README.md
In tangaot/twitterapiR: An R Wrapper for the Twitter API
twitterapiR
Description
This project is an API wrapper for Twitter in R. This package provides
user friendly functions for the users to query information from Twitter.
The current list of functions included in the wrapper are:
- set_bearer(“API key”, “API secret”)
- get_bearer()
- user_friends(screen_name, number)
- searchTweets(searchString, resultType, count)
- followersCount(screen_name)
Installation
if (!require("remotes")) {
install.packages("remotes")
}
remotes::install_github("tangaot/twitterapiR")
Authentication
You will need to follow these instructions to continue. We are accessing
Twitter programatically. twitterapiR uses the httr
package under the hood to manage this.
The first step is to create a Twitter application for yourself. To
do so, go to the Developer Platform’s
page
and log in.
Follow the instruction and fill in some basic information. After your project
is created, you can generate your consumer API and Secret key.
In your R session, you will want to do the following using the
appropriate values from the web page:
set_bearer("API key", "API secret")
This will authenticate via httr, we recommend looking at the Token
man page of this package for more information regarding how to manage
the authentication and caching processes.
Getting Started
This document is intended to describe the usage of each function and to
show examples of each function. To explore the source code or report
some issues, we recommend going to our
GitHub. Before
exploring our functionality, please make sure you complete the following
steps.
Function Usage
The set_bearer() function sets key and secret as environment
variables.
- key: Twitter API key is used to authenticate requests.
- secret: Twitter API secret is used to authenticate
requests.
set_bearer('YOUR-KEY-HERE', 'YOUR-SECRET-HERE')
The get_bearer function is a helper function that can be used to
generate the bearer key by using API key and API secret. It is
embedded in the functions searchTweets(), user_friends(), and
followersCount(). The bearer key allows the users to query information
from Twitter.
get_bearer()
The searchTweets() function can be used to search for related tweets
that match the specified string.
Parameters:
-
searchString: a string of a maximum of 100 characters for the desired
search characters in a tweet. It can contain hashtags in order to search
specified tags in Twitter.
-
count: count will restrict the maximum number of tweets to return.
The default is 5.
-
resultType: specifies the kind of search results you
want to receive. The default is “mixed.” Valid values
include:
- mixed: returns both popular and real time results in the response
- recent: returns only the most recent results in the response
- popular: returns only the most popular results in the response
The function will return a DataFrame that contains the following columns:
- created_time: the date and time the tweet was created
- user_name: the name of the user that posted the tweet
- user_screen_name: the screen name of the user that posted the tweet
- user_followers_count: the number of followers that (re)posted the
tweet
- text: the text within the tweet
- truncated: indicates if the tweet is truncated
- favorited: indicates if the tweet was favorited
- retweeted: indicates it the tweet was retweeted
- favorite_count: the number of times the tweet was favorited
- retweet_count: the number of times the tweet was retweeted
In the example below searchTweets() function returns all the users who
recently used #ubc.
# Use `searchTweets()` function to search for the top 100 recent tweets using #ubc
ubc_tweets <- searchTweets("#ubc", resultType = "recent",count = 100)
head(ubc_tweets)
#> created_time user_name user_screen_name
#> 1 Mon Feb 21 00:03:22 +0000 2022 Park Ranger netminnow
#> 2 Sun Feb 20 23:45:04 +0000 2022 Sandy sandysva
#> 3 Sun Feb 20 23:01:07 +0000 2022 16forward 16forward
#> 4 Sun Feb 20 22:17:50 +0000 2022 Richard Ajabu richardajabu
#> 5 Sun Feb 20 20:52:01 +0000 2022 Toronto_Bot Toronto_Bot
#> 6 Sun Feb 20 20:48:22 +0000 2022 Richard Ajabu richardajabu
#> user_followers_count
#> 1 5349
#> 2 833
#> 3 43
#> 4 0
#> 5 401
#> 6 0
#> text
#> 1 RT @richardajabu: @mccaffreyr3 @selectedwisdom As Putin continues the slow motion conversion of EUkraine to RUkraine,\nwhy isn't the west re…
#> 2 RT @richardajabu: @mccaffreyr3 @selectedwisdom As Putin continues the slow motion conversion of EUkraine to RUkraine,\nwhy isn't the west re…
#> 3 RT @richardajabu: @mccaffreyr3 @selectedwisdom As Putin continues the slow motion conversion of EUkraine to RUkraine,\nwhy isn't the west re…
#> 4 Complete Fail. Sad for UK. 🙄\n\nhttps://t.co/sRps0Vf2HP\n#cdnpoli #uspolitics #ukpolitics #eu #auspol #nzpol #yow… https://t.co/V4HroUXrAq
#> 5 RT @richardajabu: @mccaffreyr3 @selectedwisdom As Putin continues the slow motion conversion of EUkraine to RUkraine,\nwhy isn't the west re…
#> 6 @mccaffreyr3 @selectedwisdom As Putin continues the slow motion conversion of EUkraine to RUkraine,\nwhy isn't the w… https://t.co/IofYO2JrWD
#> truncated favorited retweeted favorite_count retweet_count
#> 1 FALSE FALSE FALSE 0 4
#> 2 FALSE FALSE FALSE 0 4
#> 3 FALSE FALSE FALSE 0 4
#> 4 TRUE FALSE FALSE 0 0
#> 5 FALSE FALSE FALSE 0 4
#> 6 TRUE FALSE FALSE 8 4
# Select those with start=5 and end time=13
created_time <- substr(ubc_tweets$created_time, start = 5, stop = 13)
# Format the time to month, day, and hour
created_time <- strptime(created_time,format='%b %d %H')
# Save the results in a DataFrame
created_time_df <- as.data.frame(created_time)
created_time_df <- created_time_df %>% group_by(created_time) %>% summarize(num_tweets = n())
created_time_df
#> # A tibble: 36 × 2
#> created_time num_tweets
#> <dttm> <int>
#> 1 2022-02-18 19:00:00 1
#> 2 2022-02-18 20:00:00 9
#> 3 2022-02-18 21:00:00 10
#> 4 2022-02-18 22:00:00 1
#> 5 2022-02-18 23:00:00 6
#> 6 2022-02-19 00:00:00 4
#> 7 2022-02-19 01:00:00 3
#> 8 2022-02-19 02:00:00 2
#> 9 2022-02-19 03:00:00 1
#> 10 2022-02-19 06:00:00 2
#> # … with 26 more rows
# Plot the results
ggplot(created_time_df, aes(x = created_time, y = num_tweets)) +
geom_area(fill="firebrick") +
geom_line(color="black", size=1) +
geom_point(color="black", size=3) +
theme_bw()+
labs(x = "Created Time", y= "Number of Tweets")+
ggtitle("The Latest 100 Trending Tweets Containing the Hashtag #ubc")
theme(panel.grid.major.x = element_blank(),panel.grid.minor.x = element_blank())
#> List of 2
#> $ panel.grid.major.x: list()
#> ..- attr(*, "class")= chr [1:2] "element_blank" "element"
#> $ panel.grid.minor.x: list()
#> ..- attr(*, "class")= chr [1:2] "element_blank" "element"
#> - attr(*, "class")= chr [1:2] "theme" "gg"
#> - attr(*, "complete")= logi FALSE
#> - attr(*, "validate")= logi TRUE
The followersCount() function can be used to quickly get the number of
followers a user has by using the user’s screen name.
Parameters:
- screen_name: A string of a Twitter screen name.
In the plot below we use this function in a for loop to get the
followers of the following state leaders as of 2022:
- Joe Biden -President of the United States
- Justin Trudeau -Prime Minister of Canada
- Boris Johnson -Prime Mnister of the United Kingdom
- Emmanuel Macron -President of France
- Moon Jae-in -President of South Korea
# Use followersCount to find the number of follower for each country leader
list = c("POTUS", "JustinTrudeau", "BorisJohnson", "EmmanuelMacron", "moonriver365")
followers = c()
for (i in list){
followers[i]=followersCount(screen_name =i)$followers_count
Sys.sleep(1)
}
# Save the results in a DataFrame
followers <- as.data.frame(followers)
# Add name and no_of_followers as column names
followers["name"] <- c("Joe Biden", "Justin Trudeau", "Boris Johnson", "Emmanuel Macron", "Moon Jae-in" )
followers["no_of_followers"] <- followers$followers
# Remove redundant column
followers$followers = NULL
followers
#> name no_of_followers
#> POTUS Joe Biden 18144191
#> JustinTrudeau Justin Trudeau 5942556
#> BorisJohnson Boris Johnson 3811533
#> EmmanuelMacron Emmanuel Macron 7633738
#> moonriver365 Moon Jae-in 1952983
If you want to visualize the comparison, you can plot the results as
follows.
# pre-set the bw theme
theme_set(theme_bw())
# Plot total number of followers per president
g <- ggplot(followers, aes(name, no_of_followers))
# Use a barplot
g + geom_col() +
labs(subtitle="example plot",
y="number of followers",
x="name",
title="Popular President?",
)
The user_friends() function can be used to retrieve the number of
friends a user has by using the user’s screen name. By default the
maximum number of returned friends is 195.
Parameters:
- screen_name: A string of one Twitter screen name.
- number: The number of friends to output. The default is 195.
Similar to the above plot, the plot below uses the user_friends()
function in a for loop to get the friends of the following state leaders
as of 2022:
- Joe Biden - President of the United States
- Justin Trudeau - Prime Minister of Canada
- Boris Johnson - Prime Minister of the United Kingdom
- Emmanuel Macron - President of France
- Moon Jae-in - President of South Korea
# Use user_friends to find the number of friends for each country leader
list = c("POTUS", "JustinTrudeau", "BorisJohnson", "EmmanuelMacron", "moonriver365")
friends = c()
for (i in list){
friends[i]=nrow(user_friends(screen_name=i))
Sys.sleep(1)
}
# Store the results in a DataFrame
friends <- as.data.frame(friends)
# Add name and no_of_friends as column names
friends["name"] <- c("Joe Biden", "Justin Trudeau", "Boris Johnson", "Emmanuel Macron", "Moon Jae-in" )
friends["no_of_friends"] <- friends$friends
# Remove redundant column
friends$friends = NULL
friends
#> name no_of_friends
#> POTUS Joe Biden 12
#> JustinTrudeau Justin Trudeau 195
#> BorisJohnson Boris Johnson 195
#> EmmanuelMacron Emmanuel Macron 195
#> moonriver365 Moon Jae-in 195
If you want to visualize the comparison, you can plot the results as
follows.
# pre-set the bw theme
theme_set(theme_bw())
# Plot the results
g <- ggplot(friends, aes(name, no_of_friends))
# Add barplot
g + geom_col() +
labs(subtitle="example plot",
y="number of friends",
x="name",
title="President with the Most friends?",
)
Code of Conduct
Please note that the twitterapiR project is released with a Contributor
Code of
Conduct.
By contributing to this project, you agree to abide by its terms.
tangaot/twitterapiR documentation built on March 24, 2022, 12:40 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
twitterapiR
Description
This project is an API wrapper for Twitter in R. This package provides user friendly functions for the users to query information from Twitter.
The current list of functions included in the wrapper are:
- set_bearer(“API key”, “API secret”)
- get_bearer()
- user_friends(screen_name, number)
- searchTweets(searchString, resultType, count)
- followersCount(screen_name)
Installation
if (!require("remotes")) {
install.packages("remotes")
}
remotes::install_github("tangaot/twitterapiR")
Authentication
You will need to follow these instructions to continue. We are accessing Twitter programatically. twitterapiR uses the httr package under the hood to manage this. The first step is to create a Twitter application for yourself. To do so, go to the Developer Platform’s page and log in.
Follow the instruction and fill in some basic information. After your project is created, you can generate your consumer API and Secret key.
In your R session, you will want to do the following using the appropriate values from the web page:
set_bearer("API key", "API secret")
This will authenticate via httr, we recommend looking at the Token man page of this package for more information regarding how to manage the authentication and caching processes.
Getting Started
This document is intended to describe the usage of each function and to show examples of each function. To explore the source code or report some issues, we recommend going to our GitHub. Before exploring our functionality, please make sure you complete the following steps.
Function Usage
The set_bearer() function sets key and secret as environment
variables.
- key: Twitter API key is used to authenticate requests.
- secret: Twitter API secret is used to authenticate requests.
set_bearer('YOUR-KEY-HERE', 'YOUR-SECRET-HERE')
The get_bearer function is a helper function that can be used to
generate the bearer key by using API key and API secret. It is
embedded in the functions searchTweets(), user_friends(), and
followersCount(). The bearer key allows the users to query information
from Twitter.
get_bearer()
The searchTweets() function can be used to search for related tweets
that match the specified string.
Parameters:
-
searchString: a string of a maximum of 100 characters for the desired search characters in a tweet. It can contain hashtags in order to search specified tags in Twitter.
-
count: count will restrict the maximum number of tweets to return. The default is 5.
