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Data Structures and Algorithms in R

Q: How do you make a hash map in R?

With an environment: h <- new.env(), then h[[key]] <- value to store and h[[key]] to read, which gives NULL for a missing key. Environments are hashed, keyed by strings, and have reference semantics, so a function that changes one changes it for the caller too, unlike a vector or a list. A named list looks like a dictionary but behaves like a value: change it inside a function and the caller's copy is untouched.

Q: Does deep recursion fail in R?

It can. By default options(expressions = 5000) caps how deeply evaluation may nest, and a deep recursion, such as a depth-first search down a long chain, stops with an evaluation nested too deeply error. Raising the option buys some room; the reliable fix is an explicit stack in a loop, the structure you build in step one.

Q: Which courses on this path are not taught in R?

Three: dynamic programming and the Python interview series, taught in Python, and bit manipulation, taught in C++. They are listed after the steps, each with a link that opens it in its own language. Dynamic programming translates directly, a memo table in R being a preallocated vector, and C++'s &, |, ^ and << become the functions bitwAnd, bitwOr, bitwXor and bitwShiftL.

Q: Do I need to know R before starting this path?

Vectors, lists, functions, loops and indexing from 1, at least. If those are new, Coddy's R course takes you there first, free, and this path picks up where it ends.

Ask about data structures in R and most answers list vectors, lists, matrices, data frames and factors. This path is the other meaning: the stack, queue, linked list, heap, tree and graph of an algorithms course, none of which base R provides. You build each one in R, then sort, recurse and search graphs with them, and finish on graded interview problems. Free, in your browser, with a certificate on most courses.

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377 lessons228 challenges702 quiz questions

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DSA in R, step by step

38 courses377 lessons228 challenges702 quiz questions

Each step is a set of existing Coddy courses, and every Start button opens them in R. The three courses not taught in R yet are listed after the steps.

1
Step 1
Data Structures Series, part one
Start this stepStartStack, queue, binary tree, hash table and linked list, each built from scratch in R and then used to solve problems. Of the five, only the hash table has a stand-in in base R, the environment, and after this step you know what it does for you.Start
5 courses, in order
66 lessons34 challenges
2
Step 2
Data Structures Series, part two
Start this stepStartDoubly linked list, heaps and priority queues, tries, graphs and the self-balancing AVL tree. R indexes from 1, the way textbooks draw a heap, so a node's children sit at 2 * i and 2 * i + 1 with no offset to remember.Start
5 courses, in order
72 lessons42 challenges
3
Step 3
Sorting algorithms
Start this stepStartBubble, selection, insertion, merge, quick, heap, counting and radix sort, written in R and watched in the visualizer. sort() itself lets you choose shell sort, quicksort or radix sort through its method argument; after this step you know what that choice means.Start
8 courses, in order
74 lessons31 challenges319 questions
4
Step 4
Recursive thinkingDedicated page
Start this stepStartRecursion challenges in R, where options(expressions = 5000) caps how deeply calls may nest and Recall lets a function call itself without repeating its own name. Each call gets a fresh environment, so a deep recursion costs memory as well as time. Dynamic programming and bit manipulation are listed after the steps, since they are taught in Python and C++.Start Dedicated page15 lessons15 challenges
5
Step 5
Graph algorithms
Start this stepStartBreadth-first and depth-first search, Dijkstra, Bellman-Ford, topological sort, Kruskal and Prim in R, on the graph you built in step two. Base R has no priority queue, so the heap from step two is the one Dijkstra needs, and these are the algorithms network packages such as igraph run for you.Start
7 courses, in order
63 lessons21 challenges383 questions
6
Step 6
Interview practice
Start this stepStartTen interview challenge packs and two coding-problem banks in R, graded by test cases: unfamiliar problems, solved without a tutorial, in the language you already analyze data in.Start
12 courses, in order
87 lessons85 challenges

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Intro to Variables

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A variable is a named container that stores a value you can reference later in your program.

In Python, you create one by writing the name, an equals sign, then the value you want to store.

The value can change over time - reassigning the name simply points it to a new value.

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Why learn DSA in R on Coddy

The other data structures. R's own types are what most R tutorials mean by data structures, and Coddy's R course teaches them. An algorithms course means stacks, queues, linked lists, heaps, trees and graphs, which base R does not provide. Building them in R is how an analyst picks up the half of computer science that data work tends to skip.
What R's semantics cost. R copies on modify, so a vector grown with c(x, value) inside a loop is copied on every pass, quadratic time for a linear job; preallocating fixes it. Environments have reference semantics and hashed lookup, which makes them R's hash map and the natural home for a node you mean to change in place.
Nearly the whole path in R. Every data structure, sort, graph algorithm, recursion challenge and interview pack is taught in R. Three are taught elsewhere and listed after the steps with a link to each: dynamic programming and the Python interview series in Python, and bit manipulation in C++. Bit manipulation reads differently from R, where & and | are logical operators and bit operations are functions such as bitwAnd.
Graded like an interview. Every lesson ends in an R challenge checked by test cases, and when one fails, Bugsy reads your code and nudges you toward the fix without handing over the answer. A free certificate on most courses, each verifiable at its own URL.

Frequently asked questions about DSA in R

Is this path about R's data structures, like vectors and data frames?

Not mainly. R's own data types are atomic vectors, lists (which nest), matrices, data frames and factors; they are what most guides to data structures in R cover, and Coddy's R course teaches them. This path is the computer science meaning: stacks, queues, linked lists, hash tables, heaps, tries, trees and graphs, built in R and then used for sorting, recursion and graph algorithms.

Is R good for data structures and algorithms?

It works, with some friction. R is built for vectorized statistics, not for structures made of linked nodes, so a linked list or a tree takes more thought than in Python, and coding interviews are rarely held in R. For an analyst, that friction is the value: knowing that growing a vector in a loop is quadratic, or that an environment is a hash map, is what makes R code scale to real data.

How do you make a hash map in R?

With an environment: h <- new.env(), then h[[key]] <- value to store and h[[key]] to read, which gives NULL for a missing key. Environments are hashed, keyed by strings, and have reference semantics, so a function that changes one changes it for the caller too, unlike a vector or a list. A named list looks like a dictionary but behaves like a value: change it inside a function and the caller's copy is untouched.

Does deep recursion fail in R?

It can. By default options(expressions = 5000) caps how deeply evaluation may nest, and a deep recursion, such as a depth-first search down a long chain, stops with an evaluation nested too deeply error. Raising the option buys some room; the reliable fix is an explicit stack in a loop, the structure you build in step one.

Which courses on this path are not taught in R?

Three: dynamic programming and the Python interview series, taught in Python, and bit manipulation, taught in C++. They are listed after the steps, each with a link that opens it in its own language. Dynamic programming translates directly, a memo table in R being a preallocated vector, and C++'s &, |, ^ and << become the functions bitwAnd, bitwOr, bitwXor and bitwShiftL.

Do I need to know R before starting this path?

Vectors, lists, functions, loops and indexing from 1, at least. If those are new, Coddy's R course takes you there first, free, and this path picks up where it ends.

Other learning paths

The same courses, arranged for a different role. Progress carries over: a course finished on one path counts on every path that includes it.

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