{"id":13109,"date":"2021-06-08T12:56:08","date_gmt":"2021-06-08T17:56:08","guid":{"rendered":"https:\/\/rud.is\/b\/?p=13109"},"modified":"2021-06-08T12:57:09","modified_gmt":"2021-06-08T17:57:09","slug":"new-tabulardata-available-in-swift-on-macos-monterery-et-al","status":"publish","type":"post","link":"https:\/\/rud.is\/b\/2021\/06\/08\/new-tabulardata-available-in-swift-on-macos-monterery-et-al\/","title":{"rendered":"New TabularData Available in Swift on macOS Monterery (et al)"},"content":{"rendered":"<p>WWDC 2021 is on this week and many new fun things are being introduced, including some data science-friendly additions to the frameworks that come with Xcode 13 and available on macOS 12+ (and its *OS cousins).<\/p>\n<p>Specifically, Apple has made tabular data a first-class citizen with the new <a href=\"https:\/\/developer.apple.com\/documentation\/tabulardata\">TabularData<\/a> app service.<\/p>\n<p>A future post will have some more expository, but here&#8217;s a sample of core operations including:<\/p>\n<ul>\n<li>reading in tabular data from CSV or JSON<\/li>\n<li>examining the structure<\/li>\n<li>working with columns and\/or rows<\/li>\n<li>grouping and filtering operations<\/li>\n<li>transforming and removing columns<\/li>\n<\/ul>\n<p>I&#8217;ve tagged this with <code>rstats<\/code> as there are R equivalents included for each operation so R folks can translate any Swift code they see in the future.<\/p>\n<pre><code class=\"language-swift\">import TabularData\n\n\/\/ define some basic formatting options for data frame output\nlet dOpts = FormattingOptions(maximumLineWidth: 80, maximumCellWidth: 10, maximumRowCount: 20, includesColumnTypes: true)\n\n\/\/ read in a CSV file\n\/\/ R: xdf &lt;- read.csv(\"mtcars.csv\")\nvar xdf = try! DataFrame.init(contentsOfCSVFile: URL(fileURLWithPath: \"mtcars.csv\"))\n\n\/\/ take a look at it\n\/\/ R: print(xdf) # no more print() in further R equivalents; just assume interactive or wrap with print\nprint(xdf.description(options: dOpts))\n\n\u250f\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u254d\u254d\u254d\u254d\u254d\u254d\u2513\n\u2503    \u2503 mpg      \u2503 cyl   \u2503 disp     \u2503 hp    \u2503 drat     \u2503 wt       \u2503 5    \u2507\n\u2503    \u2503 &lt;Double&gt; \u2503 &lt;Int&gt; \u2503 &lt;Double&gt; \u2503 &lt;Int&gt; \u2503 &lt;Double&gt; \u2503 &lt;Double&gt; \u2503 more \u2507\n\u2521\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u254d\u254d\u254d\u254d\u254d\u254d\u2529\n\u2502 0  \u2502 21.0     \u2502 6     \u2502 160.0    \u2502 110   \u2502 3.9      \u2502 2.62     \u2502      \u2506\n\u2502 1  \u2502 21.0     \u2502 6     \u2502 160.0    \u2502 110   \u2502 3.9      \u2502 2.875    \u2502      \u2506\n\u2502 2  \u2502 22.8     \u2502 4     \u2502 108.0    \u2502 93    \u2502 3.85     \u2502 2.32     \u2502      \u2506\n\u2502 3  \u2502 21.4     \u2502 6     \u2502 258.0    \u2502 110   \u2502 3.08     \u2502 3.215    \u2502      \u2506\n\u2502 4  \u2502 18.7     \u2502 8     \u2502 360.0    \u2502 175   \u2502 3.15     \u2502 3.44     \u2502      \u2506\n\u2502 5  \u2502 18.1     \u2502 6     \u2502 225.0    \u2502 105   \u2502 2.76     \u2502 3.46     \u2502      \u2506\n\u2502 6  \u2502 14.3     \u2502 8     \u2502 360.0    \u2502 245   \u2502 3.21     \u2502 3.57     \u2502      \u2506\n\u2502 7  \u2502 24.4     \u2502 4     \u2502 146.7    \u2502 62    \u2502 3.69     \u2502 3.19     \u2502      \u2506\n\u2502 8  \u2502 22.8     \u2502 4     \u2502 140.8    \u2502 95    \u2502 3.92     \u2502 3.15     \u2502      \u2506\n\u2502 9  \u2502 19.2     \u2502 6     \u2502 167.6    \u2502 123   \u2502 3.92     \u2502 3.44     \u2502      \u2506\n\u2502 10 \u2502 17.8     \u2502 6     \u2502 167.6    \u2502 123   \u2502 3.92     \u2502 3.44     \u2502      \u2506\n\u2502 11 \u2502 16.4     \u2502 8     \u2502 275.8    \u2502 180   \u2502 3.07     \u2502 4.07     \u2502      \u2506\n\u2502 12 \u2502 17.3     \u2502 8     \u2502 275.8    \u2502 180   \u2502 3.07     \u2502 3.73     \u2502      \u2506\n\u2502 13 \u2502 15.2     \u2502 8     \u2502 275.8    \u2502 180   \u2502 3.07     \u2502 3.78     \u2502      \u2506\n\u2502 14 \u2502 10.4     \u2502 8     \u2502 472.0    \u2502 205   \u2502 2.93     \u2502 5.25     \u2502      \u2506\n\u2502 15 \u2502 10.4     \u2502 8     \u2502 460.0    \u2502 215   \u2502 3.0      \u2502 5.424    \u2502      \u2506\n\u2502 16 \u2502 14.7     \u2502 8     \u2502 440.0    \u2502 230   \u2502 3.23     \u2502 5.345    \u2502      \u2506\n\u2502 17 \u2502 32.4     \u2502 4     \u2502 78.7     \u2502 66    \u2502 4.08     \u2502 2.2      \u2502      \u2506\n\u2502 18 \u2502 30.4     \u2502 4     \u2502 75.7     \u2502 52    \u2502 4.93     \u2502 1.615    \u2502      \u2506\n\u2502 19 \u2502 33.9     \u2502 4     \u2502 71.1     \u2502 65    \u2502 4.22     \u2502 1.835    \u2502      \u2506\n\u2522\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u252a\n\u2507 12 more                                                               \u2507\n\u2517\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u251b\n\n\/\/ dimensions\n\/\/ R: dim(xdf)\nprint(xdf.shape)\n\n(rows: 32, columns: 11)\n\n\/\/ head\n\/\/ R: head(xdf)\nprint(xdf.prefix(5).description(options: dOpts))\n\n\u250f\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u254d\u254d\u254d\u254d\u254d\u254d\u2513\n\u2503   \u2503 mpg      \u2503 cyl   \u2503 disp     \u2503 hp    \u2503 drat     \u2503 wt       \u2503 5    \u2507\n\u2503   \u2503 &lt;Double&gt; \u2503 &lt;Int&gt; \u2503 &lt;Double&gt; \u2503 &lt;Int&gt; \u2503 &lt;Double&gt; \u2503 &lt;Double&gt; \u2503 more \u2507\n\u2521\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u254d\u254d\u254d\u254d\u254d\u254d\u2529\n\u2502 0 \u2502 21.0     \u2502 6     \u2502 160.0    \u2502 110   \u2502 3.9      \u2502 2.62     \u2502      \u2506\n\u2502 1 \u2502 21.0     \u2502 6     \u2502 160.0    \u2502 110   \u2502 3.9      \u2502 2.875    \u2502      \u2506\n\u2502 2 \u2502 22.8     \u2502 4     \u2502 108.0    \u2502 93    \u2502 3.85     \u2502 2.32     \u2502      \u2506\n\u2502 3 \u2502 21.4     \u2502 6     \u2502 258.0    \u2502 110   \u2502 3.08     \u2502 3.215    \u2502      \u2506\n\u2502 4 \u2502 18.7     \u2502 8     \u2502 360.0    \u2502 175   \u2502 3.15     \u2502 3.44     \u2502      \u2506\n\u2514\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u254c\u254c\u254c\u254c\u254c\u254c\u2518\n\n\/\/ tail\n\/\/ R: tail(xdf)\nprint(xdf.suffix(5).description(options: dOpts))\n\n\u250f\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u254d\u254d\u254d\u254d\u254d\u254d\u2513\n\u2503    \u2503 mpg      \u2503 cyl   \u2503 disp     \u2503 hp    \u2503 drat     \u2503 wt       \u2503 5    \u2507\n\u2503    \u2503 &lt;Double&gt; \u2503 &lt;Int&gt; \u2503 &lt;Double&gt; \u2503 &lt;Int&gt; \u2503 &lt;Double&gt; \u2503 &lt;Double&gt; \u2503 more \u2507\n\u2521\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u254d\u254d\u254d\u254d\u254d\u254d\u2529\n\u2502 27 \u2502 30.4     \u2502 4     \u2502 95.1     \u2502 113   \u2502 3.77     \u2502 1.513    \u2502      \u2506\n\u2502 28 \u2502 15.8     \u2502 8     \u2502 351.0    \u2502 264   \u2502 4.22     \u2502 3.17     \u2502      \u2506\n\u2502 29 \u2502 19.7     \u2502 6     \u2502 145.0    \u2502 175   \u2502 3.62     \u2502 2.77     \u2502      \u2506\n\u2502 30 \u2502 15.0     \u2502 8     \u2502 301.0    \u2502 335   \u2502 3.54     \u2502 3.57     \u2502      \u2506\n\u2502 31 \u2502 21.4     \u2502 4     \u2502 121.0    \u2502 109   \u2502 4.11     \u2502 2.78     \u2502      \u2506\n\u2514\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u254c\u254c\u254c\u254c\u254c\u254c\u2518\n\n\/\/ column summaries\n\/\/ summary(xdf)\nprint(xdf.summaryOfAllColumns().description(options: dOpts))\n\n\u250f\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u254d\u254d\u254d\u254d\u254d\u254d\u2513\n\u2503   \u2503 count(mpg) \u2503 uniqueCou\u2026 \u2503 top(mpg) \u2503 topFreque\u2026 \u2503 count(cyl) \u2503 39   \u2507\n\u2503   \u2503 &lt;Int&gt;      \u2503 &lt;Int&gt;      \u2503 &lt;Double&gt; \u2503 &lt;Int&gt;      \u2503 &lt;Int&gt;      \u2503 more \u2507\n\u2521\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u254d\u254d\u254d\u254d\u254d\u254d\u2529\n\u2502 0 \u2502 32         \u2502 25         \u2502 21.4     \u2502 2          \u2502 32         \u2502      \u2506\n\u2514\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u254c\u254c\u254c\u254c\u254c\u254c\u2518\n\n\/\/ sort it\n\/\/ R: library(tidyverse) # assume this going forward for R examples\n\/\/ R: arrange(xdf, cyl)\nxdf.sort(on: \"cyl\")\n\nprint(xdf.description(options: dOpts))\n\n\u250f\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u254d\u254d\u254d\u254d\u254d\u254d\u2513\n\u2503    \u2503 mpg      \u2503 cyl   \u2503 disp     \u2503 hp    \u2503 drat     \u2503 wt       \u2503 5    \u2507\n\u2503    \u2503 &lt;Double&gt; \u2503 &lt;Int&gt; \u2503 &lt;Double&gt; \u2503 &lt;Int&gt; \u2503 &lt;Double&gt; \u2503 &lt;Double&gt; \u2503 more \u2507\n\u2521\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u254d\u254d\u254d\u254d\u254d\u254d\u2529\n\u2502 0  \u2502 22.8     \u2502 4     \u2502 108.0    \u2502 93    \u2502 3.85     \u2502 2.32     \u2502      \u2506\n\u2502 1  \u2502 24.4     \u2502 4     \u2502 146.7    \u2502 62    \u2502 3.69     \u2502 3.19     \u2502      \u2506\n\u2502 2  \u2502 22.8     \u2502 4     \u2502 140.8    \u2502 95    \u2502 3.92     \u2502 3.15     \u2502      \u2506\n\u2502 3  \u2502 32.4     \u2502 4     \u2502 78.7     \u2502 66    \u2502 4.08     \u2502 2.2      \u2502      \u2506\n\u2502 4  \u2502 30.4     \u2502 4     \u2502 75.7     \u2502 52    \u2502 4.93     \u2502 1.615    \u2502      \u2506\n\u2502 5  \u2502 33.9     \u2502 4     \u2502 71.1     \u2502 65    \u2502 4.22     \u2502 1.835    \u2502      \u2506\n\u2502 6  \u2502 21.5     \u2502 4     \u2502 120.1    \u2502 97    \u2502 3.7      \u2502 2.465    \u2502      \u2506\n\u2502 7  \u2502 27.3     \u2502 4     \u2502 79.0     \u2502 66    \u2502 4.08     \u2502 1.935    \u2502      \u2506\n\u2502 8  \u2502 26.0     \u2502 4     \u2502 120.3    \u2502 91    \u2502 4.43     \u2502 2.14     \u2502      \u2506\n\u2502 9  \u2502 30.4     \u2502 4     \u2502 95.1     \u2502 113   \u2502 3.77     \u2502 1.513    \u2502      \u2506\n\u2502 10 \u2502 21.4     \u2502 4     \u2502 121.0    \u2502 109   \u2502 4.11     \u2502 2.78     \u2502      \u2506\n\u2502 11 \u2502 21.0     \u2502 6     \u2502 160.0    \u2502 110   \u2502 3.9      \u2502 2.62     \u2502      \u2506\n\u2502 12 \u2502 21.0     \u2502 6     \u2502 160.0    \u2502 110   \u2502 3.9      \u2502 2.875    \u2502      \u2506\n\u2502 13 \u2502 21.4     \u2502 6     \u2502 258.0    \u2502 110   \u2502 3.08     \u2502 3.215    \u2502      \u2506\n\u2502 14 \u2502 18.1     \u2502 6     \u2502 225.0    \u2502 105   \u2502 2.76     \u2502 3.46     \u2502      \u2506\n\u2502 15 \u2502 19.2     \u2502 6     \u2502 167.6    \u2502 123   \u2502 3.92     \u2502 3.44     \u2502      \u2506\n\u2502 16 \u2502 17.8     \u2502 6     \u2502 167.6    \u2502 123   \u2502 3.92     \u2502 3.44     \u2502      \u2506\n\u2502 17 \u2502 19.7     \u2502 6     \u2502 145.0    \u2502 175   \u2502 3.62     \u2502 2.77     \u2502      \u2506\n\u2502 18 \u2502 18.7     \u2502 8     \u2502 360.0    \u2502 175   \u2502 3.15     \u2502 3.44     \u2502      \u2506\n\u2502 19 \u2502 14.3     \u2502 8     \u2502 360.0    \u2502 245   \u2502 3.21     \u2502 3.57     \u2502      \u2506\n\u2522\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u252a\n\u2507 12 