-
resultType: specifies the kind of search results you want to receive. The default is “mixed.” Valid values include:
- mixed: returns both popular and real time results in the response - recent: returns only the most recent results in the response - popular: returns only the most popular results in the response
The function will return a DataFrame that contains the following columns:
- created_time: the date and time the tweet was created
- user_name: the name of the user that posted the tweet
- user_screen_name: the screen name of the user that posted the tweet
- user_followers_count: the number of followers that (re)posted the tweet
- text: the text within the tweet
- truncated: indicates if the tweet is truncated
- favorited: indicates if the tweet was favorited
- retweeted: indicates it the tweet was retweeted
- favorite_count: the number of times the tweet was favorited
- retweet_count: the number of times the tweet was retweeted
In the example below searchTweets() function returns all the users who
recently used #ubc.
# Use `searchTweets()` function to search for the top 100 recent tweets using #ubc
ubc_tweets <- searchTweets("#ubc", resultType = "recent",count = 100)
head(ubc_tweets)
#> created_time user_name user_screen_name
#> 1 Mon Feb 21 00:03:22 +0000 2022 Park Ranger netminnow
#> 2 Sun Feb 20 23:45:04 +0000 2022 Sandy sandysva
#> 3 Sun Feb 20 23:01:07 +0000 2022 16forward 16forward
#> 4 Sun Feb 20 22:17:50 +0000 2022 Richard Ajabu richardajabu
#> 5 Sun Feb 20 20:52:01 +0000 2022 Toronto_Bot Toronto_Bot
#> 6 Sun Feb 20 20:48:22 +0000 2022 Richard Ajabu richardajabu
#> user_followers_count
#> 1 5349
#> 2 833
#> 3 43
#> 4 0
#> 5 401
#> 6 0
#> text
#> 1 RT @richardajabu: @mccaffreyr3 @selectedwisdom As Putin continues the slow motion conversion of EUkraine to RUkraine,\nwhy isn't the west re…
#> 2 RT @richardajabu: @mccaffreyr3 @selectedwisdom As Putin continues the slow motion conversion of EUkraine to RUkraine,\nwhy isn't the west re…
#> 3 RT @richardajabu: @mccaffreyr3 @selectedwisdom As Putin continues the slow motion conversion of EUkraine to RUkraine,\nwhy isn't the west re…
#> 4 Complete Fail. Sad for UK. 🙄\n\nhttps://t.co/sRps0Vf2HP\n#cdnpoli #uspolitics #ukpolitics #eu #auspol #nzpol #yow… https://t.co/V4HroUXrAq
#> 5 RT @richardajabu: @mccaffreyr3 @selectedwisdom As Putin continues the slow motion conversion of EUkraine to RUkraine,\nwhy isn't the west re…
#> 6 @mccaffreyr3 @selectedwisdom As Putin continues the slow motion conversion of EUkraine to RUkraine,\nwhy isn't the w… https://t.co/IofYO2JrWD
#> truncated favorited retweeted favorite_count retweet_count
#> 1 FALSE FALSE FALSE 0 4
#> 2 FALSE FALSE FALSE 0 4
#> 3 FALSE FALSE FALSE 0 4
#> 4 TRUE FALSE FALSE 0 0
#> 5 FALSE FALSE FALSE 0 4
#> 6 TRUE FALSE FALSE 8 4
# Select those with start=5 and end time=13
created_time <- substr(ubc_tweets$created_time, start = 5, stop = 13)
# Format the time to month, day, and hour
created_time <- strptime(created_time,format='%b %d %H')
# Save the results in a DataFrame
created_time_df <- as.data.frame(created_time)
created_time_df <- created_time_df %>% group_by(created_time) %>% summarize(num_tweets = n())
created_time_df
#> # A tibble: 36 × 2
#> created_time num_tweets
#> <dttm> <int>
#> 1 2022-02-18 19:00:00 1
#> 2 2022-02-18 20:00:00 9
#> 3 2022-02-18 21:00:00 10
#> 4 2022-02-18 22:00:00 1
#> 5 2022-02-18 23:00:00 6
#> 6 2022-02-19 00:00:00 4
#> 7 2022-02-19 01:00:00 3
#> 8 2022-02-19 02:00:00 2
#> 9 2022-02-19 03:00:00 1
#> 10 2022-02-19 06:00:00 2
#> # … with 26 more rows
# Plot the results
ggplot(created_time_df, aes(x = created_time, y = num_tweets)) +
geom_area(fill="firebrick") +
geom_line(color="black", size=1) +
geom_point(color="black", size=3) +
theme_bw()+
labs(x = "Created Time", y= "Number of Tweets")+
ggtitle("The Latest 100 Trending Tweets Containing the Hashtag #ubc")
theme(panel.grid.major.x = element_blank(),panel.grid.minor.x = element_blank())
#> List of 2
#> $ panel.grid.major.x: list()
#> ..- attr(*, "class")= chr [1:2] "element_blank" "element"
#> $ panel.grid.minor.x: list()
#> ..- attr(*, "class")= chr [1:2] "element_blank" "element"
#> - attr(*, "class")= chr [1:2] "theme" "gg"
#> - attr(*, "complete")= logi FALSE
#> - attr(*, "validate")= logi TRUE
The followersCount() function can be used to quickly get the number of
followers a user has by using the user’s screen name.
