Review of Basic Data Analytic Methods Using R - Data Science and Big Data Analytics - page 138

The vector colors defines the color scheme for the plot. It could be changed to
something like colors <- c("gray50", "white", "black") to make the
scatterplots grayscale.
Analyzing a Variable over Time
Visualizing a variable over time is the same as visualizing any pair of variables, but
in this case the goal is to identify time-specific patterns.
Figure 3.19 plots the monthly total numbers of international airline passengers (in
thousands) from January 1940 to December 1960. Enter plot(AirPassengers)
in the R console to obtain a similar graph. The plot shows that, for each year, a large
peak occurs mid-year around July and August, and a small peak happens around
the end of the year, possibly due to the holidays. Such a phenomenon is referred to
as a seasonality effect .
Figure 3.19 Airline passenger counts from 1949 to 1960
Additionally, the overall trend is that the number of air passengers steadily
increased from 1949 to 1960. Chapter 8, “Advanced Analytical Theory and
Methods: Time Series Analysis,” discusses the analysis of such datasets in greater
detail.
3.2.5 Data Exploration Versus Presentation
Using visualization for data exploration is different from presenting results to
stakeholders. Not every type of plot is suitable for all audiences. Most of the plots
presented earlier try to detail the data as clearly as possible for data scientists to
identify structures and relationships. These graphs are more technical in nature