## 11.55 Scatter Plot Colour Choice

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ds %>%
sample_n(1000) %>%
ggplot(aes(x=min_temp, y=max_temp, colour=rain_tomorrow)) +
geom_point() +
scale_colour_brewer(palette="Paired") +
labs(x      = vnames["min_temp"],
y      = vnames["max_temp"],
colour = vnames["rain_tomorrow"]) +
theme(legend.position="bottom")

With different choices of colour different patterns may be visible. Here the value of interest for the variable of interest, rain_tomorrow, is highlighted with a darker colour. We can observe, but would want to also statistically test, that generally it rains tomorrow for lower values of the maimum temperature today.

The random sample of 1,000 rows is generated using dplyr::sample_n() and is then piped through to ggplot2::ggplot(). The function argument identifies the aesthetics of the plot so that x= associates the variable min_temp with the x-axis and y= associates the variable max_temp with the y-axis.

In addition the colour= option provides a mechanism to distinguish between days where the observation rain_tomorrow is Yex and where it is No. A colour palette can be chosen using ggplot2::scale_colour_brewer().

A graphical layer is added to the plot consisting of $$(x,y)$$ points coloured appropriately. The function ggplot2::geom_point() achieves this.

The original variable names stored as vnames are used to label the plot using ggplot2::labs(). The original names will make more sens to the reader than our chosen normalised names.

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