From 39e2c02b52b0384a65803f5e63d6c8668d71288d Mon Sep 17 00:00:00 2001 From: Mike Mahoney Date: Tue, 24 Oct 2023 11:56:28 -0400 Subject: [PATCH] Add post --- .../index/execute-results/html.json | 2 +- .../index/figure-html/unnamed-chunk-8-1.png | Bin 57443 -> 59833 bytes .../index/figure-html/unnamed-chunk-9-1.png | Bin 80973 -> 80549 bytes .../index/execute-results/html.json | 14 ++ posts/2023-10-24-fun-r-funcs/index.qmd | 157 ++++++++++++++++++ 5 files changed, 172 insertions(+), 1 deletion(-) create mode 100644 _freeze/posts/2023-10-24-fun-r-funcs/index/execute-results/html.json create mode 100644 posts/2023-10-24-fun-r-funcs/index.qmd diff --git a/_freeze/posts/2023-08-29-allocations/index/execute-results/html.json b/_freeze/posts/2023-08-29-allocations/index/execute-results/html.json index a705d07d8..61f9369cd 100644 --- a/_freeze/posts/2023-08-29-allocations/index/execute-results/html.json +++ b/_freeze/posts/2023-08-29-allocations/index/execute-results/html.json @@ -1,7 +1,7 @@ { "hash": "3da9568db9d374209e8c71e3f521de3b", "result": { - "markdown": "---\ntitle: \"Pre-allocating vectors is for nerds\"\ndescription: \"Or rather: growing objects is inefficient. But it's maybe not as big a deal as I'd believed.\"\nauthor:\n - name: Mike Mahoney\n url: {}\ndate: \"2023-08-29\"\ncategories: [R, Tutorials]\nimage: banner.jpg\nformat: \n html:\n toc: true\nengine: knitr\n---\n\n\nThe second circle of R hell, in [Patrick Burns' seminal book The R Inferno](https://www.burns-stat.com/pages/Tutor/R_inferno.pdf), is titled \"Growing Objects\". This refers to a common antipattern for R users, usually among the first things taught when dealing with iteration: it is extremely inefficient to grow a vector using `c()`, like so:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nvector_c <- function(n) {\n out <- c()\n for (i in 1:n) {\n out <- c(out, i)\n }\n out\n}\n```\n:::\n\n\nInstead, Burns says, it is better to pre-allocate our vector `out`, and assign our function's output to a specific position in `out` using either `[` or `[[`:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nvector_prealloc_one_bracket <- function(n) {\n out <- vector(\"numeric\", n)\n for (i in 1:n) {\n out[i] <- i\n }\n out\n}\n\nvector_prealloc_two_bracket <- function(n) {\n out <- vector(\"numeric\", n)\n for (i in 1:n) {\n out[[i]] <- i\n }\n out\n}\n```\n:::\n\n\nOf course, it would be better yet to avoid our loop entirely, and simply create our final object using the colon operator:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncolon_operator <- function(n) {\n 1:n\n}\n```\n:::\n\n\nBut that's beside the point right now.\n\nThis advice was originally written in 2011, but is even more important today. In Burns' book, subsetting is roughly 7 times faster when `n` is 10,000; on my computer today, subsetting is roughly 200 times faster:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nn <- 10000\nbench::mark(\n c = vector_c(n),\n one_bracket = vector_prealloc_one_bracket(n),\n two_brackets = vector_prealloc_two_bracket(n),\n colon = colon_operator(n),\n filter_gc = FALSE\n)[c(\"expression\", \"median\", \"itr/sec\", \"mem_alloc\")]\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 4 × 4\n expression median `itr/sec` mem_alloc\n \n1 c 57.8ms 17.1 191.2MB\n2 one_bracket 270.9µs 3596. 99.1KB\n3 two_brackets 271.2µs 3570. 96.7KB\n4 colon 371.1ns 1841212. 0B\n```\n:::\n:::\n\n\nBut what if `n` is unknowable? Well, to quote Burns:\n\n> Often a reasonable upper bound on the size of the final object is known. If so,\nthen create the object with that size and then remove the extra values at the\nend. If the final size is a mystery, then you can still follow the same scheme,\nbut allow for periodic growth of the object.\n\nThis is still probably a decent approach: over-allocate and trim down, or allocate in chunks and only grow when those chunks are exhausted.