Package 'tidymodlr'

Title: An R6 Class to Perform Analysis on Long Tidy Data
Description: Transforms long data into a matrix form to allow for ease of input into modelling packages for regression, principal components, imputation or machine learning. It does this by pivoting on user defined columns, generating a key-value table for variable names to ensure one-to-one mappings are preserved. It is particularly useful when the indicator names in the columns are long descriptive strings, for example "Energy imports, net (% of energy use)". High level analysis wrapper functions for correlation and principal components analysis are provided.
Authors: David Hammond [aut, cre]
Maintainer: David Hammond <[email protected]>
License: MIT + file LICENSE
Version: 1.0.0
Built: 2025-01-24 03:58:22 UTC
Source: https://github.com/david-hammond/tidymodlr

Help Index

tidymodlr: Modelling with tidy long data

Description

tidymodlr transforms long data into a matrix form to allow for ease of input into modelling packages for regression, principal components, imputation or machine learning.

Details

In many fields it is common to have data in tidy long data, with the rows representing many variables, but only one column representing the values (see ?wb for an example).

tidymodlr is particularly useful when the indicator names in the columns are long descriptive strings, for example 'Energy imports, net (% of energy use)'. In such cases a straight ⁠pivot wider⁠ generates column names that are not only cumbersome, but also generate errors in many standard modelling packages that require base column names.

High level analysis functions for correlation, imputation and principals components analysis are provided.

Author(s)

Maintainer: David Hammond [email protected]

See Also

Useful links:

Generate a key value table with unique key for a set of text

Description

Given a vector of characters, this will return a data frame of a unique key column (of, where possible, 3 characters) and value column listing the unique elements of the original text.

Usage

make_key_value(text)

Arguments

text

The text to abbreviate and create a key value table for

Value

df A ⁠Key Value⁠ table

Examples

data(wb)
make_key_value(wb$indicator)

Creates a model matrix style R6 class for modelling with long tidy data

Description

Creates a model matrix style R6 class for modelling with long tidy data

Public fields

data

(data.frame())
The original tidy long data frame

parent

(data.frame())
The parent identifiers of the original data

child

(data.frame())
The model matrix version of the data

key

(data.frame())
A ⁠key value⁠ table that links the parent and child data.frames.

Methods

Public methods

Method new()

Creates a new instance of this R6 class.

Create a new tidymodl object.

Usage
tidymodl$new(df, pivot_column, pivot_value)
Arguments
df

A tidy long data frame

pivot_column

The column name on which the pivot will occur

pivot_value

The column name of the values to be pivotted

Returns

A new tidymodl object.

Method assemble()

Adds a results matrix

Usage
tidymodl$assemble(newdata, format = "long")
Arguments
newdata

A new data set to append. Needs to be either:

  • A vector of length equal to the number of rows in the model matrix. For example, the output of predict() of a lm model. In this case the function returns a data.frame of dimensions c(nrow(parent), ncol(parent) + 1)

  • A data.frame/matrix of equal dimensions of the model matrix. For example, the output of xgb_impute(). In this case the function returns a data.frame of dimensions c(nrow(data), ncol(data) + 1)

format

The desired format of the returned data frame, can either be "long" or "wide".

Details

This returns a completed data.frame for four use cases based on user preference of the desired format.

  • Format "long":

    • Use Case 1 - "newdata" is a vector of length nrow(child): The function returns a combined data frame of the parent data and the "newdata" in a new column. Useful when the user wants to append an output of, for example, predict for a lm regression model.

    • Use Case 2 - "newdata" is a matrix of dimensions dim(child): The function returns a data.frame of the original data in long format with the "newdata" in a new column. Useful when the user wants to append an output of, for example, xgb_impute for all original data.

  • Format "wide":

    • Use Case 3 - "newdata" is a vector of length nrow(child): The function returns a combined data frame of the parent data and the "newdata" in a new column. Useful when the user wants to append an output of, for example, predict for a lm regression model.

    • Use Case 4 - "newdata" is a matrix of dimensions dim(child): The function returns a data.frame of the original data in wide format with the "newdata" as replacing the child matrix of the original data. Useful when the user is only interested in using the output of, for example, xgb_impute for all original data.

Returns

df A Data Frame

Method print()

Prints the key and the head matrix

Usage
tidymodl$print()

Method correlate()

Correlates and reutrns pearson values

Usage
tidymodl$correlate()
Returns

df A Correlation Matrix of class cor_df (see corrr)

Method pca()

Provides high level principal components analysis

Usage
tidymodl$pca()
Returns

df A principle components of class PCA (see FactoMineR

Method clone()

The objects of this class are cloneable with this method.

Usage
tidymodl$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Note

Use Cases 1 and 3 return identical results.

Examples

data(wb)
mdl <- tidymodl$new(wb,
                   pivot_column = "indicator",
                  pivot_value = "value")
### Use mdl$child for modelling
fit <- lm(data = mdl$child, gni ~ gcu + ppt)

### Can be used to add a yhat value for processed data

nc <- ncol(mdl$child)
nr <- nrow(mdl$child)
dm <- nc * nr
dummy <- matrix(runif(dm),
                ncol = nc) |>
                data.frame()
names(dummy) = names(mdl$child)
tmp <- mdl$assemble(dummy)

# In built correlation function
mdl$correlate()

tmp <- mdl$pca()
plot(tmp, choix = "var")

Dummy Long Tidy Data

Description

A dataset from the World Bank of a dummy data. The variables are as follows:

Usage

data(wb)

Format

A data frame with 975 rows and 4 variables

  • iso3c: isocode of a country.

  • indicator: World Bank indicator.

  • year: Year of observation

  • value: Value of observation