ohse: One Hot Smart Encoding (Dummy Variables)

In laresbernardo/lares: Analytics & Machine Learning Sidekick

One Hot Smart Encoding (Dummy Variables)

Description

This function lets the user automatically transform a dataframe with categorical columns into numerical by one hot encoding technic.

Usage

ohse(
  df,
  redundant = FALSE,
  drop = TRUE,
  ignore = NULL,
  dates = FALSE,
  holidays = FALSE,
  country = "Venezuela",
  currency_pair = NA,
  trim = 0,
  limit = 10,
  variance = 0.9,
  other_label = "OTHER",
  sep = "_",
  quiet = FALSE,
  ...
)

Arguments

df	Dataframe
redundant	Boolean. Should we keep redundant columns? i.e. If the column only has two different values, should we keep both new columns? Is set to NULL, only binary variables will dump redundant columns.
drop	Boolean. Drop automatically some useless features?
ignore	Vector or character. Which column should be ignored?
dates	Boolean. Do you want the function to create more features out of the date/time columns?
holidays	Boolean. Include holidays as new columns?
country	Character or vector. For which countries should the holidays be included?
currency_pair	Character. Which currency exchange do you wish to get the history from? i.e, USD/COP, EUR/USD...
trim	Integer. Trim names until the nth character
limit	Integer. Limit one hot encoding to the n most frequent values of each column. Set to NA to ignore argument.
variance	Numeric. Drop columns with more than n variance. Range: 0-1. For example: if a variable contains 91 unique different values out of 100 observations, this column will be suppressed if value is set to 0.9
other_label	Character. With which text do you wish to replace the filtered values with?
sep	Character. Separator's string
quiet	Boolean. Quiet all messages and summaries?
...	Additional parameters.

Value

data.frame on which all features are numerical by nature or transformed with one hot encoding.

See Also

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Other Feature Engineering: date_feats(), holidays()

Other One Hot Encoding: date_feats(), holidays(), ohe_commas()

Examples

data(dft)
dft <- dft[, c(2, 3, 5, 9, 11)]

ohse(dft, limit = 3) %>% head(3)
ohse(dft, limit = 3, redundant = NULL) %>% head(3)

# Getting rid of columns with no (or too much) variance
dft$no_variance1 <- 0
dft$no_variance2 <- c("A", rep("B", nrow(dft) - 1))
dft$no_variance3 <- as.character(rnorm(nrow(dft)))
dft$no_variance4 <- c(rep("A", 20), round(rnorm(nrow(dft) - 20), 4))
ohse(dft, limit = 3) %>% head(3)

laresbernardo/lares documentation built on Oct. 23, 2024, 12:05 p.m.