minor typos; I assign the copyright of this contribution to Claus O. Wilke

Author: bbolker

balance_data_context.Rmd

@@ -102,7 +102,7 @@ p_Aus_base +
 
 Figures with too little non-data ink commonly suffer from the effect that figure elements appear to float in space, without clear connection or reference to anything. This problem tends to be particularly severe in small multiples plots. Figure \@ref(fig:titanic-survival-by-gender-class-bad) shows a small-multiples plot comparing six different bar plots, but it looks more like a piece of modern art than a useful data visualization. The bars are not anchored to a clear baseline and the individual plot facets are not clearly delineated. We can resolve these issues by adding a light gray background and thin horizontal grid lines to each facet (Figure \@ref(fig:titanic-survival-by-gender-class)).
 
-(ref:titanic-survival-by-gender-class-bad) Survival of passengers on the Titanic, broken down by gender and class. This small-multiples plot is too minimalistic. The individual factes are not framed, so it's difficult to see which part of the figure belongs to which facet. Further, the individual bars are not anchored to a clear baseline, and they seem to float.
+(ref:titanic-survival-by-gender-class-bad) Survival of passengers on the Titanic, broken down by gender and class. This small-multiples plot is too minimalistic. The individual facets are not framed, so it's difficult to see which part of the figure belongs to which facet. Further, the individual bars are not anchored to a clear baseline, and they seem to float.
 
 ```{r titanic-survival-by-gender-class-bad, fig.width = 5, fig.asp = 3/4, fig.cap = '(ref:titanic-survival-by-gender-class-bad)'}
 titanic %>% mutate(surv = ifelse(survived == 0, "died", "survived")) %>%
@@ -207,7 +207,7 @@ stamp_bad(
 )
 ```
 
-At the absolute minimum, we need to add one horizontal reference line. Since the stock prices in Figure \@ref(fig:price-plot-no-grid) indexed to 100 in June 2012, marking this value with a thin horizontal line at *y* = 100 helps a lot (Figure \@ref(fig:price-plot-refline)). Alternatively, we can use a minimal "grid" of horizontal lines. For a plot where we are primarily interested in the change in *y* values, vertical grid lines are not needed. Moreover, grid lines positioned at only the major axis ticks will often be sufficient. And, the axis line can be omitted or made very thin, since the horzontal lines clearly mark the extent of the plot (Figure \@ref(fig:price-plot-hgrid)).
+At the absolute minimum, we need to add one horizontal reference line. Since the stock prices in Figure \@ref(fig:price-plot-no-grid) indexed to 100 in June 2012, marking this value with a thin horizontal line at *y* = 100 helps a lot (Figure \@ref(fig:price-plot-refline)). Alternatively, we can use a minimal "grid" of horizontal lines. For a plot where we are primarily interested in the change in *y* values, vertical grid lines are not needed. Moreover, grid lines positioned at only the major axis ticks will often be sufficient. And, the axis line can be omitted or made very thin, since the horizontal lines clearly mark the extent of the plot (Figure \@ref(fig:price-plot-hgrid)).
 
 (ref:price-plot-refline) Indexed stock price over time for four major tech companies. Adding a thin horizontal line at the index value of 100 to Figure \@ref(fig:price-plot-no-grid) helps provide an important reference throughout the entire time period the plot spans. Data source: Yahoo Finance
 

boxplots_violins.Rmd

@@ -57,7 +57,7 @@ stamp_bad(lincoln_errbar)
 
 We can address all four shortcomings of Figure \@ref(fig:lincoln-temp-points-errorbars) by using a traditional and commonly used method for visualizing distributions, the boxplot. A boxplot divides the data into quartiles and visualizes them in a standardized manner (Figure \@ref(fig:boxplot-schematic)).
 
-(ref:boxplot-schematic) Anatomy of a boxplot. Shown are a cloud of points (left) and the corresponding boxplot (right). Only the *y* values of the points are visualized in the boxplot. The line in the middle of the boxplot represents the median, and the box encloses the middle 50% of the data. The top and bottom whiskers extend either to the maximum and minimum of the data or to the maximum or minimum that falls within 1.5 times the height of the box, whichever yields the shorter whisker. The distances of 1.5 times the height of the box in either direction are called the upper and the lower fences. Individual data points that fall beyond the fences are referred to as outliers and are usually showns as individual dots.
+(ref:boxplot-schematic) Anatomy of a boxplot. Shown are a cloud of points (left) and the corresponding boxplot (right). Only the *y* values of the points are visualized in the boxplot. The line in the middle of the boxplot represents the median, and the box encloses the middle 50% of the data. The top and bottom whiskers extend either to the maximum and minimum of the data or to the maximum or minimum that falls within 1.5 times the height of the box, whichever yields the shorter whisker. The distances of 1.5 times the height of the box in either direction are called the upper and the lower fences. Individual data points that fall beyond the fences are referred to as outliers and are usually shown as individual dots.
 
 ```{r boxplot-schematic, fig.width = 5*6/4.2, fig.cap = '(ref:boxplot-schematic)'}
 set.seed(3423)

choosing_visualization_software.Rmd

@@ -108,7 +108,7 @@ A good visualization software should allow you to think separately about the con
 
 In the software I have used for this book, ggplot2, separation of content and design is achieved via themes. A theme specifies the visual appearance of a figure, and it is easy to take an existing figure and apply different themes to it (Figure \@ref(fig:unemploy-themes)). Themes can be written by third parties and distributed as R packages. Through this mechanism, a thriving ecosystem of add-on themes has developed around ggplot2, and it covers a wide range of different styles and application scenarios. If you're making figures with ggplot2, you can almost certainly find an existing theme that satisfies your design needs.
 
-(ref:unemploy-themes) Number of unemployed persons in the U.S. from 1970 to 2015. The same figure is displayed using four different ggplot2 themes: (a) the default theme for this book; (b) the default theme of ggplot2, the plotting software I have used to make all figures in this book; (c) a theme that mimicks visualizations shown in the Economist; (d) a theme that mimicks visualizations shown by FiveThirtyEight. FiveThirtyEight often foregos axis labels in favor of plot titles and subtitles, and therefore I have adjusted the figure accordingly. Data source: U.S. Bureau of Labor Statistics
+(ref:unemploy-themes) Number of unemployed persons in the U.S. from 1970 to 2015. The same figure is displayed using four different ggplot2 themes: (a) the default theme for this book; (b) the default theme of ggplot2, the plotting software I have used to make all figures in this book; (c) a theme that mimics visualizations shown in the Economist; (d) a theme that mimics visualizations shown by FiveThirtyEight. FiveThirtyEight often forgoes axis labels in favor of plot titles and subtitles, and therefore I have adjusted the figure accordingly. Data source: U.S. Bureau of Labor Statistics
 
