Twitter 3
Harold Nelson
3/26/2022
Setup
library(plyr)
library(tidyverse)
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## Attaching package: 'rtweet'
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## flatten
Compare #rstats and #pandas
Compare the two hashtags on the rate of posting. Use n = 500.
Solution
time_to_get = function(term,ntweets){
search_tweets(term, n = ntweets) %>%
filter(is.na(reply_to_screen_name) &
is_retweet == FALSE &
is_quote == FALSE) %>%
summarize(max(created_at) - min(created_at))
}
time_to_get("#rstats", 500)
## # A tibble: 1 × 1
## `max(created_at) - min(created_at)`
## <drtn>
## 1 1.910278 hours
time_to_get("#pandas",500)
## # A tibble: 1 × 1
## `max(created_at) - min(created_at)`
## <drtn>
## 1 20.35806 hours
Compare Originality
Use fract_original() to compare the originality of the postings for these two hastags.
Solution
fract_original = function(term,ntweets){
search_tweets(term,n = ntweets) %>%
mutate(is_orig = is.na(reply_to_screen_name) &
is_retweet == FALSE &
is_quote == FALSE) %>%
summarize(mean(is_orig))
}
fract_original("#rstats",500)
## # A tibble: 1 × 1
## `mean(is_orig)`
## <dbl>
## 1 0.170
fract_original("#pandas",500)
## # A tibble: 1 × 1
## `mean(is_orig)`
## <dbl>
## 1 0.119
Language Distribution
Examine the distribution of languages for these two hashtags. Use n = 2000.
search_tweets("rstats",n = 2000) %>%
ggplot(aes(x = lang)) +
geom_bar() +
ggtitle("Language Distribution for #rstats")
search_tweets("pandas",n = 2000) %>%
ggplot(aes(x = lang)) +
geom_bar() +
ggtitle("Language Distribution for #pandas")
Timelines
Use get_timeline() to get and display the followers_count, friends_count and the golden ratio for the following screen names.
- @HadleyWickham
- @juliasilge
- @wesmckinn
Solution
tl = get_timeline(c("@HadleyWickham","@juliasilge","@wesmckinn
"), n = 100)
tl %>%
group_by(screen_name) %>%
summarize(followers = mean(followers_count),
friends = mean(friends_count)) %>%
ungroup() %>%
mutate(golden_ratio = followers/friends)
## # A tibble: 3 × 4
## screen_name followers friends golden_ratio
## <chr> <dbl> <dbl> <dbl>
## 1 hadleywickham 131668 283 465.
## 2 juliasilge 45087 754 59.8
## 3 wesmckinn 56228 872 64.5
Trends
Get the current trending topics in New York. Display the top 10.
Solution
gtny = get_trends("New York")
gtny %>% arrange(desc(tweet_volume)) %>%
select(query,tweet_volume) %>%
distinct %>%
head(10)
## # A tibble: 10 × 2
## query tweet_volume
## <chr> <int>
## 1 %23Oscars 586112
## 2 Beyonc%C3%A9 146162
## 3 Duke 121845
## 4 %22World+Cup%22 105820
## 5 Zendaya 98637
## 6 Hollywood 85457
## 7 %22Final+Four%22 81408
## 8 Dune 80119
## 9 Chanel 58026
## 10 Kansas 53529
Huh??
I found a topic that I knew nothing about in my results. At that time it was “SaudiArabianGP”. How would I look at the text of some of the tweets on that topic. Do the same thing for something you don’t understand.
Solution
Maybe you won’t see this today.
search_tweets("SaudiArabianGP",n = 20) %>%
select(text)
## # A tibble: 20 × 1
## text
## <chr>
## 1 "#F1 | サウジアラビアGP🇸🇦\n決勝速報📣\n\n優勝 フェルスタッペン🚀🚀\n4位 ペレ…
## 2 "VERSTAPPEN WINS IN JEDDAH!! 🏆\n\nLeclerc finishes just behind in second, S…
## 3 "@CoachDonMega @SaudiArabianGP @fia @F1 The sound of your barking is loud an…
## 4 "Verstappen:\"It was a tricky one. One the mediums again. Just not a lot of …
## 5 "Verstappen:\"The VSC came out and you dont know which car is going to come …
## 6 "¡Muy cerca han estado los Alpine de tocarse! \n🟣https://t.co/UsNqEo9XqV\n…
## 7 "Se anota infracción del procedimiento de safety car de Pérez.\n\nSainz: \"Y…
## 8 "¡¡PROBLEMAS para Tsunoda!!\n\n\"He perdido el motor. He perdido el motor\".…
## 9 "Verstappen contra Leclerc, round 2 🍿 #SaudiArabianGP https://t.co/SFhKfKAb…
## 10 "\"UNA BUENA CARRERA PARA 'CHECO' EN CUANTO A LA SUMA DE PUNTOS\" \n\n#F1xFO…
## 11 "📌| مشهد الختام 🏎\n#فورمولا1_في_السعودية\n#SaudiaArabianGP\n https://t.co/o…
## 12 "Thumbs up all round! 👍👍\n\n#SaudiArabianGP #F1 https://t.co/E4eNCKeSnN"
## 13 "YES BOYS 🙌 P1☝️\n\nFeels amazing to be on top after such an exciting race …
## 14 "Thumbs up all round! 👍👍\n\n#SaudiArabianGP #F1 https://t.co/E4eNCKeSnN"
## 15 "Leclerc leads after two rounds \n\n#SaudiArabianGP #F1 https://t.co/JHck65s…
## 16 "The #DHLFastestPitStop of the #SaudiArabianGP goes to @McLarenF1 for the se…
## 17 "#SaudiArabianGP Saudiarabia land full of gay princes that are fucking arou…
## 18 "An incredible battle, from start to finish 👊\n\nThis, is why we love Formu…
## 19 "#F1 🇸🇦 #SaudiArabianGP - Carrera\n\n🎙️#Perez \"La entrada del SC, fue el pe…
## 20 "YES BOYS 🙌 P1☝️\n\nFeels amazing to be on top after such an exciting race …
Time Series Data
Here is the code from the course.
# Extract tweets on #walmart and exclude retweets
walmart_twts <- search_tweets("#walmart", n = 18000, include_rts = FALSE,retryonratelimit = TRUE)
## retry on rate limit...
## waiting about 12 minutes...
# View the output
head(walmart_twts)
## # A tibble: 6 × 90
## user_id status_id created_at screen_name text source
## <chr> <chr> <dttm> <chr> <chr> <chr>
## 1 25473002 1508236674811109379 2022-03-28 00:16:59 rakum "There i… Twitt…
## 2 309912680 1508236404823887874 2022-03-28 00:15:55 Annie_Acorn "Two Res… Twitt…
## 3 309912680 1508236171700277255 2022-03-28 00:14:59 Annie_Acorn "Gotten … Twitt…
## 4 309912680 1505695094971711491 2022-03-20 23:57:39 Annie_Acorn "MY NEWE… Twitt…
## 5 309912680 1506726322894221313 2022-03-23 20:15:23 Annie_Acorn "Do YOU … Twitt…
## 6 309912680 1506725548004986883 2022-03-23 20:12:18 Annie_Acorn "Can a s… Twitt…
## # … with 84 more variables: display_text_width <dbl>, reply_to_status_id <chr>,
## # reply_to_user_id <chr>, reply_to_screen_name <chr>, is_quote <lgl>,
## # is_retweet <lgl>, favorite_count <int>, retweet_count <int>,
## # quote_count <int>, reply_count <int>, hashtags <list>, symbols <list>,
## # urls_url <list>, urls_t.co <list>, urls_expanded_url <list>,
## # media_url <list>, media_t.co <list>, media_expanded_url <list>,
## # media_type <list>, ext_media_url <list>, ext_media_t.co <list>, …
# Create a time series plot
ts_plot(walmart_twts, by = "hours", color = "blue")
Periodicity
Use the hour() and wday() functions from the lubridate package to see how these tweets vary over a week.
Solution
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
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## date, intersect, setdiff, union
walmart_twts = walmart_twts %>%
select(created_at) %>%
mutate(day = wday(created_at,label = TRUE,abbr = TRUE),
hour = factor(hour(created_at)))
walmart_twts %>%
ggplot(aes(x = hour, y = day)) +
geom_count() +
ggtitle("Walmart Tweets by Day of Week and Hour")
Can you explain what you see?