ASP 460 2.0 Data Visualization
Dr Thiyanga Talagala
Colour Scales
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Color Scales

Qualitative color scale
Sequential color scale
Diverging color scale

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Qualitative color scale

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Sequential color scale

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Diverging color scale

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Setting colours in ggplot8 / 39

library(ggplot2)
ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width)) + geom_point()

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library(ggplot2)
ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width)) + geom_point(color="purple")

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library(ggplot2)
ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) + geom_point()

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library(ggplot2)
ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, color=Petal.Length < 5)) + geom_point()

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cols <- c("setosa" = "red", "versicolor" = "blue", "virginica" = "darkgreen")
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point( ) + scale_colour_manual(values = cols)

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RColorBrewer::display.brewer.all()

seq (sequential)
div (diverging)
qual (qualitative)

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cols <- c("setosa" = "red", "versicolor" = "blue", "virginica" = "darkgreen")
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point( ) + scale_color_brewer(type = 'qual', palette =  'Dark2')

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ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point( ) + scale_color_manual(values = c("#1b9e77", "#d95f02", "#7570b3"))

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Viridis colour scales from viridisLite

"The viridis scales provide colour maps that are perceptually uniform in both colour and black-and-white. They are also designed to be perceived by viewers with common forms of colour blindness. See also https://bids.github.io/colormap/."

source: https://ggplot2.tidyverse.org/reference/scale_viridis.html

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ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point( ) +  scale_colour_viridis_d()

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ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point( ) +  scale_colour_viridis_d(option = "plasma")

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ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point( ) +  scale_colour_viridis_d(option = "inferno")

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ggplot(iris) +
  geom_point(aes(x = Sepal.Length,
                 y = Sepal.Width,
                 color = Species,
                 shape = Species,
                 alpha = Species)) +
  scale_x_continuous( breaks = c(170,200,230)) +
  scale_y_log10() +
  scale_colour_viridis_d() +
  scale_shape_manual( values = c(17,18,19)) +
  scale_alpha_manual( values = c( "setosa" = 0.6, "versicolor" = 0.5, #
                                  "virginica" = 0.7))

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Highlight data points

https://thiyanga.netlify.app/post/scatterplot/

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HSL colour attributes

Hue: true colours without tint or shades

Saturation: light tints to dark shades

Lightness (Value)

image source: https://purple11.com/basics/hue-saturation-lightness/

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Coordinates26 / 39

library(palmerpenguins)
data(penguins)
ggplot(penguins) +
  geom_bar(aes(x= species, fill = species))

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ggplot(penguins) +
  geom_bar(aes(x= species, fill = species)) + coord_flip()

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ggplot(penguins) +
  geom_bar(aes(x= year, fill = species))

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## Zooming into a plot with scale
ggplot(penguins) +
  geom_bar(aes(x= year, fill = species)) +
  scale_y_continuous(limits = c(0,115))

## Warning: Removed 1 rows containing missing values (`geom_bar()`).

When zooming with scale, any data outside the limits is thrown away

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Zooming into a plot with scale

ggplot(penguins) +
  geom_bar(aes(x= year, fill = species)) +
  coord_cartesian(ylim = c(0,115))

Proper zoom with coord_cartesian()

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p <- ggplot(mtcars, aes(disp, wt)) + geom_point() + geom_smooth()
p

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

# Setting the limits on a scale converts all values outside the range to NA.
p + scale_x_continuous(limits = c(325, 500))

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

## Warning: Removed 24 rows containing non-finite values (`stat_smooth()`).

## Warning: Removed 24 rows containing missing values (`geom_point()`).

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p <- ggplot(mtcars, aes(disp, wt)) + geom_point() + geom_smooth()
p

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

# Setting the limits on the coordinate system performs a visual zoom.
# The data is unchanged, and we just view a small portion of the original
# plot. Note how smooth continues past the points visible on this plot.
p + coord_cartesian(xlim = c(325, 500))

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

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ggplot(iris, aes(y=Sepal.Length, x=Sepal.Width)) + geom_point()

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ggplot(iris, aes(y=Sepal.Length, x=Sepal.Width)) + geom_point() + coord_fixed(ratio = 1)

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ggplot(iris, aes(y=Sepal.Length, x=Sepal.Width)) + geom_point() + coord_fixed(ratio = 0.5)

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ggplot(iris, aes(y=Sepal.Length, x=Sepal.Width)) + geom_point() + coord_fixed(ratio = 5)

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ggplot(iris, aes(y=Sepal.Length, x=Sepal.Width)) + geom_point() + coord_fixed(ratio = 1/5)

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ggplot(iris, aes(y=Sepal.Length, x=Sepal.Width)) + geom_point() + coord_fixed(ylim = c(0, 6))

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