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 have some more expository, but here’s a sample of core operations including:
- reading in tabular data from CSV or JSON
- examining the structure
- working with columns and/or rows
- grouping and filtering operations
- transforming and removing columns
I’ve tagged this with rstats as there are R equivalents included for each operation so R folks can translate any Swift code they see in the future.
import TabularData
// define some basic formatting options for data frame output
let dOpts = FormattingOptions(maximumLineWidth: 80, maximumCellWidth: 10, maximumRowCount: 20, includesColumnTypes: true)
// read in a CSV file
// R: xdf <- read.csv("mtcars.csv")
var xdf = try! DataFrame.init(contentsOfCSVFile: URL(fileURLWithPath: "mtcars.csv"))
// take a look at it
// R: print(xdf) # no more print() in further R equivalents; just assume interactive or wrap with print
print(xdf.description(options: dOpts))
┏━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃ ┃ mpg ┃ cyl ┃ disp ┃ hp ┃ drat ┃ wt ┃ 5 ┇
┃ ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 0 │ 21.0 │ 6 │ 160.0 │ 110 │ 3.9 │ 2.62 │ ┆
│ 1 │ 21.0 │ 6 │ 160.0 │ 110 │ 3.9 │ 2.875 │ ┆
│ 2 │ 22.8 │ 4 │ 108.0 │ 93 │ 3.85 │ 2.32 │ ┆
│ 3 │ 21.4 │ 6 │ 258.0 │ 110 │ 3.08 │ 3.215 │ ┆
│ 4 │ 18.7 │ 8 │ 360.0 │ 175 │ 3.15 │ 3.44 │ ┆
│ 5 │ 18.1 │ 6 │ 225.0 │ 105 │ 2.76 │ 3.46 │ ┆
│ 6 │ 14.3 │ 8 │ 360.0 │ 245 │ 3.21 │ 3.57 │ ┆
│ 7 │ 24.4 │ 4 │ 146.7 │ 62 │ 3.69 │ 3.19 │ ┆
│ 8 │ 22.8 │ 4 │ 140.8 │ 95 │ 3.92 │ 3.15 │ ┆
│ 9 │ 19.2 │ 6 │ 167.6 │ 123 │ 3.92 │ 3.44 │ ┆
│ 10 │ 17.8 │ 6 │ 167.6 │ 123 │ 3.92 │ 3.44 │ ┆
│ 11 │ 16.4 │ 8 │ 275.8 │ 180 │ 3.07 │ 4.07 │ ┆
│ 12 │ 17.3 │ 8 │ 275.8 │ 180 │ 3.07 │ 3.73 │ ┆
│ 13 │ 15.2 │ 8 │ 275.8 │ 180 │ 3.07 │ 3.78 │ ┆
│ 14 │ 10.4 │ 8 │ 472.0 │ 205 │ 2.93 │ 5.25 │ ┆
│ 15 │ 10.4 │ 8 │ 460.0 │ 215 │ 3.0 │ 5.424 │ ┆
│ 16 │ 14.7 │ 8 │ 440.0 │ 230 │ 3.23 │ 5.345 │ ┆
│ 17 │ 32.4 │ 4 │ 78.7 │ 66 │ 4.08 │ 2.2 │ ┆
│ 18 │ 30.4 │ 4 │ 75.7 │ 52 │ 4.93 │ 1.615 │ ┆
│ 19 │ 33.9 │ 4 │ 71.1 │ 65 │ 4.22 │ 1.835 │ ┆
┢╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍┪
┇ 12 more ┇
┗╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍┛
// dimensions
// R: dim(xdf)
