Transformation Toolset for Vectors, Matrices, Lists and Data Frames
The package provides tools for transformation and inter-conversion of vectors, matrices, lists and data frames, which expand the tidyverse environment. Such extra tools include functions for translation of a variable using a dictionary table, reduction of lists and vectors into low-dimensional data structures, and expansion of data frames.
Additonal features making the life of a data scientist easier are e.g. a swiss army knife row binding function family, functions for setting row names and identification of individuals with complete record of consecutive data.
You may install the package and its dependency 'figur' using devtools:
devtools::install_github('PiotrTymoszuk/figur')
devtools::install_github('PiotrTymoszuk/trafo')
Exchanging values with a dictionary
This operation is done for a range of data structures with the exchange() function. In any case you'll need a dictionary in a data frame format:
library(trafo)
library(tibble)
library(tidyverse)
## example data: tibble, data frame and matrix
my_cars <- mtcars %>%
rownames_to_column('cars') %>%
select(- vs, - am) %>%
as_tibble
my_cars2 <- mtcars %>%
mutate(mtcars,
ncap = sample(c(1:10, NA),
nrow(mtcars),
replace = TRUE))
my_cyl_mtx <- matrix(sample(1:8, 20, replace = TRUE),
nrow = 2,
ncol = 10)
my_cyl_list <- list('A' = sample(1:8, 20, replace = TRUE),
'B' = sample(1:8, 20, replace = TRUE),
'C' = matrix(c(12, 4, 8, 10),
c(6, 4, 8, 10),
ncol = 2),
'D' = my_cars)
## and a dictionary mapping the cylinder number to a text label
cyl_reco <- tibble::tibble(cyl = c(4, 6, 8, 10, 12),
cyl_lab = c('4-cylinder',
'6-cylinder',
'6+ cylinder',
'6+-cylinder',
'6+-cylinder'))
Translation of the cylinder number to its text value with exchange() is straightforward:
## for data frame/tibble
exchange(my_cars,
variable = 'cyl',
dict = cyl_reco,
key = 'cyl',
value = 'cyl_lab')
# A tibble: 32 × 10
cars mpg cyl disp hp drat wt qsec gear carb
<chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Mazda RX4 21 6-cylinder 160 110 3.9 2.62 16.5 4 4
2 Mazda RX4 Wag 21 6-cylinder 160 110 3.9 2.88 17.0 4 4
3 Datsun 710 22.8 4-cylinder 108 93 3.85 2.32 18.6 4 1
4 Hornet 4 Drive 21.4 6-cylinder 258 110 3.08 3.22 19.4 3 1
5 Hornet Sportabout 18.7 6+ cylinder 360 175 3.15 3.44 17.0 3 2
6 Valiant 18.1 6-cylinder 225 105 2.76 3.46 20.2 3 1
7 Duster 360 14.3 6+ cylinder 360 245 3.21 3.57 15.8 3 4
8 Merc 240D 24.4 4-cylinder 147. 62 3.69 3.19 20 4 2
9 Merc 230 22.8 4-cylinder 141. 95 3.92 3.15 22.9 4 2
10 Merc 280 19.2 6-cylinder 168. 123 3.92 3.44 18.3 4 4
# … with 22 more rows
# ℹ Use `print(n = ...)` to see more rows
## for a matrix:
exchange(my_cyl_mtx,
dict = cyl_reco,
key = 'cyl',
value = 'cyl_lab')
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "6+ cylinder" NA "4-cylinder" "6-cylinder" "6-cylinder" "6-cylinder" "4-cylinder" "6-cylinder" NA NA
[2,] "6-cylinder" NA "4-cylinder" "6+ cylinder" NA "6+ cylinder" NA "6+ cylinder" "6+ cylinder" NA
## for a list:
exchange(my_cyl_list,
dict = cyl_reco,
key = 'cyl',
value = 'cyl_lab',
variable = 'cyl')
$A
<NA> 6 <NA> 8 4 4 8 6 8
NA "6-cylinder" NA "6+ cylinder" "4-cylinder" "4-cylinder" "6+ cylinder" "6-cylinder" "6+ cylinder"
<NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
NA NA NA NA NA NA NA NA NA
8 8
"6+ cylinder" "6+ cylinder"
$B
<NA> <NA> <NA> <NA> 6 <NA> <NA> <NA> 8
