Warning: package 'stringr' was built under R version 4.6.1
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.2.1 ✔ readr 2.2.0
✔ forcats 1.0.1 ✔ stringr 1.6.0
✔ ggplot2 4.0.3 ✔ tibble 3.3.1
✔ lubridate 1.9.5 ✔ tidyr 1.3.2
✔ purrr 1.2.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
# Investigating the available columns. head(trump_tweets)
source id_str
1 Twitter Web Client 6971079756
2 Twitter Web Client 6312794445
3 Twitter Web Client 6090839867
4 Twitter Web Client 5775731054
5 Twitter Web Client 5364614040
6 Twitter Web Client 5203117820
text
1 From Donald Trump: Wishing everyone a wonderful holiday & a happy, healthy, prosperous New Year. Let’s think like champions in 2010!
2 Trump International Tower in Chicago ranked 6th tallest building in world by Council on Tall Buildings & Urban Habitat http://bit.ly/sqvQq
3 Wishing you and yours a very Happy and Bountiful Thanksgiving!
4 Donald Trump Partners with TV1 on New Reality Series Entitled, Omarosa's Ultimate Merger: http://tinyurl.com/yk5m3lc
5 --Work has begun, ahead of schedule, to build the greatest golf course in history: Trump International – Scotland.
6 --From Donald Trump: "Ivanka and Jared’s wedding was spectacular, and they make a beautiful couple. I’m a very proud father."
created_at retweet_count in_reply_to_user_id_str favorite_count
1 2009-12-23 12:38:18 28 <NA> 12
2 2009-12-03 14:39:09 33 <NA> 6
3 2009-11-26 14:55:38 13 <NA> 11
4 2009-11-16 16:06:10 5 <NA> 3
5 2009-11-02 09:57:56 7 <NA> 6
6 2009-10-27 10:31:48 4 <NA> 5
is_retweet
1 FALSE
2 FALSE
3 FALSE
4 FALSE
5 FALSE
6 FALSE
# Installing and loading the stringr package to count words. Instructions from: https://r-statistics.co/stringr-str_count-in-R.htmlinstall.packages("stringr")
Warning: package 'stringr' is in use and will not be installed
library(stringr)# Creating a new column with the word count for each tweet. Instructions form: https://r-statistics.co/stringr-str_count-in-R.html.trump_tweets <- trump_tweets %>%mutate(word_count =str_count(text, boundary("word")))# Creating a new column denotating what political era the tweet took place in. Instructions for as.Date found here: https://rpubs.com/odenipinedo/working-with-dates-and-times-in-R.trump_tweets <- trump_tweets %>%mutate(political_era =case_when( created_at <as.Date("2015-06-15") ~"Pre Campaign", created_at <as.Date("2016-11-09") ~"Campaign",TRUE~"Post Election") )
# Using ggplot and geom point to plot tweet length on the x axis, favorite count on the y axis, and color by political era. Setting alpha = 0.3 to make the points easier to see since there are so many.tweet_scatter <- trump_tweets %>%ggplot(aes(x=word_count, y=favorite_count)) +geom_point(aes(color=political_era), alpha=0.3) +# Setting a log scale for y since there is such a wide range and the numbers go so high. scale_y_log10("Total Favorites (log scale)") +# Adding labels and a title. labs(x="Tweet Word Count", title="Trump Tweet Length vs Favorites Colored by Political Era") +# Adding the minimal ggplot theme.theme_minimal() +# Adding the color brewer set 2 palette. scale_color_brewer(name="Poltical Era", palette ="Set2") tweet_scatter
I used the trump_tweets dslabs dataset for this project, which provides data on trump’s tweets from 2009-2017. I counted the words in each tweet and placed this word count in a new column using the stringr and mutate functions. I found instructions on how to use stringr to count words here: https://r-statistics.co/stringr-str_count-in-R.html. I also made a new column noting which political era a tweet came from by using the mutate and case_when functions to send tweets to different categories based on if they were before 06/15/2015 (campaign announcement) or 11/09/2016 (the day after the election). To make the scatterplot, I used ggplot and geom_point and plotted word count in the x axis, favorite count on the y axis, and colored the points by political era. I used a log scale for the y axis.
