3.3.6 Rank Rows

OREdplyr functions for ranking rows.

The ranking functions rank the elements in an ordered ore.vector by its values. An ore.character is coerced to an ore.factor. The values of an ore.factor are based upon factor levels. To reverse the direction of the ranking, use the desc function.

Table 3-7 Ranking Rows

Function	Description
cume_dist	A cumulative distribution function: returns the proportion of all values that are less than or equal to the current rank.
dense_rank	Like min_rank but with no gaps between ranks.
first	Gets the first value from an ordered ore.vector object.
last	Gets the last value from an ordered ore.vector object.
min_rank	Equivalent to rank(ties.method = "min").
nth	Obtains the value at the specified position in the order.
ntile	A rough ranking that breaks the input vector into n buckets.
n_distinct	Gets the nth value from an ordered ore.vector object.
percent_rank	Returns a number between 0 and 1 that is computed by rescaling min_rank to [0, 1].
row_number	Equivalent to rank(ties.method = "first").
top_n	Selects the top or bottom number of rows.

Example 3-71 Ranking Rows

These examples use the ranking functions row_number, min_rank, dense_rank, percent_rank, cume_dist, and ntile.

X <- ore.push(c(5, 1, 3, 2, 2, NA))

row_number(X)
row_number(desc(X))

min_rank(X)

dense_rank(X)

percent_rank(X)

cume_dist(X)

ntile(X, 2)
ntile(ore.push(runif(100)), 10)

MTCARS <- ore.push(mtcars)
by_cyl <- group_by(MTCARS, cyl)

# Using ranking functions with an ore.frame
head(mutate(MTCARS, rank = row_number(hp)))

head(mutate(MTCARS, rank = min_rank(hp)))

head(mutate(MTCARS, rank = dense_rank(hp)))

# Using ranking functions with a grouped ore.frame
head(mutate(by_cyl, rank = row_number(hp)))

head(mutate(by_cyl, rank = min_rank(hp)))

head(mutate(by_cyl, rank = dense_rank(hp)))

Listing for This Example

R> X <- ore.push(c(5, 1, 3, 2, 2, NA))
R> 
R> row_number(X)
[1] 5 1 4 2 3 6
R> row_number(desc(X))
[1] 1 5 2 3 4 6
R> 
R> min_rank(X)
[1] 5 1 4 2 2 6
R> 
R> dense_rank(X)
[1] 4 1 3 2 2 6
R> 
R> percent_rank(X)
[1] 0.8 0.0 0.6 0.2 0.2 1.0
R> 
R> cume_dist(X)
[1] 0.8333333 0.1666667 0.6666667 0.5000000 0.5000000 1.0000000
R> 
R> ntile(X, 2)
[1] 2 1 2 1 1 2
R> ntile(ore.push(runif(100)), 10)
  [1]  6 10  5  2  1  1  8  3  8  8  7  3 10  3  7  9  9  4  4 10 10  7  2  3  7  4  5  5  3  9  4  6  8  4 10  6  1  5  5  4  6  9
 [43]  5  8  2  7  7  1  2  9  1  2  8  5  6  5  3  4  7  1  3  1 10  1  5  5 10  9  2  3  9  6  6  8  8  6  3  7  2  2  8  4  1  9
 [85]  6 10  4 10  7  2  9 10  7  2  4  9  6  3  8  1
R>
R> MTCARS <- ore.push(mtcars)
R> by_cyl <- group_by(MTCARS, cyl)
R>
R> # Using ranking functions with an ore.frame
R> head(mutate(MTCARS, rank = row_number(hp)))
                   mpg cyl disp  hp drat    wt  qsec vs am gear carb rank
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4   12
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4   13
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1    7
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1   14
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2   20
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1   10
R> 
R> head(mutate(MTCARS, rank = min_rank(hp)))
                   mpg cyl disp  hp drat    wt  qsec vs am gear carb rank
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4   12
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4   12
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1    7
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1   12
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2   20
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1   10
R> 
R> head(mutate(MTCARS, rank = dense_rank(hp)))
                   mpg cyl disp  hp drat    wt  qsec vs am gear carb rank
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4   11
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4   11
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1    6
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1   11
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2   15
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1    9
R> 
R> # Using ranking functions with a grouped ore.frame
R> head(mutate(by_cyl, rank = row_number(hp)))
                   mpg cyl disp  hp drat    wt  qsec vs am gear carb rank
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4    2
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4    3
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1    7
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1    4
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2    3
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1    1
R>
R> head(mutate(by_cyl, rank = min_rank(hp)))
                   mpg cyl disp  hp drat    wt  qsec vs am gear carb rank
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4    2
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4    2
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1    7
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1    2
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2    3
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1    1
R>
R> head(mutate(by_cyl, rank = dense_rank(hp)))
                   mpg cyl disp  hp drat    wt  qsec vs am gear carb rank
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4    2
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4    2
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1    6
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1    2
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2    2
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1    1