As I add new projects to my rstats portfolio and work collaboratively on projects with students the issue of working directories is becoming more and more complicated. Not really understanding how working directories and file paths actually work, I have been relying on the beginner logic… Everything will be just fine as long as you keep your datafiles in the same folder as your .rmd file via GIPHY
In the kind of research that we do, t-tests can only take you so far. Most often we design factorial experiments where we are interested in both main effects and interactions. Because we work with infants and young children, who are both expensive to recruit/test and notoriously variable in their behaviour, we try to design experiments that use within-subjects designs; each child gives us more than one data point and we need to use repeated-measures analyses.
It is definitely true that it takes much longer to get your data ready for analysis than it does to actually analyse it. Apparently up to 80% of the data analysis time is spent wrangling data (and cursing and swearing). Did you know up to 80% of data analysis is spent on the process of cleaning and preparing data? - cf. Wickham, 2014 and Dasu and Johnson, 2003 So here is an excellent approach to data wrangling in #rstats https://t.
I am feeling more confident about my resolution to get rid of Excel and only use R for data wrangling and visualisation. Next steps… analysis. I’m starting simple (I presume) with t-tests. Mostly commonly I want to determine whether there is a difference in the performance of independent groups of kids, or a difference between kids' performance on two different conditions, or whether kids are just guessing (i.e. whether their performance differs significantly chance).
I was trying to work out how to do t-tests using my own data and the lsr package but ended up working with Dani’s AFL data from her book while trying to work out why R insisted that my outcome variable wasn’t numeric (it definitely was). Turns out that the lsr package doesn’t deal well with tibbles (which are created by default when you use read_csv to get your data) but if you use read.