more                                                               \u2507\n\u2517\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u251b\n\n\/\/ read in a JSON File\n\/\/ R: xdf2 &lt;- jsonlite::fromJSON(\"mtcars.json\")\nvar xdf2 = try! DataFrame.init(contentsOfJSONFile: URL(fileURLWithPath: \"mtcars.json\"))\n\n\/\/ bind the rows together\n\/\/ R: xdf &lt;- bind_rows(xdf, xdf2)\nxdf.append(xdf2)\n\n\/\/ get the new summary\n\/\/ R: summary(xdf)\nprint(xdf.summaryOfAllColumns().description(options: dOpts))\n\n\u250f\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u254d\u254d\u254d\u254d\u254d\u254d\u2513\n\u2503   \u2503 count(mpg) \u2503 uniqueCou\u2026 \u2503 top(mpg) \u2503 topFreque\u2026 \u2503 count(cyl) \u2503 39   \u2507\n\u2503   \u2503 &lt;Int&gt;      \u2503 &lt;Int&gt;      \u2503 &lt;Double&gt; \u2503 &lt;Int&gt;      \u2503 &lt;Int&gt;      \u2503 more \u2507\n\u2521\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u254d\u254d\u254d\u254d\u254d\u254d\u2529\n\u2502 0 \u2502 64         \u2502 25         \u2502 21.4     \u2502 4          \u2502 64         \u2502      \u2506\n\u2514\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u254c\u254c\u254c\u254c\u254c\u254c\u2518\n\n\/\/ basic filtering\n\/\/ R: xdf.filter(cyl == 6)\nprint( xdf.filter(on: \"cyl\", Int.self) { (val) in val == 6 } )\n\n\u250f\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u254d\u254d\u254d\u254d\u254d\u254d\u2513\n\u2503    \u2503 mpg      \u2503 cyl   \u2503 disp     \u2503 hp    \u2503 drat     \u2503 wt       \u2503 5    \u2507\n\u2503    \u2503 &lt;Double&gt; \u2503 &lt;Int&gt; \u2503 &lt;Double&gt; \u2503 &lt;Int&gt; \u2503 &lt;Double&gt; \u2503 &lt;Double&gt; \u2503 more \u2507\n\u2521\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u254d\u254d\u254d\u254d\u254d\u254d\u2529\n\u2502 11 \u2502 21.0     \u2502 6     \u2502 160.0    \u2502 110   \u2502 3.9      \u2502 2.62     \u2502      \u2506\n\u2502 12 \u2502 21.0     \u2502 6     \u2502 160.0    \u2502 110   \u2502 3.9      \u2502 2.875    \u2502      \u2506\n\u2502 13 \u2502 21.4     \u2502 6     \u2502 258.0    \u2502 110   \u2502 3.08     \u2502 3.215    \u2502      \u2506\n\u2502 14 \u2502 18.1     \u2502 6     \u2502 225.0    \u2502 105   \u2502 2.76     \u2502 3.46     \u2502      \u2506\n\u2502 15 \u2502 19.2     \u2502 6     \u2502 167.6    \u2502 123   \u2502 3.92     \u2502 3.44     \u2502      \u2506\n\u2502 16 \u2502 17.8     \u2502 6     \u2502 167.6    \u2502 123   \u2502 3.92     \u2502 3.44     \u2502      \u2506\n\u2502 17 \u2502 19.7     \u2502 6     \u2502 145.0    \u2502 175   \u2502 3.62     \u2502 2.77     \u2502      \u2506\n\u2502 32 \u2502 21.0     \u2502 6     \u2502 160.0    \u2502 110   \u2502 3.9      \u2502 2.62     \u2502      \u2506\n\u2502 33 \u2502 21.0     \u2502 6     \u2502 160.0    \u2502 110   \u2502 3.9      \u2502 2.875    \u2502      \u2506\n\u2502 35 \u2502 21.4     \u2502 6     \u2502 258.0    \u2502 110   \u2502 3.08     \u2502 3.215    \u2502      \u2506\n\u2522\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u252a\n\u2507 4 more                                                                \u2507\n\u2517\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u251b\n\n\/\/ group by a column\n\/\/ R: group_by(xdf, cyl)\nprint(xdf.grouped(by: \"cyl\"))\n\n4\n\u250f\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u254d\u254d\u254d\u254d\u254d\u254d\u2513\n\u2503   \u2503 mpg      \u2503 cyl   \u2503 disp     \u2503 hp    \u2503 drat     \u2503 wt       \u2503 5    \u2507\n\u2503   \u2503 &lt;Double&gt; \u2503 &lt;Int&gt; \u2503 &lt;Double&gt; \u2503 &lt;Int&gt; \u2503 &lt;Double&gt; \u2503 &lt;Double&gt; \u2503 more \u2507\n\u2521\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u254d\u254d\u254d\u254d\u254d\u254d\u2529\n\u2502 0 \u2502 22.8     \u2502 4     \u2502 108.0    \u2502 93    \u2502 3.85     \u2502 2.32     \u2502      \u2506\n\u2502 1 \u2502 24.4     \u2502 4     \u2502 146.7    \u2502 62    \u2502 3.69     \u2502 3.19     \u2502      \u2506\n\u2502 2 \u2502 22.8     \u2502 4     \u2502 140.8    \u2502 95    \u2502 3.92     \u2502 3.15     \u2502      \u2506\n\u2502 3 \u2502 32.4     \u2502 4     \u2502 78.7     \u2502 66    \u2502 4.08     \u2502 2.2      \u2502      \u2506\n\u2502 4 \u2502 30.4     \u2502 4     \u2502 75.7     \u2502 52    \u2502 4.93     \u2502 1.615    \u2502      \u2506\n\u2502 5 \u2502 33.9     \u2502 4     \u2502 71.1     \u2502 65    \u2502 4.22     \u2502 1.835    \u2502      \u2506\n\u2502 6 \u2502 21.5     \u2502 4     \u2502 120.1    \u2502 97    \u2502 3.7      \u2502 2.465    \u2502      \u2506\n\u2502 7 \u2502 27.3     \u2502 4     \u2502 79.0     \u2502 66    \u2502 4.08     \u2502 1.935    \u2502      \u2506\n\u2502 8 \u2502 26.0     \u2502 4     \u2502 120.3    \u2502 91    \u2502 4.43     \u2502 2.14     \u2502      \u2506\n\u2502 9 \u2502 30.4     \u2502 4     \u2502 95.1     \u2502 113   \u2502 3.77     \u2502 1.513    \u2502      \u2506\n\u2522\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u252a\n\u2507 12 more                                                              \u2507\n\u2517\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u251b\n\n6\n\u250f\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u254d\u254d\u254d\u254d\u254d\u254d\u2513\n\u2503    \u2503 mpg      \u2503 cyl   \u2503 disp     \u2503 hp    \u2503 drat     \u2503 wt       \u2503 5    \u2507\n\u2503    \u2503 &lt;Double&gt; \u2503 &lt;Int&gt; \u2503 &lt;Double&gt; \u2503 &lt;Int&gt; \u2503 &lt;Double&gt; \u2503 &lt;Double&gt; \u2503 more \u2507\n\u2521\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u254d\u254d\u254d\u254d\u254d\u254d\u2529\n\u2502 11 \u2502 21.0     \u2502 6     \u2502 160.0    \u2502 110   \u2502 3.9      \u2502 2.62     \u2502      \u2506\n\u2502 12 \u2502 21.0     \u2502 6     \u2502 160.0    \u2502 110   \u2502 3.9      \u2502 2.875    \u2502      \u2506\n\u2502 13 \u2502 21.4     \u2502 6     \u2502 258.0    \u2502 110   \u2502 3.08     \u2502 3.215    \u2502      \u2506\n\u2502 14 \u2502 18.1     \u2502 6     \u2502 225.0    \u2502 105   \u2502 2.76     \u2502 3.46     \u2502      \u2506\n\u2502 15 \u2502 19.2     \u2502 6     \u2502 167.6    \u2502 123   \u2502 3.92     \u2502 3.44     \u2502      \u2506\n\u2502 16 \u2502 17.8     \u2502 6     \u2502 167.6    \u2502 123   \u2502 3.92     \u2502 3.44     \u2502      \u2506\n\u2502 17 \u2502 19.7     \u2502 6     \u2502 145.0    \u2502 175   \u2502 3.62     \u2502 2.77     \u2502      \u2506\n\u2502 32 \u2502 21.0     \u2502 6     \u2502 160.0    \u2502 110   \u2502 3.9      \u2502 2.62     \u2502      \u2506\n\u2502 33 \u2502 21.0     \u2502 6     \u2502 160.0    \u2502 110   \u2502 3.9      \u2502 2.875    \u2502      \u2506\n\u2502 35 \u2502 21.4     \u2502 6     \u2502 258.0    \u2502 110   \u2502 3.08     \u2502 3.215    \u2502      \u2506\n\u2522\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u252a\n\u2507 4 more                                                                \u2507\n\u2517\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u251b\n\n8\n\u250f\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u254d\u254d\u254d\u254d\u254d\u254d\u2513\n\u2503    \u2503 mpg      \u2503 cyl   \u2503 disp     \u2503 hp    \u2503 drat     \u2503 wt       \u2503 5    \u2507\n\u2503    \u2503 &lt;Double&gt; \u2503 &lt;Int&gt; \u2503 &lt;Double&gt; \u2503 &lt;Int&gt; \u2503 &lt;Double&gt; \u2503 &lt;Double&gt; \u2503 more \u2507\n\u2521\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u254d\u254d\u254d\u254d\u254d\u254d\u2529\n\u2502 18 \u2502 18.7     \u2502 8     \u2502 360.0    \u2502 175   \u2502 3.15     \u2502 3.44     \u2502      \u2506\n\u2502 19 \u2502 14.3     \u2502 8     \u2502 360.0    \u2502 245   \u2502 3.21     \u2502 3.57     \u2502      \u2506\n\u2502 20 \u2502 16.4     \u2502 8     \u2502 275.8    \u2502 180   \u2502 3.07     \u2502 4.07     \u2502      \u2506\n\u2502 21 \u2502 17.3     \u2502 8     \u2502 275.8    \u2502 180   \u2502 3.07     \u2502 3.73     \u2502      \u2506\n\u2502 22 \u2502 15.2     \u2502 8     \u2502 275.8    \u2502 180   \u2502 3.07     \u2502 3.78     \u2502      \u2506\n\u2502 23 \u2502 10.4     \u2502 8     \u2502 472.0    \u2502 205   \u2502 2.93     \u2502 5.25     \u2502      \u2506\n\u2502 24 \u2502 10.4     \u2502 8     \u2502 460.0    \u2502 215   \u2502 3.0      \u2502 5.424    \u2502      \u2506\n\u2502 25 \u2502 14.7     \u2502 8     \u2502 440.0    \u2502 230   \u2502 3.23     \u2502 5.345    \u2502      \u2506\n\u2502 26 \u2502 15.5     \u2502 8     \u2502 318.0    \u2502 150   \u2502 2.76     \u2502 3.52     \u2502      \u2506\n\u2502 27 \u2502 15.2     \u2502 8     \u2502 304.0    \u2502 150   \u2502 3.15     \u2502 3.435    \u2502      \u2506\n\u2522\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u252a\n\u2507 18 more                                                               \u2507\n\u2517\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u251b\n\n\/\/ number of groups\n\/\/ R: group_by(xdf, cyl) %&gt;% group_keys() %&gt;% nrow()\nprint(xdf.grouped(by: \"cyl\").count)\n\n3\n\n\/\/ group, manipulate (in this case, filter), and re-combine\n\/\/ R: group_by(xdf) %&gt;% filter(mpg &lt; 20) %&gt;% ungroup()\nprint(\n  xdf.grouped(by: \"cyl\").mapGroups { (val) in\n    val.filter(on: \"mpg\", Double.self) { (val) in val! &lt; 20 }.base\n  }.ungrouped()\n)\n\n\u250f\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u254d\u254d\u254d\u254d\u254d\u254d\u2513\n\u2503   \u2503 mpg      \u2503 disp     \u2503 hp    \u2503 drat     \u2503 wt       \u2503 qsec     \u2503 5    \u2507\n\u2503   \u2503 &lt;Double&gt; \u2503 &lt;Double&gt; \u2503 &lt;Int&gt; \u2503 &lt;Double&gt; \u2503 &lt;Double&gt; \u2503 &lt;Double&gt; \u2503 more \u2507\n\u2521\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u254d\u254d\u254d\u254d\u254d\u254d\u2529\n\u2502 0 \u2502 22.8     \u2502 108.0    \u2502 93    \u2502 3.85     \u2502 2.32     \u2502 18.61    \u2502      \u2506\n\u2502 1 \u2502 24.4     \u2502 146.7    \u2502 62    \u2502 3.69     \u2502 3.19     \u2502 20.0     \u2502      \u2506\n\u2502 2 \u2502 22.8     \u2502 140.8    \u2502 95    \u2502 3.92     \u2502 3.15     \u2502 22.9     \u2502      \u2506\n\u2502 3 \u2502 32.4     \u2502 78.7     \u2502 66    \u2502 4.08     \u2502 2.2      \u2502 19.47    \u2502      \u2506\n\u2502 4 \u2502 30.4     \u2502 75.7     \u2502 52    \u2502 4.93     \u2502 1.615    \u2502 18.52    \u2502      \u2506\n\u2502 5 \u2502 33.9     \u2502 71.1     \u2502 65    \u2502 4.22     \u2502 1.835    \u2502 19.9     \u2502      \u2506\n\u2502 6 \u2502 21.5     \u2502 120.1    \u2502 97    \u2502 3.7      \u2502 2.465    \u2502 20.01    \u2502      \u2506\n\u2502 7 \u2502 27.3     \u2502 79.0     \u2502 66    \u2502 4.08     \u2502 1.935    \u2502 18.9     \u2502      \u2506\n\u2502 8 \u2502 26.0     \u2502 120.3    \u2502 91    \u2502 4.43     \u2502 2.14     \u2502 16.7     \u2502      \u2506\n\u2502 9 \u2502 30.4     \u2502 95.1     \u2502 113   \u2502 3.77     \u2502 1.513    \u2502 16.9     \u2502      \u2506\n\u2522\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u252a\n\u2507 182 more                                                                \u2507\n\u2517\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u251b\n\n\/\/ look at one column\n\/\/ R: xdf$cyl\nprint( xdf[\"cyl\"] )\n\n\u250f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2513\n\u2503 cyl   \u2503\n\u2503 &lt;Int&gt; \u2503\n\u2521\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2529\n\u2502 4     \u2502\n\u2502 4     \u2502\n\u2502 4     \u2502\n\u2502 4     \u2502\n\u2502 4     \u2502\n\u2502 4     \u2502\n\u2502 4     \u2502\n\u2502 4     \u2502\n\u2502 4     \u2502\n\u2502 4     \u2502\n\u2522\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u252a\n\u2507 54 m\u2026 \u2507\n\u2517\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u251b\n\n\/\/ combine two columns and look at it\n\/\/ R: mutate(xdf, cyl_mpg = sprintf(\"%s:%s\", cyl, mpg) %&gt;% select(-cyl, -mpg)\n\/\/ R: unite(xdf, cyl_mpg, cyl, mpg, sep = \":\") # alternate way\nxdf.combineColumns(\"cyl\", \"mpg\", into: \"cyl_mpg\") { (val1: Int?, val2: Double?) -&gt; String in\n  String(val1 ?? 0) + \":\" + String(val2 ?? 0.0)\n}\n\nprint(xdf[\"cyl_mpg\"])\n\n\u250f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2513\n\u2503 cyl_mpg  \u2503\n\u2503 &lt;String&gt; \u2503\n\u2521\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2529\n\u2502 4:22.8   \u2502\n\u2502 4:24.4   \u2502\n\u2502 4:22.8   \u2502\n\u2502 4:32.4   \u2502\n\u2502 4:30.4   \u2502\n\u2502 4:33.9   \u2502\n\u2502 4:21.5   \u2502\n\u2502 4:27.3   \u2502\n\u2502 4:26.0   \u2502\n\u2502 4:30.4   \u2502\n\u2522\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u252a\n\u2507 54 more  \u2507\n\u2517\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u251b\n\n\/\/ look at the colnames (^^ removes \"cyl\" and \"mpg\"\n\/\/ R: colnames(xdf)\nprint(xdf.columns.map{ col in col.name })\n\n[\"cyl_mpg\", \"disp\", \"hp\", \"drat\", \"wt\", \"qsec\", \"vs\", \"am\", \"gear\", \"carb\"]\n\n\/\/ turn an Int into a Double\n\/\/ R: xdf$hp &lt;- as.double(xdf$hp) # or use dplyr::mutate()\nxdf.transformColumn(\"hp\") { (val1: Int?) -&gt; Double? in\n  Double(val1 ?? 0)\n}\n\nprint(xdf[\"hp\"])\n\n\u250f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2513\n\u2503 hp       \u2503\n\u2503 &lt;Double&gt; \u2503\n\u2521\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2529\n\u2502 93.0     \u2502\n\u2502 62.0     \u2502\n\u2502 95.0     \u2502\n\u2502 66.0     \u2502\n\u2502 52.0     \u2502\n\u2502 65.0     \u2502\n\u2502 97.0     \u2502\n\u2502 66.0     \u2502\n\u2502 91.0     \u2502\n\u2502 113.0    \u2502\n\u2522\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u252a\n\u2507 54 more  \u2507\n\u2517\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u251b\n\n\/\/ look at the coltypes\n\/\/ R: sapply(mtcars, typeof)\nprint(xdf.columns.map{ col in col.wrappedElementType })\n\n[Swift.String, Swift.Double, Swift.Double, Swift.Double, Swift.Double, Swift.Double, Swift.Int, Swift.Int, Swift.Int, Swift.Int]\n\n\/\/ distinct horsepower\n\/\/ R: distinct(xdf, hp)\nprint(xdf[\"hp\"].distinct())\n\n\u250f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2513\n\u2503 hp       \u2503\n\u2503 &lt;Double&gt; \u2503\n\u2521\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2529\n\u2502 93.0     \u2502\n\u2502 62.0     \u2502\n\u2502 95.0     \u2502\n\u2502 66.0     \u2502\n\u2502 52.0     \u2502\n\u2502 65.0     \u2502\n\u2502 97.0     \u2502\n\u2502 91.0     \u2502\n\u2502 113.0    \u2502\n\u2502 109.0    \u2502\n\u2522\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u252a\n\u2507 12 more  \u2507\n\u2517\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u251b\n\n\/\/ row slices\n\/\/ R: xdf[10,]\nprint(xdf.rows[10])\n\n\u250f\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u254d\u254d\u254d\u254d\u254d\u254d\u2513\n\u2503    \u2503 cyl_mpg  \u2503 disp     \u2503 hp       \u2503 drat     \u2503 wt       \u2503 qsec     \u2503 4    \u2507\n\u2503    \u2503 &lt;String&gt; \u2503 &lt;Double&gt; \u2503 &lt;Double&gt; \u2503 &lt;Double&gt; \u2503 &lt;Double&gt; \u2503 &lt;Double&gt; \u2503 more \u2507\n\u2521\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u254d\u254d\u254d\u254d\u254d\u254d\u2529\n\u2502 10 \u2502 4:21.4   \u2502 121.0    \u2502 109.0    \u2502 4.11     \u2502 2.78     \u2502 18.6     \u2502      \u2506\n\u2514\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u254c\u254c\u254c\u254c\u254c\u254c\u2518\n\n\/\/ R: xdf[3:10,]\nprint(xdf.rows[3...10])\n\nRows(base: \n\u250f\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u254d\u254d\u254d\u254d\u254d\u254d\u2513\n\u2503   \u2503 cyl_mpg  \u2503 disp     \u2503 hp       \u2503 drat     \u2503 wt       \u2503 qsec     \u2503 4    \u2507\n\u2503   \u2503 &lt;String&gt; \u2503 &lt;Double&gt; \u2503 &lt;Double&gt; \u2503 &lt;Double&gt; \u2503 &lt;Double&gt; \u2503 &lt;Double&gt; \u2503 more \u2507\n\u2521\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u254d\u254d\u254d\u254d\u254d\u254d\u2529\n\u2502 0 \u2502 4:22.8   \u2502 108.0    \u2502 93.0     \u2502 3.85     \u2502 2.32     \u2502 18.61    \u2502      \u2506\n\u2502 1 \u2502 4:24.4   \u2502 146.7    \u2502 62.0     \u2502 3.69     \u2502 3.19     \u2502 20.0     \u2502      \u2506\n\u2502 2 \u2502 4:22.8   \u2502 140.8    \u2502 95.0     \u2502 3.92     \u2502 3.15     \u2502 22.9     \u2502      \u2506\n\u2502 3 \u2502 4:32.4   \u2502 78.7     \u2502 66.0     \u2502 4.08     \u2502 2.2      \u2502 19.47    \u2502      \u2506\n\u2502 4 \u2502 4:30.4   \u2502 75.7     \u2502 52.0     \u2502 4.93     \u2502 1.615    \u2502 18.52    \u2502      \u2506\n\u2502 5 \u2502 4:33.9   \u2502 71.1     \u2502 65.0     \u2502 4.22     \u2502 1.835    \u2502 19.9     \u2502      \u2506\n\u2502 6 \u2502 4:21.5   \u2502 120.1    \u2502 97.0     \u2502 3.7      \u2502 2.465    \u2502 20.01    \u2502      \u2506\n\u2502 7 \u2502 4:27.3   \u2502 79.0     \u2502 66.0     \u2502 4.08     \u2502 1.935    \u2502 18.9     \u2502      \u2506\n\u2502 8 \u2502 4:26.0   \u2502 120.3    \u2502 91.0     \u2502 4.43     \u2502 2.14     \u2502 16.7     \u2502      \u2506\n\u2502 9 \u2502 4:30.4   \u2502 95.1     \u2502 113.0    \u2502 3.77     \u2502 1.513    \u2502 16.9     \u2502      \u2506\n\u2522\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u2537\u254d\u254d\u254d\u254d\u254d\u254d\u252a\n\u2507 54 more                                                                    \u2507\n\u2517\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u254d\u251b\n, subranges: _RangeSet(3..&lt;11))\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>WWDC 2021 is on this week and many new fun things are being introduced, including some data science-friendly additions to the frameworks that come with Xcode 13 and available on macOS 12+ (and its *OS cousins). Specifically, Apple has made tabular data a first-class citizen with the new TabularData app service. A future post will [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"activitypub_content_warning":"","activitypub_content_visibility":"","activitypub_max_image_attachments":3,"activitypub_interaction_policy_quote":"anyone","activitypub_status":"","footnotes":""},"categories":[663,780,91,830],"tags":[],"class_list":["post-13109","post","type-post","status-publish","format-standard","hentry","category-apple","category-macos","category-r","category-swift"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>New TabularData Available in Swift on macOS Monterery (et al) - rud.is<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/rud.is\/b\/2021\/06\/08\/new-tabulardata-available-in-swift-on-macos-monterery-et-al\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"New TabularData Available in Swift on macOS Monterery (et al) - rud.is\" \/>\n<meta property=\"og:description\" content=\"WWDC 2021 is on this week and many new fun things are being introduced, including some data science-friendly additions to the frameworks that come with Xcode 13 and available on macOS 12+ (and its *OS cousins). 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Specifically, Apple has made tabular data a first-class citizen with the new TabularData app service. 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