'Eight Queens' Problem with Observable

@coolbutuseless has a great blog on solving the “Eight Queens” problem using R. To challenge myself in translating “R” code, I created an Observable notebook. See the final product here; read on to learn how I did it.

Setdiff is Nonsymmetrical

This step makes sense; it eliminates diagonals movements that would take a piece outside the boundaries of the chessboard. But it isn’t strictly necessary…

  diags <- diags[diags > 0 & diags < 9]

… it isn’t necessary because we use setdiff The setdiff function will only remove items from the first argument (.x), if they appear in the second argument (.y); (in other words, the setdiff function will not include any items from the second argument). That’s because the setdiff function is not the symmetric difference…

possible_placements <- setdiff(possible_placements, diags)

… as described here:

the elements of setdiff(x,y) are those elements in x but not in y.

EDIT I noticed the author skipped the filtering step in the “compacted” solution:


Tilde plus dot

I didn’t understand what this code was doing:

  possible_placements %>% 
    map(~place_queen(c(queens, .x))) %>%
    keep(~length(.x) > 0) %>%

Specifically I didn’t understand what the tilde ~ and dot operator . were doing in this line:

    map(~place_queen(c(queens, .x))) %>%

Turns out the tilde ~ creates a simple lambda function, whose arguments are accessed via the dot ., as explained here:

As MrFlick pointed out, these are two separate operators. Together, they provide a special mechanism that allows tidyverse packages to construct lambda functions on the fly. This is best described in ?purrr::as_mapper. Specifically,

If a formula, e.g. ~ .x + 2, it is converted to a function. There are three ways to refer to the arguments:

  1. For a single argument function, use .
  2. For a two argument function, use .x and .y
  3. For more arguments, use ..1, ..2, ..3 etc

In this case, could replace the first argument of a single-argument function, so would this be equivalent (replace .x with .)?

    map(~place_queen(c(queens, .))) %>%

In fact, if the inner function is c(...), then isn’t c(., queens) the equivalent of c(queens, .); so we could use the “implicit” version: (do we need braces?) No, this is for the piping dot operator, vs the tilde-lambda dot operator…

    map(~place_queen(c(queens))) %>%

Last statements of R functions are automatically returned

I didn’t understand how the recursive place_queen R function would return a value; I saw a return statement once it found a solution, but I didn’t see a return statement at the end of the function, to return the possible_placements once finished recursively searching all solutions and flattening them.

I learned that R functions will automatically return the last statement:

You can transmit a value back to the caller by explicitly calling return(). Without this call, the value of the last executed statement will be returned by default. For instance, consider the oddcount() example from Chapter 1:

Recursive functions in Observable

Observable will complain about a “circular definition” unless we follow this approach; i.e. using the function declaration, vs assigning a function to the block’s value.

flatten() in Javascript

I struggled to figure out the equivalent of the ` %>% flatten()`.

In fact, the Javascript Array.prototype.flat() is the equivalent of flatten() When I first used .flat(), my result was a single array of 736 numbers (that’s all 92 solutions x 8 positions per solution) - vs an array with 92 elements, each of which is an 8-element Array.

I realized my .flat() was “over-flattening”, because my “solutions” were a normal Javascript array (i.e. my solutions were susceptible to being flattened!):

if (queens.length===8){
  return queens;

This would be the equivalent to the R code, in the sense that it returns an object that can be flattened


So I changed my solutions to an object, with a solution property and a length property.

  if (queens.length == 8) {
    let result = { solution: queens, length: queens.length };
    return result;

This is closer to the original R code, in the sense that it returns an object that is not flattened:


Notice the use of unlist() later on (in the “compact” version of R code), and remember flatten() is different than unlist() as described here:

flatten() removes a level hierarchy from a list. They are similar to unlist(), but they only ever remove a single layer of hierarchy and they are type-stable, so you always know what the type of the output is.

Notice that the “compact” version of R code

ggplot requires show() inside a function

I had to call show() or print() to see the geom_tile visualization, as described here, and here.

You will, however, need to call print() explicitly if you want to draw a plot inside a function or for loop.

Notice that the plots are rotated 90 degrees, so they aren’t oriented the way I expect. The queens_df should use cols as the x-axis, not the rows:

  p <- ggplot(queens_df, aes(rows, cols)) + # ...

… so just swap the rows, and cols, then we get the right orientation:

  p <- ggplot(queens_df, aes(cols, rows)) + # ...

Code golf

The original blogpost compresses all the R code necessary to 1) find all solutions and 2) visualize all solutions, into < 160 characters (so it can fit in a tweet!)

Visualizing Recursive solutions

Some links: