I have been thinking a lot this weekend about the homework assignment for this week. The assignment was to attempt to "tidy" one's own data, using some of the tools we have been practicing -- i.e. packages of the so-called "tidyverse". There are many functions and approaches within the tidyverse, and it has been my experience that learning really begins when one starts applying these skills to one's own data.
It was my hope that after an introduction on Tuesday and Thursday that we could begin to actually work with our own data to start earning some of this priceless experience. My plan was to introduce some general functions, and point you towards resources for more specific ones that you might need.
However, I realize now that this was a mistake. I fear that some of you are feeling frustrated and unsupported. If that is you, I truly apologize. We are exactly in the midpoint of the course, and I believe there is still time to arrive at our goal: a fully documented and tidied dataset, ready for analysis/publication/archiving. If you stick with me, I promise I will help you get there.
I am creating an altered plan for this week, hopefully one that will prove helpful:
Tuesday
I have scheduled two "data workshops" tomorrow (Tuesday). The first from 11:00 to 13:00 and the second from 16:30 to 17:30, both taking place in our usual room 224. In these we are just going to work on our homework assigment -- that is, on cleaning up our data. I'll be walking around giving help whenever necessary, and we can occasionally put something up on the screen
Tuesday class
During class tomorrow I'm going to do the third and final part of our "data cleaning" trilogy. I'll pick a few specific data-cleaning tasks that I think are relatively common -- perhaps using data from volunteers, if anyone is willing -- and we can see how they might be resolved.
Thursday class
We will go over "assertions and metadata", which are two key concepts in data validation. This lecture will give you the tools to test the contents of a dataset, and also to document those contents so that strangers can interpret it.
From looking at the work inside your repositories, I can see that all of you have made a lot of progress. I'm looking forward to continuing the journey with you,
Hello @BIOL548O/2017_students ,
I have been thinking a lot this weekend about the homework assignment for this week. The assignment was to attempt to "tidy" one's own data, using some of the tools we have been practicing -- i.e. packages of the so-called "tidyverse". There are many functions and approaches within the tidyverse, and it has been my experience that learning really begins when one starts applying these skills to one's own data.
It was my hope that after an introduction on Tuesday and Thursday that we could begin to actually work with our own data to start earning some of this priceless experience. My plan was to introduce some general functions, and point you towards resources for more specific ones that you might need.
However, I realize now that this was a mistake. I fear that some of you are feeling frustrated and unsupported. If that is you, I truly apologize. We are exactly in the midpoint of the course, and I believe there is still time to arrive at our goal: a fully documented and tidied dataset, ready for analysis/publication/archiving. If you stick with me, I promise I will help you get there.
I am creating an altered plan for this week, hopefully one that will prove helpful:
Tuesday
I have scheduled two "data workshops" tomorrow (Tuesday). The first from 11:00 to 13:00 and the second from 16:30 to 17:30, both taking place in our usual room 224. In these we are just going to work on our homework assigment -- that is, on cleaning up our data. I'll be walking around giving help whenever necessary, and we can occasionally put something up on the screen
Tuesday class
During class tomorrow I'm going to do the third and final part of our "data cleaning" trilogy. I'll pick a few specific data-cleaning tasks that I think are relatively common -- perhaps using data from volunteers, if anyone is willing -- and we can see how they might be resolved.
Thursday class
We will go over "assertions and metadata", which are two key concepts in data validation. This lecture will give you the tools to test the contents of a dataset, and also to document those contents so that strangers can interpret it.
From looking at the work inside your repositories, I can see that all of you have made a lot of progress. I'm looking forward to continuing the journey with you,
Andrew