Use dataset instead of data set

model-basics.Rmd

@@ -1,3 +1,15 @@
+```{r include=FALSE, cache=FALSE}
+set.seed(1014)
+options(digits = 3)
+
+knitr::opts_chunk$set(
+  comment = "#>",
+  collapse = TRUE,
+  cache = TRUE
+)
+
+options(dplyr.print_min = 6, dplyr.print_max = 6)
+```
 # Model
 
 The goal of a fitted model is to provide a simple low-dimensional summary of a dataset. Ideally, the fitted model will capture true "signals" (i.e. patterns generated by the phenomenon of interest), and ignore "noise" (i.e. random variation that you're not interested in). 

model.Rmd

@@ -1,3 +1,15 @@
+```{r include=FALSE, cache=FALSE}
+set.seed(1014)
+options(digits = 3)
+
+knitr::opts_chunk$set(
+  comment = "#>",
+  collapse = TRUE,
+  cache = TRUE
+)
+
+options(dplyr.print_min = 6, dplyr.print_max = 6)
+```
 # (PART) Model {-}
 
 # Introduction

tidy.Rmd

@@ -31,20 +31,20 @@ library(dplyr)
 You can organize tabular data in many ways. For example, the datasets below show the same data organized in four different ways. Each dataset shows the same values of four variables *country*, *year*, *population*, and *cases*, but each dataset organizes the values into a different layout . You can access the datasets in tidyr.
 
 ```{r}
-# Data set one
+# dataset one
 table1
 
-# Data set two
+# dataset two
 table2
 
-# Data set three
+# dataset three
 table3
 ```
 
 The last dataset is a collection of two tables.
 
 ```{r}
-# Data set four
+# dataset four
 table4  # cases
 table5  # population
 ```