| title | author | date | output | ||||
|---|---|---|---|---|---|---|---|
Chapter 4 |
Julin Maloof |
2022-09-24 |
|
- What is the result of subsetting a vector with positive integers, negative integers, a logical vector, or a character vector?
_Positive integers select elements by positions; negative integers exclude elements by positions; a logical vector selects by position and needs to be the same length as the vector (or maybe it can be recycled); a character vector selects by name.
- What’s the difference between [, [[, and $ when applied to a list?
[ returns a list with those elements, [[ extracts that element and doesn't return a list (unless the element itself is a list), $ will pull out an element by name
- When should you use drop = FALSE?
When you do not want a data frame to be converted to a vector (or maybe when you don't want to lose dimensions)
- If x is a matrix, what does x[] <- 0 do? How is it different from x <- 0?
x[] <- 0 will set all elements to 0. x <- 0 will change x to a vector of length 1 with a value of 0.
- How can you use a named vector to relabel categorical variables?
I am not sure what this is asking
skipped what I knew... this is the code for selecting from matrices with matrices:
vals <- outer(1:5, 1:5, FUN = "paste", sep = ",")
vals## [,1] [,2] [,3] [,4] [,5]
## [1,] "1,1" "1,2" "1,3" "1,4" "1,5"
## [2,] "2,1" "2,2" "2,3" "2,4" "2,5"
## [3,] "3,1" "3,2" "3,3" "3,4" "3,5"
## [4,] "4,1" "4,2" "4,3" "4,4" "4,5"
## [5,] "5,1" "5,2" "5,3" "5,4" "5,5"
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] "1,1" "1,2" "1,3" "1,4" "1,5"
#> [2,] "2,1" "2,2" "2,3" "2,4" "2,5"
#> [3,] "3,1" "3,2" "3,3" "3,4" "3,5"
#> [4,] "4,1" "4,2" "4,3" "4,4" "4,5"
#> [5,] "5,1" "5,2" "5,3" "5,4" "5,5"
vals[c(4, 15)] #Selects the 4th and 15th elements, going down columns## [1] "4,1" "5,3"
select <- matrix(ncol = 2, byrow = TRUE, c(
1, 1,
3, 1,
2, 4
))
select## [,1] [,2]
## [1,] 1 1
## [2,] 3 1
## [3,] 2 4
vals[select]## [1] "1,1" "3,1" "2,4"
Oh, cool, so these are selecting by "address"
Original
mtcars[mtcars$cyl = 4, ]
mtcars[-1:4, ]
mtcars[mtcars$cyl <= 5]
mtcars[mtcars$cyl == 4 | 6, ]Fixed
mtcars[mtcars$cyl == 4, ]## mpg cyl disp hp drat wt qsec vs am gear carb
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
mtcars[-1:-4, ]## mpg cyl disp hp drat wt qsec vs am gear carb
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
mtcars[mtcars$cyl <= 5,]## mpg cyl disp hp drat wt qsec vs am gear carb
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
mtcars[mtcars$cyl == 4 | mtcars$cyl == 6, ]## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
2. Why does the following code yield five missing values? (Hint: why is it different from x[NA_real_]?)
x <- 1:5
x[NA]## [1] NA NA NA NA NA
x[NA_real_]## [1] NA
I guess because [NA] is recycled and when you have an NA value it always returns NA in extraction. But why is [NA_real] different? Maybe because it is testing for NA_Real?
3. What does upper.tri() return? How does subsetting a matrix with it work? Do we need any additional subsetting rules to describe its behaviour?
x <- outer(1:5, 1:5, FUN = "*")
x## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 2 3 4 5
## [2,] 2 4 6 8 10
## [3,] 3 6 9 12 15
## [4,] 4 8 12 16 20
## [5,] 5 10 15 20 25
upper.tri(x)## [,1] [,2] [,3] [,4] [,5]
## [1,] FALSE TRUE TRUE TRUE TRUE
## [2,] FALSE FALSE TRUE TRUE TRUE
## [3,] FALSE FALSE FALSE TRUE TRUE
## [4,] FALSE FALSE FALSE FALSE TRUE
## [5,] FALSE FALSE FALSE FALSE FALSE
x[upper.tri(x)]## [1] 2 3 6 4 8 12 5 10 15 20
upper.tri() returns a matrix of TRUES and FALSES where TRUES correspond to the upper triangle. We do not need any additional rules
dim(mtcars)## [1] 32 11
When you are subsetting a data frame and do not include a comma, it subsets columns. mtcars has less than 20 columns.