Parameters:
- screen_name: A string of a Twitter screen name.
In the plot below we use this function in a for loop to get the followers of the following state leaders as of 2022:
- Joe Biden -President of the United States
- Justin Trudeau -Prime Minister of Canada
- Boris Johnson -Prime Mnister of the United Kingdom
- Emmanuel Macron -President of France
- Moon Jae-in -President of South Korea
# Use followersCount to find the number of follower for each country leader
list = c("POTUS", "JustinTrudeau", "BorisJohnson", "EmmanuelMacron", "moonriver365")
followers = c()
for (i in list){
followers[i]=followersCount(screen_name =i)$followers_count
Sys.sleep(1)
}
# Save the results in a DataFrame
followers <- as.data.frame(followers)
# Add name and no_of_followers as column names
followers["name"] <- c("Joe Biden", "Justin Trudeau", "Boris Johnson", "Emmanuel Macron", "Moon Jae-in" )
followers["no_of_followers"] <- followers$followers
# Remove redundant column
followers$followers = NULL
followers
#> name no_of_followers
#> POTUS Joe Biden 18144191
#> JustinTrudeau Justin Trudeau 5942556
#> BorisJohnson Boris Johnson 3811533
#> EmmanuelMacron Emmanuel Macron 7633738
#> moonriver365 Moon Jae-in 1952983
If you want to visualize the comparison, you can plot the results as follows.
# pre-set the bw theme
theme_set(theme_bw())
# Plot total number of followers per president
g <- ggplot(followers, aes(name, no_of_followers))
# Use a barplot
g + geom_col() +
labs(subtitle="example plot",
y="number of followers",
x="name",
title="Popular President?",
)
The user_friends() function can be used to retrieve the number of
friends a user has by using the user’s screen name. By default the
maximum number of returned friends is 195.
Parameters:
- screen_name: A string of one Twitter screen name.
- number: The number of friends to output. The default is 195.
Similar to the above plot, the plot below uses the user_friends()
function in a for loop to get the friends of the following state leaders
as of 2022:
- Joe Biden - President of the United States
- Justin Trudeau - Prime Minister of Canada
- Boris Johnson - Prime Minister of the United Kingdom
- Emmanuel Macron - President of France
- Moon Jae-in - President of South Korea
# Use user_friends to find the number of friends for each country leader
list = c("POTUS", "JustinTrudeau", "BorisJohnson", "EmmanuelMacron", "moonriver365")
friends = c()
for (i in list){
friends[i]=nrow(user_friends(screen_name=i))
Sys.sleep(1)
}
# Store the results in a DataFrame
friends <- as.data.frame(friends)
# Add name and no_of_friends as column names
friends["name"] <- c("Joe Biden", "Justin Trudeau", "Boris Johnson", "Emmanuel Macron", "Moon Jae-in" )
friends["no_of_friends"] <- friends$friends
# Remove redundant column
friends$friends = NULL
friends
#> name no_of_friends
#> POTUS Joe Biden 12
#> JustinTrudeau Justin Trudeau 195
#> BorisJohnson Boris Johnson 195
#> EmmanuelMacron Emmanuel Macron 195
#> moonriver365 Moon Jae-in 195
If you want to visualize the comparison, you can plot the results as follows.
# pre-set the bw theme
theme_set(theme_bw())
# Plot the results
g <- ggplot(friends, aes(name, no_of_friends))
# Add barplot
g + geom_col() +
labs(subtitle="example plot",
y="number of friends",
x="name",
title="President with the Most friends?",
)
Code of Conduct
Please note that the twitterapiR project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
R Package Documentation
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