 ```{r unemploy-themes, fig.width = 5.5*6/4.2, fig.asp = 0.75, fig.cap = '(ref:unemploy-themes)'}
 unemploy_base <- ggplot(economics, aes(x = date, y = unemploy)) +

color_basics.Rmd

@@ -139,9 +139,9 @@ texas_income %>% st_transform(crs = texas_crs) %>%
   )
 ```
 
-In some cases, we need to visualize the deviation of data values in one of two directions relative to a neutral midpoint. One straightforward example is a dataset containing both positive and negative numbers. We may want to show those with different colors, so that it is immediately obvious whether a value is positive or negative as well as how far in either direction it deviates from zero. The appropriate color scale in this situation is a *diverging* color scale. We can think of a diverging scale as two sequential scales stiched together at a common midpoint, which usually is represented by a light color (Figure \@ref(fig:diverging-scales)). Diverging scales need to be balanced, so that the progression from light colors in the center to dark colors on the outside is approximately the same in either direction. Otherwise, the perceived magnitude of a data value would depend on whether it fell above or below the midpoint value.
+In some cases, we need to visualize the deviation of data values in one of two directions relative to a neutral midpoint. One straightforward example is a dataset containing both positive and negative numbers. We may want to show those with different colors, so that it is immediately obvious whether a value is positive or negative as well as how far in either direction it deviates from zero. The appropriate color scale in this situation is a *diverging* color scale. We can think of a diverging scale as two sequential scales stitched together at a common midpoint, which usually is represented by a light color (Figure \@ref(fig:diverging-scales)). Diverging scales need to be balanced, so that the progression from light colors in the center to dark colors on the outside is approximately the same in either direction. Otherwise, the perceived magnitude of a data value would depend on whether it fell above or below the midpoint value.
 
-(ref:diverging-scales) Example diverging color scales. Diverging scales can be thought of as two sequential scales stiched together at a common midpoint color. Common color choices for diverging scales include brown to greenish blue, pink to yellow-green, and blue to red.
+(ref:diverging-scales) Example diverging color scales. Diverging scales can be thought of as two sequential scales stitched together at a common midpoint color. Common color choices for diverging scales include brown to greenish blue, pink to yellow-green, and blue to red.
 
 ```{r diverging-scales, fig.width=5*6/4.2, fig.asp=3*.14, fig.cap = '(ref:diverging-scales)'}
 p1 <- gg_color_swatches(7, title_family = dviz_font_family) + 

figure_titles_captions.Rmd

@@ -131,7 +131,7 @@ One of the most common mistakes I see in figure captions is the omission of a pr
 
 Just like every plot needs a title, axes and legends need titles as well. (Axis titles are often colloquially referred to as *axis labels*.) Axis and legend titles and labels explain what the displayed data values are and how they map to plot aesthetics.
 
-To present an example of a plot where all axes and legends are appropriately labeled and titled, I have taken the blue jay dataset discussed at length in Chapter \@ref(visualizing-associations) and visualized it as a bubble plot (Figure \@ref(fig:blue-jays-scatter-bubbles2)). In this plot, the axis titles clearly indicate that the *x* axis shows body mass in grams and the *y* axis shows head length in milimeters. Similarly, the legend titles show that point coloring indicates the birds' sex and point size indicates the birds' skull size in milimeters. I emphasize that for all numerical variables (body mass, head length, and skull size) the relevant titles not only state the variables shown but also the units in which the variables are measured. This is good practice and should be done whenever possible. Categorical variables (such as sex) do not require units.
+To present an example of a plot where all axes and legends are appropriately labeled and titled, I have taken the blue jay dataset discussed at length in Chapter \@ref(visualizing-associations) and visualized it as a bubble plot (Figure \@ref(fig:blue-jays-scatter-bubbles2)). In this plot, the axis titles clearly indicate that the *x* axis shows body mass in grams and the *y* axis shows head length in millimeters. Similarly, the legend titles show that point coloring indicates the birds' sex and point size indicates the birds' skull size in millimeters. I emphasize that for all numerical variables (body mass, head length, and skull size) the relevant titles not only state the variables shown but also the units in which the variables are measured. This is good practice and should be done whenever possible. Categorical variables (such as sex) do not require units.
 
 (ref:blue-jays-scatter-bubbles2) Head length versus body mass for 123 blue jays. The birds' sex is indicated by color, and the birds' skull size by symbol size. Head-length measurements include the length of the bill while skull-size measurements do not. Data source: Keith Tarvin, Oberlin College