README.md
In UBC-MDS/mealprepR: Helps Prepare Your Data Meal
mealprepR
MealprepR offers a toolkit, made with care, to help users save time in
the data preprocessing kitchen.
Overview
Recognizing that the preparation step of a data science project often
requires the most time and effort, mealprepR aims to help data science
chefs of all specialties master their recipes of analysis. This package
tackles pesky tasks such as classifying columns as categorical or
numeric ingredients, straining NA values and outliers, and automating a
preprocessing recipe pipeline.
Functions
find_fruits_veg(): This function returns the indices of numeric and
categorical variables in the dataset.
find_missing_ingredients(): For each column with missing values, this
function will create a reference list of row indices, sum the number,
and calculate the proportion of missing values.
find_bad_apples(): This function uses a univariate approach to outlier
detection. For each column with outliers (values that are 2 or more
standard deviations from the mean), this function will create a
reference list of row indices with outliers, and the total number of
outliers in that column.
make_recipe(): This function is used to quickly apply common data
preprocessing techniques.
mealprepR and R’s Ecosystem
mealprepR complements many of the existing packages in the R
ecosystem around the theme of data preprocessing. With respect to
categorizing the columns of a dataframe by type, the base R code
sapply(yourdataframe, class) will produce the data types of each
column. However, there appears to be no easy way to divide the columns
into categorical and numerical groups. find_fruits_veg() aims to fill
this void.
In terms of missing values, the package
na.tools
provides a suite of tools to check the number and proportion of missing
values in a dataset as well as methods controlling how they may be
replaced. The
forecast
package includes the function na.interp() which provides a similar
replacement tool for time series data. The gap between these packages is
that neither provides a summary of the missing values including the list
of indices where they occur. find_missing_ingredients() augments these
tools by providing a summary dataframe detailing which columns have
missing values, as well as their count and proportion.
The base R summary() function is a popular first function to run
during the data exploration stage of a project because it returns the
mean, median, minimum, and maximum of each variable in a dataset, which
allows users to easily see whether there are possible outliers. However,
the output of this function does not tell you which rows of data these
outliers are found in, or how many outliers are present in the
dataframe. Packages like
outliers
and
OutlierDetection
provide many more ways of detecting outliers using both univariate and
multivariate outlier detection methods, and some functions such as the
outlierTest() function from the
car package also
output the row within a dataframe which has the most extreme observation
given a model. The find_bad_apples() function provides more detail
than the base R summary() function for outlier detection, but does not
provide multivariate outlier detection like other functions from
packages specifically made to detect outliers.
Lastly, there are many great tools in the data science ecosystem for
pre-processing data such as
caret in R.
However, you may find yourself frequently writing the same lengthy code
for common preprocessing tasks (e.g scale numeric features and one hot
encode categorical features). preprocess_recipe() provides a shortcut
function to apply your favourite recipes quickly to preprocess data in
one line of code.
Installation
You can install the released version of mealprepR from
CRAN with:
install.packages("mealprepR")
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("UBC-MDS/mealprepR")
Documentation
To view mealprepR documentation online, please visit
https://ubc-mds.github.io/mealprepR/
Examples
To load the package:
library(mealprepR)
library(devtools)
load_all()
find_fruits_veg()
find_fruits_veg() will help you find the wanted columns index. The
example below shows how to use find_fruits_veg() to find numerical
columns’ index of iris data frame.
head(iris)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
find_fruits_veg(iris, 'num') # find the numeric column indices
#> [1] 1 2 3 4
find_fruits_veg(iris, 'categ') # find the categorical column indices
#> [1] 5
find_missing_ingredients()
Before launching into a new data analysis, running the function
find_missing_ingredients() on a data frame of interest will produce a
report on each column with missing values.
To demonstrate how this function works, follow the example below with
toy data
df <- data.frame("letters" = c(1,2,NA,"b"),"numbers" = c(3,4,NA,NA))
df
#> letters numbers
#> 1 1 3
#> 2 2 4
#> 3 <NA> NA
#> 4 b NA
find_missing_ingredients(df)
#> Column_name NA_count NA_proportion NA_indices
#> letters letters 1 25% 3
#> numbers numbers 2 50% 3, 4
find_bad_apples()
If you don’t already have a dataframe to work with, run this code to set
up a toy dataframe (df) for testing.
df <- data.frame('A' = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
'B' = c(1, 1, 1, 1, 1, 1, 1, 1, 1, -100,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 100),
'C' = c(19, 1, 1, 1, 17, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 20, 1, 1, 1))
df
#> A B C
#> 1 1 1 19
#> 2 1 1 1
#> 3 1 1 1
#> 4 1 1 1
#> 5 1 1 17
#> 6 1 1 1
#> 7 1 1 1
#> 8 1 1 1
#> 9 1 1 1
#> 10 1 -100 1
#> 11 1 1 1
#> 12 1 1 1
#> 13 1 1 1
#> 14 1 1 1
#> 15 1 1 1
#> 16 1 1 1
#> 17 1 1 1
#> 18 1 1 1
#> 19 1 1 1
#> 20 1 1 1
#> 21 1 1 1
#> 22 1 1 1
#> 23 1 1 1
#> 24 1 1 1
#> 25 1 1 1
#> 26 1 1 1
#> 27 1 1 20
#> 28 1 1 1
#> 29 1 1 1
#> 30 1 100 1
To find the outliers in the dataframe, run find_bad_apples(df) and
you’ll get a dataframe that shows which columns have outliers.
find_bad_apples(df)
#> # A tibble: 2 x 3
#> # Groups: variable, total_outliers [2]
#> variable total_outliers indices
#> <chr> <dbl> <list>
#> 1 B 2 <tibble [2 × 1]>
#> 2 C 3 <tibble [3 × 1]>
To see the nested tibbles that show the indices of the outliers, add
$indices to the end of the function. This output shows that column ‘B’
has outliers in rows 10 and 30, and column ‘C’ has outliers in rows 1,
5, and 27.
find_bad_apples(df)$indices
#> [[1]]
#> # A tibble: 2 x 1
#> indices
#> <dbl>
#> 1 10
#> 2 30
#>
#> [[2]]
#> # A tibble: 3 x 1
#> indices
#> <dbl>
#> 1 1
#> 2 5
#> 3 27
make_recipe()
Do you find yourslef constantly applying the same data preprocessing
techniques time and time again? make_recipe can help by applying your
favourite preprocessing recips in only a few lines of code.
Below make_recipe applies the following common recipe in only one line
of code:
- Split data into training, validation, and testing
- Standardise and scale numeric features
- One hot encode categorical features
First load the classic mtcars data set.
library(dplyr)
X <- dplyr::as_tibble(mtcars) %>%
mutate(
carb = as.factor(carb),
gear = as.factor(gear),
vs = as.factor(vs),
am = as.factor(am)
)
head(X)
#> # A tibble: 6 x 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <fct> <fct> <fct>
#> 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
Use make_recipe to quickly apply split your data and apply your
favourite preprocessing techniques!
library(mealprepR)
mtcars_splits <- make_recipe(
X = X,
y = "gear",
recipe = "ohe_and_standard_scaler",
splits_to_return = "train_test"
)
head(mtcars_splits$X_train)
#> # A tibble: 6 x 17
#> mpg cyl disp hp drat wt qsec vs_0 vs_1 am_0 am_1
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.116 -0.0442 -0.515 -0.497 0.537 -0.545 -0.796 1 0 0 1
#> 2 0.116 -0.0442 -0.515 -0.497 0.537 -0.275 -0.486 1 0 0 1
#> 3 0.403 -1.19 -0.935 -0.755 0.446 -0.862 0.394 0 1 0 1
#> 4 0.180 -0.0442 0.278 -0.497 -0.956 0.0846 0.854 0 1 1 0
#> 5 -0.250 1.10 1.10 0.489 -0.828 0.323 -0.486 1 0 1 0
#> 6 -0.345 -0.0442 0.0109 -0.573 -1.54 0.344 1.29 0 1 1 0
#> # … with 6 more variables: carb_1 <dbl>, carb_2 <dbl>, carb_3 <dbl>,
#> # carb_4 <dbl>, carb_6 <dbl>, carb_8 <dbl>
Calling make_recipe will then return a list containing all of your
transformed data.
str(mtcars_splits)
#> List of 6
#> $ X_train:Classes 'tbl_df', 'tbl' and 'data.frame': 26 obs. of 17 variables:
#> ..$ mpg : num [1:26] 0.116 0.116 0.403 0.18 -0.25 ...
