stavemåde af colour

episodes/different_plots.Rmd

@@ -73,7 +73,7 @@ Not to be confused with histograms, barcharts count the number of
 observations in different groups. Where the scale in histograms is 
 continuous, and split into bins, the scale in barcharts is discrete. 
 
-Here we map the color-variable to the x-axis in the barchart. `geom_bar` counts 
+Here we map the `color`-variable to the x-axis in the barchart. `geom_bar` counts 
 the number of observations itself - we do not need to
 provide a count:
 
@@ -89,17 +89,17 @@ Why are the columns in the barchart above in that order?
 
 One might guess that they are simply in alphabetical order. 
 
-Not so! Color is a categorical variable. Diamonds either have the color 
-"D" (which is the best color), or another color (like "J", which is the worst).
+Not so! `color` is a categorical variable. Diamonds either have the colour 
+"D" (which is the best colour), or another colour (like "J", which is the worst).
 
-There are no "D.E" colors, they do not exist on a continous range.
+There are no "D.E" colours, they do not exist on a continuous range.
 
 This is called "factors" in R.
 The data in a factor can take one of several values, called levels. And the
 order of these levels are what control the order in the plot.
 
 The order can be either arbitrary, or there can exist an implicit order in the 
-data, like with the color of the diamonds, where D is the best color, and J is 
+data, like with the colour of the diamonds, where D is the best colour, and J is 
 the worst. These types of ordered categorical data are called ordinal data.
 
 They look like this:
@@ -109,7 +109,7 @@ diamonds %>%
   str()
 ```
 Note that even though the colour "D" is better than "E", the levels 
-of the color factor indicates that "D<E". 
+of the `color` factor indicates that "D<E". 
 
 All this just to say: We can control the order of columns in the plot, by
 controlling the order of the levels of the categorical value we are plotting:

episodes/facets.Rmd

@@ -25,11 +25,11 @@ library(tidyverse)
 
 If we only make one plot we quickly runs into the problem of trying to plot 
 too much information in the plot. Here we plot the price against carat,
-color by the color of the diamonds. And represent their clarity by the
+colour by the `color` of the diamonds. And represent their clarity by the
 shape of the points:
 
 ```{r too_much_in_plot}
-ggplot(data = diamonds, mapping = aes(x = carat, y = price, color = color, shape = clarity)) +
+ggplot(data = diamonds, mapping = aes(x = carat, y = price, colour = color, shape = clarity)) +
   geom_point()
 ```
 
@@ -43,7 +43,7 @@ along with all the other information, we make one plot for each value of
 clarity. This is called facetting:
 
 ```{r first_facet}
-ggplot(data = diamonds, mapping = aes(x = carat, y = price, color = color)) +
+ggplot(data = diamonds, mapping = aes(x = carat, y = price, colour = color)) +
   geom_point() +
   facet_wrap(~clarity)
 ```
@@ -76,14 +76,14 @@ through them one category at a time, and make comparisons.
 ## Exercise
 
 Plot price as a function of depth (price on the y-axis, depth on the x-axis),
-and facet by cut. If you want a colorful plot, color the points
-by color.
+and facet by cut. If you want a colourful plot, colour the points
+by `color`.
 
 :::: solution
 ## Solution
 
 ```{r solution, eval = FALSE}
-ggplot(data = diamonds, mapping = aes(x = depth, y = price, color = color)) +
+ggplot(data = diamonds, mapping = aes(x = depth, y = price, colour = color)) +
   geom_point() +
   facet_wrap(~cut)
 
@@ -103,11 +103,11 @@ We can expand on the "small multiple" concept, by plotting the
 facets in a grid, defined by two categorical values.
 
 In this plot we plot price as a function of carate, and 
-make individual plots for each combination of clarity and color:
+make individual plots for each combination of `clarity` and `color`:
 
 ```{r facet_grid}
 diamonds %>% 
-  ggplot(aes(x = carat, y = price, color = color)) +
+  ggplot(aes(x = carat, y = price, colour = color)) +
     geom_point() +
     facet_grid(clarity ~ color)
 ```

