My Unique Method
Here is a fresh implementation that follows the steps outlined above. Initially, we perform a numeric sorting. Subsequently, utilizing reduce, we segment the numbers into consecutive groups. Finally, we utilize map to extract the first and last values from each array.
To streamline the process, we introduce a straightforward helper function called last which retrieves the final element of an array.
const last = (xs) =>
xs [xs .length - 1]
const nonConsecutives = (ns) =>
[...ns]
.sort ((a, b) => a - b)
.reduce (
(r, n, i) => (i == 0 || n > last (last (r)) + 1)
? r .concat ([[n]])
: r .slice (0, -1) .concat ([last (r) .concat (n)]),
[]
)
.map (ns => [ns [0], last (ns)])
console .log (
nonConsecutives ([2, 3, 4, 5, 9, 8, 10, 13])
)
Revised Approach
If you are wondering about the flaws in your approach, it mainly lies at the boundaries. The line
if (input [i + 1] - input [i] > 1)
encounters issues at the last index since there is no
input [i + 1]. Similarly, updating
start = input [i + 1] during the initial iteration should be avoided.
A more effective version address these concerns by testing between the current and previous indices. An additional pre-check ensures accuracy even though it may seem redundant. Following the loop, a final group is pushed for completion.
Check out this implementation:
const array = [2, 3, 4, 5, 9, 8, 10, 13]
const input = array .sort ((a, b) => a - b)
const group = []
let start = input [0]
for (let i = 0; i < input .length; i++){
if (i == 0 || input [i] - input [i - 1] > 1) {
if (i > 0) {
group .push ([start, input [i - 1]])
}
start = input [i]
}
}
group .push ([start, input [input .length - 1]])
console.log(group);
Divergence in Strategies
The disparity between the two approaches is quite significant. One notable variance is my encapsulation within a function, following a functional programming paradigm. Moreover, I employ distinct transformations such as sorting, grouping, and capturing endpoints explicitly to enhance clarity. Although this may sacrifice efficiency, it prioritizes transparency.
Furthermore, a crucial differentiator is the absence of variable mutations in my version. Embracing immutable data transformation aligns with core principles of functional programming.
Exploring Ramda
As a co-founder of the Ramda functional programming library, I gravitate towards its streamlined approach to problem-solving. By leveraging tailored functions without altering the data state, Ramda promotes clean code structure. A simplified rendition using Ramda might look like this:
const {pipe, sortBy, identity, groupWith, map, juxt, head, last} = R
const nonConsecutives = pipe (
sortBy (identity),
groupWith ((a, b) => b - a <= 1),
map (juxt ([head, last]))
)
console .log (
nonConsecutives ([2, 3, 4, 5, 9, 8, 10, 13])
)
<script src="//cdnjs.cloudflare.com/ajax/libs/ramda/0.27.1/ramda.min.js"></script>
Delving into the intricacies of this method exceeds the scope here. Regardless, embracing smaller, pure functions can pave the way for efficient functional programming practices.