set.seed(26439) %>% ds sample_n(1000) %>% ggplot(aes(x=min_temp, y=max_temp, colour=rain_tomorrow, shape=rain_tomorrow)) + geom_point() + labs(x = vnames["min_temp"], y = vnames["max_temp"], colour = vnames["rain_tomorrow"]) + theme_bw() + theme(legend.position="bottom")
There are many circumstances where it makes sense to colour the points according to some scheme. For example, in a predictive modelling context the colour might correspond to the prediction made (yes or no). For a cluster analysis the colour might represent the cluster each point is allocated to.
A key variable of interest in the rattle::weatherAUS dataset is
rain_tomorrow. By colouring the dots according to the
rain_tomorrow we may begin to see relationships in the data.
The colour is added simply by specifying a further aesthetic,
colour=rain_tomorrow. Different values of the variable
rain_tomorrow will then be coloured differently.
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