From d8688e80299f1aeecee64aa79050208aa0e41374 Mon Sep 17 00:00:00 2001 From: Hadley Wickham Date: Mon, 23 Jan 2023 08:27:51 -0600 Subject: [PATCH] Tweak package install advice --- intro.qmd | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/intro.qmd b/intro.qmd index 8a76850..8aaafbf 100644 --- a/intro.qmd +++ b/intro.qmd @@ -213,15 +213,26 @@ This doesn't make them better or worse, just different. In other words, the complement to the tidyverse is not the messyverse but many other universes of interrelated packages. As you tackle more data science projects with R, you'll learn new packages and new ways of thinking about data. -In this book, we'll use five data packages from outside the tidyverse: +We'll use many packages from outside the tidyverse in this book. +For example, we use the following four data packages to provide interesting applications: ```{r} #| eval: false -install.packages(c("gapminder", "Lahman", "nycflights13", "palmerpenguins")) +install.packages(c("babynames", "gapminder", "nycflights13", "palmerpenguins")) ``` -These packages provide data on world development, baseball, airline flights, and body measurements of penguins that we'll use to illustrate key data science ideas, while the final one helps generate random data sets. +We'll also use a selection of other packages for one off examples. +You don't need to install them now, just remember that whenever you see an error like this: + +```{r} +#| eval: false + +library(ggrepel) +#> Error in library(ggrepel) : there is no package called ‘ggrepel’ +``` + +You need to run `install.packages("ggrepel")` to install the package. ## Running R code