@drsimonj here to introduce my latest tidymodelling package for R, “twidlr”. twidlr wraps model and predict functions you already know and love with a consistent data.framebased API!
All models wrapped by twidlr can be fit to data and used to make predictions as follows:
library(twidlr)
fit < model(data, formula, ...)
predict(fit, data, ...)

data
is a data.frame (or object that can be corced to one) and is required 
formula
describes the model to be fit
The motivation
The APIs of model and predict functions in R are inconsistent and messy.
Some models like linear regression want a formula and data.frame:
lm(hp ~ ., mtcars)
Models like gradientboosted decision trees want vectors and matrices:
library(xgboost)
y < mtcars$hp
x < as.matrix(mtcars[names(mtcars) != "hp"])
xgboost(x, y, nrounds = 5)
Models like generalized linear models want you to work. For example, to create interactions and dummycoded variables:
library(glmnet)
y < iris$Petal.Length
x < model.matrix(Petal.Length ~ Sepal.Width * Sepal.Length + Species, iris)
glmnet(x, y)
Some models like kmeans don’t have a corresponding predict function:
fit < kmeans(iris[1:120,5], centers = 3)
predict(fit, iris[121:150,])
## Error in UseMethod("predict") :
## no applicable method for 'predict' applied to an object of class "kmeans"
Some predict functions are impure and return unexpected results. For example, linear discriminant analysis:
library(MASS)
d < iris
fit < lda(Species ~ ., d)
table(predict(fit)$class)
#>
#> setosa versicolor virginica
#> 50 49 51
d < d[1:10,]
table(predict(fit)$class)
#>
#> setosa versicolor virginica
#> 10 0 0
~ twidlr
twidlr helps to solve these problems by wrapping model and predict functions you already know and love with a consistent data.framebased API!
Load twidlr and your favourite models can be fit to a data.frame with a formula and any additional arguments! To demonstrate, compare API to above:
library(twidlr)
lm(mtcars, hp ~ .)
xgboost(mtcars, hp ~ ., nrounds = 5)
glmnet(iris, Petal.Length ~ Sepal.Width * Sepal.Length + Species)
What’s more, predictions can be made with all fitted models via predict
and a data.frame. This even works for models that don’t traditionally have a predict
method:
library(twidlr)
fit < kmeans(iris[1:140,5], centers = 3)
predict(fit, iris[141:150,])
#> [1] 3 3 3 3 3 3 3 3 3 3
Bonus example
Although useful in itself, a consistent data.framebased API expands the capabilities of other tidy and data.framebased packages like the tidyverse packages and pipelearner.
For the motivated, this demonstrates how to fit multiple models and compare their RMSE on new data. It’s streamlined because purrr’s map functions can exploit the consistent API for each model and predict
.
library(twidlr)
library(purrr)
train < cars[ 1:40, ]
test < cars[41:50, ]
f < c("lm", "randomForest", "rpart")
# Fit each model to training data and compute RMSE on test data
rmse < invoke_map(f, data = train, formula = speed ~ dist) %>%
map(predict, data = test) %>%
map_dbl(~ sqrt(mean((.  test$speed)^2)))
setNames(rmse, f)
#> lm randomForest rpart
#> 3.832426 6.129539 6.034932
If you can’t see the value, try doing this without twidlr.
Take home messages
twidlr attempts to brings the follwing to modelling in R:
 a consistent and tidy model APIs
 pure and available predict functions
 the power of formula operators
 tidyverse philosophy (eg keep piping)
But twidlr is new, and needs your help to grow. So if your favourite model isn’t listed here, fork twidlr on GitHub and add it to help improve modelling in R! Advice for contributing can be found here.
Thanks already to Joran Elias and Mathew Ling for their contributions!
Sign off
Thanks for reading and I hope this was useful for you.
For updates of recent blog posts, follow @drsimonj on Twitter, or email me at [email protected] to get in touch.
If you’d like the code that produced this blog, check out the blogR GitHub repository.
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