\n\nOr... perhaps we might try growing a vector with `[` or `[[`, rather than with `c()`? To anyone raised on R traditions, this might seem like a code smell:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nvector_unalloc_one_bracket <- function(n) {\n out <- c()\n for (i in 1:n) {\n out[i] <- i\n }\n out\n}\n\nvector_unalloc_two_bracket <- function(n) {\n out <- c()\n for (i in 1:n) {\n out[[i]] <- i\n }\n unlist(out)\n}\n```\n:::\n\n\nBut if we test it out:^[I dropped `prealloc_two_brackets` from the benchmarks because it was performing ~the same as the one-bracket alternative.]\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbench::mark(\n c = vector_c(n),\n prealloc_one_bracket = vector_prealloc_one_bracket(n),\n unalloc_one_bracket = vector_unalloc_one_bracket(n),\n unalloc_two_brackets = vector_unalloc_two_bracket(n),\n filter_gc = FALSE\n)[c(\"expression\", \"median\", \"itr/sec\", \"mem_alloc\")]\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 4 × 4\n expression median `itr/sec` mem_alloc\n \n1 c 61.23ms 14.8 191.23MB\n2 prealloc_one_bracket 269.86µs 3632. 78.17KB\n3 unalloc_one_bracket 1.27ms 679. 871.73KB\n4 unalloc_two_brackets 2.86ms 316. 1.72MB\n```\n:::\n:::\n\n\nGrowing a vector via `[` is still notably slower than assigning values to a pre-allocated vector; it looks like it's roughly ~5 times slower. But that still means it's ~50 times faster than growing a vector via `c()`, and allocates ~200 times less memory to do so. Growing a vector via `[[` isn't quite as efficient -- taking roughly twice the time and memory as `[` here -- but still blows `c()` out of the water.\n\nThat's not too shabby, for a code smell. How does a method like `vapply()` compare?\n\n\n::: {.cell}\n\n```{.r .cell-code}\nvapply_lambda <- function(n) {\n vapply(1:n, \\(i) i, numeric(1))\n}\n\nbench::mark(\n c = vector_c(n),\n prealloc_one_bracket = vector_prealloc_one_bracket(n),\n unalloc_one_bracket = vector_unalloc_one_bracket(n),\n unalloc_two_brackets = vector_unalloc_two_bracket(n),\n vapply = vapply_lambda(n),\n filter_gc = FALSE\n)[c(\"expression\", \"median\", \"itr/sec\", \"mem_alloc\")]\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 5 × 4\n expression median `itr/sec` mem_alloc\n \n1 c 51.14ms 19.2 191.2MB\n2 prealloc_one_bracket 268.72µs 3643. 78.2KB\n3 unalloc_one_bracket 1.33ms 567. 853KB\n4 unalloc_two_brackets 2.72ms 341. 1.7MB\n5 vapply 3.44ms 270. 78.2KB\n```\n:::\n:::\n\n\n`vapply()` uses as little memory as our pre-allocation approaches, but is slower than either of our un-allocated methods.^[Usual disclaimer that this is probably not a type of slowness that matters for your code, that you should look into moving computation to C++/Rust if you care about a few milliseconds execution time, and that the real benefits of *apply functions come from readability and their potential for parallelization, not speed.]\n\nIt's worth emphasizing that the differences between these methods are _microscopic_ compared to the difference between them and `c()` for growing vectors:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbenchmarks <- bench::press(\n bench::mark(\n c = vector_c(n),\n prealloc_one_bracket = vector_prealloc_one_bracket(n),\n unalloc_one_bracket = vector_unalloc_one_bracket(n),\n unalloc_two_brackets = vector_unalloc_two_bracket(n),\n vapply = vapply_lambda(n),\n filter_gc = FALSE\n ),\n n = c(10, 100, 1000, 10000, 100000)\n)\n\nlibrary(ggplot2)\nggplot(benchmarks, aes(n, median, color = as.character(expression))) + \n geom_line() + \n theme_minimal() + \n labs(y = \"Median execution time (s)\")\n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/unnamed-chunk-8-1.png){width=672}\n:::\n:::\n\n\nBut as far as execution speed goes, well, maybe growing objects in general isn't worthy of its own circle of hell anymore:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbenchmarks[as.character(benchmarks$expression) != \"c\", ] |> \n ggplot(aes(n, median, color = as.character(expression))) + \n geom_line() + \n theme_minimal() + \n labs(y = \"Median execution time (s)\")\n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/unnamed-chunk-9-1.png){width=672}\n:::\n:::\n\n\nThough of course, `vapply()` and the pre-allocated methods still win out in terms of memory allocation:^[The pre-allocated line is hidden by the `vapply()` line; they're practically identical, and possibly also literally identical.]\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbenchmarks[as.character(benchmarks$expression) != \"c\", ] |> \n ggplot(aes(n, mem_alloc, color = as.character(expression))) + \n geom_line() + \n theme_minimal() + \n labs(y = \"Memory allocation (bytes)\")\n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/unnamed-chunk-10-1.png){width=672}\n:::\n:::\n\n\nSo: pre-allocate your vectors when you're able. But maybe it's fine to grow an object every once in a while, as a treat. It probably won't get you sent to hell.\n\nI have no idea when things changed to make growing vectors via `[` so much more efficient now than in 2011 -- and please let me know in the comments/[Mastodon](https://fosstodon.org/@MikeMahoney218)/[BlueSky](https://bsky.app/profile/mikemahoney218.com) if you know any more details here. \n", + "markdown": "---\ntitle: \"Pre-allocating vectors is for nerds\"\ndescription: \"Or rather: growing objects is inefficient. But it's maybe not as big a deal as I'd believed.\"\nauthor:\n - name: Mike Mahoney\n url: {}\ndate: \"2023-08-29\"\ncategories: [R, Tutorials]\nimage: banner.jpg\nformat: \n html:\n toc: true\nengine: knitr\n---\n\n\nThe second circle of R hell, in [Patrick Burns' seminal book The R Inferno](https://www.burns-stat.com/pages/Tutor/R_inferno.pdf), is titled \"Growing Objects\". This refers to a common antipattern for R users, usually among the first things taught when dealing with iteration: it is extremely inefficient to grow a vector using `c()`, like so:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nvector_c <- function(n) {\n out <- c()\n for (i in 1:n) {\n out <- c(out, i)\n }\n out\n}\n```\n:::\n\n\nInstead, Burns says, it is better to pre-allocate our vector `out`, and assign our function's output to a specific position in `out` using either `[` or `[[`:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nvector_prealloc_one_bracket <- function(n) {\n out <- vector(\"numeric\", n)\n for (i in 1:n) {\n out[i] <- i\n }\n out\n}\n\nvector_prealloc_two_bracket <- function(n) {\n out <- vector(\"numeric\", n)\n for (i in 1:n) {\n out[[i]] <- i\n }\n out\n}\n```\n:::\n\n\nOf course, it would be better yet to avoid our loop entirely, and simply create our final object using the colon operator:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncolon_operator <- function(n) {\n 1:n\n}\n```\n:::\n\n\nBut that's beside the point right now.\n\nThis advice was originally written in 2011, but is even more important today. In Burns' book, subsetting is roughly 7 times faster when `n` is 10,000; on my computer today, subsetting is roughly 200 times faster:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nn <- 10000\nbench::mark(\n c = vector_c(n),\n one_bracket = vector_prealloc_one_bracket(n),\n two_brackets = vector_prealloc_two_bracket(n),\n colon = colon_operator(n),\n filter_gc = FALSE\n)[c(\"expression\", \"median\", \"itr/sec\", \"mem_alloc\")]\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 4 × 4\n expression median `itr/sec` mem_alloc\n \n1 c 51ms 19.3 191.2MB\n2 one_bracket 277µs 3548. 99.1KB\n3 two_brackets 276µs 3538. 96.7KB\n4 colon 361ns 2124339. 0B\n```\n:::\n:::\n\n\nBut what if `n` is unknowable? Well, to quote Burns:\n\n> Often a reasonable upper bound on the size of the final object is known. If so,\nthen create the object with that size and then remove the extra values at the\nend. If the final size is a mystery, then you can still follow the same scheme,\nbut allow for periodic growth of the object.\n\nThis is still probably a decent approach: over-allocate and trim down, or allocate in chunks and only grow when those chunks are exhausted.\n\nOr... perhaps we might try growing a vector with `[` or `[[`, rather than with `c()`? To anyone raised on R traditions, this might seem like a code smell:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nvector_unalloc_one_bracket <- function(n) {\n out <- c()\n for (i in 1:n) {\n out[i] <- i\n }\n out\n}\n\nvector_unalloc_two_bracket <- function(n) {\n out <- c()\n for (i in 1:n) {\n out[[i]] <- i\n }\n unlist(out)\n}\n```\n:::\n\n\nBut if we test it out:^[I dropped `prealloc_two_brackets` from the benchmarks because it was performing ~the same as the one-bracket alternative.]\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbench::mark(\n c = vector_c(n),\n prealloc_one_bracket = vector_prealloc_one_bracket(n),\n unalloc_one_bracket = vector_unalloc_one_bracket(n),\n unalloc_two_brackets = vector_unalloc_two_bracket(n),\n filter_gc = FALSE\n)[c(\"expression\", \"median\", \"itr/sec\", \"mem_alloc\")]\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 4 × 4\n expression median `itr/sec` mem_alloc\n \n1 c 54.02ms 16.6 191.23MB\n2 prealloc_one_bracket 285.52µs 3428. 78.17KB\n3 unalloc_one_bracket 1.24ms 710. 871.73KB\n4 unalloc_two_brackets 2.76ms 337. 1.72MB\n```\n:::\n:::\n\n\nGrowing a vector via `[` is still notably slower than assigning values to a pre-allocated vector; it looks like it's roughly ~5 times slower. But that still means it's ~50 times faster than growing a vector via `c()`, and allocates ~200 times less memory to do so. Growing a vector via `[[` isn't quite as efficient -- taking roughly twice the time and memory as `[` here -- but still blows `c()` out of the water.\n\nThat's not too shabby, for a code smell. How does a method like `vapply()` compare?\n\n\n::: {.cell}\n\n```{.r .cell-code}\nvapply_lambda <- function(n) {\n vapply(1:n, \\(i) i, numeric(1))\n}\n\nbench::mark(\n c = vector_c(n),\n prealloc_one_bracket = vector_prealloc_one_bracket(n),\n unalloc_one_bracket = vector_unalloc_one_bracket(n),\n unalloc_two_brackets = vector_unalloc_two_bracket(n),\n vapply = vapply_lambda(n),\n filter_gc = FALSE\n)[c(\"expression\", \"median\", \"itr/sec\", \"mem_alloc\")]\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 5 × 4\n expression median `itr/sec` mem_alloc\n \n1 c 50.87ms 19.5 191.2MB\n2 prealloc_one_bracket 279.79µs 3501. 78.2KB\n3 unalloc_one_bracket 1.18ms 649. 853KB\n4 unalloc_two_brackets 2.69ms 345. 1.7MB\n5 vapply 3.41ms 272. 78.2KB\n```\n:::\n:::\n\n\n`vapply()` uses as little memory as our pre-allocation approaches, but is slower than either of our un-allocated methods.^[Usual disclaimer that this is probably not a type of slowness that matters for your code, that you should look into moving computation to C++/Rust if you care about a few milliseconds execution time, and that the real benefits of *apply functions come from readability and their potential for parallelization, not speed.]\n\nIt's worth emphasizing that the differences between these methods are _microscopic_ compared to the difference between them and `c()` for growing vectors:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbenchmarks <- bench::press(\n bench::mark(\n c = vector_c(n),\n prealloc_one_bracket = vector_prealloc_one_bracket(n),\n unalloc_one_bracket = vector_unalloc_one_bracket(n),\n unalloc_two_brackets = vector_unalloc_two_bracket(n),\n vapply = vapply_lambda(n),\n filter_gc = FALSE\n ),\n n = c(10, 100, 1000, 10000, 100000)\n)\n\nlibrary(ggplot2)\nggplot(benchmarks, aes(n, median, color = as.character(expression))) + \n geom_line() + \n theme_minimal() + \n labs(y = \"Median execution time (s)\")\n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/unnamed-chunk-8-1.png){width=672}\n:::\n:::\n\n\nBut as far as execution speed goes, well, maybe growing objects in general isn't worthy of its own circle of hell anymore:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbenchmarks[as.character(benchmarks$expression) != \"c\", ] |> \n ggplot(aes(n, median, color = as.character(expression))) + \n geom_line() + \n theme_minimal() + \n labs(y = \"Median execution time (s)\")\n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/unnamed-chunk-9-1.png){width=672}\n:::\n:::\n\n\nThough of course, `vapply()` and the pre-allocated methods still win out in terms of memory allocation:^[The pre-allocated line is hidden by the `vapply()` line; they're practically identical, and possibly also literally identical.]