Data Science Desktop Survival Guide by Graham Williams
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Pipe and Plot

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 Temp3pm data 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 not missing. 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 alpha= argument. We also add a title and label the axes using ggplot2::labs().

cities <- c("Canberra", "Darwin", "Melbourne", "Sydney")

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.