Rank and Order in R

rank and order in R

set.seed(1)
x <- sample(1:50, 30)    
x
# [1] 14 19 28 43 10 41 42 29 27  3  9  7 44 15 48 18 25 33 13 34 47 39 49  4 30 46  1 40 20  8
rank(x)
# [1]  9 12 16 25  7 23 24 17 15  2  6  4 26 10 29 11 14 19  8 20 28 21 30  3 18 27  1 22 13  5
order(x)
# [1] 27 10 24 12 30 11  5 19  1 14 16  2 29 17  9  3  8 25 18 20 22 28  6  7  4 13 26 21 15 23

rank returns a vector with the "rank" of each value. the number in the first position is the 9th lowest. order returns the indices that would put the initial vector x in order.

The 27th value of x is the lowest, so 27 is the first element of order(x) - and if you look at rank(x), the 27th element is 1.

x[order(x)]
# [1]  1  3  4  7  8  9 10 13 14 15 18 19 20 25 27 28 29 30 33 34 39 40 41 42 43 44 46 47 48 49

Difference between sort(), rank(), and order()

sort() sorts the vector in an ascending order.

rank() gives the respective rank of the numbers present in the vector, the smallest number receiving the rank 1.

order() returns the indices of the vector in a sorted order.

for example: if we apply these functions are applied to the vector - c (3, 1, 2, 5, 4)

sort(c (3, 1, 2, 5, 4)) will give c(1,2,3,4,5)

rank(c (3, 1, 2, 5, 4)) will give c(3,1,2,5,4)

order(c (3, 1, 2, 5, 4)) will give c(2,3,1,5,4).
if you put these indices in this order, you will get the sorted vector. Notice how v[2] = 1, v[3] = 2, v[1] = 3, v[5] = 4 and v[4] = 5

also there is a tie handling method in R. If you run rank(c (3, 1, 2, 5, 4, 2)) it will give Rank 1 to 1, since there are two 2 present R will rank them on 2 and 3 but assign Rank 2.5 to each of them, next 3 will get Rank 4.0, so

rank(c (3, 1, 2, 5, 4, 2)) will give you output [4.0 1.0 2.5 6.0 5.0 2.5]

Hope this is helpful.

understanding output of order function in R

I think you are looking for rank rather than order but rank can take only one column value. So we can first order the data based on names and then use rank.

test <- test[order(test$names), ]

rank(-test$scores, ties.method = "first")
#[1] 1 5 2 3 4

See ?rank for different ties.method options. If we use ties.method = "first" when there is a tie small number is given to the entry which occurs first and opposite when ties.method = "last".

rank(-test$scores, ties.method = "last")
#[1] 2 5 1 3 4

---

order returns indices of the original vector in sorted order.

a1 <- order(test$scores, decreasing = TRUE)
a1
#[1] 1 3 4 5 2

a2 <- test$scores
a2
#[1] 10  5 10  9  8

Here, the output of order can be interpreted as a2[a1[1]] (10) is the biggest number followed by a2[a1[2]] (10) and a2[a1[3]] (9) and so on.

data

names <- c("Anna", "Bella", "Christian", "Derrick", "Emma")
scores <- c(10,5,10,9,8)
age <- c(16,16,17,18,21)
test <- data.frame(names, scores, age)

How to rank order dates in R

use dplyr::dense_rank()

df %>% mutate(new = dense_rank(date))
  acc product date new
1  a1      p1   d1   1
2  a1      p1   d2   2
3  a1      p1   d3   3
4  a1      p1   d4   4
5  a1      p2   d1   1
6  a1      p2   d2   2
7  a1      p2   d3   3
8  a1      p3   d3   3
9  a1      p3   d4   4

If however, you want to restart ranks for each acc use group_by before the mutate statement.

dput used

df <- structure(list(acc = c("a1", "a1", "a1", "a1", "a1", "a1", "a1", 
"a1", "a1"), product = c("p1", "p1", "p1", "p1", "p2", "p2", 
"p2", "p3", "p3"), date = c("d1", "d2", "d3", "d4", "d1", "d2", 
"d3", "d3", "d4")), class = "data.frame", row.names = c(NA, -9L
))

Elegant way to obtain order(rank) of each column of a matrix

Perhaps you can try this

> apply(A, 2, rank)
     [,1] [,2]
[1,]  2.5    2
[2,]  1.0    3
[3,]  2.5    1

How to get reverse rank order when using chain syntax in R

If we can block the code within {}, it works as intended

c(5,7,14,3) %>% 
      {rank(-.)}
#[1] 3 2 1 4

---

Or make use of multiply_by

c(5,7,14,3) %>% 
      magrittr::multiply_by(-1) %>%
     rank
#[1] 3 2 1 4

ranking dataframe using two columns in R

You can use data.table::frank or dplyr::min_rank:

data.table::frank

dt$Rank <- frank(dt, B, A, ties.method = "min")
dt
  A B Rank
1 1 1    1
2 2 1    2
3 2 1    2
4 4 4    5
5 5 3    4

dplyr::min_rank

mutate(dt, Rank = min_rank(paste(B,A)))
  A B Rank
1 1 1    1
2 2 1    2
3 2 1    2
4 4 4    5
5 5 3    4

Data

dt <- data.frame(A = c(1,2,2,4,5), B = c(1,1,1,4,3))

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