\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbenchmarks[as.character(benchmarks$expression) != \"c\", ] |> \n ggplot(aes(n, mem_alloc, color = as.character(expression))) + \n geom_line() + \n theme_minimal() + \n labs(y = \"Memory allocation (bytes)\")\n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/unnamed-chunk-10-1.png){width=672}\n:::\n:::\n\n\nSo: pre-allocate your vectors when you're able. 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+{ + "hash": "42b4215721831fabf6f6edfd653b3a43", + "result": { + "markdown": "---\ntitle: \"Three fun R functions\"\ndescription: \"All the cool kids were jumping off the bridge!\"\nauthor:\n - name: Mike Mahoney\n url: {}\ndate: \"2023-10-24\"\ncategories: [R, Tutorials]\nimage: banner.jpg\nformat: \n html:\n toc: true\nengine: knitr\n---\n\n\nInspired by [Maëlle](https://masalmon.eu/2023/10/20/three-neat-functions/) who was inspired by [Yihui](https://yihui.org/en/2023/10/three-functions/) who was inspired by [Maëlle](https://masalmon.eu/2023/09/29/three-functions/)(who has [a whole](https://masalmon.eu/2023/08/31/three-shorten/) [series](https://masalmon.eu/2023/06/06/basic-patterns/) [about](https://masalmon.eu/2023/08/30/three-r-functions/) [this](https://masalmon.eu/2023/07/24/basic-notions/)), I wanted to share three useful base R functions that I think maybe don't get enough love. And inspired by [Maëlle again](https://masalmon.eu/2023/10/20/three-neat-functions/), my list here is actually four functions.\n\n## `sweep()`\n\nIf you ever need to do math with matrices, then `sweep()` is going to be your best friend. Say for instance we want to center and scale each column in a matrix. This is a pretty straightforward operation -- we need to calculate the mean and standard deviations for each column, subtract the column mean from each observation, and then divide those by the corresponding standard deviation.\n\nWe can use `apply` to get our means and standard deviations:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Generate some fake data in a 10x10 matrix:\nx <- matrix(data = rnorm(100), nrow = 10)\n# Calculate one mean and sd for each column of our matrix:\ncol_means <- apply(x, 2, mean)\ncol_sds <- apply(x, 2, sd)\n```\n:::\n\n\nThe subtraction and division are a bit less straightforward. R's base math operators will attempt to do element-wise operations, treating our vector as a one-column array and replicating as needed. That's not what we want:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nall.equal(\n (x - col_means) / col_sds,\n scale(x)\n)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"Attributes: < Length mismatch: comparison on first 1 components >\"\n[2] \"Mean relative difference: 0.5463949\" \n```\n:::\n:::\n\n\nWe could replicate our vector ourself, in order to take advantage of these element-wise operations:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nall.equal(\n ((x - matrix(rep(col_means, 10), 10, byrow = TRUE)) / \n matrix(rep(col_sds, 10), 10, byrow = TRUE)) |> as.vector(),\n scale(x) |> as.vector()\n)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] TRUE\n```\n:::\n:::\n\n\nBut that's silly, especially if we were working with more observations.\n\nBetter instead is to use `sweep()` to perform some operation between each element of our vector and each column of the matrix:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Take every value in our matrix, and subtract its corresponding column mean:\ncentered <- sweep(\n x = x, \n MARGIN = 2, # just like in apply()\n STATS = col_means, \n FUN = \"-\" # \"-\" is the default argument -- we don't NEED to provide it here\n)\n```\n:::\n\n\nAnd we can similarly use `sweep()` to divide each column by its corresponding standard deviation, finishing up our centering and scaling:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Divide each value by its corresponding column sd:\ncentered_and_scaled <- sweep(centered, 2, col_sds, \"/\")\n\n# Works