Comparison between electoral college predictions and actual result:

polls <- polls_us_election_2016 %>%
  filter(state != "U.S." & enddate >= "2016-10-31") %>%
  mutate(spread = rawpoll_clinton/100 - rawpoll_trump/100)

cis <- polls %>%
  mutate(X_hat = (spread + 1)/2,
         se = 2*sqrt(X_hat*(1-X_hat)/samplesize),
         lower = spread - qnorm(0.975)*se,
         upper = spread + qnorm(0.975)*se) %>%
  select(state, startdate, enddate, pollster, grade, spread, lower, upper)

add <- results_us_election_2016 %>%
  mutate(actual_spread = clinton/100 - trump/100) %>%
  select(state, actual_spread)

ci_data <- cis %>%
  mutate(state = as.character(state)) %>%
  left_join(add, by = "state")

Calculating proportion of colleges that has the actual value in your confidence interval:

p_hits <- ci_data %>%
  mutate(hit = (actual_spread >= lower & actual_spread <= upper)) %>%
  summarize(hits = mean(hit))
p_hits$hits

0.661330049261084

Summarizing states that has 5 or more polls and showing the proportion of hits for each state:

p_hits <- ci_data %>%
  mutate(hit = (actual_spread >= lower & actual_spread <= upper)) %>%
  group_by(state) %>%
  filter(n() >= 5) %>%
  summarize(proportion_hits = mean(hit), n = n())

p_hits %>%
  mutate(state = reorder(state, proportion_hits)) %>%
  ggplot(aes(state, proportion_hits)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  theme(axis.text = element_text(size=14))

Calculating the difference between predicted and actual spreads:

errors <- ci_data %>%
  mutate(error = spread - actual_spread,
         hit = sign(spread) == sign(actual_spread))
median(errors$error)

0.037

Boxplot of the errors by state for polls with grades B+ or higher:

errors %>% filter(grade %in% c("A+","A","A-","B+")) %>%
  mutate(state = reorder(state, error)) %>%
  ggplot(aes(state, error)) +
  geom_boxplot() +
  geom_point() +
  theme(axis.text = element_text(size=14),
        axis.text.x = element_text(angle = 90, hjust = 1))

Boxplot of the errors by state for polls only for states with at least 5 polls with grades B+ or higher:

errors %>% filter(grade %in% c("A+","A","A-","B+")) %>%
  group_by(state) %>%
  filter(n() >= 5) %>%
  ungroup() %>%
  mutate(state = reorder(state, error)) %>%
  ggplot(aes(state, error)) +
  geom_boxplot() +
  geom_point() +
  theme(axis.text = element_text(size=14))