# Keep just profs' salaries, >2011
library(dplyr) # for %>%
# Load the data ------------------------------------------------------------
data("salaries")
# 1. filter salary data to profs only ----------------------------------------
dontcare <- "emer|vstg|res|adj|affil|collab|clin"
# salary data filtered for professors positions only
s1 <-
salaries %>%
filter(pay_period == "year") %>%
filter(grepl("prof", title)) %>%
filter(!grepl(dontcare, title)) %>%
filter(year > 2011) %>% # only have dir data from 2012-2019
select(-base_salary_date)
# 2. remove librarians ----------------------------------------------------
#--only identified ones from 2019 so far. can add to list as needed
libs <- read_csv("data-raw/professors/librarians.csv") %>% pull(value)
s2 <-
s1 %>%
filter(!(name_lfm20 %in% libs))
# 3. manual ---------------------------------------------------------------
#--crecelius must have been hired in 2019, salary is weird
#--same with lerman (both are in marketing)
#--holly bender is an admin thing at celt
#--gary taylor switched to outreach, which is not a college, in 2017
#--elena cotos is listed as being part of the grad college, google says she's english
#--adeleke raimi olatun is an administrator 2012-2019
s3 <-
s2 %>%
filter(!(grepl("crecelius", name_lfm20) & year == 2019)) %>%
filter(!(grepl("lerman", name_lfm20) & year == 2019)) %>%
filter(!(grepl("bender holly", name_lfm20))) %>%
filter(!(grepl("taylor gary", name_lfm20) & year > 2016)) %>%
filter(!(grepl("cotos elena", name_lfm20))) %>%
filter(!(grepl("adeleke raimi olatun", name_lfm20)))
# write final -------------------------------------------------------------
sal_profs <- s3
write_csv(sal_profs, "data-raw/professors/01_sals-profs.csv")
# examine -----------------------------------------------------------------
#--who has a base salary of 0?!
sal_profs %>%
filter(base_salary == 0)
#--a lot of people need to check that out later...
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