#> ..$ cyl : num [1:26] -0.0442 -0.0442 -1.1924 -0.0442 1.1041 ...
#> ..$ disp : num [1:26] -0.515 -0.515 -0.935 0.278 1.102 ...
#> ..$ hp : num [1:26] -0.497 -0.497 -0.755 -0.497 0.489 ...
#> ..$ drat : num [1:26] 0.537 0.537 0.446 -0.956 -0.828 ...
#> ..$ wt : num [1:26] -0.5449 -0.2751 -0.8623 0.0846 0.3226 ...
#> ..$ qsec : num [1:26] -0.796 -0.486 0.394 0.854 -0.486 ...
#> ..$ vs_0 : num [1:26] 1 1 0 0 1 0 1 0 0 0 ...
#> ..$ vs_1 : num [1:26] 0 0 1 1 0 1 0 1 1 1 ...
#> ..$ am_0 : num [1:26] 0 0 0 1 1 1 1 1 1 1 ...
#> ..$ am_1 : num [1:26] 1 1 1 0 0 0 0 0 0 0 ...
#> ..$ carb_1: num [1:26] 0 0 1 1 0 1 0 0 0 0 ...
#> ..$ carb_2: num [1:26] 0 0 0 0 1 0 0 1 0 0 ...
#> ..$ carb_3: num [1:26] 0 0 0 0 0 0 0 0 0 0 ...
#> ..$ carb_4: num [1:26] 1 1 0 0 0 0 1 0 1 1 ...
#> ..$ carb_6: num [1:26] 0 0 0 0 0 0 0 0 0 0 ...
#> ..$ carb_8: num [1:26] 0 0 0 0 0 0 0 0 0 0 ...
#> $ X_valid:Classes 'tbl_df', 'tbl' and 'data.frame': 0 obs. of 0 variables
#> $ X_test :Classes 'tbl_df', 'tbl' and 'data.frame': 6 obs. of 17 variables:
#> ..$ mpg : num [1:6] 0.658 -0.616 -0.473 -0.887 1.12 ...
#> ..$ cyl : num [1:6] -1.19 1.1 1.1 1.1 -1.19 ...
#> ..$ disp : num [1:6] -0.622 0.422 0.422 1.749 -1.169 ...
#> ..$ hp : num [1:6] -1.225 0.565 0.565 1.323 -1.164 ...
#> ..$ drat : num [1:6] 0.155 -0.974 -0.974 -0.683 0.865 ...
#> ..$ wt : num [1:6] 0.0581 0.989 0.6294 2.3379 -1.2696 ...
#> ..$ qsec : num [1:6] 1.164 -0.276 -0.165 -0.265 0.555 ...
#> ..$ vs_0 : num [1:6] 0 1 1 1 0 1
#> ..$ vs_1 : num [1:6] 1 0 0 0 1 0
#> ..$ am_0 : num [1:6] 1 1 1 1 0 0
#> ..$ am_1 : num [1:6] 0 0 0 0 1 1
#> ..$ carb_1: num [1:6] 0 0 0 0 1 0
#> ..$ carb_2: num [1:6] 1 0 0 0 0 0
#> ..$ carb_3: num [1:6] 0 1 1 0 0 0
#> ..$ carb_4: num [1:6] 0 0 0 1 0 1
#> ..$ carb_6: num [1:6] 0 0 0 0 0 0
#> ..$ carb_8: num [1:6] 0 0 0 0 0 0
#> $ y_train:Classes 'tbl_df', 'tbl' and 'data.frame': 26 obs. of 1 variable:
#> ..$ gear: Factor w/ 3 levels "3","4","5": 2 2 2 1 1 1 1 2 2 2 ...
#> $ y_valid:Classes 'tbl_df', 'tbl' and 'data.frame': 0 obs. of 0 variables
#> $ y_test :Classes 'tbl_df', 'tbl' and 'data.frame': 6 obs. of 1 variable:
#> ..$ gear: Factor w/ 3 levels "3","4","5": 2 1 1 1 2 3
UBC-MDS/mealprepR documentation built on April 1, 2020, 4:36 a.m.