5. Implement your own function that extracts the diagonal entries from a matrix (it should behave like diag(x) where x is a matrix).
m <- matrix(1:32, ncol=8)
m## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1 5 9 13 17 21 25 29
## [2,] 2 6 10 14 18 22 26 30
## [3,] 3 7 11 15 19 23 27 31
## [4,] 4 8 12 16 20 24 28 32
diag(m)## [1] 1 6 11 16
diag2 <- function(x) {
end <- ifelse(nrow(x) > ncol(x), length(x), nrow(x)^2) #anyway more clever to do this?
d <- seq(1, end, by=nrow(x)+1)
x[d]
}
diag2(m)## [1] 1 6 11 16
alternate way
diag3 <- function(x) {
select <- 1:min(dim(x))
select <- cbind(select,select)
x[select]
}
diag(matrix(1:32,ncol=4))## [1] 1 10 19 28
diag3(matrix(1:32,ncol=4))## [1] 1 10 19 28
diag(matrix(1:32,ncol=8))## [1] 1 6 11 16
diag3(matrix(1:32,ncol=8))## [1] 1 6 11 16
df <- as.data.frame(m)
df## V1 V2 V3 V4 V5 V6 V7 V8
## 1 1 5 9 13 17 21 25 29
## 2 2 6 10 14 18 22 26 30
## 3 3 7 11 15 19 23 27 31
## 4 4 8 12 16 20 24 28 32
df[3,2] <- NA
df## V1 V2 V3 V4 V5 V6 V7 V8
## 1 1 5 9 13 17 21 25 29
## 2 2 6 10 14 18 22 26 30
## 3 3 NA 11 15 19 23 27 31
## 4 4 8 12 16 20 24 28 32
df[is.na(df)] <- 0
df## V1 V2 V3 V4 V5 V6 V7 V8
## 1 1 5 9 13 17 21 25 29
## 2 2 6 10 14 18 22 26 30
## 3 3 0 11 15 19 23 27 31
## 4 4 8 12 16 20 24 28 32
subsets the df to positions with NA and replaces them with 0
Huh?
for (i in 2:length(x)) {
out[[i]] <- fun(x[[i]], out[[i - 1]])
}1. Brainstorm as many ways as possible to extract the third value from the cyl variable in the mtcars dataset.
mtcars[3,"cyl"]## [1] 4
mtcars[3,2]## [1] 4
mtcars$cyl[3]## [1] 4
mtcars[["cyl"]][3]## [1] 4
purrr::pluck(mtcars, "cyl", 3)## [1] 4
2. Given a linear model, e.g., mod <- lm(mpg ~ wt, data = mtcars), extract the residual degrees of freedom. Then extract the R squared from the model summary (summary(mod))
mod <- lm(mpg ~ wt, data = mtcars)
str(mod)## List of 12
## $ coefficients : Named num [1:2] 37.29 -5.34
## ..- attr(*, "names")= chr [1:2] "(Intercept)" "wt"
## $ residuals : Named num [1:32] -2.28 -0.92 -2.09 1.3 -0.2 ...
## ..- attr(*, "names")= chr [1:32] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
## $ effects : Named num [1:32] -113.65 -29.116 -1.661 1.631 0.111 ...
## ..- attr(*, "names")= chr [1:32] "(Intercept)" "wt" "" "" ...
## $ rank : int 2
## $ fitted.values: Named num [1:32] 23.3 21.9 24.9 20.1 18.9 ...
## ..- attr(*, "names")= chr [1:32] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
## $ assign : int [1:2] 0 1
## $ qr :List of 5
## ..$ qr : num [1:32, 1:2] -5.657 0.177 0.177 0.177 0.177 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:32] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
## .. .. ..$ : chr [1:2] "(Intercept)" "wt"
## .. ..- attr(*, "assign")= int [1:2] 0 1
## ..$ qraux: num [1:2] 1.18 1.05
## ..$ pivot: int [1:2] 1 2
## ..$ tol : num 1e-07
## ..$ rank : int 2
## ..- attr(*, "class")= chr "qr"
## $ df.residual : int 30
## $ xlevels : Named list()
## $ call : language lm(formula = mpg ~ wt, data = mtcars)
## $ terms :Classes 'terms', 'formula' language mpg ~ wt
## .. ..- attr(*, "variables")= language list(mpg, wt)
## .. ..- attr(*, "factors")= int [1:2, 1] 0 1
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:2] "mpg" "wt"
## .. .. .. ..$ : chr "wt"
## .. ..- attr(*, "term.labels")= chr "wt"
## .. ..- attr(*, "order")= int 1
## .. ..- attr(*, "intercept")= int 1
## .. ..- attr(*, "response")= int 1
## .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## .. ..- attr(*, "predvars")= language list(mpg, wt)
## .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
## .. .. ..- attr(*, "names")= chr [1:2] "mpg" "wt"
## $ model :'data.frame': 32 obs. of 2 variables:
## ..$ mpg: num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## ..$ wt : num [1:32] 2.62 2.88 2.32 3.21 3.44 ...
## ..- attr(*, "terms")=Classes 'terms', 'formula' language mpg ~ wt
## .. .. ..- attr(*, "variables")= language list(mpg, wt)
## .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
## .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. ..$ : chr [1:2] "mpg" "wt"
## .. .. .. .. ..$ : chr "wt"
## .. .. ..- attr(*, "term.labels")= chr "wt"
## .. .. ..- attr(*, "order")= int 1
## .. .. ..- attr(*, "intercept")= int 1
## .. .. ..- attr(*, "response")= int 1
## .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## .. .. ..- attr(*, "predvars")= language list(mpg, wt)
## .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
## .. .. .. ..- attr(*, "names")= chr [1:2] "mpg" "wt"
## - attr(*, "class")= chr "lm"
mod$df.residual## [1] 30
mod.sum <- summary(mod)
str(mod.sum)## List of 11
## $ call : language lm(formula = mpg ~ wt, data = mtcars)
## $ terms :Classes 'terms', 'formula' language mpg ~ wt
## .. ..- attr(*, "variables")= language list(mpg, wt)
## .. ..- attr(*, "factors")= int [1:2, 1] 0 1
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:2] "mpg" "wt"
## .. .. .. ..$ : chr "wt"
## .. ..- attr(*, "term.labels")= chr "wt"
## .. ..- attr(*, "order")= int 1
## .. ..- attr(*, "intercept")= int 1
## .. ..- attr(*, "response")= int 1
## .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## .. ..- attr(*, "predvars")= language list(mpg, wt)
## .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
## .. .. ..- attr(*, "names")= chr [1:2] "mpg" "wt"
## $ residuals : Named num [1:32] -2.28 -0.92 -2.09 1.3 -0.2 ...
## ..- attr(*, "names")= chr [1:32] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
## $ coefficients : num [1:2, 1:4] 37.285 -5.344 1.878 0.559 19.858 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2] "(Intercept)" "wt"
## .. ..$ : chr [1:4] "Estimate" "Std. Error" "t value" "Pr(>|t|)"
## $ aliased : Named logi [1:2] FALSE FALSE
## ..- attr(*, "names")= chr [1:2] "(Intercept)" "wt"
## $ sigma : num 3.05
## $ df : int [1:3] 2 30 2
## $ r.squared : num 0.753
## $ adj.r.squared: num 0.745
## $ fstatistic : Named num [1:3] 91.4 1 30
## ..- attr(*, "names")= chr [1:3] "value" "numdf" "dendf"
## $ cov.unscaled : num [1:2, 1:2] 0.38 -0.1084 -0.1084 0.0337
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2] "(Intercept)" "wt"
## .. ..$ : chr [1:2] "(Intercept)" "wt"
## - attr(*, "class")= chr "summary.lm"
mod.sum$r.squared## [1] 0.7528328
1. How would you randomly permute the columns of a data frame? (This is an important technique in random forests.) Can you simultaneously permute the rows and columns in one step?
df <- data.frame(x=LETTERS[1:5], y=1:5, z=letters[26:22])
df## x y z
## 1 A 1 z
## 2 B 2 y
## 3 C 3 x
## 4 D 4 w
## 5 E 5 v
#columns
df[,sample(ncol(df))]## y z x
## 1 1 z A
## 2 2 y B
## 3 3 x C
## 4 4 w D
## 5 5 v E
#columns and rows
df[sample(nrow(df)), sample(ncol(df))]## y z x
## 3 3 x C
## 2 2 y B
## 5 5 v E
## 4 4 w D
## 1 1 z A
2. How would you select a random sample of m rows from a data frame? What if the sample had to be contiguous (i.e., with an initial row, a final row, and every row in between)?
I think the question means a random subset or random subsample
# 5 rows at random
mtcars[sample(nrow(mtcars), size = 5),]## mpg cyl disp hp drat wt qsec vs am gear carb
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
# contiguous 5 rows
start <- sample(nrow(mtcars)-4, size=1)
mtcars[start:(start+4),]## mpg cyl disp hp drat wt qsec vs am gear carb
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
mtcars[, order(colnames(mtcars))]## am carb cyl disp drat gear hp mpg qsec vs wt
## Mazda RX4 1 4 6 160.0 3.90 4 110 21.0 16.46 0 2.620
## Mazda RX4 Wag 1 4 6 160.0 3.90 4 110 21.0 17.02 0 2.875
## Datsun 710 1 1 4 108.0 3.85 4 93 22.8 18.61 1 2.320
## Hornet 4 Drive 0 1 6 258.0 3.08 3 110 21.4 19.44 1 3.215
## Hornet Sportabout 0 2 8 360.0 3.15 3 175 18.7 17.02 0 3.440
## Valiant 0 1 6 225.0 2.76 3 105 18.1 20.22 1 3.460
## Duster 360 0 4 8 360.0 3.21 3 245 14.3 15.84 0 3.570
## Merc 240D 0 2 4 146.7 3.69 4 62 24.4 20.00 1 3.190
## Merc 230 0 2 4 140.8 3.92 4 95 22.8 22.90 1 3.150
## Merc 280 0 4 6 167.6 3.92 4 123 19.2 18.30 1 3.440
## Merc 280C 0 4 6 167.6 3.92 4 123 17.8 18.90 1 3.440
## Merc 450SE 0 3 8 275.8 3.07 3 180 16.4 17.40 0 4.070
## Merc 450SL 0 3 8 275.8 3.07 3 180 17.3 17.60 0 3.730
## Merc 450SLC 0 3 8 275.8 3.07 3 180 15.2 18.00 0 3.780
## Cadillac Fleetwood 0 4 8 472.0 2.93 3 205 10.4 17.98 0 5.250
## Lincoln Continental 0 4 8 460.0 3.00 3 215 10.4 17.82 0 5.424
## Chrysler Imperial 0 4 8 440.0 3.23 3 230 14.7 17.42 0 5.345
## Fiat 128 1 1 4 78.7 4.08 4 66 32.4 19.47 1 2.200
## Honda Civic 1 2 4 75.7 4.93 4 52 30.4 18.52 1 1.615
## Toyota Corolla 1 1 4 71.1 4.22 4 65 33.9 19.90 1 1.835
## Toyota Corona 0 1 4 120.1 3.70 3 97 21.5 20.01 1 2.465
## Dodge Challenger 0 2 8 318.0 2.76 3 150 15.5 16.87 0 3.520
## AMC Javelin 0 2 8 304.0 3.15 3 150 15.2 17.30 0 3.435
## Camaro Z28 0 4 8 350.0 3.73 3 245 13.3 15.41 0 3.840
## Pontiac Firebird 0 2 8 400.0 3.08 3 175 19.2 17.05 0 3.845
## Fiat X1-9 1 1 4 79.0 4.08 4 66 27.3 18.90 1 1.935
## Porsche 914-2 1 2 4 120.3 4.43 5 91 26.0 16.70 0 2.140
## Lotus Europa 1 2 4 95.1 3.77 5 113 30.4 16.90 1 1.513
## Ford Pantera L 1 4 8 351.0 4.22 5 264 15.8 14.50 0 3.170
## Ferrari Dino 1 6 6 145.0 3.62 5 175 19.7 15.50 0 2.770
## Maserati Bora 1 8 8 301.0 3.54 5 335 15.0 14.60 0 3.570
## Volvo 142E 1 2 4 121.0 4.11 4 109 21.4 18.60 1 2.780