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 anotherdavidhammond@gmail.com

See Also

Useful links:

• https://github.com/david-hammond/tidymodlr

• Report bugs at https://github.com/david-hammond/tidymodlr/issues

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

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

Methods

Public methods

• tidymodl$new()

• tidymodl$assemble()

• tidymodl$print()

• tidymodl$correlate()

• tidymodl$pca()

• tidymodl$clone()

Method new()

Creates a new instance of this R6 class.

Create a new tidymodl object.

Usage

tidymodl$new(df, pivot_column, pivot_value)

Arguments

Returns

A new tidymodl object.

Method assemble()

Adds a results matrix

Usage

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

Arguments

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

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