Fix figure manipulation

oreilly/base-R.html

@@ -333,24 +333,24 @@ <h2>
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-<figure id="fig-pepper-3"><p><img src="images/pepper.jpg" style="width:25.0%" alt="A photo of a glass pepper shaker. Instead of the pepper shaker containing pepper, it contains many packets of pepper."/></p>
-<figcaption>Figure 26.1: A pepper shaker that Hadley once found in his hotel room.</figcaption>
+<figure id="fig-pepper-1"><p><img src="images/pepper.jpg" style="width:25.0%" alt="A photo of a glass pepper shaker. Instead of the pepper shaker containing pepper, it contains many packets of pepper."/></p>
+<figcaption>A pepper shaker that Hadley once found in his hotel room.</figcaption>
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-<figure class="figure"><p><img src="images/pepper-1.jpg" style="width:25.0%" alt="A photo of the glass pepper shaker containing just one packet of pepper."/></p>
-<figcaption class="figure-caption">Figure 26.2: <code>pepper[1]</code></figcaption>
+<figure id="fig-pepper-2"><p><img src="images/pepper-1.jpg" style="width:25.0%" alt="A photo of the glass pepper shaker containing just one packet of pepper."/></p>
+<figcaption>pepper[1]<code>pepper[1]</code></figcaption>
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-<figure class="figure"><p><img src="images/pepper-2.jpg" style="width:25.0%" alt="A photo of single packet of pepper."/></p>
-<figcaption class="figure-caption">Figure 26.3: <code>pepper[[1]]</code></figcaption>
+<figure id="fig-pepper-3"><p><img src="images/pepper-2.jpg" style="width:25.0%" alt="A photo of single packet of pepper."/></p>
+<figcaption>pepper[[1]]<code>pepper[[1]]</code></figcaption>
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oreilly/communicate-plots.html

@@ -204,8 +204,8 @@ <h1>
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-<figure id="fig-themes"><p><img src="communicate-plots_files/figure-html/fig-just-1.png" style="width:60.0%"/></p>
-<figcaption>Figure 28.1: All nine combinations of hjust and vjust.<code>hjust</code> and <code>vjust</code>.</figcaption>
+<figure id="fig-just"><p><img src="communicate-plots_files/figure-html/fig-just-1.png" style="width:60.0%"/></p>
+<figcaption>All nine combinations of hjust and vjust.<code>hjust</code> and <code>vjust</code>.</figcaption>
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@@ -398,8 +398,8 @@ <h2>
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-<figure class="figure"><p><img src="communicate-plots_files/figure-html/fig-brewer-1.png" width="576"/></p>
-<figcaption class="figure-caption">Figure 28.2: All ColourBrewer scales.</figcaption>
+<figure id="fig-brewer"><p><img src="communicate-plots_files/figure-html/fig-brewer-1.png" width="576"/></p>
+<figcaption>All ColourBrewer scales.</figcaption>
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@@ -591,8 +591,8 @@ <h1>
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-<figure class="figure"><p><img src="images/visualization-themes.png" alt="Eight barplots created with ggplot2, each with one of the eight built-in themes: theme_bw() - White background with grid lines, theme_light() - Light axes and grid lines, theme_classic() - Classic theme, axes but no grid lines, theme_linedraw() - Only black lines, theme_dark() - Dark background for contrast, theme_minimal() - Minimal theme, no background, theme_gray() - Gray background (default theme), theme_void() - Empty theme, only geoms are visible." width="1600"/></p>
-<figcaption class="figure-caption">Figure 28.3: The eight themes built-in to ggplot2.</figcaption>
+<figure id="fig-themes"><p><img src="images/visualization-themes.png" alt="Eight barplots created with ggplot2, each with one of the eight built-in themes: theme_bw() - White background with grid lines, theme_light() - Light axes and grid lines, theme_classic() - Classic theme, axes but no grid lines, theme_linedraw() - Only black lines, theme_dark() - Dark background for contrast, theme_minimal() - Minimal theme, no background, theme_gray() - Gray background (default theme), theme_void() - Empty theme, only geoms are visible." width="1600"/></p>
+<figcaption>The eight themes built-in to ggplot2.</figcaption>
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oreilly/data-tidy.html

@@ -97,8 +97,8 @@ <h1>
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-<figure id="fig-pivot-names-and-values"><p><img src="images/tidy-1.png" alt="Three panels, each representing a tidy data frame. The first panel shows that each variable is a column. The second panel shows that each observation is a row. The third panel shows that each value is a cell." width="683"/></p>
-<figcaption>Figure 6.1: The following three rules make a dataset tidy: variables are columns, observations are rows, and values are cells.</figcaption>
+<figure id="fig-tidy-structure"><p><img src="images/tidy-1.png" alt="Three panels, each representing a tidy data frame. The first panel shows that each variable is a column. The second panel shows that each observation is a row. The third panel shows that each value is a cell." width="683"/></p>
