3.17 Pipes and Plots
REVIEW A common scenario for pipeline processing is to prepare data for plotting. Indeed, plotting itself has a pipeline type concept where we build a plot by adding layers to it.
Below the rattle::weatherAUS dataset is
stats::filter()ed for observations from four Australian
cities. We stats::filter() observations that have missing
values for the variable
Temp3pm using an embedded
pipeline. The embedded pipeline pipes the
through the base::is.na() function which tests if the value
is missing. These results are then piped to magrittr::not()
which inverts the true/false values so that we include those that are
A plot is generated using ggplot2::ggplot() into which we pipe the processed dataset. We add a geometric layer using ggplot2::geom_density() which consists of a density plot with transparency specified through the argument. We also add a title and label the axes using ggplot2::labs().
<- c("Canberra", "Darwin", "Melbourne", "Sydney") cities %>% ds filter(location %in% cities) %>% filter(temp_3pm %>% is.na() %>% not()) %>% ggplot(aes(x=temp_3pm, colour=location, fill=location)) + geom_density(alpha=0.55) + labs(title = "Density Distributions of the 3pm Temperature", x = "Temperature Recorded at 3pm", y = "Density")
We now observe and tell a story from the plot. Our narrative will begin with the observation that Darwin has quite a different and warmer pattern of temperatures at 3pm than Canberra, Melbourne and Sydney. Canberra is on the colder side with Sydney generally warmer than Melbourne!
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