episodes/further_mapping.Rmd

@@ -25,15 +25,16 @@ knitr::opts_chunk$set(echo = TRUE)
 ```
 
 We saw how to map data to a position in a scatterplot. But we are able to map 
-the data to other elements of a plot, eg the color of the points.
+the data to other elements of a plot, eg the colour of the points.
 
 
 ```{r more_mapping}
 ggplot(data = diamonds, mapping = aes(x = carat, y = price, colour = color)) +
   geom_point() 
 ```
 
-The argument to which we are mapping the values in the column *color* is also called *colour*, making the code look a bit weird.
+The argument to which we are mapping the values in the column *color* is also
+called *colour*, making the code look a bit weird.
 
 Are these colours suitable? Probably not. The authors of this course
 material are not able to distinguish all of the colours. We will return to how 
@@ -43,20 +44,23 @@ to change colours in plots later in this course.
 :::: callout
 ## Spelling
 
-Color, and some other words can be spelled in more than one way.
-For arguments ggplot understands both the correct english spelling
+Colour, and some other words can be spelled in more than one way.
+For arguments, ggplot understands both the correct english spelling
 *colour* and the american spelling *color*.
 
 Note that this only applies to the arguments in the functions. If the
-column in the dataset is called *color* ggplot will not find it if
+column in the dataset is called *color*, ggplot will not find it if
 you write *colour* instead.
 
+In an attempt to reduce confusion, we use *colour* for the arguments and
+*color* when we refer to the variable `color`.
+
 ::::
 
-Not surprisingly, the "best" color, D have higher prices than the "worst"
-color, "J".
+Not surprisingly, the "best" colour, D have higher prices than the "worst"
+colour, "J".
 
-A common mistake is to place the color argument a wrong place:
+A common mistake is to place the colour argument a wrong place:
 ```{r chunk2}
 ggplot(data = diamonds, mapping = aes(x = carat, y = price), colour = color) +
   geom_point()
@@ -106,7 +110,7 @@ values directly. One very useful aesthetic to play with, at least when
 we have as many datapoints as we have here, is `alpha`:
 
 ```{r}
-ggplot(data = diamonds, mapping = aes(x = carat, y = price, color = color)) +
+ggplot(data = diamonds, mapping = aes(x = carat, y = price, colour = color)) +
   geom_point(alpha = 0.1)
 ```
 

episodes/getting_started.Rmd

@@ -57,7 +57,7 @@ We are going to cover each element in the following.
 ## What is the difference?
 
 ggplot2 is the library, containing different types of 
-functions for plotting, theming the plots, changing colors and lots of other stuff.
+functions for plotting, theming the plots, changing colours and lots of other stuff.
 
 ggplot is one of these functions in ggplot2, and the one that begins every plot we make.
 

episodes/introduction.Rmd

@@ -11,11 +11,20 @@ exercises: 5
 
 
 :::: objectives
-  - "Get to know the importance of visualisations"
-  - "Get to know the data we are going to work with"
+- "Get to know the importance of visualisations"
+- "Get to know the data we are going to work with"
 
 ::::
 
+::::instructor
+We have a slight problem with the column `color` in the dataset, which
+can be confusing when we also have a `colour` argument in play.
+
+We try to be consistent in this material, and only use `color` when referring
+to the variable, and the correct spelling when referring to the arguments, or
+the phenomenon "colour".
+
+::::
 
 
 ```{r setup, include=FALSE}
@@ -63,7 +72,7 @@ There are 10 variables in the dataset:
 |----------|-------------|
 | carat    | Weight of the diamond in carat (0.200 gram) |
 | cut      | Quality of the cut of the diamond (Fair, Good, Very Good, Premium, Ideal) | 
-| color    | Color of the diamond from D (best), to J (worst) |
+| color    | Colour of the diamond from D (best), to J (worst) |
 | clarity  | How clear is the diamond. I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best) |
 | depth    | Total depth percentage = z / mean(x, y) |
 | table    | Width of the top of the diamond relative to its widest point |

episodes/scaling-and-coordinates.Rmd

@@ -7,15 +7,15 @@ exercises: 5
 :::: questions
 - "How can we adjust the scales in a plot?"
 - "How can we zoom-in to specific parts of a plot?"
-- "How can we change the colors of the plot?"
+- "How can we change the colours of the plot?"
 - "How do I make a pie-chart?"
 ::::
 
 ::::objectives
 - "Learn to zoom by adjusting scales"
 - "Learn how to make log-scale plots"
 - "Learn why you should not make a pie-chart"
-- "Learn how to control the color-scale"
+- "Learn how to control the colour-scale"
 :::: 
 
 
@@ -143,7 +143,7 @@ could interchange the x and y values in the mapping argument.
 Or we could add a coordinate function that changes the coordinate system:
 
 ```{r flipped-coords}
-ggplot(data = diamonds, mapping = aes(x = carat, y = price, color = color)) +
+ggplot(data = diamonds, mapping = aes(x = carat, y = price, colour = color)) +
   geom_point() +
   coord_flip()
 ```
@@ -213,7 +213,7 @@ can be defined by making a stacked bar-chart, and changing the
 coordinate system to polar.
 
 We begin by filtering the data set to only include diamonds
-with the color "G", and then make a barchart. We add the 
+with the `color` "G", and then make a barchart. We add the 
 argument `position = "stack"` to `geom_bar` to stack the bars
 rather than having them side by side. And then we adjust
 the coordinate system to be polar (the y-axis specifically), 
@@ -223,7 +223,7 @@ beginning at 0:
 diamonds %>% 
   mutate(color = as.character(color)) %>% 
   filter(color == "G") %>% 
-  ggplot(aes(x= color, fill = cut)) +
+  ggplot(aes(x = color, fill = cut)) +
   geom_bar(position = "stack") +
   coord_polar("y", start=0) 
 ```
@@ -264,61 +264,61 @@ Rare exceptions exists. But making pie charts should be done with EXTREME cautio
 ::::
 
 
-## Coloring the scale
+## Colouring the scale
 
 Looking at the plot below, the authors of this course get pretty frustrated.
 