+<figcaption>The following three rules make a dataset tidy: variables are columns, observations are rows, and values are cells.</figcaption>
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@@ -274,8 +274,8 @@ <h2>
   scale_y_reverse()</pre>
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-<figure class="figure"><p><img src="data-tidy_files/figure-html/fig-billboard-ranks-1.png" alt="A line plot with week on the x-axis and rank on the y-axis, where each line represents a song. Most songs appear to start at a high rank, rapidly accelerate to a low rank, and then decay again. There are suprisingly few tracks in the region when week is &gt;20 and rank is &gt;50." width="576"/></p>
-<figcaption class="figure-caption">Figure 6.2: A line plot showing how the rank of a song changes over time.</figcaption>
+<figure id="fig-billboard-ranks"><p><img src="data-tidy_files/figure-html/fig-billboard-ranks-1.png" alt="A line plot with week on the x-axis and rank on the y-axis, where each line represents a song. Most songs appear to start at a high rank, rapidly accelerate to a low rank, and then decay again. There are suprisingly few tracks in the region when week is &gt;20 and rank is &gt;50." width="576"/></p>
+<figcaption>A line plot showing how the rank of a song changes over time.</figcaption>
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@@ -315,26 +315,26 @@ <h2>
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-<figure class="figure"><p><img src="diagrams/tidy-data/variables.png" alt="A diagram showing how `pivot_longer()` transforms a simple dataset, using color to highlight how the values in the `var` column (&quot;A&quot;, &quot;B&quot;, &quot;C&quot;) are each repeated twice in the output because there are two columns being pivotted (&quot;col1&quot; and &quot;col2&quot;)." width="469"/></p>
-<figcaption class="figure-caption">Figure 6.3: Columns that are already variables need to be repeated, once for each column that is pivotted.</figcaption>
+<figure id="fig-pivot-variables"><p><img src="diagrams/tidy-data/variables.png" alt="A diagram showing how `pivot_longer()` transforms a simple dataset, using color to highlight how the values in the `var` column (&quot;A&quot;, &quot;B&quot;, &quot;C&quot;) are each repeated twice in the output because there are two columns being pivotted (&quot;col1&quot; and &quot;col2&quot;)." width="469"/></p>
+<figcaption>Columns that are already variables need to be repeated, once for each column that is pivotted.</figcaption>
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 <p>The column names become values in a new variable, whose name is given by <code>names_to</code>, as shown in <a href="#fig-pivot-names" data-type="xref">#fig-pivot-names</a>. They need to be repeated once for each row in the original dataset.</p>
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-<figure class="figure"><p><img src="diagrams/tidy-data/column-names.png" alt="A diagram showing how `pivot_longer()` transforms a simple data set, using color to highlight how column names (&quot;col1&quot; and &quot;col2&quot;) become the values in a new `var` column. They are repeated three times because there were three rows in the input." width="469"/></p>
-<figcaption class="figure-caption">Figure 6.4: The column names of pivoted columns become a new column.</figcaption>
+<figure id="fig-pivot-names"><p><img src="diagrams/tidy-data/column-names.png" alt="A diagram showing how `pivot_longer()` transforms a simple data set, using color to highlight how column names (&quot;col1&quot; and &quot;col2&quot;) become the values in a new `var` column. They are repeated three times because there were three rows in the input." width="469"/></p>
+<figcaption>The column names of pivoted columns become a new column.</figcaption>
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 <p>The cell values also become values in a new variable, with a name given by <code>values_to</code>. They are unwound row by row. <a href="#fig-pivot-values" data-type="xref">#fig-pivot-values</a> illustrates the process.</p>
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-<figure class="figure"><p><img src="diagrams/tidy-data/cell-values.png" alt="A diagram showing how `pivot_longer()` transforms data, using color to highlight how the cell values (the numbers 1 to 6) become the values in a new `value` column. They are unwound row-by-row, so the original rows (1,2), then (3,4), then (5,6), become a column running from 1 to 6." width="469"/></p>
-<figcaption class="figure-caption">Figure 6.5: The number of values is preserved (not repeated), but unwound row-by-row.</figcaption>
+<figure id="fig-pivot-values"><p><img src="diagrams/tidy-data/cell-values.png" alt="A diagram showing how `pivot_longer()` transforms data, using color to highlight how the cell values (the numbers 1 to 6) become the values in a new `value` column. They are unwound row-by-row, so the original rows (1,2), then (3,4), then (5,6), become a column running from 1 to 6." width="469"/></p>
+<figcaption>The number of values is preserved (not repeated), but unwound row-by-row.</figcaption>
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@@ -389,8 +389,8 @@ <h2>
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-<figure class="figure"><p><img src="diagrams/tidy-data/multiple-names.png" alt="A diagram that uses color to illustrate how supplying `names_sep` and multiple `names_to` creates multiple variables in the output. The input has variable names &quot;x_1&quot; and &quot;y_2&quot; which are split up by &quot;_&quot; to create name and number columns in the output. This is is similar case with a single `names_to`, but what would have been a single output variable is now separated into multiple variables." width="600"/></p>
-<figcaption class="figure-caption">Figure 6.6: Pivotting with many variables in the column names means that each column name now fills in values in multiple output columns.</figcaption>
+<figure id="fig-pivot-multiple-names"><p><img src="diagrams/tidy-data/multiple-names.png" alt="A diagram that uses color to illustrate how supplying `names_sep` and multiple `names_to` creates multiple variables in the output. The input has variable names &quot;x_1&quot; and &quot;y_2&quot; which are split up by &quot;_&quot; to create name and number columns in the output. This is is similar case with a single `names_to`, but what would have been a single output variable is now separated into multiple variables." width="600"/></p>
+<figcaption>Pivotting with many variables in the column names means that each column name now fills in values in multiple output columns.</figcaption>
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@@ -439,8 +439,8 @@ <h2>
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-<figure class="figure"><p><img src="diagrams/tidy-data/names-and-values.png" alt="A diagram that uses color to illustrate how the special &quot;.value&quot; sentinel works. The input has names &quot;x_1&quot;, &quot;x_2&quot;, &quot;y_1&quot;, and &quot;y_2&quot;, and we want to use the first component (&quot;x&quot;, &quot;y&quot;) as a variable name and the second (&quot;1&quot;, &quot;2&quot;) as the value for a new &quot;id&quot; column." width="540"/></p>
-<figcaption class="figure-caption">Figure 6.7: Pivoting with <code>names_to = c(".value", "id")</code> splits the column names into two components: the first part determines the output column name (<code>x</code> or <code>y</code>), and the second part determines the value of the <code>id</code> column.</figcaption>
+<figure id="fig-pivot-names-and-values"><p><img src="diagrams/tidy-data/names-and-values.png" alt="A diagram that uses color to illustrate how the special &quot;.value&quot; sentinel works. The input has names &quot;x_1&quot;, &quot;x_2&quot;, &quot;y_1&quot;, and &quot;y_2&quot;, and we want to use the first component (&quot;x&quot;, &quot;y&quot;) as a variable name and the second (&quot;1&quot;, &quot;2&quot;) as the value for a new &quot;id&quot; column." width="540"/></p>
+<figcaption>Pivoting with names_to = c(".value", "id") splits the column names into two components: the first part determines the output column name (x or y), and the second part determines the value of the id column.<code>names_to = c(".value", "id")</code> splits the column names into two components: the first part determines the output column name (<code>x</code> or <code>y</code>), and the second part determines the value of the <code>id</code> column.</figcaption>
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oreilly/data-visualize.html

@@ -177,8 +177,8 @@ <h1>
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-<figure id="fig-vis-stat-bar"><p><img src="data-visualize_files/figure-html/fig-shapes-1.png" alt="Mapping between shapes and the numbers that represent them: 0 - square, 1 - circle, 2 - triangle point up, 3 - plus, 4 - cross, 5 - diamond, 6 - triangle point down, 7 - square cross, 8 - star, 9 - diamond plus, 10 - circle plus, 11 - triangles up and down, 12 - square plus, 13 - circle cross, 14 - square and triangle down, 15 - filled square, 16 - filled circle, 17 - filled triangle point-up, 18 - filled diamond, 19 - solid circle, 20 - bullet (smaller circle), 21 - filled circle blue, 22 - filled square blue, 23 - filled diamond blue, 24 - filled triangle point-up blue, 25 - filled triangle point down blue." width="576"/></p>
-<figcaption>Figure 2.1: R has 25 built in shapes that are identified by numbers. There are some seeming duplicates: for example, 0, 15, and 22 are all squares. The difference comes from the interaction of the color and fill aesthetics. The hollow shapes (0–14) have a border determined by color; the solid shapes (15–20) are filled with color; the filled shapes (21–24) have a border of color and are filled with fill.<code>color</code> and <code>fill</code> aesthetics. The hollow shapes (0–14) have a border determined by <code>color</code>; the solid shapes (15–20) are filled with <code>color</code>; the filled shapes (21–24) have a border of <code>color</code> and are filled with <code>fill</code>.</figcaption>
