Candidate screening script

candidates.R

library(tidyr)
library(dplyr)

x <- readr::read_csv('~/Downloads/Candidate Scores (Responses) - Form Responses 1 (2).csv')

x2 <- x %>% dplyr::select(evaluator = `Email Address`,
             candidate = `Candidate's name`,
             collaboration_communication = `Collaboration and Communication Skills`,
             software = `Software development / production code`,
             organization = `Organization and Planning`,
             databases = `Databases / Programming`,
             other = `Other Skills`,
             overall = Overall,
             mean = Mean) %>% 
  mutate(evaluator = as.factor(str_remove(str_remove(evaluator, '@email.arizona.edu'), '@nceas.ucsb.edu')),
         candidate = as.factor(candidate)) #%>% 
  #filter(evaluator %in% c('dlebauer', 'julianp', 'kristinariemer', 'schnaufer'))

xlong <- x2 %>% pivot_longer(collaboration_communication:mean, names_to = 'category', values_to = 'score')

library(ggplot2)
theme_set(theme_bw())
ggplot(data = xlong, aes(candidate, score, color = evaluator)) +
  geom_point() +
  geom_line(aes(group = evaluator)) +
  facet_wrap(~category) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

ggplot(data = xlong, aes(candidate, score) )) +
  #geom_violin() +
  geom_boxplot(outlier.size = 0.5, color = 'grey') +
  geom_point(alpha = 0.25, aes(color = evaluator)) +
  geom_line(aes(color = evaluator, group = evaluator))+
  facet_wrap(~category, ncol = 3) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  ylim(c(0,5))

r <- aov(rank(mean)~candidate, data = x2)
TukeyHSD(r)  

r <- aov(score~candidate+evaluator+category, data = xlong[!xlong$category == 'mean',])
TukeyHSD(r)  

#coef         coerce       extractAIC   initialize   model.tables print        proj         se.contrast  show         slotsFromS3  summary      TukeyHSD     vcov        

model.tables(r, type = 'means', se = TRUE)

l <- lm(mean~candidate+evaluator, data = x2)

library(ggpubr)
compare_means(score~candidate, group.by = c('evaluator', 'category'), method = 'anova', data = xlong[!xlong$category == 'mean',])