out identically to the built-in scale function:\nall.equal(\n as.vector(centered_and_scaled),\n as.vector(scale(x))\n)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] TRUE\n```\n:::\n:::\n\n\nThis is the main way I use `sweep()`, but there's no requirement you use it for math -- it works just as well with non-mathematical functions or non-numeric matrices:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nletter_mat <- matrix(rep(letters[1:5], 5), 5)\nletter_mat\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [,1] [,2] [,3] [,4] [,5]\n[1,] \"a\" \"a\" \"a\" \"a\" \"a\" \n[2,] \"b\" \"b\" \"b\" \"b\" \"b\" \n[3,] \"c\" \"c\" \"c\" \"c\" \"c\" \n[4,] \"d\" \"d\" \"d\" \"d\" \"d\" \n[5,] \"e\" \"e\" \"e\" \"e\" \"e\" \n```\n:::\n\n```{.r .cell-code}\nsweep(letter_mat, 2, LETTERS[1:5], paste0)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] \"aA\" \"bA\" \"cA\" \"dA\" \"eA\" \"aB\" \"bB\" \"cB\" \"dB\" \"eB\" \"aC\" \"bC\" \"cC\" \"dC\" \"eC\"\n[16] \"aD\" \"bD\" \"cD\" \"dD\" \"eD\" \"aE\" \"bE\" \"cE\" \"dE\" \"eE\"\n```\n:::\n:::\n\n\n## `reformulate()` and `DF2formula()`\n\nThe `reformulate()` function is a lifesaver if you're trying to write long or complicated formulas, or multiple formulas generated by some other logic in your code.\n\nThe function is pretty straightforward. If you're trying to make a formula `y ~ x + z`, provide your predictors as the first argument and your outcome as the second:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nreformulate(c(\"x\", \"z\"), \"y\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\ny ~ x + z\n```\n:::\n:::\n\n\nThe nice thing is that `reformulate` accepts vectors as inputs, making it easy to construct a vector of predictors and automatically turn them into a formula:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nreformulate(letters, \"outcome\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\noutcome ~ a + b + c + d + e + f + g + h + i + j + k + l + m + \n n + o + p + q + r + s + t + u + v + w + x + y + z\n```\n:::\n\n```{.r .cell-code}\nreformulate(names(Orange), \"age\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nage ~ Tree + age + circumference\n```\n:::\n:::\n\n\nAnd in particular, this is an excellent alternative to dropping a few columns in order to use `outcome ~ .` -- instead, you can use `setdiff()` to exclude those columns from your formula:\n\n\n::: {.cell}\n\n```{.r .cell-code}\noutcome_variable <- \"age\"\nreformulate(setdiff(names(Orange), outcome_variable), outcome_variable)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nage ~ Tree + circumference\n```\n:::\n:::\n\n\nRelatedly, the function `DF2formula()` will automatically turn the column names from a data frame into a formula. The first column will become the outcome variable, and the rest will be used as predictors:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nDF2formula(Orange)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nTree ~ age + circumference\n```\n:::\n:::\n\n\nTo change what column is used as the outcome variable, reorder the columns in your data frame:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nDF2formula(Orange[3:1])\n```\n\n::: {.cell-output .cell-output-stdout}\n```\ncircumference ~ age + Tree\n```\n:::\n:::\n\n\n## `str2lang()`\n\nShockingly enough, `str2lang()` function turns a string into a language object:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ngrowth_rate <- \"circumference / age\"\nstr2lang(growth_rate)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\ncircumference/age\n```\n:::\n\n```{.r .cell-code}\nclass(str2lang(growth_rate))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"call\"\n```\n:::\n:::\n\n\nWooooo!\n\nI think that, to most people, this does not sound immediately useful.^[I think, to most people, this barely sounds like English.] But the idea that your code can turn plain text into code at runtime is pretty powerful, and some of the most R-esque nonsense that R has to offer.