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If you want to see how I recreated the OP's graph, just fork my GitHub repo. It has R code and the data sets, too: https://github.com/tmoertel/nyva-cursory ht
by tmoertel 14y ago
If you want to see how I recreated the OP's graph, just fork my GitHub repo. It has R code and the data sets, too:
https://github.com/tmoertel/nyva-cursory https://github.com/tmoertel/nyva-cursory
Anyway, here are the variables I used to identify comparable observations:
## these variables identify comparable teacher observations
id_vars <- c("subject", "grade", "dbn",
"teacher_name_first_1", "teacher_name_last_1")
So I used subject, grade, school (identified by dbn) and the teacher's first and last names.
- carbocation 14y agoNice, thanks for sharing. I used the same variables with the exception of dbn; my code is here https://gist.github.com/4652968 https://gist.github.com/4652968 (I later started applying filters such as 4th-grade teachers only, etc., which is reflected in the gist.) I was pretty curious how the author specifically munged his data, since then we could put to rest the speculation about the degree of correlation.