allow y as df in fit_xy for #129

NEWS.md

@@ -3,6 +3,7 @@
 ## New Features
 
 * A "null model" is now available that fits a predictor-free model (using the mean of the outcome for regression or the mode for classification).  
+* `fit_xy()` can take a single column data frame or matrix for `y` without error 
 
 ## Other Changes
 
@@ -38,7 +39,7 @@ First CRAN release
 
 # parsnip 0.0.0.9005
 
-* The engine, and any associated arguments, are now specified using `set_engine`. There is no `engine` argument 
+* The engine, and any associated arguments, are now specified using `set_engine()`. There is no `engine` argument 
 
 
 # parsnip 0.0.0.9004
@@ -64,7 +65,7 @@ First CRAN release
 
 # parsnip 0.0.0.9000
 
-* The `fit` interface was previously used to cover both the x/y interface as well as the formula interface. Now, `fit` is the formula interface and [`fit_xy` is for the x/y interface](https://github.com/topepo/parsnip/issues/33). 
+* The `fit` interface was previously used to cover both the x/y interface as well as the formula interface. Now, `fit()` is the formula interface and [`fit_xy()` is for the x/y interface](https://github.com/topepo/parsnip/issues/33). 
 * Added a `NEWS.md` file to track changes to the package.
 * `predict` methods were [overhauled](https://github.com/topepo/parsnip/issues/34) to be [consistent](https://github.com/topepo/parsnip/issues/41).
 * MARS was added. 

R/fit.R

@@ -180,6 +180,14 @@ fit_xy.model_spec <-
     if (any(names(dots) == "engine"))
       stop("Use `set_engine()` to supply the engine.", call. = FALSE)
 
+    if (object$engine != "spark" & NCOL(y) == 1 & !(is.vector(y) | is.factor(y))) {
+      if (is.matrix(y)) {
+        y <- y[, 1]
+      } else {
+        y <- y[[1]]
+      }
+    }
+
     cl <- match.call(expand.dots = TRUE)
     eval_env <- rlang::env()
     eval_env$x <- x

tests/testthat/test_fit_interfaces.R

@@ -23,7 +23,7 @@ test_that('good args', {
   expect_equal(   tester(NULL, formula = f, data = iris, model = rmod), "formula")
   expect_equal(tester_xy(NULL, x = iris, y = iris, model = rmod), "data.frame")
   expect_equal(   tester(NULL, f, data = iris, model = rmod), "formula")
-  expect_equal(   tester(NULL, f, data = sprk, model = rmod), "formula")  
+  expect_equal(   tester(NULL, f, data = sprk, model = rmod), "formula")
 })
 
 #test_that('unnamed args', {
@@ -37,3 +37,26 @@ test_that('wrong args', {
  expect_error(tester(NULL, f,  data = as.matrix(iris[, 1:4])))
 })
 
+test_that('single column df for issue #129', {
+
+  expect_error(
+    lm1 <-
+      linear_reg() %>%
+      set_engine("lm") %>%
+      fit_xy(x = mtcars[, 2:4], y = mtcars[,1, drop = FALSE]),
+    regexp = NA
+  )
+  expect_error(
+    lm2 <-
+      linear_reg() %>%
+      set_engine("lm") %>%
+      fit_xy(x = mtcars[, 2:4], y = as.matrix(mtcars)[,1, drop = FALSE]),
+    regexp = NA
+  )
+  lm3 <-
+    linear_reg() %>%
+    set_engine("lm") %>%
+    fit_xy(x = mtcars[, 2:4], y = mtcars$mpg)
+  expect_equal(coef(lm1), coef(lm3))
+  expect_equal(coef(lm2), coef(lm3))
+})