print(xdf.shape)
(rows: 32, columns: 11)
// head
// R: head(xdf)
print(xdf.prefix(5).description(options: dOpts))
┏━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃ ┃ mpg ┃ cyl ┃ disp ┃ hp ┃ drat ┃ wt ┃ 5 ┇
┃ ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 0 │ 21.0 │ 6 │ 160.0 │ 110 │ 3.9 │ 2.62 │ ┆
│ 1 │ 21.0 │ 6 │ 160.0 │ 110 │ 3.9 │ 2.875 │ ┆
│ 2 │ 22.8 │ 4 │ 108.0 │ 93 │ 3.85 │ 2.32 │ ┆
│ 3 │ 21.4 │ 6 │ 258.0 │ 110 │ 3.08 │ 3.215 │ ┆
│ 4 │ 18.7 │ 8 │ 360.0 │ 175 │ 3.15 │ 3.44 │ ┆
└───┴──────────┴───────┴──────────┴───────┴──────────┴──────────┴╌╌╌╌╌╌┘
// tail
// R: tail(xdf)
print(xdf.suffix(5).description(options: dOpts))
┏━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃ ┃ mpg ┃ cyl ┃ disp ┃ hp ┃ drat ┃ wt ┃ 5 ┇
┃ ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 27 │ 30.4 │ 4 │ 95.1 │ 113 │ 3.77 │ 1.513 │ ┆
│ 28 │ 15.8 │ 8 │ 351.0 │ 264 │ 4.22 │ 3.17 │ ┆
│ 29 │ 19.7 │ 6 │ 145.0 │ 175 │ 3.62 │ 2.77 │ ┆
│ 30 │ 15.0 │ 8 │ 301.0 │ 335 │ 3.54 │ 3.57 │ ┆
│ 31 │ 21.4 │ 4 │ 121.0 │ 109 │ 4.11 │ 2.78 │ ┆
└────┴──────────┴───────┴──────────┴───────┴──────────┴──────────┴╌╌╌╌╌╌┘
// column summaries
// summary(xdf)
print(xdf.summaryOfAllColumns().description(options: dOpts))
┏━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳╍╍╍╍╍╍┓
┃ ┃ count(mpg) ┃ uniqueCou… ┃ top(mpg) ┃ topFreque… ┃ count(cyl) ┃ 39 ┇
┃ ┃ <Int> ┃ <Int> ┃ <Double> ┃ <Int> ┃ <Int> ┃ more ┇
┡━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 0 │ 32 │ 25 │ 21.4 │ 2 │ 32 │ ┆
└───┴────────────┴────────────┴──────────┴────────────┴────────────┴╌╌╌╌╌╌┘
// sort it
// R: library(tidyverse) # assume this going forward for R examples
// R: arrange(xdf, cyl)
xdf.sort(on: "cyl")
print(xdf.description(options: dOpts))
┏━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃ ┃ mpg ┃ cyl ┃ disp ┃ hp ┃ drat ┃ wt ┃ 5 ┇
┃ ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 0 │ 22.8 │ 4 │ 108.0 │ 93 │ 3.85 │ 2.32 │ ┆
│ 1 │ 24.4 │ 4 │ 146.7 │ 62 │ 3.69 │ 3.19 │ ┆
│ 2 │ 22.8 │ 4 │ 140.8 │ 95 │ 3.92 │ 3.15 │ ┆
│ 3 │ 32.4 │ 4 │ 78.7 │ 66 │ 4.08 │ 2.2 │ ┆
│ 4 │ 30.4 │ 4 │ 75.7 │ 52 │ 4.93 │ 1.615 │ ┆
│ 5 │ 33.9 │ 4 │ 71.1 │ 65 │ 4.22 │ 1.835 │ ┆
│ 6 │ 21.5 │ 4 │ 120.1 │ 97 │ 3.7 │ 2.465 │ ┆
│ 7 │ 27.3 │ 4 │ 79.0 │ 66 │ 4.08 │ 1.935 │ ┆