\n\nFor instance, we can use `eval()` to actually execute the call created by `str2lang()` in our global environment:\n\n\n::: {.cell}\n\n```{.r .cell-code}\neval(str2lang(\"2 + 2\"))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 4\n```\n:::\n:::\n\n\nAnd that string can do anything that regular R code can do -- assign variables, manage connections, any procedure that normal R code can do:\n\n\n::: {.cell}\n\n```{.r .cell-code}\neval(str2lang(\"x <- 3\"))\nx\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 3\n```\n:::\n:::\n\n\nWe can also use this with `with()` or `local()` to execute our code inside of other environments. For instance, if we want to calculate our `growth_rate` from earlier, we can run that code with the `Orange` data frame:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwith(Orange, eval(str2lang(growth_rate)))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 0.25423729 0.11983471 0.13102410 0.11454183 0.09748172 0.10349854\n [7] 0.09165613 0.27966102 0.14256198 0.16716867 0.15537849 0.13972380\n[13] 0.14795918 0.12831858 0.25423729 0.10537190 0.11295181 0.10756972\n[19] 0.09341998 0.10131195 0.08849558 0.27118644 0.12809917 0.16867470\n[25] 0.16633466 0.14541024 0.15233236 0.13527181 0.25423729 0.10123967\n[31] 0.12198795 0.12450199 0.11535337 0.12682216 0.11188369\n```\n:::\n:::\n\n\nThis can be a powerful way to \"import\" code from other sources, for instance if you have a CSV of equations you want to run against a data frame. You want to be careful when using this with untrusted inputs, of course -- if your input includes a call to `system()`, it might wind up wrecking your computer! \n", + "supporting": [], + "filters": [ + "rmarkdown/pagebreak.lua" + ], + "includes": {}, + "engineDependencies": {}, + "preserve": {}, + "postProcess": true + } +} \ No newline at end of file diff --git a/posts/2023-10-24-fun-r-funcs/index.qmd b/posts/2023-10-24-fun-r-funcs/index.qmd new file mode 100644 index 000000000..febccc25c --- /dev/null +++ b/posts/2023-10-24-fun-r-funcs/index.qmd @@ -0,0 +1,157 @@ +--- +title: "Three fun R functions" +description: "All the cool kids were jumping off the bridge!" +author: + - name: Mike Mahoney + url: {} +date: "2023-10-24" +categories: [R, Tutorials] +image: banner.jpg +format: + html: + toc: true +engine: knitr +--- + +Inspired by [Maëlle](https://masalmon.eu/2023/10/20/three-neat-functions/) who was inspired by [Yihui](https://yihui.org/en/2023/10/three-functions/) who was inspired by [Maëlle](https://masalmon.eu/2023/09/29/three-functions/)(who has [a whole](https://masalmon.eu/2023/08/31/three-shorten/) [series](https://masalmon.eu/2023/06/06/basic-patterns/) [about](https://masalmon.eu/2023/08/30/three-r-functions/) [this](https://masalmon.eu/2023/07/24/basic-notions/)), I wanted to share three useful base R functions that I think maybe don't get enough love. And inspired by [Maëlle again](https://masalmon.eu/2023/10/20/three-neat-functions/), my list here is actually four functions. + +## `sweep()` + +If you ever need to do math with matrices, then `sweep()` is going to be your best friend. Say for instance we want to center and scale each column in a matrix. This is a pretty straightforward operation -- we need to calculate the mean and standard deviations for each column, subtract the column mean from each observation, and then divide those by the corresponding standard deviation. + +We can use `apply` to get our means and standard deviations: + +```{r} +# Generate some fake data in a 10x10 matrix: +x <- matrix(data = rnorm(100), nrow = 10) +# Calculate one mean and sd for each column of our matrix: +col_means <- apply(x, 2, mean) +col_sds <- apply(x, 2, sd) +``` + +The subtraction and division are a bit less straightforward. R's base math operators will attempt to do element-wise operations, treating our vector as a one-column array and replicating as needed. That's not what we want: + +```{r} +all.equal( + (x - col_means) / col_sds, + scale(x) +) +``` + +We could replicate our vector ourself, in order to take advantage of these element-wise operations: + +```{r} +all.equal( + ((x - matrix(rep(col_means, 