From 5a5e65357fd83d1cb20e6e172fcd84d1366e7606 Mon Sep 17 00:00:00 2001 From: hadley Date: Tue, 19 Jul 2016 08:33:04 -0500 Subject: [PATCH] Missing package decl --- model-assess.Rmd | 2 +- tidy.Rmd | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/model-assess.Rmd b/model-assess.Rmd index 6b76274..a4adbd1 100644 --- a/model-assess.Rmd +++ b/model-assess.Rmd @@ -222,7 +222,7 @@ Both the boostrap and cross-validation are build on top of a "resample" object. These functions return an object of class "resample", which represents the resample in a memory efficient way. Instead of storing the resampled dataset itself, it instead stores the integer indices, and a "pointer" to the original dataset. This makes resamples take up much less memory. ```{r} -x <- resample_bootstrap(as_tibble(mtcars)) +x <- resample_bootstrap(tibble::as_tibble(mtcars)) class(x) x diff --git a/tidy.Rmd b/tidy.Rmd index 170b19b..1b73504 100644 --- a/tidy.Rmd +++ b/tidy.Rmd @@ -304,12 +304,12 @@ gather(table5, "year", "population", -1) You can also identify columns by name with the notation introduced by the `select` function in `dplyr`, see Section 3.1. -You can easily combine the new versions of `table4` and `table5` into a single data frame because the new versions are both tidy. To combine the datasets, use the `left_join()` function from Section 3.6. +You can easily combine the new versions of `table4` and `table5` into a single data frame because the new versions are both tidy. To combine the datasets, use the `dplyr::left_join()` function which you'll learn about in [relational data]. ```{r} tidy4 <- gather(table4, "year", "cases", 2:3) tidy5 <- gather(table5, "year", "population", 2:3) -left_join(tidy4, tidy5) +dplyr::left_join(tidy4, tidy5) ``` ## `separate()` and `unite()`