load_data function

Parsing of raw data

Parsing of raw data

Data related to the PNAS paper. Accessed on Nov 14, 2015.

load_data(type = "extdata", ili.weighted = TRUE)

Arguments

  • type: the type of the data to be loaded. If type=="extdata" it loads the data to reproduce the PNAS paper, and if type=="athdata" it loads the data to reproduce the CID(?) paper.
  • ili.weighted: logical indicator to specify whether to load weighted ILI or not, if FALSE unweighted ILI is loaded.

Returns

A list of following named xts objects if type=="extdata"

  • GC10 Google Correlate trained with ILI available as of 2010. Google Correlate has been deprecated by Google as of Dec 2019 and is no longer publicly available.
  • GC09 Google Correlate trained with ILI available as of 2009.
  • GT Google Trends data for search queries identified using Google Correlate. Not directly available online, you have to manually input query terms at https://trends.google.com/trends/
  • CDC CDC's ILI dataset. Available online at https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html
  • GFT Google Flu Trend (historical predictions).

A list of following named xts objects if type=="athdata"

Details

Parse and load CDC's ILI data, Google Flu Trend data, Google Correlate data trained with ILI as of 2010, Google Correlate data trained with ILI as of 2009, Google Trend data with search terms identified from Google Correlate (2010 version).

Each week ends on the Saturday indicated in the xts object

Google Correlate data is standardized by Google, and we rescale it to 0 -- 100 during parsing. Google Trends data is in the scale of 0 -- 100.

Examples

system.file("extdata", "correlate-Influenza_like_Illness_h1n1_CDC_.csv", package = "argo") system.file("extdata", "correlate-Influenza_like_Illness_CDC_.csv", package = "argo") system.file("extdata", "GFT.csv", package = "argo") system.file("extdata", "ILINet.csv", package = "argo") load_data()

References

Yang, S., Santillana, M., & Kou, S. C. (2015). Accurate estimation of influenza epidemics using Google search data via ARGO. Proceedings of the National Academy of Sciences. doi:10.1073/pnas.1515373112.

  • Maintainer: Shihao Yang
  • License: GPL-2
  • Last published: 2023-05-24

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