IAFF 6501 – Plotting Confidence Intervals

NYTimes Sienna Poll (9/29 - 10/6)

3,385 voters polled
49% say will vote for Harris
46% say will vote for Trump

Build Confidence Intervals

library(tidyverse)
library(estimatr)

Build Confidence Intervals

Create data

n_harris <- .49*3385
harris <- as_tibble(c(rep(1, 1658), rep(0, 3385-1658)))
mean(harris$value)

[1] 0.489808

n_trump <- .46*3385
trump <- as_tibble(c(rep(1, 1557), rep(0, 3385-1557)))
mean(trump$value)

[1] 0.4599705

Build Confidence Intervals

harris %>% 
  lm_robust(value ~ 1, data = .)

            Estimate  Std. Error  t value Pr(>|t|)  CI Lower  CI Upper   DF
(Intercept) 0.489808 0.008593391 56.99822        0 0.4729592 0.5066567 3384

Build Confidence Intervals

trump %>% 
  lm_robust(value ~ 1, data = .)

             Estimate  Std. Error  t value Pr(>|t|)  CI Lower  CI Upper   DF
(Intercept) 0.4599705 0.008567588 53.68728        0 0.4431723 0.4767686 3384

Plot the Confidence Intervals

First, store the information we need in format for plotting

h <- harris %>% 
  lm_robust(value ~ 1, data = .) %>% 
  tidy() %>% 
  mutate(candidate = "Harris")

t <- trump %>% 
  lm_robust(value ~ 1, data = .) %>% 
  tidy() %>% 
  mutate(candidate = "Trump")

plotData <- rbind(h, t)
plotData

         term  estimate   std.error statistic p.value  conf.low conf.high   df
1 (Intercept) 0.4898080 0.008593391  56.99822       0 0.4729592 0.5066567 3384
2 (Intercept) 0.4599705 0.008567588  53.68728       0 0.4431723 0.4767686 3384
  outcome candidate
1   value    Harris
2   value     Trump

Plot

Code

ggplot(plotData, aes(y = estimate, x = candidate, ymin = conf.low, ymax = conf.high)) +
  geom_col(fill = "steelblue4", width = .5) +
  geom_errorbar(width = .05) +
  theme_bw()  +
  ylim(0, .6) +
  labs(x = "Candidate",
       y = "Percent Intending to Vote",
       title = "Point Estimates and 95% Confidence Intervals")ggplot(plotData, aes(y = estimate, x = candidate, ymin = conf.low, ymax = conf.high)) +
  geom_col(fill = "steelblue4", width = .5) +
  geom_errorbar(width = .05) +
  theme_bw()  +
  ylim(0, .6) +
  labs(x = "Candidate",
       y = "Percent Intending to Vote",
       title = "Point Estimates and 95% Confidence Intervals")ggplot(plotData, aes(y = estimate, x = candidate, ymin = conf.low, ymax = conf.high)) +
  geom_col(fill = "steelblue4", width = .5) +
  geom_errorbar(width = .05) +
  theme_bw()  +
  ylim(0, .6) +
  labs(x = "Candidate",
       y = "Percent Intending to Vote",
       title = "Point Estimates and 95% Confidence Intervals")

Point Estimates with CI

Code

ggplot(plotData, aes(y = estimate, x = candidate, ymin = conf.low, ymax = conf.high)) +
  geom_point() +
  geom_errorbar(width = .05) +
  theme_bw()  +
 ylim(.4, .6) +
  labs(x = "Candidate",
       y = "Percent Intending to Vote",
       title = "Vote Intention in 2024",
       subtitle = "Point Estimates and 95% Confidence Intervals") +
  geom_hline(yintercept = .5, linetype = "dashed", color = "grey")ggplot(plotData, aes(y = estimate, x = candidate, ymin = conf.low, ymax = conf.high)) +
  geom_point() +
  geom_errorbar(width = .05) +
  theme_bw()  +
 ylim(.4, .6) +
  labs(x = "Candidate",
       y = "Percent Intending to Vote",
       title = "Vote Intention in 2024",
       subtitle = "Point Estimates and 95% Confidence Intervals") +
  geom_hline(yintercept = .5, linetype = "dashed", color = "grey")ggplot(plotData, aes(y = estimate, x = candidate, ymin = conf.low, ymax = conf.high)) +
  geom_point() +
  geom_errorbar(width = .05) +
  theme_bw()  +
 ylim(.4, .6) +
  labs(x = "Candidate",
       y = "Percent Intending to Vote",
       title = "Vote Intention in 2024",
       subtitle = "Point Estimates and 95% Confidence Intervals") +
  geom_hline(yintercept = .5, linetype = "dashed", color = "grey")

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