Playing Around with the Prismatic Topic Graph API using R

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 prismaticR package.

## 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 admission

tids[str_detect(tids$topic, "admission"),]

     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 …

tids[str_detect(tids$topic, "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[1])
html_node(x_resp, "title") %>% html_text()

[1] "2,120 Students Offered Acceptance Into the Class of 2019 | Dartmouth Now"

Summary

Have fun. I make no warantees for the R package, but with a few calls, you can do some really cool things.