NA NA NA NA "6-cylinder" NA NA NA "6+ cylinder"
8 <NA> 8 4 4 6 <NA> 6 <NA>
"6+ cylinder" NA "6+ cylinder" "4-cylinder" "4-cylinder" "6-cylinder" NA "6-cylinder" NA
<NA> <NA>
NA NA
$C
[,1] [,2]
[1,] "6+-cylinder" "6+ cylinder"
[2,] "4-cylinder" "6+-cylinder"
[3,] "6+ cylinder" "6+-cylinder"
[4,] "6+-cylinder" "4-cylinder"
[5,] "6+-cylinder" "6+ cylinder"
[6,] "4-cylinder" "6+-cylinder"
$D
# A tibble: 32 × 10
cars mpg cyl disp hp drat wt qsec gear carb
<chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Mazda RX4 21 6-cylinder 160 110 3.9 2.62 16.5 4 4
2 Mazda RX4 Wag 21 6-cylinder 160 110 3.9 2.88 17.0 4 4
3 Datsun 710 22.8 4-cylinder 108 93 3.85 2.32 18.6 4 1
4 Hornet 4 Drive 21.4 6-cylinder 258 110 3.08 3.22 19.4 3 1
5 Hornet Sportabout 18.7 6+ cylinder 360 175 3.15 3.44 17.0 3 2
6 Valiant 18.1 6-cylinder 225 105 2.76 3.46 20.2 3 1
7 Duster 360 14.3 6+ cylinder 360 245 3.21 3.57 15.8 3 4
8 Merc 240D 24.4 4-cylinder 147. 62 3.69 3.19 20 4 2
9 Merc 230 22.8 4-cylinder 141. 95 3.92 3.15 22.9 4 2
10 Merc 280 19.2 6-cylinder 168. 123 3.92 3.44 18.3 4 4
# … with 22 more rows
# ℹ Use `print(n = ...)` to see more rows
Compressing lists and vectors to data frames
compress() allows for reduction of multi-dimensional list to a simple data frame:
## the input list storing data of various types
my_cyl_list
$A
[1] 2 1 8 4 7 1 3 7 8 3 8 1 4 6 8 8 1 1 5 2
$B
[1] 8 7 2 7 8 2 8 5 5 1 3 3 1 4 1 3 6 6 4 3
$C
[,1] [,2]
[1,] 12 8
[2,] 4 10
[3,] 8 12
[4,] 10 4
[5,] 12 8
[6,] 4 10
$D
# A tibble: 32 × 10
cars mpg cyl disp hp drat wt qsec gear carb
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Mazda RX4 21 6 160 110 3.9 2.62 16.5 4 4
2 Mazda RX4 Wag 21 6 160 110 3.9 2.88 17.0 4 4
3 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 4 1
4 Hornet 4 Drive 21.4 6 258 110 3.08 3.22 19.4 3 1
5 Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.0 3 2
6 Valiant 18.1 6 225 105 2.76 3.46 20.2 3 1
7 Duster 360 14.3 8 360 245 3.21 3.57 15.8 3 4
8 Merc 240D 24.4 4 147. 62 3.69 3.19 20 4 2
9 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 4 2
10 Merc 280 19.2 6 168. 123 3.92 3.44 18.3 4 4
# … with 22 more rows
# ℹ Use `print(n = ...)` to see more rows
## gets compacted to a simple data frame
compress(my_cyl_list,
names_to = 'lst_names',
values_to = 'lst_elements',
simplify = TRUE)
# A tibble: 4 × 2
lst_names lst_elements
<chr> <named list>
1 A <int [20]>
2 B <int [20]>
3 C <dbl [6 × 2]>
4 D <tibble [32 × 10]>
The compress() function reverts the effects of e.g. plyr::dlply() or split() functions using for conversin of data frames to lists:
## split the 'cars' data frame by cyclinder count
my_cars %>%
dlply('cyl')
$`4`
cars mpg cyl disp hp drat wt qsec gear carb
1 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 4 1
2 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 4 2
3 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 4 2
4 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 4 1
5 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 4 2
6 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 4 1
7 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 3 1
8 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 4 1
9 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 5 2
10 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 5 2
11 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 4 2
$`6`
cars mpg cyl disp hp drat wt qsec gear carb
1 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 4 4
2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 4 4
3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 3 1
4 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 3 1
5 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 4 4
6 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 4 4
7 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 5 6
$`8`
cars mpg cyl disp hp drat wt qsec gear carb
1 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 3 2
2 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 3 4
3 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 3 3
4 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 3 3
5 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 3 3
6 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 3 4
7 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 3 4
8 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 3 4
9 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 3 2
10 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 3 2
11 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 3 4
12 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 3 2
13 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 5 4
14 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 5 8
## and stitch the list back to a data frame with 'compress()'
my_cars %>%
dlply('cyl') %>%
compress(names_to = 'cyl') %>%
as_tibble
# A tibble: 32 × 10
cars mpg cyl disp hp drat wt qsec gear carb
<chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 4 1
2 Merc 240D 24.4 4 147. 62 3.69 3.19 20 4 2
3 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 4 2
4 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.5 4 1
5 Honda Civic 30.4 4 75.7 52 4.93 1.62 18.5 4 2
6 Toyota Corolla 33.9 4 71.1 65 4.22 1.84 19.9 4 1
7 Toyota Corona 21.5 4 120. 97 3.7 2.46 20.0 3 1
8 Fiat X1-9 27.3 4 79 66 4.08 1.94 18.9 4 1
9 Porsche 914-2 26 4 120. 91 4.43 2.14 16.7 5 2
10 Lotus Europa 30.4 4 95.1 113 3.77 1.51 16.9 5 2
# … with 22 more rows
# ℹ Use `print(n = ...)` to see more rows
Of practical importance, 'compress()' may be employed to create data frame from named vectors:
c('4' = '4-cylinder',
'6' = '6-cylinder',
'8' = '6+ cylinder',
'10' = '6+-cylinder',
'12' = '6+-cylinder') %>%
compress(names_to = 'cyclinder_number',
values_to = 'cylinder_category')
# A tibble: 5 × 2
cyclinder_number cylinder_category
<chr> <chr>
1 4 4-cylinder
2 6 6-cylinder
3 8 6+ cylinder
4 10 6+-cylinder
5 12 6+-cylinder
Binding by rows
The old R's rbind() is a true workhorse of tabular data manipulation. Yet, on occasions, rbind() is too strict: what about stitching two data frames with non-overlapping or partially overlapping variable sets? There are also situations when, rbind() is not strict enough, i.e. you intend to include in the result only the variables shared by both input data frames. For such tasks, full_rbind() and inner_rbind() are implemented, which take a broad range of arguments: vectors, matrices and data frames:
## overlapping variable sets
full_rbind(my_cars, my_cars2)
# A tibble: 64 × 13
cars mpg cyl disp hp drat wt qsec gear carb vs am ncap
* <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
1 Mazda RX4 21 6 160 110 3.9 2.62 16.5 4 4 NA NA NA
2 Mazda RX4 Wag 21 6 160 110 3.9 2.88 17.0 4 4 NA NA NA
3 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 4 1 NA NA NA
4 Hornet 4 Drive 21.4 6 258 110 3.08 3.22 19.4 3 1 NA NA NA
5 Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.0 3 2 NA NA NA
6 Valiant 18.1 6 225 105 2.76 3.46 20.2 3 1 NA NA NA
7 Duster 360 14.3 8 360 245 3.21 3.57 15.8 3 4 NA NA NA
8 Merc 240D 24.4 4 147. 62 3.69 3.19 20 4 2 NA NA NA
9 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 4 2 NA NA NA
10 Merc 280 19.2 6 168. 123 3.92 3.44 18.3 4 4 NA NA NA
# … with 54 more rows
# ℹ Use `print(n = ...)` to see more rows
inner_rbind(my_cars, my_cars2)
# A tibble: 64 × 9
mpg cyl disp hp drat wt qsec gear carb
* <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 21 6 160 110 3.9 2.62 16.5 4 4
2 21 6 160 110 3.9 2.88 17.0 4 4
3 22.8 4 108 93 3.85 2.32 18.6 4 1
4 21.4 6 258 110 3.08 3.22 19.4 3 1
5 18.7 8 360 175 3.15 3.44 17.0 3 2
6 18.1 6 225 105 2.76 3.46 20.2 3 1
7 14.3 8 360 245 3.21 3.57 15.8 3 4
8 24.4 4 147. 62 3.69 3.19 20 4 2
9 22.8 4 141. 95 3.92 3.15 22.9 4 2
10 19.2 6 168. 123 3.92 3.44 18.3 4 4
# … with 54 more rows
# ℹ Use `print(n = ...)` to see more rows
## non-overlapping variable sets
full_rbind(my_cars[1:3], my_cars[6:7])
# A tibble: 64 × 5
cars mpg cyl drat wt
<chr> <dbl> <dbl> <dbl> <dbl>
1 Mazda RX4 21 6 NA NA
2 Mazda RX4 Wag 21 6 NA NA
3 Datsun 710 22.8 4 NA NA
4 Hornet 4 Drive 21.4 6 NA NA
5 Hornet Sportabout 18.7 8 NA NA
6 Valiant 18.1 6 NA NA
7 Duster 360 14.3 8 NA NA
8 Merc 240D 24.4 4 NA NA
9 Merc 230 22.8 4 NA NA
10 Merc 280 19.2 6 NA NA
# … with 54 more rows
# ℹ Use `print(n = ...)` to see more rows
inner_rbind(my_cars[1:3], my_cars[6:7])
data frame with 0 columns and 0 rows
Splitting by a factor
Splitting of a data frame is accomplished by blast(), which takes one or more splitting variables in a quoted or unquoted form:
## splitting: a variable name vector and an unquoted argument
my_cars %>%
blast(all_of(c('cyl', 'gear')),
carb)
$`4.3.1`
# A tibble: 1 × 10
cars mpg cyl disp hp drat wt qsec gear carb
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Toyota Corona 21.5 4 120. 97 3.7 2.46 20.0 3 1
$`6.3.1`
# A tibble: 2 × 10
cars mpg cyl disp hp drat wt qsec gear carb
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Hornet 4 Drive 21.4 6 258 110 3.08 3.22 19.4 3 1
2 Valiant 18.1 6 225 105 2.76 3.46 20.2 3 1
$`4.4.1`
# A tibble: 4 × 10
cars mpg cyl disp hp drat wt qsec gear carb
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 4 1
2 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.5 4 1
3 Toyota Corolla 33.9 4 71.1 65 4.22 1.84 19.9 4 1
4 Fiat X1-9 27.3 4 79 66 4.08 1.94 18.9 4 1
$`8.3.2`
# A tibble: 4 × 10
cars mpg cyl disp hp drat wt qsec gear carb
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.0 3 2
2 Dodge Challenger 15.5 8 318 150 2.76 3.52 16.9 3 2
3 AMC Javelin 15.2 8 304 150 3.15 3.44 17.3 3 2
4 Pontiac Firebird 19.2 8 400 175 3.08 3.84 17.0 3 2
$`4.4.2`
# A tibble: 4 × 10
cars mpg cyl disp hp drat wt qsec gear carb
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Merc 240D 24.4 4 147. 62 3.69 3.19 20 4 2
2 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 4 2
3 Honda Civic 30.4 4 75.7 52 4.93 1.62 18.5 4 2
4 Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 4 2
$`4.5.2`
# A tibble: 2 × 10
cars mpg cyl disp hp drat wt qsec gear carb
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Porsche 914-2 26 4 120. 91 4.43 2.14 16.7 5 2
2 Lotus Europa 30.4 4 95.1 113 3.77 1.51 16.9 5 2
$`8.3.3`
# A tibble: 3 × 10
cars mpg cyl disp hp drat wt qsec gear carb
