![]() ![]() This file contains the data used in the Commission’s report Compassionate Release: The Impact of the First Step Act and COVID-19 Pandemic (2022).įederal Robbery: Prevalence, Trends, and Factors in Sentencing (2022) § 851.Ĭompassionate Release: The Impact of the First Step Act and COVID-19 Pandemic (2022) Theses penalties are commonly called “section 851 enhancements” because the procedural statute that often applies in these cases is 21 U.S.C. This data relates to drug trafficking cases in which an enhanced penalty may have applied because the offender previously had been convicted of a felony drug offense. This datafile provides the economic crime type variable collected by the project.Įnhanced Penalties for Federal Drug Trafficking Offenders The economic crime offense type coding project is designed to assign all offenders sentenced under §2B1.1 to one of 29 specific offense categories. ![]() ![]() Convictions under more than 300 federal statutes fall under §2B1.1. That section provides sentencing provisions for a broad variety of economic crimes. Most federal economic crimes are addressed in section §2B1.1 of the federal sentencing guidelines. This dataset provides additional information on prior offenses committed, the points assigned to those convictions under the federal sentencing guidelines, and the types of offenses that did not receive points. The annual Individual Offender datafiles include the criminal history points and Criminal History Category calculated under the guidelines. The Commission expanded its collection of criminal history information using recent technological improvements. Guidance on how to avoid common pitfalls can be found in the Office of Research and Data's Research Notes series and presentation, " Effective Use of Federal Sentencing Data." Available Sets of Datafilesĭatafiles (downloads are SAS and SPSS compatible) The complexity of the federal sentencing guidelines, and by extension, federal sentencing data can lead researchers to make errors when using this data. When available for release, these datafiles may contain information collected during a special coding project performed for a particular report or publication and, therefore, will not be available in the Commission's fiscal year datafiles. The Commission may also make available datasets which it has utilized in the course of conducting research and issuing reports or publications, unless release of such data would impact confidentiality or is otherwise prohibited by an interagency agreement relating to the Commission’s access to the data. The Commission's individual and organizational datafiles may also be accessed through the University of Michigan's Inter-University Consortium for Political and Social Research (ICPSR). These datafiles exclude identifiers and provide fiscal year data for researchers interested in studying federal sentencing practices through quantitative methods. To work with SAS, Stata, and other formats try Part 2.This page provides users with access to the Commission's annual and other special datafiles that support the Commission's research agenda. Try this interactive course: Importing Data in R (Part 1), to work with csv and xlsx files in R. ![]() (To practice importing Stata data with the foreign package, try this exercise.) From systat # character variables are converted to R factors (To practice importing SPSS data with the foreign package, try this exercise.) From SAS # last option converts value labels to R factors Mydata <- spss.get("c:/mydata.por", =TRUE) (To practice, try this exercise on importing an Excel worksheet into R.) From SPSS Mydata <- read.xlsx("c:/myexcel.xlsx", sheetName = "mysheet") Mydata <- read.xlsx("c:/myexcel.xlsx", 1) # read in the first worksheet from the workbook myexcel.xlsx The first row should contain variable/column names. Alternatively you can use the xlsx package to access Excel files. One of the best ways to read an Excel file is to export it to a comma delimited file and import it using the method above. (To practice importing a csv file, try this exercise.) From Excel Mydata <- read.table("c:/mydata.csv", header=TRUE, # note the / instead of \ on mswindows systems # first row contains variable names, comma is separator Example of importing data are provided below. See the Quick-R section on packages, for information on obtaining and installing the these packages. For SPSS and SAS I would recommend the Hmisc package for ease and functionality. For Stata and Systat, use the foreign package. ![]()
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