Exercise 6 - Back to Election Polls

Florida is one of the most closely watched states in the U.S. election because it has many electoral votes and the election is generally close. Create a table with the poll spread results from Florida taken during the last days before the election using the sample code.

The CLT tells us that the average of these spreads is approximately normal. Calculate a spread average and provide an estimate of the standard error.

This exercise is part of the course

HarvardX Data Science Module 4 - Inference and Modeling

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Exercise instructions

Calculate the average of the spreads. Call this average avg in the final table.
Calculate an estimate of the standard error of the spreads. Call this standard error se in the final table.
Use the mean and sd functions nested within summarize to find the average and standard deviation of the grouped spread data.
Save your results in an object called results.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Load the libraries and poll data
library(dplyr)
library(dslabs)
data(polls_us_election_2016)

# Create an object `polls` that contains the spread of predictions for each candidate in Florida during the last polling days
polls <- polls_us_election_2016 %>% 
  filter(state == "Florida" & enddate >= "2016-11-04" ) %>% 
  mutate(spread = rawpoll_clinton/100 - rawpoll_trump/100)

# Examine the `polls` object using the `head` function
head(polls)

# Create an object called `results` that has two columns containing the average spread (`avg`) and the standard error (`se`). Print the results to the console.

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