A reprex can be simply cut-and-pasted.
dput(mtcars)
#> structure(list(mpg = c(21, 21, 22.8, 21.4, 18.7, 18.1, 14.3,
#> 24.4, 22.8, 19.2, 17.8, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4,
#> 30.4, 33.9, 21.5, 15.5, 15.2, 13.3, 19.2, 27.3, 26, 30.4, 15.8,
#> 19.7, 15, 21.4), cyl = c(6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8,
#> 8, 8, 8, 8, 8, 4, 4, 4, 4, 8, 8, 8, 8, 4, 4, 4, 8, 6, 8, 4),
#> disp = c(160, 160, 108, 258, 360, 225, 360, 146.7, 140.8,
#> 167.6, 167.6, 275.8, 275.8, 275.8, 472, 460, 440, 78.7, 75.7,
#> 71.1, 120.1, 318, 304, 350, 400, 79, 120.3, 95.1, 351, 145,
#> 301, 121), hp = c(110, 110, 93, 110, 175, 105, 245, 62, 95,
#> 123, 123, 180, 180, 180, 205, 215, 230, 66, 52, 65, 97, 150,
#> 150, 245, 175, 66, 91, 113, 264, 175, 335, 109), drat = c(3.9,
#> 3.9, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92,
#> 3.07, 3.07, 3.07, 2.93, 3, 3.23, 4.08, 4.93, 4.22, 3.7, 2.76,
#> 3.15, 3.73, 3.08, 4.08, 4.43, 3.77, 4.22, 3.62, 3.54, 4.11
#> ), wt = c(2.62, 2.875, 2.32, 3.215, 3.44, 3.46, 3.57, 3.19,
#> 3.15, 3.44, 3.44, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 2.2,
#> 1.615, 1.835, 2.465, 3.52, 3.435, 3.84, 3.845, 1.935, 2.14,
#> 1.513, 3.17, 2.77, 3.57, 2.78), qsec = c(16.46, 17.02, 18.61,
#> 19.44, 17.02, 20.22, 15.84, 20, 22.9, 18.3, 18.9, 17.4, 17.6,
#> 18, 17.98, 17.82, 17.42, 19.47, 18.52, 19.9, 20.01, 16.87,
#> 17.3, 15.41, 17.05, 18.9, 16.7, 16.9, 14.5, 15.5, 14.6, 18.6
#> ), vs = c(0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0,
#> 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1), am = c(1,
#> 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
#> 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1), gear = c(4, 4, 4, 3,
#> 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3,
#> 3, 3, 4, 5, 5, 5, 5, 5, 4), carb = c(4, 4, 1, 1, 2, 1, 4,
#> 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2, 2, 4, 2, 1,
#> 2, 2, 4, 6, 8, 2)), row.names = c("Mazda RX4", "Mazda RX4 Wag",
#> "Datsun 710", "Hornet 4 Drive", "Hornet Sportabout", "Valiant",
#> "Duster 360", "Merc 240D", "Merc 230", "Merc 280", "Merc 280C",
#> "Merc 450SE", "Merc 450SL", "Merc 450SLC", "Cadillac Fleetwood",
#> "Lincoln Continental", "Chrysler Imperial", "Fiat 128", "Honda Civic",
#> "Toyota Corolla", "Toyota Corona", "Dodge Challenger", "AMC Javelin",
#> "Camaro Z28", "Pontiac Firebird", "Fiat X1-9", "Porsche 914-2",
#> "Lotus Europa", "Ford Pantera L", "Ferrari Dino", "Maserati Bora",
#> "Volvo 142E"), class = "data.frame")
It doesn't have to be the complete data, it can be a built-in data set with the same structure or even made-up data. What is shown is only a collection of variable names, one typeof integer and the others typeof chr.
It appears that what is sought is a model, f in which y, the response variable, earnings, can be estimated given some combination of x_i ... x_n, variables that represent pedigree attributes, such as GEN.1.top
The threshold task will be to encode pedigrees as dummy variables from lists of sires and dams by generation.
For example, consider two sires, Threepenny and Opera, each of which produces in one generation, three sires (A,B,C,D,E,F) and three dams (G,H,I,J,K,L). In a subsequent generation, there may be matings of A-G, B-J, E-K, etc. How will these be encoded in the data?