diff --git a/README.md b/README.md
index b22056a..117867b 100644
--- a/README.md
+++ b/README.md
@@ -25,7 +25,6 @@ devtools::install_github("hadley/r4ds")
 ```
     #| echo: FALSE
     #| out.width: NULL
-    #| fig.retina: 1.5
     knitr::include_graphics("diagrams/transform.png", dpi = 270)
 
 ## Code of Conduct
diff --git a/data-tidy.Rmd b/data-tidy.Rmd
index 9392522..4c1f375 100644
--- a/data-tidy.Rmd
+++ b/data-tidy.Rmd
@@ -50,7 +50,9 @@ There are three interrelated rules that make a dataset tidy:
 
 Figure \@ref(fig:tidy-structure) shows the rules visually.
 
-```{r tidy-structure, echo = FALSE, out.width = "100%"}
+```{r tidy-structure}
+#| echo: FALSE
+#| out.width: NULL
 #| fig.cap: > 
 #|   Following three rules makes a dataset tidy: variables are columns,
 #|   observations are rows, and values are cells.
@@ -59,7 +61,7 @@ Figure \@ref(fig:tidy-structure) shows the rules visually.
 #|   shows that each variable is column. The second panel shows that each
 #|   observation is a row. The third panel shows that each value is
 #|   a cell.
-knitr::include_graphics("images/tidy-1.png")
+knitr::include_graphics("images/tidy-1.png", dpi = 270)
 ```
 
 Why ensure that your data is tidy?
@@ -259,23 +261,23 @@ It's easier to see if we take it component by component.
 Columns that are already variables need to be repeated, once for each column in `cols`, as shown in Figure \@ref(fig:pivot-variables).
 
 ```{r pivot-variables}
-#| echo: false
-#| out.width: ~
+#| echo: FALSE
+#| out.width: NULL
 #| fig.cap: > 
 #|   Columns that are already variables need to be repeated, once for
 #|   each column that is pivotted.
-knitr::include_graphics("diagrams/tidy-data/variables.png", dpi = 144)
+knitr::include_graphics("diagrams/tidy-data/variables.png", dpi = 270)
 ```
 
 The column names become values in a new variable, whose name is given by `names_to`, as shown in Figure \@ref(fig:pivot-names).
 They need to be repeated for each row in the original dataset.
 
 ```{r pivot-names}
-#| echo: false
-#| out.width: ~
+#| echo: FALSE
+#| out.width: NULL
 #| fig.cap: >
 #|   The column names of pivoted columns become a new column.
-knitr::include_graphics("diagrams/tidy-data/column-names.png", dpi = 144)
+knitr::include_graphics("diagrams/tidy-data/column-names.png", dpi = 270)
 ```
 
 The cell values also become values in a new variable, with name given by `values_to`.
@@ -283,12 +285,12 @@ The are unwound row by row.
 Figure \@ref(fig:pivot-values) illustrates the process.
 
 ```{r pivot-values}
-#| echo: false
-#| out.width: ~
+#| echo: FALSE
+#| out.width: NULL
 #| fig.cap: >
 #|   The number of values are preserved (not repeated), but unwound
 #|   row-by-row.
-knitr::include_graphics("diagrams/tidy-data/cell-values.png", dpi = 144)
+knitr::include_graphics("diagrams/tidy-data/cell-values.png", dpi = 270)
 ```
 
 ### Many variables in column names
@@ -328,7 +330,7 @@ The next step up in complexity is when the column names include a mix of variabl
 For example, take the `household` dataset:
 
 ```{r}
-family
+household
 ```
 
 This dataset contains data about five families, with the names and dates of birth of up to two children.
diff --git a/diagrams/tidy-data/cell-values.png b/diagrams/tidy-data/cell-values.png
index 71bd640..dc8edc4 100644
Binary files a/diagrams/tidy-data/cell-values.png and b/diagrams/tidy-data/cell-values.png differ
diff --git a/diagrams/tidy-data/column-names.png b/diagrams/tidy-data/column-names.png
index b33a16f..1e8ccb6 100644
Binary files a/diagrams/tidy-data/column-names.png and b/diagrams/tidy-data/column-names.png differ
diff --git a/diagrams/tidy-data/variables.png b/diagrams/tidy-data/variables.png
index dcbf166..5f14aa1 100644
Binary files a/diagrams/tidy-data/variables.png and b/diagrams/tidy-data/variables.png differ