Instructor notes

[tavareshugo]

To make it easier for us, the exercises for the course have been compiled here.

Some notes on the things to cover:

• Create Rproj and a project folder with sub-folders "data", "data_output" and "scripts"
  • try to make students use a "tidy" directory structure
• Intro
  • Covers exercises 1.1 and 1.2
• data.frames
  • use read_csv() from the beginning to simplify things? (optional, but I find it useful to keep consistency across the course)
    • although not in the current materials, I think it's worth insisting on explicitly using option on how missing values are encoded
  • skip factors - I've found it more intuitive to cover them in plotting lesson (see below)
  • Main thing is to explain [rows, columns] for subset and $ to access column. For example I personally avoid showing the 4 different ways to access a column listed in the materials (usually it's quite confusing for beginners)
  • Covers exercise 1.3
• dplyr
  • Covers exercises 2.1, 2.2, 2.3, 2.4
    • Often do exercise 2.4 with students to save time
• ggplot2:
  • skip themes and customisation - usually we just demonstrate a couple of things and then refer to the materials showing other things that can be done
  • encourage learners to use web search when wanting to customise their ggplots. E.g. "how to change axis label orientation ggplot2".
  • extra: see note below to mention factors
  • exercises 3.1-4 (if time is short do some exercises together)
    • I've changed some of the exercises in relation to the materials, for example to include one where we use factors to improve visualisation.
    • There's "extra" exercises for more advanced/quick learners.

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note: extra material for ggplot2 section

So that students intuitively understand factors, introduce them in the plotting section.

The narrative goes something like this:

When doing this plot:

surveys_complete %>% 
  ggplot(aes(sex, hindfoot_length)) +
  geom_boxplot()

What if we want to change the order of the x-axis labels to be "M" first?

Then we need to learn about factors, which are a special way that R has to encode categorical variables.

Let's look at factors using a simple example first. Then go through the example of the course materials here, but only the very first section of it.

From there, jump back to the plotting problem and resolve it:

surveys_complete %>% 
  mutate(sex = factor(sex, levels = c("M", "F")))
  ggplot(aes(sex, hindfoot_length)) +
  geom_boxplot()

Exercise 3.4 applies this concept.

[tavareshugo]

Rough timings for sessions:

Day 1 AM:

• Spreadsheets (~30 min)
• Intro to R - skip factors and lubridate

Day 1 PM:

• Starting with data
• Manipulating data frames - roughly up to split-apply-combine (challenge might carry on to next day)

Day 2 AM:

• Finish Manipulating data frames
• Maybe start ggplot2

Day 2 PM:

• ggplot2
  • ggplot2 themes and Customization sections are demonstrated but not live-coded (takes too long)
  • cover factors here (see comment above)
• R and SQL - short session ~1h at the end.

[mvanrongen]

Exercise 3.3

Part two, updating existing graph to add sex, does not include a solution.

yearly_diversity <- surveys_complete %>% 
  group_by(sex, year, plot_type) %>% 
  summarise(n_species = n_distinct(species_id)) %>% 
  ungroup()

yearly_diversity %>% 
  ggplot(aes(year, n_species)) +
  geom_line(aes(colour = plot_type)) +
  facet_grid(sex ~ .) +
  labs(colour = "Plot Type", x = "Year", y = "# species")