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mealprepR
MealprepR offers a toolkit, made with care, to help users save time in the data preprocessing kitchen.
Overview
Recognizing that the preparation step of a data science project often
requires the most time and effort, mealprepR aims to help data science
chefs of all specialties master their recipes of analysis. This package
tackles pesky tasks such as classifying columns as categorical or
numeric ingredients, straining NA values and outliers, and automating a
preprocessing recipe pipeline.
Functions
find_fruits_veg(): This function returns the indices of numeric and
categorical variables in the dataset.
find_missing_ingredients(): For each column with missing values, this
function will create a reference list of row indices, sum the number,
and calculate the proportion of missing values.
find_bad_apples(): This function uses a univariate approach to outlier
detection. For each column with outliers (values that are 2 or more
standard deviations from the mean), this function will create a
reference list of row indices with outliers, and the total number of
outliers in that column.
make_recipe(): This function is used to quickly apply common data
preprocessing techniques.
mealprepR and R’s Ecosystem
mealprepR complements many of the existing packages in the R
ecosystem around the theme of data preprocessing. With respect to
categorizing the columns of a dataframe by type, the base R code
sapply(yourdataframe, class) will produce the data types of each
column. However, there appears to be no easy way to divide the columns
into categorical and numerical groups. find_fruits_veg() aims to fill
this void.
In terms of missing values, the package
na.tools
provides a suite of tools to check the number and proportion of missing
values in a dataset as well as methods controlling how they may be
replaced. The
forecast
package includes the function na.interp() which provides a similar
replacement tool for time series data. The gap between these packages is
that neither provides a summary of the missing values including the list
of indices where they occur. find_missing_ingredients() augments these
tools by providing a summary dataframe detailing which columns have
missing values, as well as their count and proportion.
The base R summary() function is a popular first function to run
during the data exploration stage of a project because it returns the
mean, median, minimum, and maximum of each variable in a dataset, which
allows users to easily see whether there are possible outliers. However,
the output of this function does not tell you which rows of data these
outliers are found in, or how many outliers are present in the
dataframe. Packages like
outliers
and
OutlierDetection
provide many more ways of detecting outliers using both univariate and
multivariate outlier detection methods, and some functions such as the
outlierTest() function from the
car package also
output the row within a dataframe which has the most extreme observation
given a model. The find_bad_apples() function provides more detail
than the base R summary() function for outlier detection, but does not
provide multivariate outlier detection like other functions from
packages specifically made to detect outliers.
Lastly, there are many great tools in the data science ecosystem for
pre-processing data such as
caret in R.
However, you may find yourself frequently writing the same lengthy code
for common preprocessing tasks (e.g scale numeric features and one hot
encode categorical features). preprocess_recipe() provides a shortcut
function to apply your favourite recipes quickly to preprocess data in
one line of code.
Installation
You can install the released version of mealprepR from CRAN with:
install.packages("mealprepR")
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("UBC-MDS/mealprepR")
Documentation
To view mealprepR documentation online, please visit https://ubc-mds.github.io/mealprepR/
Examples
To load the package:
library(mealprepR)
library(devtools)
load_all()
find_fruits_veg()
find_fruits_veg() will help you find the wanted columns index. The
example below shows how to use find_fruits_veg() to find numerical
columns’ index of iris data frame.
head(iris)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
find_fruits_veg(iris, 'num') # find the numeric column indices
#> [1] 1 2 3 4
find_fruits_veg(iris, 'categ') # find the categorical column indices
#> [1] 5
find_missing_ingredients()
Before launching into a new data analysis, running the function
find_missing_ingredients() on a data frame of interest will produce a
report on each column with missing values.
To demonstrate how this function works, follow the example below with toy data
df <- data.frame("letters" = c(1,2,NA,"b"),"numbers" = c(3,4,NA,NA))
df
#> letters numbers
#> 1 1 3
#> 2 2 4
#> 3 <NA> NA
#> 4 b NA
find_missing_ingredients(df)
#> Column_name NA_count NA_proportion NA_indices
#> letters letters 1 25% 3
#> numbers numbers 2 50% 3, 4
find_bad_apples()
If you don’t already have a dataframe to work with, run this code to set
up a toy dataframe (df) for testing.
df <- data.frame('A' = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
'B' = c(1, 1, 1, 1, 1, 1, 1, 1, 1, -100,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 100),
'C' = c(19, 1, 1, 1, 17, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 20, 1, 1, 1))
df
#> A B C
#> 1 1 1 19
#> 2 1 1 1
#> 3 1 1 1
#> 4 1 1 1
#> 5 1 1 17
#> 6 1 1 1
#> 7 1 1 1
#> 8 1 1 1
#> 9 1 1 1
#> 10 1 -100 1
#> 11 1 1 1
#> 12 1 1 1
#> 13 1 1 1
#> 14 1 1 1
#> 15 1 1 1
#> 16 1 1 1
#> 17 1 1 1
#> 18 1 1 1
#> 19 1 1 1
#> 20 1 1 1
#> 21 1 1 1
#> 22 1 1 1
#> 23 1 1 1
#> 24 1 1 1
#> 25 1 1 1
#> 26 1 1 1
#> 27 1 1 20
#> 28 1 1 1
#> 29 1 1 1
#> 30 1 100 1
To find the outliers in the dataframe, run find_bad_apples(df) and
you’ll get a dataframe that shows which columns have outliers.
find_bad_apples(df)
#> # A tibble: 2 x 3
#> # Groups: variable, total_outliers [2]
#> variable total_outliers indices
#> <chr> <dbl> <list>
#> 1 B 2 <tibble [2 × 1]>
#> 2 C 3 <tibble [3 × 1]>
To see the nested tibbles that show the indices of the outliers, add
$indices to the end of the function. This output shows that column ‘B’
has outliers in rows 10 and 30, and column ‘C’ has outliers in rows 1,
5, and 27.
find_bad_apples(df)$indices
#> [[1]]
#> # A tibble: 2 x 1
#> indices
#> <dbl>
#> 1 10
#> 2 30
#>
#> [[2]]
#> # A tibble: 3 x 1
#> indices
#> <dbl>
#> 1 1
#> 2 5
#> 3 27
make_recipe()
Do you find yourslef constantly applying the same data preprocessing
techniques time and time again? make_recipe can help by applying your
favourite preprocessing recips in only a few lines of code.
Below make_recipe applies the following common recipe in only one line
of code:
- Split data into training, validation, and testing
- Standardise and scale numeric features
- One hot encode categorical features
First load the classic mtcars data set.
library(dplyr)
X <- dplyr::as_tibble(mtcars) %>%
mutate(
carb = as.factor(carb),
gear = as.factor(gear),
vs = as.factor(vs),
am = as.factor(am)
)
head(X)
#> # A tibble: 6 x 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <fct> <fct> <fct>
#> 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
Use make_recipe to quickly apply split your data and apply your
favourite preprocessing techniques!
library(mealprepR)
mtcars_splits <- make_recipe(
X = X,
y = "gear",
recipe = "ohe_and_standard_scaler",
splits_to_return = "train_test"
)
head(mtcars_splits$X_train)
#> # A tibble: 6 x 17
#> mpg cyl disp hp drat wt qsec vs_0 vs_1 am_0 am_1
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.116 -0.0442 -0.515 -0.497 0.537 -0.545 -0.796 1 0 0 1
#> 2 0.116 -0.0442 -0.515 -0.497 0.537 -0.275 -0.486 1 0 0 1
#> 3 0.403 -1.19 -0.935 -0.755 0.446 -0.862 0.394 0 1 0 1
#> 4 0.180 -0.0442 0.278 -0.497 -0.956 0.0846 0.854 0 1 1 0
#> 5 -0.250 1.10 1.10 0.489 -0.828 0.323 -0.486 1 0 1 0
#> 6 -0.345 -0.0442 0.0109 -0.573 -1.54 0.344 1.29 0 1 1 0
#> # … with 6 more variables: carb_1 <dbl>, carb_2 <dbl>, carb_3 <dbl>,
#> # carb_4 <dbl>, carb_6 <dbl>, carb_8 <dbl>
Calling make_recipe will then return a list containing all of your
transformed data.
str(mtcars_splits)
#> List of 6
#> $ X_train:Classes 'tbl_df', 'tbl' and 'data.frame': 26 obs. of 17 variables:
#> ..$ mpg : num [1:26] 0.116 0.116 0.403 0.18 -0.25 ...
#> ..$ cyl : num [1:26] -0.0442 -0.0442 -1.1924 -0.0442 1.1041 ...
#> ..$ disp : num [1:26] -0.515 -0.515 -0.935 0.278 1.102 ...
#> ..$ hp : num [1:26] -0.497 -0.497 -0.755 -0.497 0.489 ...
#> ..$ drat : num [1:26] 0.537 0.537 0.446 -0.956 -0.828 ...
#> ..$ wt : num [1:26] -0.5449 -0.2751 -0.8623 0.0846 0.3226 ...
#> ..$ qsec : num [1:26] -0.796 -0.486 0.394 0.854 -0.486 ...
#> ..$ vs_0 : num [1:26] 1 1 0 0 1 0 1 0 0 0 ...
#> ..$ vs_1 : num [1:26] 0 0 1 1 0 1 0 1 1 1 ...
#> ..$ am_0 : num [1:26] 0 0 0 1 1 1 1 1 1 1 ...
#> ..$ am_1 : num [1:26] 1 1 1 0 0 0 0 0 0 0 ...
#> ..$ carb_1: num [1:26] 0 0 1 1 0 1 0 0 0 0 ...
#> ..$ carb_2: num [1:26] 0 0 0 0 1 0 0 1 0 0 ...
#> ..$ carb_3: num [1:26] 0 0 0 0 0 0 0 0 0 0 ...
#> ..$ carb_4: num [1:26] 1 1 0 0 0 0 1 0 1 1 ...
#> ..$ carb_6: num [1:26] 0 0 0 0 0 0 0 0 0 0 ...
#> ..$ carb_8: num [1:26] 0 0 0 0 0 0 0 0 0 0 ...
#> $ X_valid:Classes 'tbl_df', 'tbl' and 'data.frame': 0 obs. of 0 variables
#> $ X_test :Classes 'tbl_df', 'tbl' and 'data.frame': 6 obs. of 17 variables:
#> ..$ mpg : num [1:6] 0.658 -0.616 -0.473 -0.887 1.12 ...
#> ..$ cyl : num [1:6] -1.19 1.1 1.1 1.1 -1.19 ...
#> ..$ disp : num [1:6] -0.622 0.422 0.422 1.749 -1.169 ...
#> ..$ hp : num [1:6] -1.225 0.565 0.565 1.323 -1.164 ...
#> ..$ drat : num [1:6] 0.155 -0.974 -0.974 -0.683 0.865 ...
#> ..$ wt : num [1:6] 0.0581 0.989 0.6294 2.3379 -1.2696 ...
#> ..$ qsec : num [1:6] 1.164 -0.276 -0.165 -0.265 0.555 ...
#> ..$ vs_0 : num [1:6] 0 1 1 1 0 1
#> ..$ vs_1 : num [1:6] 1 0 0 0 1 0
#> ..$ am_0 : num [1:6] 1 1 1 1 0 0
#> ..$ am_1 : num [1:6] 0 0 0 0 1 1
#> ..$ carb_1: num [1:6] 0 0 0 0 1 0
#> ..$ carb_2: num [1:6] 1 0 0 0 0 0
#> ..$ carb_3: num [1:6] 0 1 1 0 0 0
#> ..$ carb_4: num [1:6] 0 0 0 1 0 1
#> ..$ carb_6: num [1:6] 0 0 0 0 0 0
#> ..$ carb_8: num [1:6] 0 0 0 0 0 0
#> $ y_train:Classes 'tbl_df', 'tbl' and 'data.frame': 26 obs. of 1 variable:
#> ..$ gear: Factor w/ 3 levels "3","4","5": 2 2 2 1 1 1 1 2 2 2 ...
#> $ y_valid:Classes 'tbl_df', 'tbl' and 'data.frame': 0 obs. of 0 variables
#> $ y_test :Classes 'tbl_df', 'tbl' and 'data.frame': 6 obs. of 1 variable:
#> ..$ gear: Factor w/ 3 levels "3","4","5": 2 1 1 1 2 3
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