## Possible answers for the free response question on Test 1, Spring 2025

# 5 + 2 extra credit
#(a)
ga <- ggplot(data = schooldata, aes(y = math_score, x = reading_score))
ga + 
  geom_point(aes(color = sex)) + 
  facet_wrap(~race, nrow = 1) + geom_smooth()

# 4 pts
#(b)
gb <- ggplot(data = schooldata, aes(x = math_score))
gb + geom_histogram(bins = 10) + labs(x = "Math score")

# 5 pts
#(c)
schoolsmall <- schooldata %>%
filter(free_lunch == 'no') %>%
select(math_score, reading_score, class_type, race) %>%
mutate(total_score = math_score + reading_score)

# 5 pts
#(d)
schooldata %>%
group_by(race) %>%
summarize(
N=n(),
MeanReading = mean(reading_score)
) %>%
arrange(desc(MeanReading))

# 3 rows, for the 3 races.


# 3 pts
#(e)
Combined <- schooldata %>%
inner_join(StudentNames, by=c("studentID"="studentID"))