in the factor.
answered Nov 22 '18 at 7:42
mervmerv
25.3k674109
I think @prosoitos already has a great answer. I just wanted to illustrate why the reorder function exists and how it's useful.
Ordering Groups in a Plot
Let's consider the classic iris dataset
> data(iris)
> head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
and suppose we want to plot the Sepal.Width values, comparing by Species
boxplot(Sepal.Width ~ Species, iris)
but the order here is by species name, whereas we think the plot would look nicer if we ordered by the mean sepal width of each species. And that's where reorder is a quick, powerful solution for this:
iris$Species <- reorder(iris$Species, iris$Sepal.Width, FUN=mean)
boxplot(Sepal.Width ~ Species, iris)
What happened here is that the iris$Sepal.Width values corresponding to each level in iris$Species had the function mean applied to them and the result was attached to the factor as the scores attribute:
> attr(iris$Species, 'scores')
setosa versicolor virginica
3.428 2.770 2.974
These scores were then used to rank (in ascending order) the levels in the factor, and assign them that order:
> levels(iris$Species)
[1] "versicolor" "virginica" "setosa"
Note that this doesn't change the order of any of the data in the data frame, but only the order of the codes used in the factor. The FUN argument makes the reorder function quite general, so that one could order by min or max or whatever function you'd like to compute on the grouped data.
Overall, I think the key thing is that the second argument in the reorder function, which was thought to be a desired order, is instead for inputting the weights or values associated to each entry in the factor.
answered Nov 22 '18 at 7:42
mervmerv
25.3k674109
edited Nov 22 '18 at 7:48
answered Nov 22 '18 at 7:42
mervmerv
25.3k674109
answered Nov 22 '18 at 7:42
mervmerv
25.3k674109
answered Nov 22 '18 at 7:42
mervmerv
25.3k674109
25.3k674109
Thanks for your answer. This is in fact what I was looking for :)
– Milad
Nov 22 '18 at 21:49
1
And the forcats function which does that isfct_reorder()
– prosoitos
Nov 23 '18 at 0:59
add a comment |
Thanks for your answer. This is in fact what I was looking for :)
– Milad
Nov 22 '18 at 21:49
1
And the forcats function which does that isfct_reorder()
– prosoitos
Nov 23 '18 at 0:59
Thanks for your answer. This is in fact what I was looking for :)
– Milad
Nov 22 '18 at 21:49
Thanks for your answer. This is in fact what I was looking for :)
– Milad
Nov 22 '18 at 21:49
1
1
And the forcats function which does that is
fct_reorder()– prosoitos
Nov 23 '18 at 0:59
And the forcats function which does that is
fct_reorder()– prosoitos
Nov 23 '18 at 0:59
add a comment |
The function lvls_reorder() from the tidyverse package forcats does what you want:
Var <- factor(c(0.2, 0.1, -0.1))
Needed_Order <- c(3, 1, 2)
Var <- forcats::lvls_reorder(Var, Needed_Order)
order(Var)
Result
[1] 3 1 2
Explanations
Let's use an example with distinct elements for the values, the levels and the order positions of the levels to make it easier to visualize what is going on:
f <- factor(c(a = "A", b = "B", c = "C"))
f
# a b c
# A B C
# Levels: A B C
order(f)
# [1] 1 2 3
Now, let's use stats::reorder():
reorder(f, c(3, 1, 2))
# a b c
# A B C
# attr(,"scores")
# A B C
# 3 1 2
# Levels: B C A
reorder() assigns the values 3 1 2 as "scores" attributes for the levels A B C and reorders those levels according to these scores: reordering the scores to 1 2 3 reorders the levels to B C A.
Since order() returns a permutation which rearranges the factor into (by default) an ascending order, we get:
order(reorder(f, c(3, 1, 2)))
# [1] 2 3 1
In comparison, forcats::lvls_reorder() simply reorders the levels by indexing them with the values 3 1 2 (what you were trying to do):
lvls_reorder(f, c(3, 1, 2))
# a b c
# A B C
# Levels: C A B
Which gives the order:
order(lvls_reorder(f, c(3, 1, 2)))
# [1] 3 1 2
answered Nov 22 '18 at 4:27
prosoitosprosoitos
935419
One thing I'd suggest adding to this is howreorderactually computes thescoresattribute, because that is then what is used to determine the new order for thelevelsattribute. It is sort of obscured in both OP and here because you both used vectors where the number of levels equals the number of entries, so you never see what theFUN=meanargument is doing.
