mirror of
https://github.com/agdamsbo/FreesearchR.git
synced 2025-09-12 09:59:39 +02:00
533 lines
20 KiB
Markdown
533 lines
20 KiB
Markdown
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# getfun works
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Code
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getfun("stats::lm")
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Output
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function (formula, data, subset, weights, na.action, method = "qr",
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model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE,
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contrasts = NULL, offset, ...)
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{
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ret.x <- x
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ret.y <- y
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cl <- match.call()
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mf <- match.call(expand.dots = FALSE)
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m <- match(c("formula", "data", "subset", "weights", "na.action",
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"offset"), names(mf), 0L)
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mf <- mf[c(1L, m)]
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mf$drop.unused.levels <- TRUE
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mf[[1L]] <- quote(stats::model.frame)
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mf <- eval(mf, parent.frame())
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if (method == "model.frame")
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return(mf)
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else if (method != "qr")
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warning(gettextf("method = '%s' is not supported. Using 'qr'",
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method), domain = NA)
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mt <- attr(mf, "terms")
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y <- model.response(mf, "numeric")
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w <- as.vector(model.weights(mf))
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if (!is.null(w) && !is.numeric(w))
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stop("'weights' must be a numeric vector")
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offset <- model.offset(mf)
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mlm <- is.matrix(y)
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ny <- if (mlm)
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nrow(y)
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else length(y)
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if (!is.null(offset)) {
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if (!mlm)
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offset <- as.vector(offset)
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if (NROW(offset) != ny)
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stop(gettextf("number of offsets is %d, should equal %d (number of observations)",
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NROW(offset), ny), domain = NA)
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}
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if (is.empty.model(mt)) {
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x <- NULL
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z <- list(coefficients = if (mlm) matrix(NA_real_, 0,
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ncol(y)) else numeric(), residuals = y, fitted.values = 0 *
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y, weights = w, rank = 0L, df.residual = if (!is.null(w)) sum(w !=
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0) else ny)
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if (!is.null(offset)) {
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z$fitted.values <- offset
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z$residuals <- y - offset
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}
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}
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else {
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x <- model.matrix(mt, mf, contrasts)
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z <- if (is.null(w))
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lm.fit(x, y, offset = offset, singular.ok = singular.ok,
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...)
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else lm.wfit(x, y, w, offset = offset, singular.ok = singular.ok,
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...)
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}
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class(z) <- c(if (mlm) "mlm", "lm")
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z$na.action <- attr(mf, "na.action")
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z$offset <- offset
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z$contrasts <- attr(x, "contrasts")
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z$xlevels <- .getXlevels(mt, mf)
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z$call <- cl
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z$terms <- mt
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if (model)
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z$model <- mf
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if (ret.x)
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z$x <- x
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if (ret.y)
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z$y <- y
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if (!qr)
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z$qr <- NULL
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z
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}
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<bytecode: 0x12c7f2dd8>
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<environment: namespace:stats>
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# argsstring2list works
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Code
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argsstring2list("A=1:5,b=2:4")
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Output
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$A
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[1] 1 2 3 4 5
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$b
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[1] 2 3 4
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# factorize works
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Code
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factorize(mtcars, names(mtcars))
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Output
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mpg cyl disp hp drat wt qsec vs am gear carb
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Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4
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Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4
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Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1
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Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
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Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2
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Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1
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Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4
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Merc 240D 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2
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Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2
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Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4
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Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4
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Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3
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Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3
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Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18 0 0 3 3
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Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4
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Lincoln Continental 10.4 8 460 215 3 5.424 17.82 0 0 3 4
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Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4
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Fiat 128 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4 1
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Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
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Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.9 1 1 4 1
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Toyota Corona 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3 1
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Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2
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AMC Javelin 15.2 8 304 150 3.15 3.435 17.3 0 0 3 2
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Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4
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Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2
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Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1
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Porsche 914-2 26 4 120.3 91 4.43 2.14 16.7 0 1 5 2
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Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2
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Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4
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Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6
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Maserati Bora 15 8 301 335 3.54 3.57 14.6 0 1 5 8
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Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2
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# default_parsing works
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Code
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default_parsing(mtcars)
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Output
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# A tibble: 32 x 11
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mpg cyl disp hp drat wt qsec vs am gear carb
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<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <fct> <fct>
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1 21 6 160 110 3.9 2.62 16.5 FALSE TRUE 4 4
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2 21 6 160 110 3.9 2.88 17.0 FALSE TRUE 4 4
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3 22.8 4 108 93 3.85 2.32 18.6 TRUE TRUE 4 1
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4 21.4 6 258 110 3.08 3.22 19.4 TRUE FALSE 3 1
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5 18.7 8 360 175 3.15 3.44 17.0 FALSE FALSE 3 2
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6 18.1 6 225 105 2.76 3.46 20.2 TRUE FALSE 3 1
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7 14.3 8 360 245 3.21 3.57 15.8 FALSE FALSE 3 4
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8 24.4 4 147. 62 3.69 3.19 20 TRUE FALSE 4 2
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9 22.8 4 141. 95 3.92 3.15 22.9 TRUE FALSE 4 2
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10 19.2 6 168. 123 3.92 3.44 18.3 TRUE FALSE 4 4
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# i 22 more rows
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# remove_empty_attr works
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Code
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remove_empty_attr(ds)
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Output
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$mpg
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[1] 21.0 21.0 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
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[16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
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[31] 15.0 21.4
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$cyl
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[1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4
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$disp
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[1] 160.0 160.0 108.0 258.0 360.0 225.0 360.0 146.7 140.8 167.6 167.6 275.8
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[13] 275.8 275.8 472.0 460.0 440.0 78.7 75.7 71.1 120.1 318.0 304.0 350.0
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[25] 400.0 79.0 120.3 95.1 351.0 145.0 301.0 121.0
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$hp
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[1] 110 110 93 110 175 105 245 62 95 123 123 180 180 180 205 215 230 66 52
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[20] 65 97 150 150 245 175 66 91 113 264 175 335 109
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$drat
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[1] 3.90 3.90 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
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[16] 3.00 3.23 4.08 4.93 4.22 3.70 2.76 3.15 3.73 3.08 4.08 4.43 3.77 4.22 3.62
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[31] 3.54 4.11
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$wt
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[1] 2.620 2.875 2.320 3.215 3.440 3.460 3.570 3.190 3.150 3.440 3.440 4.070
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[13] 3.730 3.780 5.250 5.424 5.345 2.200 1.615 1.835 2.465 3.520 3.435 3.840
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[25] 3.845 1.935 2.140 1.513 3.170 2.770 3.570 2.780
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$qsec
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[1] 16.46 17.02 18.61 19.44 17.02 20.22 15.84 20.00 22.90 18.30 18.90 17.40
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[13] 17.60 18.00 17.98 17.82 17.42 19.47 18.52 19.90 20.01 16.87 17.30 15.41
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[25] 17.05 18.90 16.70 16.90 14.50 15.50 14.60 18.60
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$vs
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[1] 0 0 1 1 0 1 0 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 1 0 0 0 1
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$am
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[1] 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
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$gear
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[1] 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 5 5 5 5 5 4
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$carb
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[1] 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
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---
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Code
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remove_empty_attr(dplyr::bind_cols(ds))
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Output
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# A tibble: 32 x 11
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mpg cyl disp hp drat wt qsec vs am gear carb
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<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
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1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
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2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
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3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
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4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
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5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
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6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
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7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
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8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
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9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
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10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
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# i 22 more rows
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---
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Code
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remove_empty_attr(ds[[1]])
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Output
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[1] 21.0 21.0 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
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[16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
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[31] 15.0 21.4
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# remove_empty_cols works
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Code
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remove_empty_cols(data.frame(a = 1:10, b = NA, c = c(2, NA)), cutoff = 0.5)
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Output
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a c
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1 1 2
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2 2 NA
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3 3 2
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4 4 NA
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5 5 2
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6 6 NA
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7 7 2
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8 8 NA
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9 9 2
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10 10 NA
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# append_list works
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Code
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append_list(data.frame(letters[1:20], 1:20), ls_d, "letters")
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Output
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$letters
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letters.1.20. X1.20
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1 a 1
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2 b 2
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3 c 3
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4 d 4
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5 e 5
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6 f 6
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7 g 7
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8 h 8
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9 i 9
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10 j 10
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11 k 11
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12 l 12
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13 m 13
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14 n 14
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15 o 15
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16 p 16
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17 q 17
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18 r 18
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19 s 19
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20 t 20
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---
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Code
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append_list(letters[1:20], ls_d, "letters")
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Output
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$letters
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[1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
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[20] "t"
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# missing_fraction works
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Code
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missing_fraction(c(NA, 1:10, rep(NA, 3)))
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Output
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[1] 0.2857143
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# data_description works
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Code
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data_description(data.frame(sample(1:8, 20, TRUE), sample(c(1:8, NA), 20, TRUE)),
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data_text = "This data")
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Output
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[1] "This data has 20 observations and 2 variables, with 16 (80%) complete cases."
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# Data type filter works
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Code
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data_type_filter(default_parsing(mtcars), type = c("categorical", "continuous"))
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Output
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# A tibble: 32 x 9
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mpg cyl disp hp drat wt qsec gear carb
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<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <fct>
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1 21 6 160 110 3.9 2.62 16.5 4 4
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2 21 6 160 110 3.9 2.88 17.0 4 4
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3 22.8 4 108 93 3.85 2.32 18.6 4 1
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4 21.4 6 258 110 3.08 3.22 19.4 3 1
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5 18.7 8 360 175 3.15 3.44 17.0 3 2
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6 18.1 6 225 105 2.76 3.46 20.2 3 1
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7 14.3 8 360 245 3.21 3.57 15.8 3 4
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8 24.4 4 147. 62 3.69 3.19 20 4 2
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9 22.8 4 141. 95 3.92 3.15 22.9 4 2
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10 19.2 6 168. 123 3.92 3.44 18.3 4 4
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# i 22 more rows
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---
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Code
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data_type_filter(default_parsing(mtcars), type = NULL)
|
||
|
|
Output
|
||
|
|
# A tibble: 32 x 11
|
||
|
|
mpg cyl disp hp drat wt qsec vs am gear carb
|
||
|
|
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <fct> <fct>
|
||
|
|
1 21 6 160 110 3.9 2.62 16.5 FALSE TRUE 4 4
|
||
|
|
2 21 6 160 110 3.9 2.88 17.0 FALSE TRUE 4 4
|
||
|
|
3 22.8 4 108 93 3.85 2.32 18.6 TRUE TRUE 4 1
|
||
|
|
4 21.4 6 258 110 3.08 3.22 19.4 TRUE FALSE 3 1
|
||
|
|
5 18.7 8 360 175 3.15 3.44 17.0 FALSE FALSE 3 2
|
||
|
|
6 18.1 6 225 105 2.76 3.46 20.2 TRUE FALSE 3 1
|
||
|
|
7 14.3 8 360 245 3.21 3.57 15.8 FALSE FALSE 3 4
|
||
|
|
8 24.4 4 147. 62 3.69 3.19 20 TRUE FALSE 4 2
|
||
|
|
9 22.8 4 141. 95 3.92 3.15 22.9 TRUE FALSE 4 2
|
||
|
|
10 19.2 6 168. 123 3.92 3.44 18.3 TRUE FALSE 4 4
|
||
|
|
# i 22 more rows
|
||
|
|
|
||
|
|
# sort_by works
|
||
|
|
|
||
|
|
Code
|
||
|
|
sort_by(c("Multivariable", "Univariable"), c("Univariable", "Minimal",
|
||
|
|
"Multivariable"))
|
||
|
|
Output
|
||
|
|
[1] "Univariable" NA "Multivariable"
|
||
|
|
|
||
|
|
# if_not_missing works
|
||
|
|
|
||
|
|
Code
|
||
|
|
if_not_missing(NULL, "new")
|
||
|
|
Output
|
||
|
|
[1] "new"
|
||
|
|
|
||
|
|
---
|
||
|
|
|
||
|
|
Code
|
||
|
|
if_not_missing(c(2, "a", NA))
|
||
|
|
Output
|
||
|
|
[1] "2" "a"
|
||
|
|
|
||
|
|
---
|
||
|
|
|
||
|
|
Code
|
||
|
|
if_not_missing("See")
|
||
|
|
Output
|
||
|
|
[1] "See"
|
||
|
|
|
||
|
|
# merge_expression, expression_string and pipe_string works
|
||
|
|
|
||
|
|
Code
|
||
|
|
merge_expression(list(rlang::call2(.fn = "select", !!!list(c("cyl", "disp")),
|
||
|
|
.ns = "dplyr"), rlang::call2(.fn = "default_parsing", .ns = "FreesearchR")))
|
||
|
|
Output
|
||
|
|
dplyr::select(c("cyl", "disp")) %>% FreesearchR::default_parsing()
|
||
|
|
|
||
|
|
---
|
||
|
|
|
||
|
|
Code
|
||
|
|
expression_string(pipe_string(lapply(list("mtcars", rlang::call2(.fn = "select",
|
||
|
|
!!!list(c("cyl", "disp")), .ns = "dplyr"), rlang::call2(.fn = "default_parsing",
|
||
|
|
.ns = "FreesearchR")), expression_string)), "data<-")
|
||
|
|
Output
|
||
|
|
[1] "data<-mtcars|>\ndplyr::select(c('cyl','disp'))|>\nFreesearchR::default_parsing()"
|
||
|
|
|
||
|
|
---
|
||
|
|
|
||
|
|
Code
|
||
|
|
expression_string(merge_expression(list(as.symbol(paste0("mtcars$", "mpg")),
|
||
|
|
rlang::call2(.fn = "select", !!!list(c("cyl", "disp")), .ns = "dplyr"), rlang::call2(
|
||
|
|
.fn = "default_parsing", .ns = "FreesearchR"))))
|
||
|
|
Output
|
||
|
|
[1] "mtcars$mpg|>\ndplyr::select(c('cyl','disp'))|>\nFreesearchR::default_parsing()"
|
||
|
|
|
||
|
|
# remove_nested_list works
|
||
|
|
|
||
|
|
Code
|
||
|
|
remove_nested_list(dplyr::tibble(a = 1:10, b = rep(list("a"), 10)))
|
||
|
|
Output
|
||
|
|
# A tibble: 10 x 1
|
||
|
|
a
|
||
|
|
<int>
|
||
|
|
1 1
|
||
|
|
2 2
|
||
|
|
3 3
|
||
|
|
4 4
|
||
|
|
5 5
|
||
|
|
6 6
|
||
|
|
7 7
|
||
|
|
8 8
|
||
|
|
9 9
|
||
|
|
10 10
|
||
|
|
|
||
|
|
---
|
||
|
|
|
||
|
|
Code
|
||
|
|
remove_nested_list(as.data.frame(dplyr::tibble(a = 1:10, b = rep(list(c("a",
|
||
|
|
"b")), 10))))
|
||
|
|
Output
|
||
|
|
a
|
||
|
|
1 1
|
||
|
|
2 2
|
||
|
|
3 3
|
||
|
|
4 4
|
||
|
|
5 5
|
||
|
|
6 6
|
||
|
|
7 7
|
||
|
|
8 8
|
||
|
|
9 9
|
||
|
|
10 10
|
||
|
|
|
||
|
|
# set_column_label works
|
||
|
|
|
||
|
|
Code
|
||
|
|
set_column_label(set_column_label(set_column_label(mtcars, ls), ls2), ls3)
|
||
|
|
Output
|
||
|
|
# A tibble: 32 x 11
|
||
|
|
mpg cyl disp hp drat wt qsec vs am gear carb
|
||
|
|
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
|
||
|
|
1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
|
||
|
|
2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
|
||
|
|
3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
|
||
|
|
4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
|
||
|
|
5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
|
||
|
|
6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
|
||
|
|
7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
|
||
|
|
8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
|
||
|
|
9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
|
||
|
|
10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
|
||
|
|
# i 22 more rows
|
||
|
|
|
||
|
|
---
|
||
|
|
|
||
|
|
Code
|
||
|
|
expression_string(rlang::expr(FreesearchR::set_column_label(label = !!ls3)))
|
||
|
|
Output
|
||
|
|
[1] "FreesearchR::set_column_label(label=c(mpg='',cyl='',disp='',hp='Horses',drat='',wt='',qsec='',vs='',am='',gear='',carb=''))"
|
||
|
|
|
||
|
|
# append_column works
|
||
|
|
|
||
|
|
Code
|
||
|
|
append_column(dplyr::mutate(mtcars, mpg_cut = mpg), mtcars$mpg, "mpg_cutter")
|
||
|
|
Output
|
||
|
|
mpg cyl disp hp drat wt qsec vs am gear carb mpg_cut
|
||
|
|
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 21.0
|
||
|
|
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 21.0
|
||
|
|
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 22.8
|
||
|
|
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 21.4
|
||
|
|
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 18.7
|
||
|
|
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 18.1
|
||
|
|
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 14.3
|
||
|
|
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 24.4
|
||
|
|
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 22.8
|
||
|
|
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 19.2
|
||
|
|
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 17.8
|
||
|
|
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 16.4
|
||
|
|
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 17.3
|
||
|
|
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 15.2
|
||
|
|
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 10.4
|
||
|
|
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 10.4
|
||
|
|
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 14.7
|
||
|
|
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 32.4
|
||
|
|
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 30.4
|
||
|
|
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 33.9
|
||
|
|
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 21.5
|
||
|
|
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 15.5
|
||
|
|
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 15.2
|
||
|
|
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 13.3
|
||
|
|
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 19.2
|
||
|
|
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 27.3
|
||
|
|
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 26.0
|
||
|
|
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 30.4
|
||
|
|
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 15.8
|
||
|
|
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 19.7
|
||
|
|
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 15.0
|
||
|
|
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 21.4
|
||
|
|
mpg_cutter
|
||
|
|
Mazda RX4 21.0
|
||
|
|
Mazda RX4 Wag 21.0
|
||
|
|
Datsun 710 22.8
|
||
|
|
Hornet 4 Drive 21.4
|
||
|
|
Hornet Sportabout 18.7
|
||
|
|
Valiant 18.1
|
||
|
|
Duster 360 14.3
|
||
|
|
Merc 240D 24.4
|
||
|
|
Merc 230 22.8
|
||
|
|
Merc 280 19.2
|
||
|
|
Merc 280C 17.8
|
||
|
|
Merc 450SE 16.4
|
||
|
|
Merc 450SL 17.3
|
||
|
|
Merc 450SLC 15.2
|
||
|
|
Cadillac Fleetwood 10.4
|
||
|
|
Lincoln Continental 10.4
|
||
|
|
Chrysler Imperial 14.7
|
||
|
|
Fiat 128 32.4
|
||
|
|
Honda Civic 30.4
|
||
|
|
Toyota Corolla 33.9
|
||
|
|
Toyota Corona 21.5
|
||
|
|
Dodge Challenger 15.5
|
||
|
|
AMC Javelin 15.2
|
||
|
|
Camaro Z28 13.3
|
||
|
|
Pontiac Firebird 19.2
|
||
|
|
Fiat X1-9 27.3
|
||
|
|
Porsche 914-2 26.0
|
||
|
|
Lotus Europa 30.4
|
||
|
|
Ford Pantera L 15.8
|
||
|
|
Ferrari Dino 19.7
|
||
|
|
Maserati Bora 15.0
|
||
|
|
Volvo 142E 21.4
|
||
|
|
|