Coding goals this week are still fairly modest, but I think things will start to ramp up from Week 4. Goals for this week were:
This week’s coding tutorials were quite challenging compared to the last two weeks because of the length and some of the content towards the end.
There was about three hours of content this week, which took quite a long time to go through since I tend to pause the videos so I can implement the content myself. This made me feel a bit like Homer on the Odyssey, especially when the concepts became more involved.
Initially, things were quite easy to understand: writing, reading, and arranging data. But once I reached the videos about pivoting data, I became quite confused. I understood the purpose of moving the data, but I’m still not exactly sure how those commands work and how to translate between pivot_longer and pivot_wider. Specifically, I’m unsure how the pivot_wider commands work since the explanation was not as detailed. I will have to ask about this in the upcoming Q&A since I struggled quite a lot with the third step of exercise 7. Additionally, I had some trouble wrapping my head around this section of code:
mutate( woman = replace_na(1/woman, 0), man = replace_na(1/man, 0), diff = woman - man
I just don’t understand the content inside the replace_na brackets. Again, I understand what the purpose of this is: it is so that the tables don’t show up with NA and we get a numerical value we can rank (though do correct me if I am wrong). But I don’t understand what the content of the brackets means. Will definitely follow this up in the Week 4 Q&A as well.
This was a nice surprise! I thought we were going to use RStudio cloud the entire term, so it was a welcome surprise that we ran out of hours and were forced to use the app. This is because it meant that Jenny and Danielle would provide tutorial videos on using the app and downloading packages - both of which were challenges I faced last week.
Now, I feel quite confident using the app, and I even helped a few other student’s customise the appearance of the IDE.
Now that the learning content is done, here comes the hard part: the application.
Since we’re not too far into the project, we haven’t had any setbacks yet. Also my group members are lovely, so no problems there! For now, we are planning how we can split the work and what parts we need to code.
Everything is nicely set out on our hack.md and Google doc.
There is one set of descriptive statistics, three tables, and three graphs. This was easy enough to find out.
However, the first challenge we have had to overcome is the data file format. For our article (Humiston & Wamsley, 2019) the data is in SPSS. So, we have to find a package that can read SPSS in R. I think I have found something, but I have yet to test it out.
The second challenge we face is to do with the codebook, or lack thereof. Our data file does not come with a codebook, so to find out which titles correspond with which variables, we need to use SPSS. I haven’t used SPSS since the beginning of last year, so this could be a bit of a challenge.
#Next steps in my coding journey
Now that we’re starting the project, there are a lot of goals I would like to meet in the next week: