Package 'tidybins'

add_clusters

Description

Wraps KMeans_rcpp to create a column that is a cluster formed from select columns in the data frame. Clusters names are specified by capital letters.

Usage

add_clusters(.data, ..., n_clusters = 4, cluster_name = "cluster")

Arguments

.data	dataframe
...	columns to cluster (tidyselect)
n_clusters	integer
cluster_name	column name

Value

data frame

Examples

iris %>%
tibble::as_tibble() %>%
add_clusters(Sepal.Width, Sepal.Length, n_clusters = 3, cluster_name = "Sepal_Cluster") -> iris1

iris1

iris1 %>%
numeric_summary(original_col = Sepal.Width, bucket_col = Sepal_Cluster)

---

Bin Cols

Description

Make bins in a tidy fashion. Adds a column to your data frame containing the integer codes of the specified bins of a certain column. Specifying multiple columns is only intended for supervised binning, so mutliple columns can be simultaneously binned optimally with respect to a target variable.

Usage

bin_cols(
  .data,
  col,
  n_bins = 10,
  bin_type = "frequency",
  ...,
  target = NULL,
  pretty_labels = FALSE,
  seed = 1,
  method = "mdlp"
)

Arguments

.data	a data frame
col	a column, vector of columns, or tidyselect
n_bins	number of bins
bin_type	method to make bins
...	params to be passed to selected binning method
target	unquoted column for supervised binning
pretty_labels	logical. If T returns interval label rather than integer rank
seed	seed for stochastic binning (xgboost)
method	method for bin mdlp

Details

Description of the arguments for bin_type

frequency (fr)

creates bins of equal content via quantiles. Wraps bin with method "content". Similar to ntile

width (wi)

create bins of equal numeric width. Wraps bin with method "length"

kmeans (km)

create bins using 1-dimensional kmeans. Wraps bin with method "clusters"

value (va)

each bin has equal sum of values

xgboost (xg)

column is binned by best predictor of a target column using step_discretize_xgb

cart (ca)

if the col does not have enough distinct values, xgboost will fail and automatically revert to step_discretize_cart

woe (wo)

column is binned by weight of evidence. Requires binary target

logreg (lr)

column is binned by logistic regression. Requires binary target.

mdlp

uses the discretizeDF.supervised algorithm with a variety of methods.

Value

a data frame

Examples

iris %>%
bin_cols(Sepal.Width, n_bins = 5, pretty_labels = TRUE) %>%
bin_cols(Petal.Width, n_bins = 3, bin_type = c("width", "kmeans")) %>%
bin_cols(Sepal.Width, bin_type = "xgboost", target = Species, seed = 1) -> iris1

#binned columns are named by original name + method abbreviation + number bins created.
#Sometimes the actual number of bins is less than n_bins if the col lacks enough variance.
iris1 %>%
print(width = Inf)

iris1 %>%
bin_summary() %>%
print(width = Inf)

---

bin equal value

Description

Bins a numeric column such that each bin contains 10 Intended for positive numeric vectors that make sense to sum, such as sales. Negative and NAs get treated as 0. The function never puts two rows with the same value into different bins. Accessed by the "value" method of the bin_cols function.

Usage

bin_equal_value(mdb, col, n_bins = 10)

Arguments

mdb	dataframe
col	a numeric vector
n_bins	number of bins

Value

an integer vector

---

summarize bins

Description

Returns a summary of all bins created by 'bin_cols' in a data frame. Takes no arguments other than the data frame but relies on regular expressions based of the 'bin_cols' output in order to identify the corresponding columns.

Usage

bin_summary(mdb, ...)

Arguments

mdb	dataframe output from bin_cols
...	optional tidyselect specification for specific cols

Value

a tibble

Examples

iris %>%
bin_cols(Sepal.Width) %>%
bin_summary()

---

Drop Original Cols

Description

Drops the original column from the dataframe once bins are made. Throws an error if the same column has multiple bin cols.

Usage

drop_original_cols(.data, ..., restore_names = FALSE)

Arguments

.data	dataframe output from bin_cols
...	tidyselect. default chooses all cols created from binning
restore_names	Logical, default FALSE. rename the binned cols with the original column names?

Value

dataframe

Examples

iris %>%
 bin_cols(Sepal.Length) %>%
 bin_cols(Sepal.Width, pretty_labels = TRUE) -> iris1

iris1

iris1 %>%
 drop_original_cols(restore_names = TRUE)

iris1 %>%
 drop_original_cols(restore_names = FALSE)

---

five number summary

Description

The five number summary of a numeric vector you would get from 'summary' but returned with a tidy output.

Usage

five_number_summary(x)

Arguments

x	a numeric vector

Value

a tibble

Examples

iris$Petal.Width %>%
five_number_summary()

---

numeric summary

Description

This function summarizes an arbitrary bin column, with respect to its original column. Can be used to summarize bins created from any package, or any arbitrary categorical column paired with a numeric column.

Usage

numeric_summary(mdb, original_col, bucket_col)

Arguments

mdb	a data frame
original_col	original numeric column
bucket_col	columns of bins

Value

a tibble

Examples

iris %>%
numeric_summary(original_col = Sepal.Length, bucket_col = Species)

---