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by Graham Williams
Duck Duck Go


Scatter Plot Colour Alternative



ds %>%
  sample_n(1000) %>%
  ggplot(aes(x=min_temp, y=max_temp, colour=rain_tomorrow)) +
  geom_point() +
  scale_colour_brewer(palette="Set2") +
  labs(x      = vnames["min_temp"],
       y      = vnames["max_temp"],
       colour = vnames["rain_tomorrow"])

The simplest plot is a scatter plot which displays points scattered over a plot. If the dataset is large the resulting plot will be rather dense. For illustrative purposes a random subset of just 1,000 observations is used. A linear relationship between the two variables can be seen.

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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