+<figure id="fig-shapes"><p><img src="data-visualize_files/figure-html/fig-shapes-1.png" alt="Mapping between shapes and the numbers that represent them: 0 - square, 1 - circle, 2 - triangle point up, 3 - plus, 4 - cross, 5 - diamond, 6 - triangle point down, 7 - square cross, 8 - star, 9 - diamond plus, 10 - circle plus, 11 - triangles up and down, 12 - square plus, 13 - circle cross, 14 - square and triangle down, 15 - filled square, 16 - filled circle, 17 - filled triangle point-up, 18 - filled diamond, 19 - solid circle, 20 - bullet (smaller circle), 21 - filled circle blue, 22 - filled square blue, 23 - filled diamond blue, 24 - filled triangle point-up blue, 25 - filled triangle point down blue." width="576"/></p>
+<figcaption>R has 25 built in shapes that are identified by numbers. There are some seeming duplicates: for example, 0, 15, and 22 are all squares. The difference comes from the interaction of the color and fill aesthetics. The hollow shapes (0–14) have a border determined by color; the solid shapes (15–20) are filled with color; the filled shapes (21–24) have a border of color and are filled with fill.<code>color</code> and <code>fill</code> aesthetics. The hollow shapes (0–14) have a border determined by <code>color</code>; the solid shapes (15–20) are filled with <code>color</code>; the filled shapes (21–24) have a border of <code>color</code> and are filled with <code>fill</code>.</figcaption>
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@@ -499,8 +499,8 @@ <h1>
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-<figure class="figure"><p><img src="images/visualization-stat-bar.png" style="width:100.0%" alt="A figure demonstrating three steps of creating a bar chart. Step 1. geom_bar() begins with the diamonds data set. Step 2. geom_bar() transforms the data with the count stat, which returns a data set of cut values and counts. Step 3. geom_bar() uses the transformed data to build the plot. cut is mapped to the x-axis, count is mapped to the y-axis."/></p>
-<figcaption class="figure-caption">Figure 2.2: When create a bar chart we first start with the raw data, then aggregate it to count the number of observations in each bar, and finally map those computed variables to plot aesthetics.</figcaption>
+<figure id="fig-vis-stat-bar"><p><img src="images/visualization-stat-bar.png" style="width:100.0%" alt="A figure demonstrating three steps of creating a bar chart. Step 1. geom_bar() begins with the diamonds data set. Step 2. geom_bar() transforms the data with the count stat, which returns a data set of cut values and counts. Step 3. geom_bar() uses the transformed data to build the plot. cut is mapped to the x-axis, count is mapped to the y-axis."/></p>
+<figcaption>When create a bar chart we first start with the raw data, then aggregate it to count the number of observations in each bar, and finally map those computed variables to plot aesthetics.</figcaption>
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oreilly/intro.html

@@ -7,8 +7,8 @@ <h1>
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-<figure id="fig-rstudio-console"><p><img src="diagrams/data-science/base.png" alt="A diagram displaying the data science cycle: Import -&gt; Tidy -&gt; Understand (which has the phases Transform -&gt; Visualize -&gt; Model in a cycle) -&gt; Communicate. Surrounding all of these is Communicate. " width="535"/></p>
-<figcaption>Figure 1.1: In our model of the data science process you start with data import and tidying. Next you understand your data with an iterative cycle of transforming, visualizing, and modeling. You finish the process by communicating your results to other humans.</figcaption>
+<figure id="fig-ds-diagram"><p><img src="diagrams/data-science/base.png" alt="A diagram displaying the data science cycle: Import -&gt; Tidy -&gt; Understand (which has the phases Transform -&gt; Visualize -&gt; Model in a cycle) -&gt; Communicate. Surrounding all of these is Communicate. " width="535"/></p>
+<figcaption>In our model of the data science process you start with data import and tidying. Next you understand your data with an iterative cycle of transforming, visualizing, and modeling. You finish the process by communicating your results to other humans.</figcaption>
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@@ -79,8 +79,8 @@ <h2>
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-<figure class="figure"><p><img src="diagrams/rstudio/console.png" alt="The RStudio IDE with the panes Console and Output highlighted." width="520"/></p>
-<figcaption class="figure-caption">Figure 1.2: The RStudio IDE has two key regions: type R code in the console pane on the left, and look for plots in the output pane on the right.</figcaption>
+<figure id="fig-rstudio-console"><p><img src="diagrams/rstudio/console.png" alt="The RStudio IDE with the panes Console and Output highlighted." width="520"/></p>
+<figcaption>The RStudio IDE has two key regions: type R code in the console pane on the left, and look for plots in the output pane on the right.</figcaption>
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