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.
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.
Install R package from CRAN:
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.
Find the documentation in R for each function in this package. Example:
To learn more about each Symplur API endpoint used in this package and the accepted parameters please see the Symplur API Documentation.
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")
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.
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)