– merv
Nov 22 '18 at 6:05
Right. I've been editing my answer a million times already and I still have a very obscure explanation. I am still working on it :P Thank you for your suggestion!
– prosoitos
Nov 22 '18 at 6:06
Note: feel free to post a new answer with a great explanation if you want. I am still battling it and I am sure that you would do this much better (I never use factors and while I know what works, it isn't yet very clear to me why things work the way they do. Which is why it is taking me so much time to explain it...)
– prosoitos
Nov 22 '18 at 6:09
1
Oh, it's definitely already a good, insightful answer (+1)! Thatreorderfunction is just weird though. Definitely an artifact of statisticians designing the base language.
– merv
Nov 22 '18 at 6:11
1
@prosoitos: Thanks for your insightful answer. This was helpful for me
– Milad
Nov 22 '18 at 21:48
|
show 2 more comments
The function lvls_reorder() from the tidyverse package forcats does what you want:
Var <- factor(c(0.2, 0.1, -0.1))
Needed_Order <- c(3, 1, 2)
Var <- forcats::lvls_reorder(Var, Needed_Order)
order(Var)
Result
[1] 3 1 2
Explanations
Let's use an example with distinct elements for the values, the levels and the order positions of the levels to make it easier to visualize what is going on:
f <- factor(c(a = "A", b = "B", c = "C"))
f
# a b c
# A B C
# Levels: A B C
order(f)
# [1] 1 2 3
Now, let's use stats::reorder():
reorder(f, c(3, 1, 2))
# a b c
# A B C
# attr(,"scores")
# A B C
# 3 1 2
# Levels: B C A
reorder() assigns the values 3 1 2 as "scores" attributes for the levels A B C and reorders those levels according to these scores: reordering the scores to 1 2 3 reorders the levels to B C A.
Since order() returns a permutation which rearranges the factor into (by default) an ascending order, we get:
order(reorder(f, c(3, 1, 2)))
# [1] 2 3 1
In comparison, forcats::lvls_reorder() simply reorders the levels by indexing them with the values 3 1 2 (what you were trying to do):
lvls_reorder(f, c(3, 1, 2))
# a b c
# A B C
# Levels: C A B
Which gives the order:
order(lvls_reorder(f, c(3, 1, 2)))
# [1] 3 1 2
answered Nov 22 '18 at 4:27
prosoitosprosoitos
935419
One thing I'd suggest adding to this is howreorderactually computes thescoresattribute, because that is then what is used to determine the new order for thelevelsattribute. It is sort of obscured in both OP and here because you both used vectors where the number of levels equals the number of entries, so you never see what theFUN=meanargument is doing.
– merv
Nov 22 '18 at 6:05
Right. I've been editing my answer a million times already and I still have a very obscure explanation. I am still working on it :P Thank you for your suggestion!
– prosoitos
Nov 22 '18 at 6:06
Note: feel free to post a new answer with a great explanation if you want. I am still battling it and I am sure that you would do this much better (I never use factors and while I know what works, it isn't yet very clear to me why things work the way they do. Which is why it is taking me so much time to explain it...)
– prosoitos
Nov 22 '18 at 6:09
1
Oh, it's definitely already a good, insightful answer (+1)! Thatreorderfunction is just weird though. Definitely an artifact of statisticians designing the base language.
