Description Details Author(s) References Examples
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)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | 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))
|
Loading required package: grid
Summary of accrued data object with time points.
Upload Proportion Mean Proportion Mean Count Quartile 1 Median
Lag
0 0.61 0.00 1.5 0.00 0.00
1 0.84 0.26 122.9 0.19 0.25
2 0.86 0.56 268.8 0.50 0.57
3 0.86 0.75 358.8 0.71 0.77
4 0.93 0.92 437.4 0.89 0.93
5 1.00 0.97 462.3 0.95 1.00
6 1.00 0.99 471.1 0.99 1.00
7 1.00 0.99 470.5 1.00 1.00
8 1.00 0.99 473.0 1.00 1.00
9 1.00 1.00 477.3 1.00 1.00
10 1.00 1.00 477.4 1.00 1.00
11 1.00 1.00 477.5 1.00 1.00
12 1.00 1.00 477.6 1.00 1.00
final 1.00 1.00 477.6 1.00 1.00
Quartile 3
Lag
0 0.00
1 0.33
2 0.65
3 0.81
4 0.97
5 1.00
6 1.00
7 1.00
8 1.00
9 1.00
10 1.00
11 1.00
12 1.00
final 1.00
0 1 2 3 4 5 6 7 8 9 10 11 12
0.1 437.2 288 136.2 53.2 5.0 0.0 0 0.0 0 0 0 0 0
0.5 472.0 351 206.0 116.0 32.0 1.5 0 0.0 0 0 0 0 0
0.9 513.8 439 291.6 178.8 78.6 45.0 24 11.1 0 0 0 0 0
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