Sticky note feedback

[tavareshugo]

Day 1

Good

• 13x plots
  • having examples of different plot types
  • overlaying geometries
• 5x all clear/great/nothing bad to say
• 4x good atmosphere to ask questions
• 3x good pace
• 3x great instructors, patient
• 2x having helpers so the whole class can continue
• 2x sticky notes
• 2x lots of functions
• 2x clear/good explanation of syntax
• 2x useful exercises
• 2x good introduction
• dynamic/interactive
• overall intro and what can be achieved after the course
• materials easy to follow
• cheatsheets
• useful tips
• missing values
• good as a beginner

To improve

• 2x Not clear what the course schedule and objectives are
• 2x Getting number of NAs using $ and sum() (e.g. this caused confusion: sum(!is.na(surveys$weight))
• 2x bit fast (but the instructors are very helpful)
• bit slow
• Better do data manipulation before plots
• more 5min breaks
• more references to the cheatsheets
• ggplot aesthetics (fill, colour, etc.)
• go through syntax more
• difference between square [] and round () brackets
• why is c() used inside the scale_y_continuous(limits = c(0, 100))
• more data reading examples
• too much to remember
• too many options for plotting, hard to remember
• more spreadsheet analysis
• how to plot using open access data
• more exercises

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• Elixir talk - was not clear what the objective was
• More veggie food

[mvanrongen]

Day 2 - AM

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Good

• you are awesome
• 2x pace was good
• 3x exercises were good
• 2x very useful session
• recap was good & much needed
• 3x clear explanations of not-always-obvious concepts in R
• good to know about basic counts, means etc
• 3x data manipulation (overall)
  • 2x pipes
  • cleaning
  • 4x mutate
  • 2x summarise
  • separate
  • 6x filtering & selecting
• new concepts learnt
• useful for data analysis
• will apply to own data
• feel more confident
• learning about tidyverse, intuitive & really useful
• helpful customised support
• liked troubleshooting code with mistakes

To improve

• more examples in materials
• 2x more exercises for those who are quick (especially data manipulation)
• 2x speed of teaching a bit fast
• 2x overview of raw data structure
• better data set please
• too slow
• 3x a lot to take in/too fast
  • last half hour before lunch - brain was full
• include comments above every command for future reference
• more explanation on functions used, especially ones that appear in exercises
• list of functions we could use
• more recap needed
• a lot of commands to take in
• 2x titles implied something different until introduction today
• some examples give confusing results if you paste in answer (specifically 2.3 question 2)
• 3x very last exercise a bit of a jump (but good that it integrated things from day before)
• teaching could be more clear
• problems hearing what was said
• project overview on GitHub is bad
• 3x lunch was poor quality/quantity

to improve syntax-specific

• 2x when to use !is.na and when na.rm = TRUE (why/when chosen), n = n()
• difference group_by and select
• when and why to use/create data.frame & is this the same as environment?
• syntax for sorting data unclear
• order in which commands are written could be clearer
  • 2x more in-depth about group_by and summarise functions
  • count function not very clear
• struggling with grouping & summary
• pipes

[tavareshugo]

Day 2 - PM

Good

• 2x Nothing to complain
• 5x Everything useful, learned a lot
• 3x Great/patient instructors
• Supportive environment (no question is stupid)
• Recap is good
• 4x Data organisation spreadsheets useful
• 3x ggplot
• Exercises good
• 4x Data manipulation
• pipes
• using band_members and band_instruments useful for joins

To improve

• 2x lots of info for one day
• How to extract and combine data from open source data
• 3x Re-shaping data (gather/spread)
  • and understand which shape we need for plotting
• How to use categorical data in ggplot
• 2x more context for the data / different data
• too much time for exercise before lunch (can't remember which one it was...)
• too slow, too many breaks, don't stop to help others (wrong course for this person?)
  • could have done more today (but hard with mixed ability group)
• 2x more breaks
• more time for joins
• sometimes too fast
• identifying aims and what one should be able to do after each session
• some concepts hard to get on screen - more cartoons would help
• when to use c() function?

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• food could be better
• lots of single-use plastic with the milk

[mvanrongen]

Day 4 - AM

You guys are so good at explaining this. Thank you so much for your patience. I finally feel like I understand how to think about data.

Good

• you are a great teacher
• 4x enjoyed working with real data
• 16x good/enjoyed explanation of PCA
• 3x pace is good
• fewer breaks
• plotting was good
• 2x good explanation of everything
• good explanation of theory and practical
• enjoyed working with case study
• good consolidation of previous knowledge
• good help
• friendly atmosphere
• thanks for entire course
• 3x nothing to complain
• very didactic, great!
• good example of exercise
• finally a bit of bioinformatics
• although more abstract, so far the best session
• good to bring lots of skills together
• very useful but need time outside the course to digest

To improve

• a little slow
• confusion eigenvalues and eigenvectors in prcomp help
• a lot to take in
• 2x more breaks
• would be nice to see how to manipulate data sets
• quick guide of how to convert raw data to transformed data
• title course is misleading, little bioinformatics
• what does factor function do for plotting
• unclear to go from str gave pc_scores and why square sdev
• 2x components of PCA still somewhat unclear (but great effort!)
• getting data out of PCA object & transforming to plots
• how to select best PC
• preferred more time on R code instead of PC analysis
• could have watched video first
• mostly syntax & function problems
• more generic analysis, would not use these examples in line of work

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• hungry
• uncomfortable chairs

[tavareshugo]

Day 4 pm

good

• 8x general good comments: well presented; good material; informative
• 6x PCA well explained
• 3x Data reshaping, how to go from matrix to table
• 2x Useful topic that can apply to own data
• 2x Friendly instructors
• MA plots
• good advice in searching for help
• good to have extra material to look at home

to improve

• time management:
  • 6x need more time (2 days?); too long on basics to fast on advanced stuff
  • 3x Less time on PCA and some time on clustering genes
• 4x Too much technical syntax in afternoon session (PCA loadings, I think was the problem)
• 2x more examples of PCA applications (e.g. extra material with other datasets)
  • More clarity in what questions we can answer from PCA
• consolidate the data manipulation first, before going into the stats
• More plotting examples
• PCA a bit difficult
• Loadings plot was difficult to understand
• Some concepts not clear: when to use c(), $, %in%, why ungroup()
• Don't know much about gene expression

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• 2x Lunch was better