– merv
Nov 22 '18 at 6:11
1
@prosoitos: Thanks for your insightful answer. This was helpful for me
– Milad
Nov 22 '18 at 21:48
|
show 2 more comments
The function lvls_reorder() from the tidyverse package forcats does what you want:
Var <- factor(c(0.2, 0.1, -0.1))
Needed_Order <- c(3, 1, 2)
Var <- forcats::lvls_reorder(Var, Needed_Order)
order(Var)
Result
[1] 3 1 2
Explanations
Let's use an example with distinct elements for the values, the levels and the order positions of the levels to make it easier to visualize what is going on:
f <- factor(c(a = "A", b = "B", c = "C"))
f
# a b c
# A B C
# Levels: A B C
order(f)
# [1] 1 2 3
Now, let's use stats::reorder():
reorder(f, c(3, 1, 2))
# a b c
# A B C
# attr(,"scores")
# A B C
# 3 1 2
# Levels: B C A
reorder() assigns the values 3 1 2 as "scores" attributes for the levels A B C and reorders those levels according to these scores: reordering the scores to 1 2 3 reorders the levels to B C A.
Since order() returns a permutation which rearranges the factor into (by default) an ascending order, we get:
order(reorder(f, c(3, 1, 2)))
# [1] 2 3 1
In comparison, forcats::lvls_reorder() simply reorders the levels by indexing them with the values 3 1 2 (what you were trying to do):
lvls_reorder(f, c(3, 1, 2))
# a b c
# A B C
# Levels: C A B
Which gives the order:
order(lvls_reorder(f, c(3, 1, 2)))
# [1] 3 1 2
answered Nov 22 '18 at 4:27
prosoitosprosoitos
935419
The function lvls_reorder() from the tidyverse package forcats does what you want:
Var <- factor(c(0.2, 0.1, -0.1))
Needed_Order <- c(3, 1, 2)
Var <- forcats::lvls_reorder(Var, Needed_Order)
order(Var)
Result
[1] 3 1 2
Explanations
Let's use an example with distinct elements for the values, the levels and the order positions of the levels to make it easier to visualize what is going on:
f <- factor(c(a = "A", b = "B", c = "C"))
f
# a b c
# A B C
# Levels: A B C
order(f)
# [1] 1 2 3
Now, let's use stats::reorder():
reorder(f, c(3, 1, 2))
# a b c
# A B C
# attr(,"scores")
# A B C
# 3 1 2
# Levels: B C A
reorder() assigns the values 3 1 2 as "scores" attributes for the levels A B C and reorders those levels according to these scores: reordering the scores to 1 2 3 reorders the levels to B C A.
Since order() returns a permutation which rearranges the factor into (by default) an ascending order, we get:
order(reorder(f, c(3, 1, 2)))
# [1] 2 3 1
In comparison, forcats::lvls_reorder() simply reorders the levels by indexing them with the values 3 1 2 (what you were trying to do):
lvls_reorder(f, c(3, 1, 2))
# a b c
# A B C
# Levels: C A B
Which gives the order:
order(lvls_reorder(f, c(3, 1, 2)))
# [1] 3 1 2
answered Nov 22 '18 at 4:27
prosoitosprosoitos
935419
edited Nov 22 '18 at 6:45
answered Nov 22 '18 at 4:27
prosoitosprosoitos
935419
answered Nov 22 '18 at 4:27
prosoitosprosoitos
935419
answered Nov 22 '18 at 4:27
prosoitosprosoitos
935419
935419
One thing I'd suggest adding to this is howreorderactually computes thescoresattribute, because that is then what is used to determine the new order for thelevelsattribute. It is sort of obscured in both OP and here because you both used vectors where the number of levels equals the number of entries, so you never see what theFUN=meanargument is doing.
– merv
Nov 22 '18 at 6:05
Right. I've been editing my answer a million times already and I still have a very obscure explanation. I am still working on it :P Thank you for your suggestion!
– prosoitos
Nov 22 '18 at 6:06
Note: feel free to post a new answer with a great explanation if you want. I am still battling it and I am sure that you would do this much better (I never use factors and while I know what works, it isn't yet very clear to me why things work the way they do. Which is why it is taking me so much time to explain it...)
– prosoitos
Nov 22 '18 at 6:09
1
Oh, it's definitely already a good, insightful answer (+1)! Thatreorderfunction is just weird though. Definitely an artifact of statisticians designing the base language.
