Slide 14
Slide 14 text
library(tidyverse)
library(tidymodels)
evals
start with data
Approach 2: Using computational methods.
# A tibble: 463 × 23
course_id prof_id score rank ethnicity gender language age cls_perc_eval
1 1 1 4.7 tenure… minority female english 36 55.8
2 2 1 4.1 tenure… minority female english 36 68.8
3 3 1 3.9 tenure… minority female english 36 60.8
4 4 1 4.8 tenure… minority female english 36 62.6
5 5 2 4.6 tenured not mino… male english 59 85
6 6 2 4.3 tenured not mino… male english 59 87.5
7 7 2 2.8 tenured not mino… male english 59 88.6
8 8 3 4.1 tenured not mino… male english 51 100
9 9 3 3.4 tenured not mino… male english 51 56.9
10 10 4 4.5 tenured not mino… female english 40 87.0
# ℹ 453 more rows
# ℹ 14 more variables: cls_did_eval , cls_students , cls_level ,
# cls_profs , cls_credits , bty_f1lower , bty_f1upper ,
# bty_f2upper , bty_m1lower , bty_m1upper , bty_m2upper ,
# bty_avg , pic_outf
i
t , pic_color
# ℹ Use `print(n =
...
)` to see more rows