│ 8 │ 26.0 │ 4 │ 120.3 │ 91 │ 4.43 │ 2.14 │ ┆
│ 9 │ 30.4 │ 4 │ 95.1 │ 113 │ 3.77 │ 1.513 │ ┆
│ 10 │ 21.4 │ 4 │ 121.0 │ 109 │ 4.11 │ 2.78 │ ┆
│ 11 │ 21.0 │ 6 │ 160.0 │ 110 │ 3.9 │ 2.62 │ ┆
│ 12 │ 21.0 │ 6 │ 160.0 │ 110 │ 3.9 │ 2.875 │ ┆
│ 13 │ 21.4 │ 6 │ 258.0 │ 110 │ 3.08 │ 3.215 │ ┆
│ 14 │ 18.1 │ 6 │ 225.0 │ 105 │ 2.76 │ 3.46 │ ┆
│ 15 │ 19.2 │ 6 │ 167.6 │ 123 │ 3.92 │ 3.44 │ ┆
│ 16 │ 17.8 │ 6 │ 167.6 │ 123 │ 3.92 │ 3.44 │ ┆
│ 17 │ 19.7 │ 6 │ 145.0 │ 175 │ 3.62 │ 2.77 │ ┆
│ 18 │ 18.7 │ 8 │ 360.0 │ 175 │ 3.15 │ 3.44 │ ┆
│ 19 │ 14.3 │ 8 │ 360.0 │ 245 │ 3.21 │ 3.57 │ ┆
┢╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍┪
┇ 12 more ┇
┗╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍┛
// read in a JSON File
// R: xdf2 <- jsonlite::fromJSON("mtcars.json")
var xdf2 = try! DataFrame.init(contentsOfJSONFile: URL(fileURLWithPath: "mtcars.json"))
// bind the rows together
// R: xdf <- bind_rows(xdf, xdf2)
xdf.append(xdf2)
// get the new summary
// R: summary(xdf)
print(xdf.summaryOfAllColumns().description(options: dOpts))
┏━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳╍╍╍╍╍╍┓
┃ ┃ count(mpg) ┃ uniqueCou… ┃ top(mpg) ┃ topFreque… ┃ count(cyl) ┃ 39 ┇
┃ ┃ <Int> ┃ <Int> ┃ <Double> ┃ <Int> ┃ <Int> ┃ more ┇
┡━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 0 │ 64 │ 25 │ 21.4 │ 4 │ 64 │ ┆
└───┴────────────┴────────────┴──────────┴────────────┴────────────┴╌╌╌╌╌╌┘
// basic filtering
// R: xdf.filter(cyl == 6)
print( xdf.filter(on: "cyl", Int.self) { (val) in val == 6 } )
┏━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃ ┃ mpg ┃ cyl ┃ disp ┃ hp ┃ drat ┃ wt ┃ 5 ┇
┃ ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 11 │ 21.0 │ 6 │ 160.0 │ 110 │ 3.9 │ 2.62 │ ┆
│ 12 │ 21.0 │ 6 │ 160.0 │ 110 │ 3.9 │ 2.875 │ ┆
│ 13 │ 21.4 │ 6 │ 258.0 │ 110 │ 3.08 │ 3.215 │ ┆
│ 14 │ 18.1 │ 6 │ 225.0 │ 105 │ 2.76 │ 3.46 │ ┆
│ 15 │ 19.2 │ 6 │ 167.6 │ 123 │ 3.92 │ 3.44 │ ┆
│ 16 │ 17.8 │ 6 │ 167.6 │ 123 │ 3.92 │ 3.44 │ ┆
│ 17 │ 19.7 │ 6 │ 145.0 │ 175 │ 3.62 │ 2.77 │ ┆
│ 32 │ 21.0 │ 6 │ 160.0 │ 110 │ 3.9 │ 2.62 │ ┆
│ 33 │ 21.0 │ 6 │ 160.0 │ 110 │ 3.9 │ 2.875 │ ┆
│ 35 │ 21.4 │ 6 │ 258.0 │ 110 │ 3.08 │ 3.215 │ ┆
┢╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍┪
┇ 4 more ┇
┗╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍┛
// group by a column
// R: group_by(xdf, cyl)
print(xdf.grouped(by: "cyl"))
4
┏━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃ ┃ mpg ┃ cyl ┃ disp ┃ hp ┃ drat ┃ wt ┃ 5 ┇
┃ ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 0 │ 22.8 │ 4 │ 108.0 │ 93 │ 3.85 │ 2.32 │ ┆
│ 1 │ 24.4 │ 4 │ 146.7 │ 62 │ 3.69 │ 3.19 │ ┆
│ 2 │ 22.8 │ 4 │ 140.8 │ 95 │ 3.92 │ 3.15 │ ┆
│ 3 │ 32.4 │ 4 │ 78.7 │ 66 │ 4.08 │ 2.2 │ ┆
│ 4 │ 30.4 │ 4 │ 75.7 │ 52 │ 4.93 │ 1.615 │ ┆
│ 5 │ 33.9 │ 4 │ 71.1 │ 65 │ 4.22 │ 1.835 │ ┆
│ 6 │ 21.5 │ 4 │ 120.1 │ 97 │ 3.7 │ 2.465 │ ┆
│ 7 │ 27.3 │ 4 │ 79.0 │ 66 │ 4.08 │ 1.935 │ ┆
│ 8 │ 26.0 │ 4 │ 120.3 │ 91 │ 4.43 │ 2.14 │ ┆
│ 9 │ 30.4 │ 4 │ 95.1 │ 113 │ 3.77 │ 1.513 │ ┆
┢╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍┪
┇ 12 more ┇
┗╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍┛
6
┏━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃ ┃ mpg ┃ cyl ┃ disp ┃ hp ┃ drat ┃ wt ┃ 5 ┇
┃ ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 11 │ 21.0 │ 6 │ 160.0 │ 110 │ 3.9 │ 2.62 │ ┆
│ 12 │ 21.0 │ 6 │ 160.0 │ 110 │ 3.9 │ 2.875 │ ┆
│ 13 │ 21.4 │ 6 │ 258.0 │ 110 │ 3.08 │ 3.215 │ ┆
│ 14 │ 18.1 │ 6 │ 225.0 │ 105 │ 2.76 │ 3.46 │ ┆
│ 15 │ 19.2 │ 6 │ 167.6 │ 123 │ 3.92 │ 3.44 │ ┆
│ 16 │ 17.8 │ 6 │ 167.6 │ 123 │ 3.92 │ 3.44 │ ┆
│ 17 │ 19.7 │ 6 │ 145.0 │ 175 │ 3.62 │ 2.77 │ ┆
│ 32 │ 21.0 │ 6 │ 160.0 │ 110 │ 3.9 │ 2.62 │ ┆
│ 33 │ 21.0 │ 6 │ 160.0 │ 110 │ 3.9 │ 2.875 │ ┆
│ 35 │ 21.4 │ 6 │ 258.0 │ 110 │ 3.08 │ 3.215 │ ┆
┢╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍┪
┇ 4 more ┇
┗╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍┛
8
┏━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃ ┃ mpg ┃ cyl ┃ disp ┃ hp ┃ drat ┃ wt ┃ 5 ┇
┃ ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 18 │ 18.7 │ 8 │ 360.0 │ 175 │ 3.15 │ 3.44 │ ┆
│ 19 │ 14.3 │ 8 │ 360.0 │ 245 │ 3.21 │ 3.57 │ ┆
│ 20 │ 16.4 │ 8 │ 275.8 │ 180 │ 3.07 │ 4.07 │ ┆
│ 21 │ 17.3 │ 8 │ 275.8 │ 180 │ 3.07 │ 3.73 │ ┆
│ 22 │ 15.2 │ 8 │ 275.8 │ 180 │ 3.07 │ 3.78 │ ┆
│ 23 │ 10.4 │ 8 │ 472.0 │ 205 │ 2.93 │ 5.25 │ ┆
│ 24 │ 10.4 │ 8 │ 460.0 │ 215 │ 3.0 │ 5.424 │ ┆
│ 25 │ 14.7 │ 8 │ 440.0 │ 230 │ 3.23 │ 5.345 │ ┆
│ 26 │ 15.5 │ 8 │ 318.0 │ 150 │ 2.76 │ 3.52 │ ┆
