Announcements

Assignments

  • HW 03 due Nov 4 at 11:59p
  • Tomorrow’s lab: Project peer review
    • Push a draft of your report to the Github repo by today at 11:59p

Tea with a TA

Hang out with the TAs from STA 199! This is a casual conversation and a fun opportunity to meet the members of the STA 199 teaching team. The only rule is these can’t turn into office hours!

Tea with a TA counts as a statistics experience.

Caroline Levenson, Mon, Nov 2, 1p - 2p

Submit your questions about statistics and the US election

What questions do you have about statistics and the US election? Click here to submit your questions by Friday, Oct 30. We will discuss some of the questions during the Nov 2 live lecture.

And…if you’re eligible, VOTE!

Other events

Stats in Spring 2021

Project - Draft due Oct 28 at 11:59p

  • Write the draft in the written-report.Rmd file in the project repo.

  • Draft should include

    • the research context and motivation
    • exploratory data analysis
    • any inference, modeling, or conclusions.

Exercises

For today’s AE, we are analyzing the tweets from the statistics experience!

# load packages
library(tidyverse)
library(tidytext)
library(stringr)
#library(wordcloud) #word cloud
tweets <- read_csv("data/stats-tweets.csv") %>%
  mutate(tweet_num = 1:nrow())

Exercise 1

Are these Tweets or Tweet threads? Let’s see how long the statistics experience tweets are!

Exercise 2

Let’s use the unnest_tokens to make a tidy data frame of words from the tweets.

Exercise 3

What are the most common words used in the tweets? Are the most common words interesting? If not, what can we do to make this more interesting?

Exercise 4

Make a graph to visualize the top 10 words from your tweets.

Exercise 5

Now let’s see the general sentiment of the tweets. Use the bing lexicon to identify the top 10 most common words and their sentiments.

Exercise 6

Let’s visualize the top 10 most common words for each sentiment. Make a plot to display the top 10 most commonly used positive words and negative words.

Exercise 7

We can also visualize the most common words using a word cloud.

#library(wordcloud)
#set.seed(10282020)
# _____ %>%
#  anti_join(stop_words) %>%
 # count(word) %>%
#  with(wordcloud(word, n, max.words = 100))

Exercise 8

Based on this analysis, what are some general conclusions you draw about the class’s first statistics experiences?

More with text analysis!