I just noticed that the corrr package, which is used on day 3, is not available on CRAN anymore:

https://cran.r-project.org/web/packages/corrr/index.html

How about using corrplot instead?

Morning 1: Spreadsheets & intro to R

Good:

Content:

• Introduction for using function building and using vector
• examples to explain functions
• I always used length() to find out how many elements in my vector fit the condition. "sum" really help, thanks.
• The basic of R. Definition of vectors and objects, always I had problem with them :)
• finding values in vectors + understanding them
• excel best practices. finally (for me) getting started in R

Style:

• I enjoyed the interactive session: listening and doing exercises
• I really enjoyed the rhythm of the class. Being able to do the exercises. Understand that sum works because logical operator gives true/false results.
• I like that there are exercises to practice
• Was really good to follow the session and exercises, good speed (not too slow/fast)
• live learning on screen
  *good way of explaining things. Like the signal post-it system. Great to have hands on possibility. Great preparation.
• the pace is excellent. great that you always checking with us if all is ok. sticky notes are great.

General

• All good so far :)
• An interesting introduction to RStudio. Enjoyed it :)
• all was well explained!
• I generally liked the content of the course and the way it was presented
• nicely done: explaining, how orders/tasks/functions and how to use them. just a little much ;)
• very didactic and good explanations of the subject
• very nice intro into R
• I enjoyed learning a new language. great course!

Could be better:

• excel data a little bit boring :)
• could be a bit quicker for the basics
• great deal of new info. a little fast if you haven't used R before but for majority should be ok. takes time to get used to new tasks and find everything.
• more exercises to catch the logic of the basic skills before we continue - thx!
• a bit more practical example on the filtering of tabular data
• could not eat in computer lab :)
• the room is a little cold

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

Outline of 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
  • 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)
  • exercise 1.3
• dplyr
  • skip spread/gather? (covered in extra RNAseq lesson) - but if we're good on time we could do it then and then repeat in the extra lesson.
  • 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.

note: extra material for ggplot2 section

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

For example:

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 again.

Some things to do before workshop:

• Ensure software is installed in training machines (see setup instructions)

• Send carpentries pre-workshop survey to participants?

• Print base R, tidyr, dplyr and ggplot2 cheatsheets? (here, here, here and here)