R Dataset / Package Ecdat / USclassifiedDocuments

How To Create a Barplot

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How To Create a Stacked Barplot

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How To Create a Pie Chart

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How To Compute the Mean

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How To Create a Plot

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How to Compute the Median

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Boxplot

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Correlation Coefficient

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Cumulative Frequency Histogram

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Dotplot

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Hollow Histogram

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Numerical Summaries

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Pie Chart

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Plot

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Ratio

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Regression

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Stem and Leaf Plots

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Visual Summaries

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Submitted by pmagunia on March 9, 2018 - 1:06 PM
<iframe src="https://embed.picostat.com/r-dataset-package-ecdat-usclassifieddocuments.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-90051.csv 823 bytes
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Documentation

On this Picostat.com statistics page, you will find information about the USclassifiedDocuments data set which pertains to Official Secrecy of the United States Government . The USclassifiedDocuments data set is found in the Ecdat R package. Try to load the USclassifiedDocuments data set in R by issuing the following command at the console data("USclassifiedDocuments"). This may load the data into a variable called USclassifiedDocuments. If R says the USclassifiedDocuments data set is not found, you can try installing the package by issuing this command install.packages("Ecdat") and then attempt to reload the data with library("Ecdat") followed by data("USclassifiedDocuments"). Perhaps strangley, if R gives you no output after entering a command, it means the command succeeded. If it succeeded you can see the data by typing USclassifiedDocuments at the command-line which should display the entire dataset.

If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the USclassifiedDocuments R data set. The size of this file is about 823 bytes.


Official Secrecy of the United States Government

Description

Data on classification activity of the United States government.

Fitzpatrick (2013) notes that the dramatic jump in derivative classification activity (DerivClassActivity) that occurred in 2009 coincided with "New guidance issued to include electronic environment". Apart from the jump in 2009, the DerivClassActivity tended to increase by roughly 12 percent per year (with a standard deviation of the increase in the natural logarithm of DerivClassActivity of 0.18).

Usage

data(USclassifiedDocuments)

Format

A dataframe containing :

year

the calendar year

OCAuthority

Number of people in the government designated as Original Classification Authorities for the indicated year.

OCActivity

Original classification activity for the indicated year: These are the number of documents created with an original classification, i.e., so designated by an official Original Classification Authority.

TenYearDeclass

Percent of OCActivity covered by the 10 year declassification rules.

DerivClassActivity

Derivative classification activity for the indicated year: These are the number of documents created that claim another document as the authority for classification.

Details

The lag 1 autocorrrelation of the first difference of the logarithms of DerivClassActivity through 2008 is -0.52. However, because there are only 13 numbers (12 differences), this negative correlation is not statistically significant.

Source

Fitzpatrick, John P. (2013) Annual Report to the President for 2012, United States Information Security Oversight Office, National Archives and Record Administration, June 20, 2013 (https://www.archives.gov/isoo/reports)

Examples

##
## 1.  plot DerivClassActivity 
##
plot(DerivClassActivity~year, USclassifiedDocuments)
#  Exponential growth?  plot(DerivClassActivity~year, USclassifiedDocuments, 
     log='y')
# A jump in 2009 as discussed by Fitzpatrick (2013).  
# Otherwise plausibly a straight line.   ##
## 2.  First difference? 
##
plot(diff(log(DerivClassActivity))~year[-1], 
     USclassifiedDocuments)
# Jump in 2009 but otherwise on distribution ##
## 3.  autocorrelation?  
##
sel <- with(USclassifiedDocuments, 
            (1995 < year) & (year < 2009) )
acf(diff(log(USclassifiedDocuments$
             DerivClassActivity[sel])))
# lag 1 autocorrelation = (-0.52).  
# However, with only 12 numbers, 
# this is not statistically significant.  

--

Dataset imported from https://www.r-project.org.
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