 
-```{r scale_color}
-ggplot(data = diamonds, mapping = aes(x = carat, y = price, color = color)) +
+```{r scale_colour}
+ggplot(data = diamonds, mapping = aes(x = carat, y = price, colour = color)) +
   geom_point() 
 ```
 
-We are not really able to distinquish the color for "D" and "E". Or for "G" and
-"H". Controlling the colors is important not only for aesthetic reasons, but
+We are not really able to distinquish the colour for "D" and "E". Or for "G" and
+"H". Controlling the colours is important not only for aesthetic reasons, but
 also for actually illustrating what the plot is showing.
 
-Here, the color is introduced by mapping the color of the diamonds to the 
-coloring of the points. This actually is mapping a value to a scale, no different
+Here, the colour is introduced by mapping the `color` of the diamonds to the 
+colouring of the points. This actually is mapping a value to a scale, no different
 from the mapping of the price to the y-axis.
 
 In the same way we can adjust the scale of the y-axis as shown above, we are
-able to adjust the actual colors in the plot. 
+able to adjust the actual colours in the plot. 
 
 The functions for this are (almost) all called `scale_` and then continues
-with `color` if we are coloring points, `fill` if we want to control the 
-fill-color of a solid object in the plot, and finally something that specifies
+with `colour` if we are colouring points, `fill` if we want to control the 
+fill-colour of a solid object in the plot, and finally something that specifies
 either the type of data we are plotting, or specific functionality to control 
-the color. 
+the colour. 
 
-Below we adjust the color using the special family of functions `brewer`:
-`scale_color_brewer`. Nice colors, but even worse:
+Below we adjust the colour using the special family of functions `brewer`:
+`scale_colour_brewer`. Nice colours, but even worse:
 
-```{r color_brewer}
-ggplot(data = diamonds, mapping = aes(x = carat, y = price, color = color)) +
+```{r colour_brewer}
+ggplot(data = diamonds, mapping = aes(x = carat, y = price, colour = color)) +
   geom_point() +
-  scale_color_brewer() +
+  scale_colour_brewer() +
   theme(panel.background = element_rect(fill = "black"))
 ```
 What we did to change the background will be covered in the next episode.
 
-Finding the optimal colors usually requires a lot of fiddling around. Rather
-than using functions to choose the colors, we can chose the manually, 
+Finding the optimal colours usually requires a lot of fiddling around. Rather
+than using functions to choose the colours, we can choose them manually, 
 like this:
 
-```{r manual_colors}
-ggplot(data = diamonds, mapping = aes(x = carat, y = price, color = color)) +
+```{r manual_colours}
+ggplot(data = diamonds, mapping = aes(x = carat, y = price, colour = color)) +
   geom_point() +
-  scale_color_manual(values=c('#7fc97f','#beaed4','#fdc086','#ffff99','#386cb0','#f0027f','#bf5b17'))
+  scale_colour_manual(values=c('#7fc97f','#beaed4','#fdc086','#ffff99','#386cb0','#f0027f','#bf5b17'))
 ```
 
-The codes #7fc97f are "hex-codes", specifying the colors. You can find websites
-allowing you to chose a color, and get the code. A good place to get suggestions
-for color-pallettes is [Colorbrewer2](https://colorbrewer2.org/).
+The codes #7fc97f are "hex-codes", specifying the colours. You can find websites
+allowing you to chose a colour, and get the code. A good place to get suggestions
+for colour-pallettes is [Colorbrewer2](https://colorbrewer2.org/).
 
 
 :::: keypoints
 - "Pie charts are a bad idea!"
 - "Zooming might exclude data if done wrong"
-- "Play around to find the colors you like"
+- "Play around to find the colours you like"
 ::::

episodes/theming.Rmd

@@ -140,7 +140,7 @@ Most of the `elements` of the plot need to be defined in a special way. If we
 want the "theme" a text element, we set the `axis.text` to be an `element_text()` 
 function with specific arguments to specify *what* we want to do. For the background
 of the plot we are changing a rectangular object `element_rect`, and can set the background
-color like this: 
+colour like this: 
 
 ```{r background}
 diamonds %>%