The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
Reporting with tidylearn
Overview
tidylearn is designed so that analysis results flow directly into
reports. Every model produces tidy tibbles, ggplot2 visualisations, and
— with the tl_table_*() functions — polished
gt tables, all with a consistent interface. This vignette
walks through the reporting tools available.
library(tidylearn)
library(dplyr)
library(ggplot2)
library(gt)
Plots
tidylearn’s plot() method dispatches to the right
visualisation for each model type. All plots are ggplot2 objects —
themeable, composable, and convertible to plotly.
Regression
model_reg <- tl_model(mtcars, mpg ~ wt + hp, method = "linear")
# Actual vs predicted — one call
plot(model_reg, type = "actual_predicted")
Classification
split <- tl_split(iris, prop = 0.7, stratify = "Species", seed = 42)
model_clf <- tl_model(split$train, Species ~ ., method = "forest")
plot(model_clf, type = "confusion")
PCA
pca <- tidy_pca(USArrests, scale = TRUE)
tidy_pca_screeplot(pca)
tidy_pca_biplot(pca, label_obs = TRUE)
Regularisation
model_lasso <- tl_model(mtcars, mpg ~ ., method = "lasso")
tl_plot_regularization_path(model_lasso)
tl_plot_regularization_cv(model_lasso)
Tables
The tl_table() family mirrors the plot interface but
produces formatted gt tables instead. Like
plot(), tl_table() dispatches based on model
type and a type parameter:
tl_table(model) # auto-selects the best table type
tl_table(model, type = "coefficients") # specific type
Evaluation Metrics
tl_table_metrics(model_reg)
| Model Evaluation Metrics |
| Metric |
Value |
| Rmse |
2.4689 |
| Mae |
1.9015 |
| Rsq |
0.8268 |
| tidylearn | linear (regression) | mpg ~ wt + hp | n = 32 |
Coefficients
For linear and logistic models, the table includes standard errors,
test statistics, and p-values, with significant terms highlighted:
tl_table_coefficients(model_reg)
| Linear Model Coefficients |
| Term |
Estimate |
Std. Error |
t value |
p |
|
| (Intercept) |
37.2273 |
1.5988 |
23.2847 |
2.57 × 10−20 |
* |
| wt |
−3.8778 |
0.6327 |
−6.1287 |
1.12 × 10−6 |
* |
| hp |
−0.0318 |
0.0090 |
−3.5187 |
1.45 × 10−3 |
* |
| tidylearn | linear (regression) | mpg ~ wt + hp | n = 32 |
For regularised models, coefficients are sorted by magnitude and zero
coefficients are greyed out:
tl_table_coefficients(model_lasso)
| Lasso Coefficients |
| lambda = 1.536 (1se) |
| Term |
Coefficient |
|Coefficient| |
| (Intercept) |
33.4721 |
33.4721 |
| wt |
−2.2863 |
2.2863 |
| cyl |
−0.8339 |
0.8339 |
| hp |
−0.0059 |
0.0059 |
| disp |
0.0000 |
0.0000 |
| drat |
0.0000 |
0.0000 |
| qsec |
0.0000 |
0.0000 |
| vs |
0.0000 |
0.0000 |
| am |
0.0000 |
0.0000 |
| gear |
0.0000 |
0.0000 |
| carb |
0.0000 |
0.0000 |
| tidylearn | lasso (regression) | mpg ~ . | n = 32 |
Confusion Matrix
A formatted confusion matrix with correct predictions highlighted on
the diagonal:
tl_table_confusion(model_clf, new_data = split$test)
| Confusion Matrix |
| Actual |
Predicted
|
| setosa |
versicolor |
virginica |
| setosa |
15 |
0 |
0 |
|---|
| versicolor |
0 |
14 |
1 |
|---|
| virginica |
0 |
2 |
13 |
|---|
| tidylearn | forest (classification) | Species ~ . | n = 105 |
Feature Importance
A ranked importance table with a colour gradient:
tl_table_importance(model_clf)
| Feature Importance |
| Top 4 features |
| Feature |
Importance |
| Petal.Length |
100.00 |
| Petal.Width |
93.33 |
| Sepal.Length |
27.43 |
| Sepal.Width |
10.52 |
| tidylearn | forest (classification) | Species ~ . | n = 105 |
PCA Variance Explained
Cumulative variance is coloured green to highlight how many
components are needed:
pca_model <- tl_model(USArrests, method = "pca")
tl_table_variance(pca_model)
| PCA Variance Explained |
| Component |
Std. Dev. |
Variance |
Proportion |
Cumulative |
| PC1 |
1.5749 |
2.4802 |
62.0% |
62.0% |
| PC2 |
0.9949 |
0.9898 |
24.7% |
86.8% |
| PC3 |
0.5971 |
0.3566 |
8.9% |
95.7% |
| PC4 |
0.4164 |
0.1734 |
4.3% |
100.0% |
| tidylearn | pca | n = 50 |
PCA Loadings
A diverging red–blue colour scale highlights strong positive and
negative loadings:
tl_table_loadings(pca_model)
| PCA Loadings |
| Variable |
PC1 |
PC2 |
PC3 |
PC4 |
| Murder |
−0.536 |
−0.418 |
0.341 |
0.649 |
| Assault |
−0.583 |
−0.188 |
0.268 |
−0.743 |
| UrbanPop |
−0.278 |
0.873 |
0.378 |
0.134 |
| Rape |
−0.543 |
0.167 |
−0.818 |
0.089 |
| tidylearn | pca | n = 50 |
Cluster Summary
Cluster sizes and mean feature values:
km <- tl_model(iris[, 1:4], method = "kmeans", k = 3)
tl_table_clusters(km)
| Cluster Summary |
| kmeans | 3 clusters |
| Cluster |
Size |
Sepal.Length |
Sepal.Width |
Petal.Length |
Petal.Width |
| 1 |
50 |
5.01 |
3.43 |
1.46 |
0.25 |
| 2 |
38 |
6.85 |
3.07 |
5.74 |
2.07 |
| 3 |
62 |
5.90 |
2.75 |
4.39 |
1.43 |
| tidylearn | kmeans | n = 150 |
Model Comparison
Compare multiple models side-by-side:
m1 <- tl_model(split$train, Species ~ ., method = "logistic")
m2 <- tl_model(split$train, Species ~ ., method = "forest")
m3 <- tl_model(split$train, Species ~ ., method = "tree")
tl_table_comparison(
m1, m2, m3,
new_data = split$test,
names = c("Logistic", "Random Forest", "Decision Tree")
)
| Model Comparison |
| 3 models compared |
| Metric |
Logistic |
Random Forest |
Decision Tree |
| Accuracy |
0.0000 |
0.9556 |
0.8889 |
| tidylearn | n = 45 |
Interactive Reporting with plotly
Because all plot functions return ggplot2 objects, converting to
interactive plotly charts is a one-liner:
library(plotly)
ggplotly(plot(model_reg, type = "actual_predicted"))
ggplotly(tidy_pca_biplot(pca, label_obs = TRUE))
ggplotly(tl_plot_regularization_path(model_lasso))
Putting It Together
A typical reporting workflow combines plots and tables for the same
model. Because the interface is consistent, the same pattern works
regardless of the algorithm:
# Fit
model <- tl_model(split$train, Species ~ ., method = "forest")
# Evaluate
tl_table_metrics(model, new_data = split$test)
| Model Evaluation Metrics |
| Metric |
Value |
| Accuracy |
0.9333 |
| tidylearn | forest (classification) | Species ~ . | n = 105 |
# Visualise
plot(model, type = "confusion")
# Drill into feature importance
tl_table_importance(model, top_n = 4)
| Feature Importance |
| Top 4 features |
| Feature |
Importance |
| Petal.Length |
100.00 |
| Petal.Width |
94.05 |
| Sepal.Length |
33.96 |
| Sepal.Width |
12.28 |
| tidylearn | forest (classification) | Species ~ . | n = 105 |
Swap method = "forest" for method = "tree"
or method = "svm" and the reporting code above works
without modification.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.