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Merc 450SE 16.4 8 276. 180 3.07 4.07 17.4 3 3
2 Merc 450SL 17.3 8 276. 180 3.07 3.73 17.6 3 3
3 Merc 450SLC 15.2 8 276. 180 3.07 3.78 18 3 3
$`8.3.4`
# A tibble: 5 × 10
cars mpg cyl disp hp drat wt qsec gear carb
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Duster 360 14.3 8 360 245 3.21 3.57 15.8 3 4
2 Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 18.0 3 4
3 Lincoln Continental 10.4 8 460 215 3 5.42 17.8 3 4
4 Chrysler Imperial 14.7 8 440 230 3.23 5.34 17.4 3 4
5 Camaro Z28 13.3 8 350 245 3.73 3.84 15.4 3 4
$`6.4.4`
# A tibble: 4 × 10
cars mpg cyl disp hp drat wt qsec gear carb
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Mazda RX4 21 6 160 110 3.9 2.62 16.5 4 4
2 Mazda RX4 Wag 21 6 160 110 3.9 2.88 17.0 4 4
3 Merc 280 19.2 6 168. 123 3.92 3.44 18.3 4 4
4 Merc 280C 17.8 6 168. 123 3.92 3.44 18.9 4 4
$`8.5.4`
# A tibble: 1 × 10
cars mpg cyl disp hp drat wt qsec gear carb
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 5 4
$`6.5.6`
# A tibble: 1 × 10
cars mpg cyl disp hp drat wt qsec gear carb
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 5 6
$`8.5.8`
# A tibble: 1 × 10
cars mpg cyl disp hp drat wt qsec gear carb
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Maserati Bora 15 8 301 335 3.54 3.57 14.6 5 8
Regular expression search
The reglook() function enables for selecting data frame records with a regular expression match. All or user-specified variables may be searched:
## let's select all Hondas and Mercedes from the 'cars' data set:
reglook(my_cars,
regex = '^(Merc|Honda)')
# A tibble: 8 × 10
cars mpg cyl disp hp drat wt qsec gear carb
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Merc 240D 24.4 4 147. 62 3.69 3.19 20 4 2
2 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 4 2
3 Merc 280 19.2 6 168. 123 3.92 3.44 18.3 4 4
4 Merc 280C 17.8 6 168. 123 3.92 3.44 18.9 4 4
5 Merc 450SE 16.4 8 276. 180 3.07 4.07 17.4 3 3
6 Merc 450SL 17.3 8 276. 180 3.07 3.73 17.6 3 3
7 Merc 450SLC 15.2 8 276. 180 3.07 3.78 18 3 3
8 Honda Civic 30.4 4 75.7 52 4.93 1.62 18.5 4 2
## or all cars with 4- or 6-cylinder engines
reglook(my_cars,
regex = '4|6',
keys = 'cyl')
# A tibble: 18 × 10
cars mpg cyl disp hp drat wt qsec gear carb
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Mazda RX4 21 6 160 110 3.9 2.62 16.5 4 4
2 Mazda RX4 Wag 21 6 160 110 3.9 2.88 17.0 4 4
3 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 4 1
4 Hornet 4 Drive 21.4 6 258 110 3.08 3.22 19.4 3 1
5 Valiant 18.1 6 225 105 2.76 3.46 20.2 3 1
6 Merc 240D 24.4 4 147. 62 3.69 3.19 20 4 2
7 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 4 2
8 Merc 280 19.2 6 168. 123 3.92 3.44 18.3 4 4
9 Merc 280C 17.8 6 168. 123 3.92 3.44 18.9 4 4
10 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.5 4 1
11 Honda Civic 30.4 4 75.7 52 4.93 1.62 18.5 4 2
12 Toyota Corolla 33.9 4 71.1 65 4.22 1.84 19.9 4 1
13 Toyota Corona 21.5 4 120. 97 3.7 2.46 20.0 3 1
14 Fiat X1-9 27.3 4 79 66 4.08 1.94 18.9 4 1
15 Porsche 914-2 26 4 120. 91 4.43 2.14 16.7 5 2
16 Lotus Europa 30.4 4 95.1 113 3.77 1.51 16.9 5 2
17 Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 5 6
18 Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 4 2
The package is available under a GPL-3 license.
The package maintainer is Piotr Tymoszuk.
trafo uses tools provided by the rlang, tidyverse and stringi