accrued-package {accrued} | R Documentation |
Package for visualizing data quality of partially accruing data.
Package: | accrued |
Type: | Package |
Version: | 1.4.1 |
Date: | 2016-06-07 |
License: GPL-3 | |
Julie Eaton (jreaton@uw.edu) and Ian Painter
[1] Painter I, Eaton J, Olson D, Revere D, Lober W. How good is your data. In conference abstracts for the International Society for Disease Surveillance Conference 2011: Building the Future of Public Health Surveillance. Emerging Health Threats Journal. 2011;4. (http://www.eht-journal.net/index.php/ehtj/article/view/11907)
[2] Painter I, Eaton J, Olson D, Lober W, Revere D. (2011). Visualizing data quality: tools and views. In conference abstracts for the International Society for Disease Surveillance Conference 2011: Building the Future of Public Health Surveillance. Emerging Health Threats Journal. 2011;4. (http://www.eht-journal.net/index.php/ehtj/article/view/11907)
[3] Lober W, Reeder B, Painter I, Revere D, Bugni P, McReynolds J, Goldov K, Webster E, Olson D. Technical Description of the Distribute Project: A Community-based Syndromic Surveillance System Implementation. Online Journal of Public Health Informatics. 2014;5(3). (http://dx.doi.org/10.5210/ojphi.v5i3.4938)
[4] J. Eaton, I. Painter, D. Olson, W. Lober. Visualizing the quality of partially accruing data for use in decision making. Online Journal of Public Health Informatics. 2015;7(3). (http://dx.doi.org/10.5210/ojphi.v7i3.6096)
data(accruedDataExample) testData <- data.accrued(accruedDataExample) plot(testData) summary(testData) plot(summary(testData)) uploadPattern(testData) laggedTSarray(testData, lags=c(1,3,5,7) ) lagHistogram(testData) summary(accruedErrors(testData)) plot(accruedErrors(testData)) currentValues = asOf(testData, currentDate=20) # plot(currentValues) data(accruedDataILIExample) testData2 <- data.accrued(accruedDataILIExample) plot(accruedErrors(testData, testData2))