– merv
Nov 22 '18 at 6:11
1
@prosoitos: Thanks for your insightful answer. This was helpful for me
– Milad
Nov 22 '18 at 21:48
|
show 2 more comments
One thing I'd suggest adding to this is howreorderactually computes thescoresattribute, because that is then what is used to determine the new order for thelevelsattribute. It is sort of obscured in both OP and here because you both used vectors where the number of levels equals the number of entries, so you never see what theFUN=meanargument is doing.
– merv
Nov 22 '18 at 6:05
Right. I've been editing my answer a million times already and I still have a very obscure explanation. I am still working on it :P Thank you for your suggestion!
– prosoitos
Nov 22 '18 at 6:06
Note: feel free to post a new answer with a great explanation if you want. I am still battling it and I am sure that you would do this much better (I never use factors and while I know what works, it isn't yet very clear to me why things work the way they do. Which is why it is taking me so much time to explain it...)
– prosoitos
Nov 22 '18 at 6:09
1
Oh, it's definitely already a good, insightful answer (+1)! Thatreorderfunction is just weird though. Definitely an artifact of statisticians designing the base language.
– merv
Nov 22 '18 at 6:11
1
@prosoitos: Thanks for your insightful answer. This was helpful for me
– Milad
Nov 22 '18 at 21:48
One thing I'd suggest adding to this is how
reorder actually computes the scores attribute, because that is then what is used to determine the new order for the levels attribute. It is sort of obscured in both OP and here because you both used vectors where the number of levels equals the number of entries, so you never see what the FUN=mean argument is doing.– merv
Nov 22 '18 at 6:05
One thing I'd suggest adding to this is how
reorder actually computes the scores attribute, because that is then what is used to determine the new order for the levels attribute. It is sort of obscured in both OP and here because you both used vectors where the number of levels equals the number of entries, so you never see what the FUN=mean argument is doing.– merv
Nov 22 '18 at 6:05
Right. I've been editing my answer a million times already and I still have a very obscure explanation. I am still working on it :P Thank you for your suggestion!
– prosoitos
Nov 22 '18 at 6:06
Right. I've been editing my answer a million times already and I still have a very obscure explanation. I am still working on it :P Thank you for your suggestion!
– prosoitos
Nov 22 '18 at 6:06
Note: feel free to post a new answer with a great explanation if you want. I am still battling it and I am sure that you would do this much better (I never use factors and while I know what works, it isn't yet very clear to me why things work the way they do. Which is why it is taking me so much time to explain it...)
– prosoitos
Nov 22 '18 at 6:09
Note: feel free to post a new answer with a great explanation if you want. I am still battling it and I am sure that you would do this much better (I never use factors and while I know what works, it isn't yet very clear to me why things work the way they do. Which is why it is taking me so much time to explain it...)
– prosoitos
Nov 22 '18 at 6:09
1
1
Oh, it's definitely already a good, insightful answer (+1)! That
reorder function is just weird though. Definitely an artifact of statisticians designing the base language.– merv
Nov 22 '18 at 6:11
Oh, it's definitely already a good, insightful answer (+1)! That
reorder function is just weird though. Definitely an artifact of statisticians designing the base language.– merv
Nov 22 '18 at 6:11
1
1
@prosoitos: Thanks for your insightful answer. This was helpful for me
– Milad
Nov 22 '18 at 21:48
@prosoitos: Thanks for your insightful answer. This was helpful for me
– Milad
Nov 22 '18 at 21:48
|
show 2 more comments
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Why do you expect to get that? If you want to reorder the levels, you should supply the actual levels. If you read the help file for reorder (?reorder) you will see
bymedian <- with(InsectSprays, reorder(spray, count, median))which shows you that you don't supply an array of nubers, you have to supply a function that will be used for the ordering.– Elin
Nov 22 '18 at 4:09
@Elin so what function should I use if I want a particular order, which cannot be defined by a function? Note that I do not want to supply the actual levels, as there are so many of them and I need to repeat this process many times
– Milad
Nov 22 '18 at 4:15
Well what is the order you want? If it's not the levels themselves then you will need to provide some other values that are ***actually found in the data ***
– Elin
Nov 22 '18 at 4:27