README.md

In symplur/SympluR: Analyze Healthcare Social Media Data from the 'Symplur' API

SympluR

SympluR is an R package for analyzing data from the Healthcare Social Graph via access to the Symplur API.

Healthcare Social Graph

The Symplur API gives access to insights from the Healthcare Social Graph® – the vast neural network of public healthcare communities, conversations and people, hand curated by Symplur and powered by machine learning.

#hcsmR

Take a look at the over 300 published journal articles that have employed or referenced Symplur data in their research. #hcsmR is a collaboration between Symplur and Stanford Medicine X.

Get started

Install R package from CRAN:

install.packages("SympluR")
library(SympluR)

For latest development version, install the package from GitHub:

library("devtools")
install_github('symplur/SympluR')
library(SympluR)

Symplur API Credentials

To make use of this R package you need to have access to the Symplur API. The package comes with free demo credentials that allow you to access the demo dataset #LCSMDemoData. This dataset is a 4-week snapshot of the conversations from #LCSM (Lung Cancer Social Media) from 08/16/2017 to 09/15/2017.

You can get a quick look at the data now by opening the same demo dataset in Symplur Signals Dashboards with a free account.

To access any other datasets, please contact Symplur for further details.

The R package will prompt you for your credentials during the first API call in your R session. Paste in your credentials when asked, or if you want to try out the demo data then hit enter without entering anything.

Documentation

Find the documentation in R for each function in this package. Example:

?symplurTweetsSummary

?symplurTweetsActivity

?symplurPeopleInfluencers

?symplurContentRetweets

?symplurContentWords

To learn more about each Symplur API endpoint used in this package and the accepted parameters please see the Symplur API Documentation.

Example Usage

Summary

The symplurTweetsSummary() function will create a list with statistics of the database and the time span selected. The stats includes tweets, mentions, impressions, users, retweets, replies, urls_shared, etc.

LCSM <- symplurTweetsSummary("09/01/2017", "09/08/2017", databases = "#LCSMDemoData")

Summary Table

The symplurTweetsSummaryTable() function will create a data frame with summary statistics of all databases and time spans defined in an existing data frame. First create in a spreadsheet columns 'database', 'start' and 'end'. Add rows according to your query needs, then export as a CSV-file. See example CSV-file.

Example table:

| database | start | end | | ------------- | ---------- | ---------- | | #LCSMDemoData | 09/01/2017 | 09/06/2017 | | #LCSMDemoData | 09/06/2017 | 09/13/2017 | | #LCSMDemoData | 09/13/2017 | 09/19/2017 |

Load such an CSV-file into R as a data frame:

library(readr)
LCSMquery <- read_csv(file.choose())

Now we're ready to run the analysis:

LCSManalysis <- symplurTweetsSummaryTable(LCSMquery)

You can also try out symplurTweetsSummaryTable() with an example CSV file:

library(readr)
datasets <- read_csv(system.file("extdata", "datasets.csv", package = "SympluR", mustWork = TRUE))
LCSMDemoDataTweetsSummaryTable <- symplurTweetsSummaryTable(datasets)

Credits

Thank you to Professor Larry Chu, MD at Stanford University School of Medicine for the idea of the SympluR R package.

symplur/SympluR documentation built on Oct. 13, 2019, 3:11 a.m.