10), 10, byrow = TRUE)) / + matrix(rep(col_sds, 10), 10, byrow = TRUE)) |> as.vector(), + scale(x) |> as.vector() +) +``` + +But that's silly, especially if we were working with more observations. + +Better instead is to use `sweep()` to perform some operation between each element of our vector and each column of the matrix: + +```{r} +# Take every value in our matrix, and subtract its corresponding column mean: +centered <- sweep( + x = x, + MARGIN = 2, # just like in apply() + STATS = col_means, + FUN = "-" # "-" is the default argument -- we don't NEED to provide it here +) +``` + +And we can similarly use `sweep()` to divide each column by its corresponding standard deviation, finishing up our centering and scaling: + +```{r} +# Divide each value by its corresponding column sd: +centered_and_scaled <- sweep(centered, 2, col_sds, "/") + +# Works out identically to the built-in scale function: +all.equal( + as.vector(centered_and_scaled), + as.vector(scale(x)) +) +``` + +This is the main way I use `sweep()`, but there's no requirement you use it for math -- it works just as well with non-mathematical functions or non-numeric matrices: + +```{r} +letter_mat <- matrix(rep(letters[1:5], 5), 5) +letter_mat + +sweep(letter_mat, 2, LETTERS[1:5], paste0) +``` + +## `reformulate()` and `DF2formula()` + +The `reformulate()` function is a lifesaver if you're trying to write long or complicated formulas, or multiple formulas generated by some other logic in your code. + +The function is pretty straightforward. If you're trying to make a formula `y ~ x + z`, provide your predictors as the first argument and your outcome as the second: + +```{r} +reformulate(c("x", "z"), "y") +``` + +The nice thing is that `reformulate` accepts vectors as inputs, making it easy to construct a vector of predictors and automatically turn them into a formula: + +```{r} +reformulate(letters, "outcome") + +reformulate(names(Orange), "age") +``` + +And in particular, this is an excellent alternative to dropping a few columns in order to use `outcome ~ .` -- instead, you can use `setdiff()` to exclude those columns from your formula: + +```{r} +outcome_variable <- "age" +reformulate(setdiff(names(Orange), outcome_variable), outcome_variable) +``` + +Relatedly, the function `DF2formula()` will automatically turn the column names from a data frame into a formula. The first column will become the outcome variable, and the rest will be used as predictors: + +```{r} +DF2formula(Orange) +``` + +To change what column is used as the outcome variable, reorder the columns in your data frame: + +```{r} +DF2formula(Orange[3:1]) +``` + +## `str2lang()` + +Shockingly enough, `str2lang()` function turns a string into a language object: + +```{r} +growth_rate <- "circumference / age" +str2lang(growth_rate) +class(str2lang(growth_rate)) +``` + +Wooooo! + +I think that, to most people, this does not sound immediately useful.^[I think, to most people, this barely sounds like English.] But the idea that your code can turn plain text into code at runtime is pretty powerful, and some of the most R-esque nonsense that R has to offer. + +For instance, we can use `eval()` to actually execute the call created by `str2lang()` in our global environment: + +```{r} +eval(str2lang("2 + 2")) +``` + +And that string can do anything that regular R code can do -- assign variables, manage connections, any procedure that normal R code can do: + +```{r} +eval(str2lang("x <- 3")) +x +``` + +We can also use this with `with()` or `local()` to execute our code inside of other environments. For instance, if we want to calculate our `growth_rate` from earlier, we can run that code with the `Orange` data frame: + +```{r} +with(Orange, eval(str2lang(growth_rate))) +``` + +This can be a powerful way to "import" code from other sources, for instance if you have a CSV of equations you want to run against a data frame. You want to be careful when using this with untrusted inputs, of course -- if your input includes a call to `system()`, it might wind up wrecking your computer!