In UBC-MDS/featurescreator: New features for temporal data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

features_creator

Helper functions to create new features for temporal data.

Contributors

Nikita Shymberg
Rakesh Pandey
Son Chau
Wenjia Zhu

Description

This package aims to speed up and simplify the process of feature engineering for temporal (e.g. weekly or monthly) data. It works with dataframes that have columns whose names follow a pattern and end with a number. For example payment_week_1, payment_week_2, ... For such datasets, commonly engineered features include, among others, the percentage change across time periods, the average across time periods, and the standard deviation across time periods.

This package defines the following four functions:

get_matching_column_names: Returns a subset of the columns whose names match the pattern. This is a prerequisite for the feature engineering
calculate_average: Returns the average value across matching columns for each row.
calculate_standard_deviation: Returns the stadard deviation across matching columns for each row.
calculate_percentage_change: Returns the percent change across consecutive time periods for each row.

How does this package fit into the existing ecosystem?

There are several existing packages that work with time series data. For example, the tsfeatures package use functions to extract features from time series data. Other package such as tscompdata is useful for comparing time series data.

For datasets that have columns that follow the pattern quantity_1, quantity_2, ... featurescreator is the simplest package for engineering features.

Installation

You can install the development version of regexcite from GitHub with:

# install.packages("devtools")
devtools::install_github("UBC-MDS/featurescreator")

Usage

library(featurescreator)
library(dplyr)

# Example data
df <- data.frame(
  subscriber_id = c(1, 2, 3),
  data_usage1 = c(10, 5, 3), # 1 represent data usage in prediction month (m) - 1
  data_usage2 = c(4, 5, 6), # m - 2
  data_usage3 = c(7, 8, 9), # m - 3
  data_usage4 = c(10, 11, 12), # m - 4
  data_usage5 = c(13, 14, 15), # m - 5
  othercolumn = c(5, 6, 7), # Other example column
  data_usage_string6 = c(5, 6, 7) # Other example column with an integer
)

# Get matching column names
columns <- get_matching_column_names(df, "data_usage")

# Calculate standard deviation across time periods
df$std_monthly_data_usage <- calculate_standard_deviation(df, "data_usage")

# Calculate average across time periods
df$avg_monthly_data_usage <- calculate_average(df, "data_usage")

# Calculate percentage change 2 months over 2 months
df$percent_change_data_usage <- calculate_percentage_change(
  df, "data_usage",
  compare_period = c(2, 2)
)

# Display data

df |> select(
  subscriber_id,
  std_monthly_data_usage,
  avg_monthly_data_usage,
  percent_change_data_usage
)

# subscriber_id std_monthly_data_usage  avg_monthly_data_usage  percent_change_data_usage
# 1 2.792848    8.8 -17.64706
# 2 3.193744    8.6 -47.36842
# 3 3.872983    9.0 -57.14286

Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.