│ 27 │ 15.2 │ 8 │ 304.0 │ 150 │ 3.15 │ 3.435 │ ┆
┢╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍┪
┇ 18 more ┇
┗╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍┛
// number of groups
// R: group_by(xdf, cyl) %>% group_keys() %>% nrow()
print(xdf.grouped(by: "cyl").count)
3
// group, manipulate (in this case, filter), and re-combine
// R: group_by(xdf) %>% filter(mpg < 20) %>% ungroup()
print(
xdf.grouped(by: "cyl").mapGroups { (val) in
val.filter(on: "mpg", Double.self) { (val) in val! < 20 }.base
}.ungrouped()
)
┏━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃ ┃ mpg ┃ disp ┃ hp ┃ drat ┃ wt ┃ qsec ┃ 5 ┇
┃ ┃ <Double> ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 0 │ 22.8 │ 108.0 │ 93 │ 3.85 │ 2.32 │ 18.61 │ ┆
│ 1 │ 24.4 │ 146.7 │ 62 │ 3.69 │ 3.19 │ 20.0 │ ┆
│ 2 │ 22.8 │ 140.8 │ 95 │ 3.92 │ 3.15 │ 22.9 │ ┆
│ 3 │ 32.4 │ 78.7 │ 66 │ 4.08 │ 2.2 │ 19.47 │ ┆
│ 4 │ 30.4 │ 75.7 │ 52 │ 4.93 │ 1.615 │ 18.52 │ ┆
│ 5 │ 33.9 │ 71.1 │ 65 │ 4.22 │ 1.835 │ 19.9 │ ┆
│ 6 │ 21.5 │ 120.1 │ 97 │ 3.7 │ 2.465 │ 20.01 │ ┆
│ 7 │ 27.3 │ 79.0 │ 66 │ 4.08 │ 1.935 │ 18.9 │ ┆
│ 8 │ 26.0 │ 120.3 │ 91 │ 4.43 │ 2.14 │ 16.7 │ ┆
│ 9 │ 30.4 │ 95.1 │ 113 │ 3.77 │ 1.513 │ 16.9 │ ┆
┢╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍┪
┇ 182 more ┇
┗╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍┛
// look at one column
// R: xdf$cyl
print( xdf["cyl"] )
┏━━━━━━━┓
┃ cyl ┃
┃ <Int> ┃
┡━━━━━━━┩
│ 4 │
│ 4 │
│ 4 │
│ 4 │
│ 4 │
│ 4 │
│ 4 │
│ 4 │
│ 4 │
│ 4 │
┢╍╍╍╍╍╍╍┪
┇ 54 m… ┇
┗╍╍╍╍╍╍╍┛
// combine two columns and look at it
// R: mutate(xdf, cyl_mpg = sprintf("%s:%s", cyl, mpg) %>% select(-cyl, -mpg)
// R: unite(xdf, cyl_mpg, cyl, mpg, sep = ":") # alternate way
xdf.combineColumns("cyl", "mpg", into: "cyl_mpg") { (val1: Int?, val2: Double?) -> String in
String(val1 ?? 0) + ":" + String(val2 ?? 0.0)
}
print(xdf["cyl_mpg"])
┏━━━━━━━━━━┓
┃ cyl_mpg ┃
┃ <String> ┃
┡━━━━━━━━━━┩
│ 4:22.8 │
│ 4:24.4 │
│ 4:22.8 │
│ 4:32.4 │
│ 4:30.4 │
│ 4:33.9 │
│ 4:21.5 │
│ 4:27.3 │
│ 4:26.0 │
│ 4:30.4 │
┢╍╍╍╍╍╍╍╍╍╍┪
┇ 54 more ┇
┗╍╍╍╍╍╍╍╍╍╍┛
// look at the colnames (^^ removes "cyl" and "mpg"
// R: colnames(xdf)
print(xdf.columns.map{ col in col.name })
["cyl_mpg", "disp", "hp", "drat", "wt", "qsec", "vs", "am", "gear", "carb"]
// turn an Int into a Double
// R: xdf$hp <- as.double(xdf$hp) # or use dplyr::mutate()
xdf.transformColumn("hp") { (val1: Int?) -> Double? in
Double(val1 ?? 0)
}
print(xdf["hp"])
┏━━━━━━━━━━┓
┃ hp ┃
┃ <Double> ┃
┡━━━━━━━━━━┩
│ 93.0 │
│ 62.0 │
│ 95.0 │
│ 66.0 │
│ 52.0 │
│ 65.0 │
│ 97.0 │
│ 66.0 │
│ 91.0 │
│ 113.0 │
┢╍╍╍╍╍╍╍╍╍╍┪
┇ 54 more ┇
┗╍╍╍╍╍╍╍╍╍╍┛
// look at the coltypes
// R: sapply(mtcars, typeof)
print(xdf.columns.map{ col in col.wrappedElementType })
[Swift.String, Swift.Double, Swift.Double, Swift.Double, Swift.Double, Swift.Double, Swift.Int, Swift.Int, Swift.Int, Swift.Int]
// distinct horsepower
// R: distinct(xdf, hp)
print(xdf["hp"].distinct())
┏━━━━━━━━━━┓
┃ hp ┃
┃ <Double> ┃
┡━━━━━━━━━━┩
│ 93.0 │
│ 62.0 │
│ 95.0 │
│ 66.0 │
│ 52.0 │
│ 65.0 │
│ 97.0 │
│ 91.0 │
│ 113.0 │
│ 109.0 │
┢╍╍╍╍╍╍╍╍╍╍┪
┇ 12 more ┇
┗╍╍╍╍╍╍╍╍╍╍┛
// row slices
// R: xdf[10,]
print(xdf.rows[10])
┏━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃ ┃ cyl_mpg ┃ disp ┃ hp ┃ drat ┃ wt ┃ qsec ┃ 4 ┇
┃ ┃ <String> ┃ <Double> ┃ <Double> ┃ <Double> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 10 │ 4:21.4 │ 121.0 │ 109.0 │ 4.11 │ 2.78 │ 18.6 │ ┆
└────┴──────────┴──────────┴──────────┴──────────┴──────────┴──────────┴╌╌╌╌╌╌┘
// R: xdf[3:10,]
print(xdf.rows[3...10])
Rows(base:
┏━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃ ┃ cyl_mpg ┃ disp ┃ hp ┃ drat ┃ wt ┃ qsec ┃ 4 ┇
┃ ┃ <String> ┃ <Double> ┃ <Double> ┃ <Double> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 0 │ 4:22.8 │ 108.0 │ 93.0 │ 3.85 │ 2.32 │ 18.61 │ ┆
│ 1 │ 4:24.4 │ 146.7 │ 62.0 │ 3.69 │ 3.19 │ 20.0 │ ┆
│ 2 │ 4:22.8 │ 140.8 │ 95.0 │ 3.92 │ 3.15 │ 22.9 │ ┆
│ 3 │ 4:32.4 │ 78.7 │ 66.0 │ 4.08 │ 2.2 │ 19.47 │ ┆
│ 4 │ 4:30.4 │ 75.7 │ 52.0 │ 4.93 │ 1.615 │ 18.52 │ ┆
│ 5 │ 4:33.9 │ 71.1 │ 65.0 │ 4.22 │ 1.835 │ 19.9 │ ┆
│ 6 │ 4:21.5 │ 120.1 │ 97.0 │ 3.7 │ 2.465 │ 20.01 │ ┆
│ 7 │ 4:27.3 │ 79.0 │ 66.0 │ 4.08 │ 1.935 │ 18.9 │ ┆
│ 8 │ 4:26.0 │ 120.3 │ 91.0 │ 4.43 │ 2.14 │ 16.7 │ ┆
│ 9 │ 4:30.4 │ 95.1 │ 113.0 │ 3.77 │ 1.513 │ 16.9 │ ┆
┢╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍┪
┇ 54 more ┇
┗╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍┛
, subranges: _RangeSet(3..<11))
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