• Take some time to set up your screen
  • RStudio, Zoom window, Zoom chat, maybe a web browser
• Leave your camera on if you are comfortable
• Feel free to ask questions by:
  • Unmuting, raising hand, putting question in chat…
• Don’t hesitate to ask questions!

1. The Grammar of Graphics
2. ggplot2 basics
3. Building plots iteratively
4. Adding labels and titles
5. Theming
6. Saving your output

NOTE: We’ll have a break about halfway through!

• Layer
  • Data
  • Mapping
  • Statistical transformation (stat)
  • Geometric object (geom)
  • Position adjustment (position)
• Scale
• Coordinate system (coord)
• Faceting (facet)
• Themes

• Data
• Aesthetic mapping (aes)
  • Describes how variables are mapped onto graphical attributes
  • Visual attribute of data including x-y axes, color, fill, shape, and alpha
• Geometric objects (geom)
  • Determines how values are rendered graphically, as bars (geom_bar), scatterplot (geom_point), line (geom_line), etc.

<DATA> %>%
    ggplot(aes(<MAPPINGS>)) +
    <GEOM_FUNCTION>()

## This is the correct syntax for adding layers
interviews_plot +
    geom_point()

## This will not add the new layer and will return an error message
interviews_plot
+ geom_point()

Use what you just learned to create a scatter plot of rooms by village with the respondent_wall_type showing in different colors. Does this seem like a good way to display the relationship between these variables? What other kinds of plots might you use to show this type of data?

When you’re done, please give a :thumbs-up: or a :green-check: in the Zoom reactions.

Boxplots are useful summaries, but hide the shape of the distribution. For example, if the distribution is bimodal, we would not see it in a boxplot. An alternative to the boxplot is the violin plot, where the shape (of the density of points) is drawn.

• Replace the box plot with a violin plot; see geom_violin().

When you’re done, please give a :thumbs-up: or a :green-check: in the Zoom reactions.

So far, we’ve looked at the distribution of room number within wall type. Try making a new plot to explore the distribution of another variable within wall type.

• Create a boxplot for liv_count for each wall type. Overlay the boxplot layer on a jitter layer to show actual measurements.

When you’re done, please give a :thumbs-up: or a :green-check: in the Zoom reactions.

Add colour to the data points on your boxplot according to whether the respondent is a member of an irrigation association (memb_assoc).

When you’re done, please give a :thumbs-up: or a :green-check: in the Zoom reactions.

Create a bar plot showing the proportion of respondents in each village who are or are not part of an irrigation association (memb_assoc). Include only respondents who answered that question in the calculations and plot. Which village had the lowest proportion of respondents in an irrigation association?

The labs function takes the following arguments:

• title – to produce a plot title
• subtitle – to produce a plot subtitle (smaller text placed beneath the title)
• caption – a caption for the plot
• ... – any pair of name and value for aesthetics used in the plot (e.g., x, y, fill, color, size)

In addition to theme_bw(), which changes the plot background to white, ggplot2 comes with several other themes which can be useful to quickly change the look of your visualization. The complete list of themes is available at https://ggplot2.tidyverse.org/reference/ggtheme.html. theme_minimal() and theme_light() are popular, and theme_void() can be useful as a starting point to create a new hand-crafted theme.

The ggthemes package provides a wide variety of options (including an Excel 2003 theme). The ggplot2 extensions website provides a list of packages that extend the capabilities of ggplot2, including additional themes.

• Experiment with at least two different themes. Build the previous plot using each of those themes. Which do you like best?

With all of this information in hand, please take another five minutes to either improve one of the plots generated in this exercise or create a beautiful graph of your own. Use the RStudio ggplot2 cheat sheet for inspiration. Here are some ideas:

• See if you can make the bars white with black outline.
• Try using a different colour palette (see http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/).

After creating your plot, you can save it to a file in your favorite format. The Export tab in the Plot pane in RStudio will save your plots at low resolution, which will not be accepted by many journals and will not scale well for posters.

Instead, use the ggsave() function, which allows you easily change the dimension and resolution of your plot by adjusting the appropriate arguments (width, height, and dpi).

Make sure you have the fig_output/ folder in your working directory.

To sign up for more sessions: go/summer-data-workshops/

Assessment survey: go/summer-data-assessment/

Ryan’s contact info: go/ryan/