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Data and donuts: Data Visualization using R
The document outlines a data visualization workshop using R and ggplot2, covering topics like data import, graphic modifications, and plot customization. It provides practical examples with a dataset of small animal surveys and includes exercises to create different types of plots, such as scatterplots, box plots, and time series. The workshop also mentions resources for further learning and troubleshooting.
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Data and donuts: Data Visualization using R
- 1. Data Visualization using R C.Tobin Magle, PhD 05-16-2017 10:00-11:30 a.m. Morgan Library Computer Classroom 175 Based on http://www.datacarpentry.org/R-ecology-lesson/
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- 3. Outline • Basic elements(data, aesthetics, geoms) • Modifications (transparency, color, grouping) • Themes (modifying default, using premade, saving your own) • Exporting plots (ggsave)
- 4. Setup • Install Rand R studio http://www.datacarpentry.org/R-ecology-lesson/index.html#setup_instructions • Download the quickstart files: http://tinyurl.com/kp6bxt4 • See the Basic Analysis with R lesson if you’re unfamiliar with R or R studio http://libguides.colostate.edu/data-and-donuts/r-analysis
- 5. Data set: surveyof small animals • Stored in a data frame • Rows: observations of individual animals • Columns: Variables that describe the animals • Species, sex, date, location, etc
- 6. Load data intoR • Import data using read.csv function • Arguments: a csv file • Output: a data frame Example: surveys <- read.csv('data/surveys_complete.csv')
- 7. Graphics with ggplot2 •data: data frame • aesthetics: looks • geoms: type of plot • Ex: points, lines, bars
- 8. ggplot2 functions • ggplot():initializes ggplot object • aes(): draws axes based on arguments • geom_XXX(): draws points/lines etc. • + operator: adds components to plot • Modular structure
- 9. Simplest ggplot Need data,aesthetics and a geom to create a plot. Example: ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) + geom_point()
- 10. ggplot() ggplot(data = surveys_complete) Argument:data frame Output: blank plot area
- 11. ggplot() + aes() ggplot(data= surveys_complete, aes(x = weight, y = hindfoot_length)) ggplot arguments: data frame + aes() aes arguments: • x = x axis variable • y = y axis variable • Output: draws axes
- 12. ggplot + aes+ geom_point ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) + geom_point() + operator: adds point to the specified plot area Output: scatterplot of weigh vs. hindfood length
- 13. Add transparency ggplot(data =surveys_complete, aes(x = weight, y = hindfoot_length)) + geom_point(alpha = 0.1) Argument: alpha = 0.1 • 1/10 opacity • Range: 0-1
- 14. Add color ggplot(data =surveys_complete, aes(x = weight, y = hindfoot_length)) + geom_point(alpha = 0.1, color = "blue") Argument: color; makes all points blue Ref chart: http://sape.inf.usi.ch/quick- reference/ggplot2/colour
- 15. Add color byspecies ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) + geom_point(alpha = 0.1, aes(color=species_id)) Argument: color = <factor variable> • Must be inside aes()
- 16. Add color byspecies ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) + geom_point(alpha = 0.1, aes(color=species_id)) Argument: color = <factor variable> • Must be inside aes() Points only Whole plot
- 17. Exercise 1 • Usethe previous example as a starting point. • Add color to the data points according to the plot from which the sample was taken (plot_id). • Hint: Check the class for plot_id. Consider changing the class of plot_id from integer to factor. Why does this change how R makes the graph?
- 18. Plot factor variableswith box plot ggplot(data = surveys_complete, aes(x = species_id, y = hindfoot_length)) + geom_boxplot() aes arguments: • x: species id (factor) • y: hinfoot length (numeric)
- 19. Overlay points ona box plot ggplot(data = surveys_complete, aes(x = species_id, y = hindfoot_length)) + geom_boxplot(alpha = 0) + geom_jitter(alpha = 0.3, color = "tomato")
- 20. Exercise 2: Violinplot • Plot the same data as in the previous example, but as a Violin plot • Hint: see geom_violin(). • What information does this give you about the data that a box plot does?
- 21. Time series data Reshapedata: yearly_counts <- surveys_complete %>% group_by(year, species_id) %>% tally
- 22. Time series data ggplot(data= yearly_counts, aes(x = year, y = n)) + geom_line() Arguments: • Data = yearly counts • X = year • Y = n (# observations)
- 23. Separate by species ggplot(data= yearly_counts, aes(x = year, y = n, group = species_id)) + geom_line() New aes argument: group • Makes a line for each species id
- 24. Color by species ggplot(data= yearly_counts, aes(x = year, y = n, group = species_id, color = species_id)) + geom_line() Combine group and color to create species_id legend
- 25. Exercise #3 • Usewhat you just learned to create a plot that depicts how the average weight of each species changes through the years. • Hint: reshape the data using the following code yearly_weight <- surveys_complete %>% group_by(year, species_id) %>% summarize(avg_weight = mean(weight))
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- 27. Applying a premadetheme ggplot(data = yearly_counts, aes(x = year, y = n, color = sex, group = sex)) + geom_line() + theme_bw() • See ?theme_bw() to see descriptions of all ggplot themes
- 28. Customize axis labelswith labs() ggplot(data = yearly_counts, aes(x = year, y = n, color = species_id)) + geom_line() + labs(title = ’Observed Species in time', x = 'Year of observation', y = 'Count') + theme_bw()
- 29. Customize font sizewith element_text() ggplot(data = yearly_counts, aes(x = year, y = n, color = species_id)) + geom_line() + labs(title = Observed Species in Time', x = 'Year of observation', y = 'Count') + theme_bw() + theme(text=element_text(size=16, family="Arial")) See ?margin for more ggplot theme elements See ?theme for more theme arguments
- 30. Create your owntheme arial_theme <- theme_bw()+ theme(text = element_text(size=16, family="Arial"))
- 31. Apply your theme ggplot(surveys_complete, aes(x= species_id, y = hindfoot_length)) + geom_boxplot() + arial_theme
- 32. Save your plotwith ggsave() • Save a plot to a variable • ggsave: saves plot to a file • Arguments: name of file, ggplot variable, width + height • Output: a png file Example: ggsave("name_of_file.png", my_plot, width=15, height=10)
- 33. Need help? • Email:tobin.magle@colostate.edu • Data Management Services website: http://lib.colostate.edu/services/data-management • Data Carpentry: http://www.datacarpentry.org/ • R Ecology Lesson: http://www.datacarpentry.org/R-ecology-lesson/04-visualization-ggplot2.html • Ggplot2 Cheat Sheets: • https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf