chapter 1 - added fixed figures where needed without creating awkward large whitespaces

Author: ttimbers

intro.Rmd

@@ -260,7 +260,7 @@ to read data into R.
 
 (ref:img-read-csv) Syntax for the `read_csv` function.
 
-``` {r img-read-csv, echo = FALSE, message = FALSE, warning = FALSE, fig.cap = "(ref:img-read-csv)", fig.retina = 2, out.width="100%"}
+``` {r img-read-csv, echo = FALSE, message = FALSE, warning = FALSE, fig.cap = "(ref:img-read-csv)", fig.retina = 2, out.width="100%", fig.pos = "H"}
 image_read("img/read_csv_function.jpeg") |>
   image_crop("1625x1900")
 ```
@@ -528,7 +528,7 @@ image_read("img/ggplot_function.jpeg") |>
   image_crop("1625x1900")
 ```
 
-```{r barplot-mother-tongue, fig.width=5, fig.height=4, warning=FALSE, fig.cap = "Bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue."}
+```{r barplot-mother-tongue, fig.width=5, fig.height=3, warning=FALSE, fig.cap = "Bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue."}
 ggplot(ten_lang, aes(x = language, y = mother_tongue)) +
   geom_bar(stat = "identity")
 ```
@@ -567,7 +567,9 @@ words (e.g. `"Mother Tongue (Number of Canadian Residents)"`) as arguments to
 layers to format the plot further, and we will explore these in Chapter
 \@ref(viz).
 
-```{r barplot-mother-tongue-labs, fig.width=5, fig.height=4, warning=FALSE, fig.cap = "Bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue with x and y labels."}
+(ref:barplot-mother-tongue-labs) Bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue with x and y labels.
+
+```{r barplot-mother-tongue-labs, fig.width=5, fig.height=3.6, warning=FALSE, fig.cap = "(ref:barplot-mother-tongue-labs)", fig.pos = "H", out.extra=""}
 ggplot(ten_lang, aes(x = language, y = mother_tongue)) +
   geom_bar(stat = "identity") +
   xlab("Language") +
@@ -581,7 +583,7 @@ currently making it difficult to read the different language names.
 One solution is to rotate the plot such that the bars are horizontal rather than vertical.
 To accomplish this, we will swap the x and y coordinate axes:
 
-```{r barplot-mother-tongue-flipped, fig.width=5, fig.height=3, warning=FALSE, fig.cap = "Horizontal bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue."}
+```{r barplot-mother-tongue-flipped, fig.width=5, fig.height=3, fig.pos = "H", warning=FALSE, fig.cap = "Horizontal bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue."}
 ggplot(ten_lang, aes(x = mother_tongue, y = language)) +
   geom_bar(stat = "identity") +
   xlab("Mother Tongue (Number of Canadian Residents)") +
@@ -597,6 +599,8 @@ each language, rather than in alphabetical order. We can reorder the bars using
 the `reorder` \index{reorder} function, which orders a variable (here `language`) based on the
 values of the second variable (`mother_tongue`). 
 
+\newpage
+
 ```{r barplot-mother-tongue-reorder, fig.width=5, fig.height=3, warning=FALSE, fig.cap = "Bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue with bars reordered."}
 ggplot(ten_lang, aes(x = mother_tongue,
                      y = reorder(language, mother_tongue))) +
@@ -633,6 +637,14 @@ of code for others, and perhaps more importantly, your future self!
 It's good practice to get in the habit of
 commenting your code to improve its readability.
 
+This exercise demonstrates the power of R. In relatively few lines of code, we
+performed an entire data science workflow with a highly effective data
+visualization! We asked a question, loaded the data into R, wrangled the data
+(using `filter`, `arrange` and `slice`) and created a data visualization to
+help answer our question. In this chapter, you got a quick taste of the data
+science workflow; continue on with the next few chapters to learn each of 
+these steps in much more detail!
+
 ```{r nachos-to-cheesecake, fig.width=5, fig.height=3, warning=FALSE, message=FALSE, fig.cap = "Putting it all together: bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue."}
 library(tidyverse)
 
@@ -652,14 +664,6 @@ ggplot(ten_lang, aes(x = mother_tongue,
   ylab("Language") 
 ```
 
-This exercise demonstrates the power of R. In relatively few lines of code, we
-performed an entire data science workflow with a highly effective data
-visualization! We asked a question, loaded the data into R, wrangled the data
-(using `filter`, `arrange` and `slice`) and created a data visualization to
-help answer our question. In this chapter, you got a quick taste of the data
-science workflow; continue on with the next few chapters to learn each of 
-these steps in much more detail!
-
 ## Accessing documentation
 
 There are many R functions in the `tidyverse` package (and beyond!), and 

pdf/index.Rmd

@@ -23,8 +23,8 @@ cover-image: img/chapter_overview.jpg
 ---
 
 ```{r setup-pdf, include=FALSE}
-knitr::opts_chunk$set(fig.pos = "H",
-                      out.extra = "")
+#knitr::opts_chunk$set(fig.pos = "H",
+#                      out.extra = "")
 
 ```
 

reading.Rmd

@@ -9,7 +9,7 @@ knitr::opts_chunk$set(message = FALSE,
 
 print_html_nodes <- function(html_nodes_object) {
   if(!is_latex_output()) {
-    html_nodes
+    html_nodes_object
   } else {
     output <- capture.output(html_nodes_object)