The Prismatic Team has slowly been rolling out a very cool API. You can read all about it here. At the same time, I have been using this as an opportunity to learn how to create an R package.
After today’s API update to identify the relevant content related to a specific topic, I wanted to highlight what is possible with a few lines of code using the
prismaticR package. Needless to say, my package is raw, but I wanted to demonstrate some of the cool things that you can do.
Let’s get started
First things first, you can use
devtools to install the
## install devtools package -- uncomment line below if you need to install # install.packages("devtools") library(devtools) ## install my prismaticR package if you havent already install_github("btibert3/prismaticR") ## now lets load it library(prismaticR)
Before you move forward, you will need to get an API token for your calls. You can get that token here.
Store your token in an object called TOKEN …
TOKEN = "YOUR_TOKEN_HERE"
Explore the API
The first thing that we should do is crawl the topic id database. We will use this later …
tids = prizTID() # ## keep everything lower case tids$topic = tolower(tids$topic)
We can use the
stringr package to filter topic names based on keywords of interest. For example, how many of the topics include the term
id topic 993 993 college admissions
A broader keyword …
tids[str_detect(tids$topic, "higher ed"),]
id topic 1929 1929 higher education
How about college?
head(tids[str_detect(tids$topic, "college"),], 10)
id topic 182 182 amherst college 433 433 bard college 434 434 barnard college 447 447 baruch college 592 592 boston college 593 593 boston college eagles football 605 605 bowdoin college 659 659 brooklyn college 993 993 college admissions 994 994 college athletics
And university? ..
head(tids[str_detect(tids$topic, "university"),], 10)
id topic 34 34 adelphi university 243 243 appalachian state university 278 278 arizona state university 348 348 auburn university 468 468 baylor university 565 565 bob jones university 636 636 brigham young university 732 732 california state university 786 786 carnegie mellon university 1558 1558 florida state university
And to close it out, how about student …
id topic 1591 1591 foreign students 1746 1746 gifted students 1780 1780 graduate schools and students 4249 4249 student loans
This might be a good time to use the similar topic API. To keep it simple, let’s identify the topics that are related to the topic of
Harvard University …
harvard = tids[str_detect(tids$topic, "harvard uni"),]$id prizSIM(TOKEN, TID = harvard)
topic_id topic score 1 1139 Dallas Mavericks 0.28756
Interesting. How about
Amherst College? …
prizSIM(TOKEN, TID = 182)
topic_id topic score 1 4866 Williams College 0.32472
The API even allows to identify the current stories relevant to
college admissions? The top 5 are …
score url 1 0.66227 http://now.dartmouth.edu/2015/03/2120-students-offered-acceptance-into-the-class-of-2019/ 2 0.64772 http://college.usatoday.com/2015/03/31/i-didnt-get-into-my-first-choice-college-now-what/ 3 0.62981 http://dailyprincetonian.com/news/2015/03/admissions/ 4 0.62837 http://www.nj.com/mercer/index.ssf/2015/03/princeton_university_has_most_selective_admissions.html 5 0.62516 http://www.nj.com/essex/index.ssf/2015/03/newark_students_get_on-the-spot_college_acceptance.html
And for the sake of bots, here is the title of the “hottest” page above …
x_resp = html(x$url) html_node(x_resp, "title") %>% html_text()
 "2,120 Students Offered Acceptance Into the Class of 2019 | Dartmouth Now"
Have fun. I make no warantees for the R package, but with a few calls, you can do some really cool things.