plotting data in the tidyverse with ggplot2

Transcript

1. The tidyverse
2. What is the tidyverse? The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. https://www.tidyverse.org/
3. Why learn the tidyverse? Unlike base R, the design philosophy provides a consistent syntax: The first argument is always the data, which means we can use the pipe: %>% All functions are designed with a consistent grammar in mind with verbs, nouns, adverbs and pronouns. The design of harp tries to follow and make use of the tidyverse where possible.
4. Which tidyverse packages will we use? readr: read and write data dplyr: a grammar of data manipulation ggplot2: a grammar of graphics tidyr: tidy "messy" data lubridate: work with date - time data
5. Load the tidyverse and read some data Demo
6. ggplot "ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details."
7. ggplot "ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details." You would begin by giving it a data frame (or tibble!), telling it which columns to map to which aesthetics and then add on layers, scales, facet specifications, coordinate systems and themes.
8. ggplot - aesthetic mapping What is an aesthetic? An aesthetic is some feature of the plot controlled by the data. This could be x-position, y-position, colour, fill colour, point shape, line style, transparency (alpha), group… How do we map aesthetics? ggplot(data, aes(x = date, y = temperature, colour = station))
9. date temperature station 2019-10-15 22 Cape Town 2019-10-16 19 Cape Town 2019-10-17 24 Cape Town 2019-10-15 12 Copenhagen 2019-10-16 13 Copenhagen 2019-10-17 9 Copenhagen ggplot - aesthetic mapping ggplot(data, aes(x = date, y = temperature, colour = station))
10. ggplot - adding graphics layers What are graphics layers? Once we've mapped the data to aesthetics, we then need to tell ggplot how to add that information to the plot. This is done by adding geometries, or 'geoms'. For the previous plot: ggplot(data, aes(x = date, y = temperature, colour = station)) + geom_line()
11. ggplot - adding graphics layers Demo
12. ggplot - adding scales Scales can be applied to: ✓ colour e.g. scale_colour_manual ✓ fill e.g. scale_fill_gradient2 ✓ axes e.g. scale_y_continuous ✓ shape e.g. scale_shape_discrete ✓ size e.g. scale_size_continuous ✓ linetype e.g. scale_linetype_manual
13. ggplot - adding scales Demo
14. ggplot - facets What are facets? Using facets, you can map variables (or groups) to different panels in a plot. There are two main methods to achieve this: • facet_wrap - wraps the panels based on the value of the faceting variable • facet_grid - for 2 variables, maps the panels to a matrix with one variable for columns and the other for rows
15. ggplot - facets Returning to our original example, you plot the temperature time series in a panel for each station. date temperature station 2019-10-15 22 Cape Town 2019-10-16 19 Cape Town 2019-10-17 24 Cape Town 2019-10-15 12 Copenhagen 2019-10-16 13 Copenhagen 2019-10-17 9 Copenhagen ggplot(data, aes(x = date, y = temperature)) + geom_line() + facet_wrap(vars(station), ncol = 1)
16. ggplot - facets Demo
17. ggplot - coordinate systems Coordinate systems have to main roles: 1. Combine the two position aesthetics to produce a 2d position on the plot. 2. In coordination with the faceter, coordinate systems draw axes and panel backgrounds. We are mostly working in cartesian coordinates, so will concentrate on those, other systems exist too, such as polar coordinates and map coordinates.
18. ggplot - coordinate systems Demo
19. ggplot - themes The theme system in ggplot controls non data elements of your plot. There is an enormous number of things that you can control, so it is best to head to the documentation. You can control text sizes, fonts and colours, legend position, background colours, axis lines, axis tick marks and more. There are also a number of complete themes built into ggplot, or available in other packages, such as ggthemes.
20. ggplot - labeling For labeling, you can add labels for • x and y axes • Title • Subtitle • Caption • Legends This is especially useful when you apply functions to your data before plotting and don't like the axis labels.
21. ggplot - themes and labeling Demo