Using smartrank

library(rank)

Rank provides a customizable alternative to the built-in rank() function. The package offers the following features:

  1. Frequency-based ranking of categorical variables: choose whether to rank based on alphabetic order or element frequency.

  2. Control over sorting order: Use desc=TRUE to rank based on descending or ascending order.

Ranking categorical vectors

fruits <- c("Apple", "Orange", "Apple", "Pear", "Orange")

# rank alphabetically
smartrank(fruits)
#> [1] 1.5 3.5 1.5 5.0 3.5

# rank based on frequency
smartrank(fruits, sort_by = "frequency")
#> [1] 2.5 4.5 2.5 1.0 4.5

# rank based on descending order of frequency
smartrank(fruits, sort_by = "frequency", desc = TRUE)
#> [1] 1.5 3.5 1.5 5.0 3.5

Ranking numeric vectors

# rank numerically
smartrank(c(1, 3, 2))
#> [1] 1 3 2

# rank numerically based on descending order
smartrank(c(1, 3, 2), desc = TRUE)
#> [1] 3 1 2

Sorting vectors by rank

We can use order to sort vectors based on their ranks. For example, we can sort the fruits vector based on the frequency of each element.

fruits <- c("Apple", "Orange", "Apple", "Pear", "Orange")
ranks <- smartrank(fruits, sort_by = "frequency")
fruits[order(ranks)]
#> [1] "Pear"   "Apple"  "Apple"  "Orange" "Orange"

Sorting Data-frames

smartrank can be used to arrange data.frames based on one or more columns, while maintaining complete control over how each column contributes to the final row order.

BaseR

For example, we can sort the following dataframe based on frequency of fruits, but break any ties based on the alphabetical order of the picker.

data = data.frame(
    fruits = c("Apple", "Orange", "Apple", "Pear", "Orange"),
    picker = c("Elizabeth", "Damian",  "Bob", "Cameron", "Alice")
)

# Rank fruits so the most frequently picked fruits will come first
fruit_ranks <- smartrank(data$fruits, sort_by = "frequency", desc=TRUE) 


# Rank pickers in alphabetical order
picker_ranks <- smartrank(data$picker, sort_by = "alphabetical", desc=FALSE) 

# Sort dataframe by the fruit_ranks, then the picker_ranks (hierarchical)
data[order(fruit_ranks, picker_ranks),]
#>   fruits    picker
#> 3  Apple       Bob
#> 1  Apple Elizabeth
#> 5 Orange     Alice
#> 2 Orange    Damian
#> 4   Pear   Cameron

Tidyverse Integration

An equivalent way to hierarchically sort data.frames is to use smartrank() in the tidyverse arrange() function.

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

arrange(
  data, 
  smartrank(fruits, "frequency", desc = TRUE), 
  smartrank(picker, "alphabetical", desc = FALSE)
)
#>   fruits    picker
#> 1  Apple       Bob
#> 2  Apple Elizabeth
#> 3 Orange     Alice
#> 4 Orange    Damian
#> 5   Pear   Cameron